# Associative Trails — full text > Associative Trails is an AI advisory practice for professional services firms. We help firms work out where AI can help, where it can't, and what knowledge and judgement sit underneath both. Generated from https://www.associativetrails.com/ on every build. Pages first, then case studies, then tools, then writing, newest first. --- # The model gets the attention. The foundations decide the outcome. URL: https://www.associativetrails.com/ The model gets the attention. The foundations decide the outcome. Professional services firms hold knowledge that matters: in people's heads, in email, in informal processes - almost never where AI can reach. We've spent twenty years building the systems underpinning these firms' knowledge. We know what has to be structured before AI can help. We know what isn't worth touching. Get in touch ## What this looks like in your firm The report that gets rebuilt from scratch every time because the good version is buried in someone's old files. The way you actually run a project, which only works because the people who know are in the room, and falls apart when they're not. The standard everyone follows and no one has written down. This is the knowledge that runs the firm, and almost none of it sits anywhere that software can reach. So when AI arrives, it works on the thin layer that is written down and misses everything that matters. That's not a model problem. It's a knowledge architecture problem, and it's fixable. Not by buying more tools. Every firm has real work an AI can help with: administration, document preparation, recurring bottlenecks, decision support. The question is: what has to be structured first, what should remain human judgement, and what simply isn't worth automating. The answer to this question is the hard part. ## What we do We help professional services firms work out where AI can help, where it can't, and what knowledge and judgement sit underneath both. Our work is practical and bounded. We start with how the firm actually operates: where knowledge lives, how decisions get made, what information people rely on, and what happens when key people are unavailable. That means talking to people at every level, not just the people who commissioned the work. The person who runs a process every day usually knows better than anyone which parts are worth automating and which parts quietly depend on judgement. Often the most useful insight comes from the most junior person in the room, because they feel the friction the directors stopped noticing years ago. This only works if people experience it as support, not extraction. Too often, knowledge capture has implied turning what someone knows into a system that makes them replaceable. We want the opposite: expertise that is easier to find and reuse, and time won back from the work that drains judgement rather than uses it. From there we help firms: - Identify knowledge that AI can realistically use - Surface expertise that currently exists only in people's heads - Remove friction from recurring workflows - Build lightweight systems where they earn their place - Avoid expensive AI projects that solve the wrong problem Sometimes the most valuable recommendation is not what to automate, but what to leave alone. ## What you get A short, practical assessment of where AI can genuinely help your firm, and what has to be fixed first. Typically this gives you: - A map of where important knowledge currently lives - A shortlist of workflows where AI or automation could help - A clear view of what should remain human judgement - The risks, ownership and governance needed before anything is rolled out - One or two low-risk pilot opportunities worth testing first ## How we think about this ## Tools we use ourselves We build small tools for our own work and publish them for anyone to use. Two of them deal with problems on this page: getting honest criticism of AI-assisted work, and making research you'll need again repeatable. See all our tools (https://www.associativetrails.com/tools/). ## If this sounds familiar We work with a small number of professional services firms at a time. If you're trying to work out where AI can genuinely help, what foundations need attention first, or whether you're solving the right problem at all, start a conversation. Start a conversation (https://www.associativetrails.com/contact/). Worst case, you get an outside read on where AI fits in your organisation and where it doesn't. Oh good. Another seagull. --- # About us URL: https://www.associativetrails.com/about/ Associative Trails / about About Us > “Wholly new forms of encyclopedias will appear, ready made with a mesh of associative trails running through them…” — Vannevar Bush, 1945 Bush was describing how knowledge should connect: not filed and forgotten, but linked, navigable and alive. That idea has been the thread through everything Associative Trails has built since 2005. ## Twenty years, one problem Matt Perdeaux (http://www.linkedin.com/in/mattperdeaux) founded Associative Trails in 2005. Since then the work has almost always come back to the same thing: helping professional services firms capture what they know and put it where people can actually use it. Technical libraries where knowledge linked up instead of piling up. Intranets that became the place a firm's knowledge lived. Systems for curating, tagging and sharing expertise that would otherwise stay locked in individuals' heads. That was the work before anyone called it AI readiness. The organisations were architects, engineers, business schools, law and insurance practices, and the problem was always structural: the knowledge that mattered most was the hardest to reach. ## Why this matters now AI has made the old problem urgent. Firms are buying tools that depend on structured, accessible knowledge and discovering they don't have it. The technology works. The foundations often don't. We've spent two decades building the layer those tools depend on. We know what it takes to get a firm's knowledge into usable shape, what that effort is worth, and, just as importantly, when it isn't worth doing. That experience shapes how we advise clients today. Experience earned from building systems inside professional services firms and seeing, repeatedly, why some approaches succeed and others fail. ## What makes us different Many AI advisers start with the technology. We start with the people. We know where expertise hides. We know why intranets fail. We know which workflows look broken but aren't, and which ones quietly drain time every day. Most importantly, we're comfortable saying: "AI won't help you here." That scepticism isn't a limitation - it's what makes the advice useful. ## People can tell the difference When you start formalising what people know, some of them get nervous, and they are right to. Knowledge capture has too often been a polite name for making someone replaceable. That is not what this is. The aim is to take the parts of the job that drain time and judgement and give them back, not to hollow out the expertise and keep the shell. People resist when they sense the first thing. They engage when they can see it is the second. Getting that distinction right, and being honest about it, is a big factor in why these projects succeed or fail. ## Selected clients Arup · Ashridge Business School · Eckersley O'Callaghan · Ellipse · Heatherwick Studio · London Business School · Safetime · Scott Brownrigg · Sheppard Robson That experience is what we bring to AI now. See what we've built. (https://www.associativetrails.com/work/) --- # Contact us URL: https://www.associativetrails.com/contact/ Associative Trails / contact Contact us Associative Trails Limited. enquiries@associativetrails.com Based in Ramsgate, Kent, UK. If you would like to contact us, please complete the form below. --- # Our work URL: https://www.associativetrails.com/work/ For twenty years we've built systems that help professional firms capture, structure and use knowledge. Today those same problems are the foundations of successful AI adoption. The projects below were delivered long before AI became a boardroom priority, but they solved the same underlying challenge: making expertise visible, reusable and available beyond the people who happen to hold it. - Eckersley O'Callaghan: An early example of a problem many organisations still face today. The technical knowledge existed, but finding the right answer often depended on knowing who to ask. (https://www.associativetrails.com/work/eckersleyocallaghanintranet/) - London Business School: A knowledge-capture tool for compiling, annotating and sharing the reading and references behind academic work. (https://www.associativetrails.com/work/lbsknowledge/) - Heatherwick Studio: A bespoke intranet built around tens of thousands of records covering projects, skills and studio history. (https://www.associativetrails.com/work/heatherwiki/) - Sling: A workflow platform that allows firms to build and run their own approval processes, supported by an Outlook integration that surfaces relevant information where people already work. (https://www.associativetrails.com/work/sling/) ## What these projects have in common Different professional firms. Different technologies. Different outcomes. The common thread is understanding how knowledge moves through organisations. Not because every firm needs another system, but because successful AI depends on understanding where knowledge lives, how decisions get made, and what should remain human judgement. ## Track record Two decades of knowledge and workflow systems for professional services firms. - Arup: CMS rebuild and web standards realignment (https://www.associativetrails.com/work/arupcms/), contacts and events database (https://www.associativetrails.com/work/arupcontacts/), marketing collateral repository (https://www.associativetrails.com/work/arupcollateral/) - Scott Brownrigg: Compass intranet, staff and project portal (https://www.associativetrails.com/work/scottbrownriggcompass/), iOS portfolio app (https://www.associativetrails.com/work/scottbrownriggapp/), CRM and events systems (https://www.associativetrails.com/work/scottbrownriggspider/) - Sheppard Robson: company intranet (https://www.associativetrails.com/work/sheppardrobsonintranet/) and CRM (https://www.associativetrails.com/work/sheppardrobsoncrm/) - Ellipse: website and content management system (https://www.associativetrails.com/work/ellipsecms/) with PDF form generation for insurance claims - Grant Thornton: full-lifecycle event management system (https://www.associativetrails.com/work/grantthorntonevents/) - Ashridge Business School: business news and analysis platform (https://www.associativetrails.com/work/perspectives/) - Old Arupians: long-running community website (https://www.associativetrails.com/work/oldarupians/) for a network of retired Arup staff --- # Eckersley O'Callaghan Intranet URL: https://www.associativetrails.com/work/eckersleyocallaghanintranet/ Client: Eckersley O'Callaghan Type: Intranet Summary: Invaluable technical information — A self-populating technical library that grew into a comprehensive company-wide information portal for one of the UK's most accomplished engineering specialists. This fully-featured intranet helps with a broad range of important responsibilities - from technical knowledge capture and dissemination to alerting staff to freshly-cut birthday cake. The fully responsive interface can be used on screens of all sizes - from phones to the biggest desktop monitor. ## The challenge Eckersley O'Callaghan initially approached us to build a searchable library of their technical documentation. The brief quickly expanded to encompass a comprehensive intranet for all their staff. We started by building an automatic process that spiders through network drives, searching for technical documents. These documents are thumbnailed and analysed for popular keywords before being added to the library. Potential duplicate documents are flagged for review and users can add their own metadata to improve discoverability. With the library being tested and populated, we then turned our attention to helping Eckersley O'Callaghan improve staff communication and knowledge sharing. ## The solution Using the technical library as a foundation, we then developed a fully-featured intranet for comprehensive knowledge sharing within the practice. Features such as rich text editing, granular administration privileges, controlled items with read receipts, staff skills assessment and office skills rosters were all featured in the new system. We used the Bootstrap framework and a modular front-end coding approach to build a rich interface that is responsive across all screen sizes. Having the solid foundation of a formal framework helped us achieve some complicated user interactions for cropping images, embedding dynamic item lists in body text, sorting and reordering lists of linked items, etc. The new system includes a number of features to help staff in their day-to-day professional lives: - Sophisticated content discovery with a skimming interface for search results allowing users to review the content of search matches without leaving the result page - Network drives containing technical documents are spidered and imported to the library after being thumbnailed and analysed for keyword frequency and potential duplicates - Comprehensive staff skills profiles, including software skills, languages, education history and professional experience - An application-wide user subscription model allowing users to quickly see updates to content that interests them - Controlled items can request users acknowledge they have read them, with targeting for all staff or individuals - Image cropping within easy-to-use web forms - Support for multiple offices, with graphical presentation of office skills profiles - Event attendance management - Project records - with links to staff profiles, technical documents, tags, etc. - A noticeboard to allow staff to post messages about personal events, stuff for sale, flats for rent, etc. - A Stack Overflow-style question and answer interface to encourage staff to share knowledge with each other > “Matt has been able to deliver an easy to use, customised product... Communicative, concise and creative. It been great to collaborate with him and I'll continue to do so.” — Graham Coult, Technical Director at Eckersley O'Callaghan ## The outcome The new intranet is available to all client employees using their web browsers. They are automatically recognised from their windows domain login. The new system has gone down well with staff, and we look forward to future development. Technologies used Adobe Coldfusion, Python, Microsoft SQL Server, HTML, CSS, Bootstrap, Javascript. Integration with Microsoft Active Directory. Hubspot API, YouTube API, Vimeo API, Egnyte API, Slack API. ## Screens - Technical library - browsable, filterable and searchable: The library shows documents as thumbnails, with filters for type, source, status, tags, linked people and linked projects. - Embedded document view with metadata, linked staff and projects: Each document record links to related staff and projects and lets users add their own metadata to improve discoverability. - Flagging of potential duplicate documents: Two possible duplicates are shown side by side, and a person decides whether they are different or which copy to delete. - Content skimming: Search results can be skimmed in place — review a match without leaving the results page. - Events calendar: Staff can see upcoming events across the month, colour-coded by category such as finance, office or social. - Personal user favourites: Users can save news articles, technical documents, notices and projects to a personal favourites list, filterable by item type. - News feed: Staff can browse company news and filter articles by news feed, office or tag. - Image cropping: A photograph can be zoomed and cropped over the page-editing form, then added to a gallery or inserted into the body text. - Home page: The home page brings together news, events, the noticeboard and library documents in one dashboard for staff. - Office skills roster: An office's skills roster opens each skill, here thermal analysis, to show who has it and their star rating. - Projects map: Projects can be browsed geographically, with a world map plotting the location of each one. --- # London Business School Knowledge Capture URL: https://www.associativetrails.com/work/lbsknowledge/ Client: London Business School Type: Web application Summary: Ravishing reading lists — Knowledge capture and sharing with a difference Fizzing with ideas for knowledge capture and sharing A web application to help compile, publish and annotate academic reading lists and other collections of links. From a central interface, administrators can set up course or client-specific reading lists and collections of links. Users then annotate, comment on and tag the links to build their own curated collections. ## The challenge We were approached by London Business School to think about how their academic staff presented course reading lists, and how students could best consume and share their new-found knowledge. What followed was a research and development prototyping exercise that explored the various different ways that a corpus of links can be curated, imported, shared, annotated and classified. A rapid development-feedback cycle meant we could explore a good number of ideas despite constraints on time and budget. ## The solution Using well-established frameworks for backend and front-end development, we were able to explore a number of functional ideas in a short space of time. The abstraction from the database layers that Django provides meant we could quickly make changes to the data model without extensive re-coding. The Bootstrap framework gave us a palette of interaction components that meant we could assemble an interface with the minimum of fuss. We considered a number of different ideas to assist administrators and faculty members in the setting up, curation, importing, grouping and addition of links to the system: - Curated reading lists can be added by faculty members, with the links annotated and ordered. - Reading lists can be exported in RSS or HTML format, with historical snapshots embedded in external systems. - A satellite site is a branded collection of links from the corpus, including reading lists. These can be set up quickly for a targeted course or client. - A browser toolbar button allows faculty members to easily add links to any of their reading lists or satellite sites. - Links can be imported from spreadsheets or CSV files that are uploaded or available remotely. A remote spreadsheet will be regularly checked for additional links, which can then be automatically imported into the system. - All links added to the system can be scanned for certain criteria (e.g. sources, tags, authors, etc.) and automatically added to any reading list or satellite site. - Links can be added using Twitter From the student/user perspective, we explored a number of different features to try to help users learn from, embellish and recall knowledge: - All links can be tagged by all users - allowing personal collections to be easily built - Fully-threaded comments for every link means students can discuss items on the reading list - A system of micro-interactions, or "bumps", allows users to quickly favourite, recommend or warn about a particular link. - Where a link's body text is available, users can highlight and annotate particular passages - We have recently been using the D3 data visualisation library to build visual models of the data and assist user interaction Technologies used Python, Django, MySQL, Bootstrap, HTML, CSS, Javascript, D3 data visualisation, Amazon Elastic Beanstalk, Twitter API. ## Screens - Satellite site home page: A branded satellite site, here for alumni, shows its links as tagged cards beside its top contributors, tags and link collections. - Feed of latest actions: The home feed shows the latest annotations, tags and bumps on subscribed reading lists and from people you follow. - Reading list administration: A reading list page shows its manager, subscribers and links, with an RSS feed and a code to embed it in another site. - Viewing a link: Users can annotate a link's text, add tags and see who else has linked to the same article. - An individual's contributions: Each contributor's profile brings together their links, comments, collections and most-used tags in one place. - Administering a link filter: A new collection is built from filters on who added a link, its tags, source, date, satellite site or reading list, tested against matching links. - Reading list curated view: Curated reading lists present ordered, annotated articles with the compiler's own notes on each entry. - Link comments and tags: A link's history shows each user's comment with the tags they added, and a form to add your own comment and tags. - Data visualisation: A force-directed diagram sizes each tag by how often it is used, and links tags by how often they are used together. --- # Heatherwick Studio Intranet URL: https://www.associativetrails.com/work/heatherwiki/ Client: Heatherwick Studio Type: Intranet Summary: Digging that studio scene — A bespoke company intranet for one of the UK's top design studios. A beautiful company intranet - fit for a multi-award winning industrial design and architecture practice. This bespoke development combines beautiful aesthetics with great functionality - staff skills, project tracking and a famed database of tens of thousands of objects dating back to the studio's earliest days. ## The challenge Heatherwick Studio initially approached us to look at ways of improving their staff appraisal system. The brief quickly expanded to encompass a comprehensive intranet for all their staff. The system needed to be highly functional, but also reflect the design aesthetic of the studio - practical and unfussy but with the flair and attention to details that characterises much of the studio's work. The client insisted upon a hands-on approach to the aesthetic design of the system, as well as having strong ideas on the informational design. ## The solution An extended HTML prototyping process before the main development began allowed Heatherwick Studio staff a large amount of input into the visual and information design of key pages - something that then informed the rest of the system. At one point in the prototype process, a restaurant menu was presented as an input into the visual design of the application. These ideas were worked in to the next iteration of the prototype to great effect. Once the prototype was agreed, we built a fully functioning intranet system with a number of features that would help studio members every day: - A fully customisable portal entry point - the layout can be easily changed depending on news item importance - An application-wide user subscription model allowing users to quickly see updates to content they are particularly interested in - Integration with OpenAsset image library - Comprehensive staff skills profiles, including software skills, languages, education history and professional experience - Project records - with links to staff profiles, external organisations and objects on the studio archive - A current project dashboard displaying key metrics such as the resource plan, milestones and key team members - Network drives containing documents can be spidered by the intranet and the documents discovered browsed from within the system - The Heatherwick Object Library - a curated and indexed collection of physical objects from the Studio's beginnings to the present day ## The outcome As one would expect with such a prestigious client, there were some highly individual features of the system that were particularly satisfying to design and implement. The completed system provides a stylish and usable interface to important company information. Technologies used Adobe Coldfusion, Microsoft SQL Server, HTML, CSS, Bootstrap, Javascript. Integration with Microsoft Active Directory, Axomic OpenAsset. ## Screens - Prototype - staff list: The staff directory prototype filters people by name, skills, location, domain and studio knowledge, with an A to Z index. - Prototype - alternative 1: An alternative staff profile layout sets a person's skills and domains beside their projects and a feed of their recent activity. - Prototype - staff view: This prototype profile lists a person's studio knowledge, skills, past experience and education, with a timeline of their recent activity. - Prototype - alternative 2: A further prototype iteration tried a different colour scheme and navigation style for the same staff-profile content. - News article: Each news story can be tagged, illustrated with photographs and discussed in a comments thread below. - Home page: The layout can be rearranged depending on story importance, and users see updates to the tags they follow. - Manage home page layout: Administrators choose a layout for each row of the home page, and fill each slot with a chosen story or the latest from a feed. - Object library: Each object in the studio's archive has its own record: category, year made, the project it belongs to, tags and photographs. - Studio handbook content page: The studio handbook sets out practical guidance for working in the studio, from desk etiquette to recycling. - Project view page: Project records link to the staff and external organisations who worked on them, alongside a gallery of images. - Tags: Controlled tags get an image and a description of the area they cover, with every other tag listed beneath. --- # Sling URL: https://www.associativetrails.com/work/sling/ Client: Scott Brownrigg Type: Workflow Summary: Wonderfully easy workflows — A flexible and innovative workflow building tool enabling companies to set up and administer approval workflows for business development, procurement, and all sorts of other key processes. The custom add-in for Outlook means staff have immediate and prominent access to useful data without having to hunt for it. A lead can be added to the system the moment it appears in your Outlook inbox. Works seamlessly with existing company CRM systems and databases. Integrates with external APIs for credit checking and more. ## The challenge The hard-coded business development workflow we had built for Scott Brownrigg a number of years before had done sterling service, but needed some updates - more flexibility, better integration with other company systems and a way of getting the relevant information in front of senior staff without them having to hunt for it. Approval and notification workflows needed to ensure that other areas of the business were aware of new leads, that sufficient time and resources were available, that the client was suitable and credit-worthy, and the project fitted in with the overall company strategy. The collaborative nature of lead generation and business development meant lots of comments and feedback were getting buried in email threads. Existing company CRM systems and databases had details of thousands of clients, teammates and projects - information that is really useful when responding to new leads and putting together pitches. We needed to unlock this potential. ## The solution We started by developing a feature-rich administration interface to build flexible workflows without requiring technical expertise. Administrators can quickly build a series of forms and gateways to capture the information required at each stage of the workflow, and send notifications, run checks, and request approval before allowing the lead to continue to the next step. The workflow forms can include any number of fields of different types: - Long and short text fields to capture qualitative information about the lead (e.g. title, description, etc.) - Select from a list of classification terms (e.g. project sectors, services provided, geographical locations, etc.), with the lists of terms being fully editable and re-usable in different flows - Add individual contacts, companies or staff members that have been imported from company CRM and staff databases. Assign them roles from lists of editable classification terms - Numeric fields to capture quantitative information, including auto-increment fields (good for assigning project numbers automatically) and spreadsheet-like equations for calculated values based on other fields - Yes/no answers. Date fields. Web links, uploaded files and links to files stored on the company network The forms are followed by editable gateways that run checks of the data supplied, send notifications and approval requests to the relevant parties, and branch to subsequent steps: - Administrators can set up groups of staff to receive notifications or approval requests. Groups can be re-used for multiple gateways or workflows - Individual staff can also be notified or asked for approval - Notifications/approval requests can be targeted based on the data provided in the form - e.g. "if Market sector is Energy, request signoff from Ethics team" and "if Region is Americas, send notification to New York office" - Gateways can be directly passed or failed based on the form data - e.g. "The value for Project must be more than £50,000" - The workflows can branch to or skip subsequent steps based on the data - e.g. "If Bid budget is less than £5,000, jump to Fee proposal form (skipping Full bid budget breakdown)" - Integration with the Creditsafe API means potential client organisation can be credit checked and enquiries approved, rejected or passed on for human evaluation based on the results Contact and staff data is imported from existing company databases via an administration interface to specify datasources and import queries. A system of administrator-defined triggers sends data back to the company databases to keep them up to date. Staff can use Sling via the web-based application, or the Outlook add-in. The web app offers a rich interface that lets them manage live leads, add and browse notes added to contacts and projects, and provides a dashboard overview of the data that can be searched and filtered. Beyond filling in the workflow forms, the app provides a rich interface for adding collaborative notes to individual answers, providing feedback in the approval process, rejection and resubmission, and a full history of the lead. The Outlook add-in provides a more focused interface that pre-filters the data based on the email the user is currently reading. The add-in can be used to quickly list the leads and projects a particular contact has been involved in, add new leads with fields pre-filled from the email metadata, and add notes to the contact for other staff members to see in future. Workflow approvals and comments can also be added directly in Outlook. > “We were looking to update our 'Enquiry process' and Sling has definitely fulfilled this - particularly by using the Outlook add-in that makes access quick and ensures great team engagement.” — Glyn Hurrey, Director at Scott Brownrigg ## The outcome Sling is available to all staff involved in business development. The new workflow is now much more flexible and responsive than before, and having personalised, relevant information available directly in Outlook has made collaborating on new business much easier. The direct link with other company systems reduced the administrative burden and made the tool immediately useful from launch. Integrating the workflow gateways directly with the Creditsafe API gives finance staff an immediate idea of a client's creditworthiness, allowing them to approve or reject leads before too many resources have been committed to the work. Simon Jardine, Technology Director at Scott Brownrigg thinks the integration with other company databases is a key feature: “From a technical standpoint the ease of setting up processes and intergrating to our back end systems has meant we can use the solution in a multitude of ways. Giving us the flexibility and agility to capture and use data to drive business processes in efficient ways.” Technologies used Python, Django, PostgreSQL, Outlook add-in API, Bootstrap, HTML, CSS, Javascript, Docker, Amazon EC2, Creditsafe API, OpenAsset API, OpenCV computer vision API. ## Screens - View the latest leads from across the business: The web app gives staff a searchable, filterable dashboard overview of live leads across the business. - Add collaborative notes to customer and collaborator contacts: Colleagues can leave notes on a contact's record, keeping feedback visible instead of buried in email threads. - View all the projects and leads a contact is involved in: Contact records link to every project and lead they are part of, drawing on data imported from existing company systems. - Set up multiple workflows for many different tasks: Administrators can build flexible workflows for different business processes without needing technical expertise. - Select from many different field types: Workflow forms can include many field types, from classification pick-lists to headings and yes or no questions. - Including text fields, dropdowns, yes/no, contacts, companies, staff members...: Fields can capture contacts, companies and staff members alongside classification choices and free text. - ...and spreadsheet-like calculated values: Numeric fields can feed spreadsheet-like formulas that calculate costs automatically from the values entered. - Add different types of approval gateways for each stage of the workflow: Gateways can send notifications, request approvals or branch the workflow based on the answers given at each stage. - Including group and individual notifications/approvals...: Group sign-offs and notifications can be triggered by the answers to compliance questions, or required for every enquiry. - ...and company credit rating scores: Credit rating results from an external check can automatically pass, block or route an enquiry for sign-off. - Manage groups: Groups of staff can be created once and reused for approvals across multiple gateways and workflows. - Outlook - View useful information about the contact who sent the email: The add-in shows the sender's details and notes in a panel beside the email they sent. - Outlook - view the leads a contact is involved in: The sender's Contacts tab lists every lead they have been involved in, each with its status. - Outlook - view lead gateways: Workflow gateway answers and approval history can be reviewed from within Outlook. - Outlook - edit contact details: The sender's name, email, role and phone numbers can be edited from the add-in. - Outlook - add a new lead by pasting information directly from the email: Highlighted email text and its metadata can be pasted straight into a new lead, pre-filling company and contact fields. - Enter workflow item details: Workflow item forms combine risk-assessment questions with fields for the people and companies involved in the item. - Approve or reject workflow items: Workflow items can be approved or rejected at each gateway, with a comment recorded alongside the decision. - View workflow item data: Each workflow item's full set of responses and gateway history can be reviewed together on one page. - View workflow item gateways: Gateway outcomes such as approvals, branching decisions and credit checks are logged against each workflow item. --- # Arup Website Technical Realignment URL: https://www.associativetrails.com/work/arupcms/ Client: Arup Type: CMS Summary: Web standards wizardry — A bespoke content management interface that maintains thousands of web pages, accommodates hundreds of authors and publishes standards-compliant code. ## The challenge The Content Management System (CMS) for the Arup corporate website had done sterling work for a number of years, but was badly in need of an overhaul to take advantage of improvements in server technology and coding techniques. Planned future enhancements to the site would also require a firm technical foundation. Arup approached us to redevelop the technical underpinnings of the site, whilst retaining the existing visual design and information architecture. ## The solution During consultation, we decided to not only completely redevelop the database and secure CMS, but also upgrade the front-end to take advantage of recent developments in web standards and stylesheets. The new CMS adds some much needed functional enhancements such as full page versioning with rollback and automatic image resizing and optimisation. The streamlined workflows save administration time and make the content authoring interface simpler and more intuitive. The updated architecture and database structure improves the modularity and long-term stability of the application. Rebuilding the front-end website code to follow the latest XHTML and CSS standards-based techniques means simpler cross-platform delivery, improved accessibility and reduced browser compatibility problems. > “I love the new system. You are a star!” — Cheryl Pulford Senior Communications Officer, Arup. ## The outcome The new technology behind the Arup website has made content management easier and given the website a firm foundation for future development. Along with other advantages, it is estimated that switching to web standards will save approximately 42GB of bandwidth per month. Technologies used Adobe Coldfusion, Verity, SQL Server, XHTML, CSS, AJAX, URL Rewriting. ## Screens - Powerful item search and retrieval: Search and filtering lets editors find a specific page among the thousands the CMS holds. - Advanced content authoring: The authoring interface lets staff write and format page text and attach project details in one screen. - Image management: Images attached to a page can have their captions and placement on the page managed from this screen. - Page linkage management: This screen manages promotional links between pages, showing both outgoing links and pages that link in. - Complies with XHTML web standards: The rebuilt front end follows current web standards for cross-platform delivery and accessibility. - Manages thousands of pages: The CMS holds the many project pages published across the site's regional sections. - Supports multiple languages: The site can present its content in other languages, shown here with a Turkish project page. - Full site usage statistics: Usage statistics track page views by month, letting the firm monitor how the site is used over time. --- # Arup Contacts and Events Database URL: https://www.associativetrails.com/work/arupcontacts/ Client: Arup Type: Intranet application Summary: An intranet application for managing client contacts and organising events. A more robust enterprise architecture, enabling the application to blossom into a fully-fledged company-wide resource. ## The challenge Arup's existing intranet application had done sterling work for a number of years but was beginning to struggle to cope with the volume of usage it was having to deal with. Our brief was to migrate the technical platform to a more robust enterprise architecture, enabling the application to blossom into a fully-fledged company-wide resource. ## The solution After an extensive consultancy period, we decided on a full rebuild of the system, taking advantage of the latest developments in web standards, application architectures and rich user interfaces. Streamlining the interface allowed us to reduce the load on the servers without sacrificing the sophisticated searching and filtering options. Integrating with central corporate HR databases reduced the administration overhead and ensured that staff data was always up to date. The ability to export contacts and events to desktop applications such as Outlook and Excel allows staff to take advantage of the system without changing their established working patterns. ## The outcome The finished application is fully integrated with central corporate applications and can scale to support all Arup staff. The new architecture ensures a solid platform for future development. The application is now ready to be rolled out to many more users and will save a great deal of time spent in managing client contacts and events. Technologies used Adobe Coldfusion, Oracle, XHTML, CSS, AJAX, WSDL. ## Screens - Sophisticated searching and filtering: The search form lets staff filter firms by business type, interest, location and account status. - Simple administration interface: A straightforward form lets staff add and edit a firm's contact record. - Holds thousands of records: The database can list and filter very large numbers of firm records at once. - Event invitation tracking: Invitation status and responses for a large guest list are tracked and filtered on a single screen. - Event management: An event's details, guest list summary and attendee list are managed together on this page. - Item versioning and validation support: Each record keeps a history of edits and validations, shown here flagging one awaiting review. --- # Arup Collateral Store and Branded Products Shop URL: https://www.associativetrails.com/work/arupcollateral/ Client: Arup Type: Intranet application Summary: An intranet application for sharing marketing collateral and internal ordering. ## The challenge Whilst Arup already had an intranet application to help them share marketing collateral with staff, the interface made it difficult to manage and find relevant documents. Our brief was to open up the system to the whole firm, make the interface more intuitive and share data with other applications on the firm's extensive intranet. ## The solution By analysing different use cases, we found that splitting the solution into two separate applications would streamline workflows and make the interface more intuitive. Integrating the two applications with existing HR systems allows the applications to recognise staff automatically and avoid unwelcoming registration, whilst still giving certain staff administrator privileges. We use managed classification schemas and ad-hoc tagging to provide a rich document browsing interface, with "serendipity" links between similar documents. Exposing document lists via web services allows other intranet applications to link directly to relevant collateral. Migrating the technical platform to Oracle allows the applications to support many concurrent users. > “[The applications] will save us significant time and money...The product is robust and adaptable - everything we asked for.” — Olivia Gadd, Head of Corporate Communications, Arup. ## The outcome Olivia Gadd, head of Corporate Communications at Arup explains: "The finished applications are fully integrated with central corporate applications and support Arup staff around the world. This will save us significant time and money and will assist in the consistency of our marketing message. The product is robust and adaptable - everything we asked for." Technologies used Adobe Coldfusion, Oracle, XHTML, CSS, WSDL. ## Screens - Yahoo-style directory listing: The directory-style browsing lets staff drill down to collateral by type, service, discipline or location. - Classification management: Administrators manage the classification schema used to categorise collateral from this screen. - Cloud of user-assigned tags: Ad-hoc tags assigned by users are displayed as a cloud, sized by how frequently each tag is used. - Browsing products: Staff can browse and add branded products to a basket as part of the internal ordering system. - Item-level metadata and serendipity links: Each document's metadata and a list of similar items are shown together, giving the serendipity links described in the brief. - Bespoke shopping basket: The bespoke basket lets staff review quantities and submit contact details to place an internal order. --- # Scott Brownrigg Compass URL: https://www.associativetrails.com/work/scottbrownriggcompass/ Client: Scott Brownrigg Type: Intranet Summary: Effective portal enhancement — An updated bespoke company information portal. Fully responsive, very well-connected. An update to one of our existing bespoke company intranets. The fully responsive interface can be used on screens of all sizes - from phones to the biggest desktop monitor. As well as providing a rich information portal, the intranet aggregates data from other corporate systems - staff information, project records and training events. ## The challenge Our original bespoke intranet for Scott Brownrigg (https://www.associativetrails.com/work/scottbrownriggintranet/) had done fine work for 6 years, but the time was right for an update. In consultation with the client, we decided to retain much of the functionality of the original system. However, an improved search interface would help users quickly discover relevant content in the increasingly large corpus of information held. We also wanted to broaden the range of other corporate applications aggregated by the system, including project records and training events. Interface-wise, a complete rewrite of the front-end to improve the design and make it responsive would ensure it was usable across a broad range of devices. ## The solution The new system retained much of the functionality of the previous one. Features such as rich text editing, granular administration privileges, content review and scheduling, internal news feeds and staff skill assessment were all featured in the new system. We used the Bootstrap framework and a modular front-end coding approach to build a rich interface that is responsive across all screen sizes. Having the solid foundation of a formal framework helped us achieve some complicated user interactions for cropping images, defining imagemap hotspots, sorting and reordering lists of linked items, etc. The new system includes a number of new or enhanced features: - User customisable news carousels - An application-wide user subscription model allowing users to quickly see updates to content they are particularly interested in - Integration with OpenAsset image library - Image cropping and definition of imagemaps within easy-to-use web forms - Support for multiple offices, with graphical presentation of office skills profiles - Event attendance management and integration with HR systems (Cascade) - Comprehensive staff skills profiles, with project summary and history pulled in from an external project records system (Union Square) - Sophisticated content discovery with a skimming interface for search results allowing users to review the content of search matches without leaving the result page ## The outcome The new intranet is available to all client employees using their web browsers. They are automatically recognised from their windows domain login. The new system has gone down well with staff, and we look forward to future development. Technologies used Adobe Coldfusion, Microsoft SQL Server, HTML, CSS, Bootstrap, Javascript. Integration with Microsoft Active Directory, Axomic OpenAsset, Union Square and Cascade. ## Screens - Home page with news carousel and subject feeds: The homepage puts a news carousel above panels for projects and people, and feeds of industry news, press coverage and practice news. - Filterable feed of latest news: The news list can be filtered by feed, such as press coverage, practice news or people news, with simple and advanced search. - News item with tags, linked staff, comments and subscriptions: News items can be tagged and linked to staff, with comments and a subscription option for future updates. - Office skills profile: Each studio page charts its staff's experience by sector at three levels, with tabs for groups, software skills, memberships and languages. - Events calendar: A monthly calendar colour-codes events by type, from management meetings and social events to CPD and mandatory training. - Event attendance management: Staff attendance at an event is tracked and displayed alongside a record of changes made to the listing. - Staff photographs: The staff directory filters people by experience, groups, software skills, languages, studio and more, showing each with a photograph, role and office. - Content skimming: A tag's pages can be skimmed in place: choosing one previews it beside the list, without leaving the tag page. - Project data including team members: Project records show their staff and their client and billing organisations, imported from Spider, which is where they must be edited. --- # Scott Brownrigg App for iPad and iPhone URL: https://www.associativetrails.com/work/scottbrownriggapp/ Client: Scott Brownrigg Type: Mobile app Summary: Stylish portable portfolio — A stylish portable portfolio of award-winning architecture projects, available via iTunes for iPad and iPhone. The App includes the latest company news, interesting research and information about over 250 projects split into lists based on sector and service. Projects have brief descriptions, quantitative data, multiple photographs and geographical locations plotted on maps. ## The challenge Having issued shiny new iPads for senior staff, architects Scott Brownrigg (http://www.scottbrownrigg.com) wanted to equip each one with a portfolio of their award-winning work that could be demonstrated anywhere. With over 200 projects, 850 high resolution photographs and a desire to see all the projects on a single map, the portfolio needed to marshal a large amount of data in a user-friendly and responsive manner, taking advantage of the iPad's large screen but also being usable on the smaller iPhone device. ## The solution Having prototyped in HTML5, we decided to implement as a native App for iPad & iPhone to maximise speed and allow it to be used when no data connection was available. The "halo effect" of having the App available on the iTunes store was also a very desirable feature for the client. The final App includes the latest company news, interesting research and information about over 250 projects split into lists based on sector and service. Most projects have brief descriptions, quantitative data, multiple photographs and geographical locations. Projects are individually and group-plotted on maps - the "Projects near me" plot shows the closest projects to the device's current location. The iPad version has a dashboard interface that displays an overview of all the news, research and project lists. The larger screen size allows the display of full metadata in project lists and high resolution photographs. The iPhone version cuts down the information displayed in menus and lists to make the App more usable on smaller screens. All project data is stored locally on the device meaning no data connection is required to use the App. Updates are discreetly downloaded in the background, without disrupting the user experience. ## The outcome The portfolio is live on the Apple iTunes App store and is available for Scott Brownrigg employees, potential clients, students, journalists and anyone who is interested to download. We're pleased to have had some positive user reviews: "Downloaded the app upon recommendation by an employee. I am very impressed by the structure and layout of the application. Furthermore, the content is just right. Not too much information yet not too little. Five stars from me." Technologies used Sencha Touch (for prototyping), Objective-C, the iOS SDK & Xcode, XML, Python (for data import), RSS. Available via iTunes for iPad and iPhone. ## Screens - Dashboard-style homepage with overview of news, sectors and R&D: The iPad's larger screen shows full news, sector and service listings on one dashboard-style home screen. - Full project description: Most projects include a description, quantitative data such as size and value, and multiple photographs. - Project locations plotted on detailed maps: Projects are plotted individually and in groups on maps covering their geographical locations worldwide. - iPhone homepage menu: The iPhone version simplifies the menu into a single stacked list suited to its smaller screen. - List of project sectors: Projects are split into lists by sector, such as business space or residential, and by service. - Project photos, location and description: Each project page lets a user switch between its photographs, map location and written description. - Project locations plotted on detailed maps: The same map view of project locations is also available on the smaller iPhone screen. --- # Scott Brownrigg Spider URL: https://www.associativetrails.com/work/scottbrownriggspider/ Client: Scott Brownrigg Type: CRM Summary: Spinning customer stories — Building a bespoke CRM system to sit proudly at the centre of an award-winning architecture practice. Managing contact, companies, projects, bids and events - all in a mobile-friendly web interface. ## The challenge When award-winning architects Scott Brownrigg asked us to build a system to keep track of their interactions with clients, partners and prospects we were confident we could deliver usable and robust software that met their brief. When the brief was expanded to include event and guest list management, bid tracking, aggregation of project media and the assignment of project numbers, we knew we had something to really get our teeth into... ## The solution The intranet-based web application presents an easy-to-use interface to access and contribute information about projects, individual contacts, companies and events. New projects are created and assigned project numbers (a very big deal for companies in construction) via a simple web form, which then integrates with Windows scripts to set up server resources and Newforma to set up project management. The project setup process, which used to take days and involve multiple manual interventions, now takes a matter of minutes. New contacts are added, assigned roles with companies and projects, and automatically integrated with accounting systems (Iris PROJECTminder) and project management software (Newforma). The ability to record quick and easy Twitter-style messages and associate them with contacts means a full history of interactions is built up without the hassle of filling in lots of forms. When staff are out of the office, they can use the simplified mobile interface to quickly record interactions with Clients and partners. > “The feedback during the training has been exceptionally positive, so I must thank you for all your amazing hard work in creating this wonderful system for us.” — Charlotte Wensley, Marketing & Communications Manager, Scott Brownrigg ## The outcome The SPiDER web application (named via a company-wide competition) now sits at the heart of the business and manages the entire lifecycle of project, contact and event records within the firm. "We have now rolled out Spider across the offices and the feedback during the training has been exceptionally positive, so I must thank you for all your amazing hard work in creating this wonderful system for us. Pretty much every question I have been asked I have been able to offer a response / solution to. Thank you!" - Charlotte Wensley, Marketing & Communications Manager. Technologies used Adobe Coldfusion, Microsoft SQL Server. Integration with Iris PROJECTminder and Newforma (http://www.newforma.com/) ## Screens - Overview of latest activity: A home feed gives an overview of the latest notes, meetings and events logged across the business. - Project details and associated media: Project records store statistics, financial details and photographs alongside a full list of the team involved. - Search entire contacts database: The entire contacts database can be searched and filtered by criteria such as sector, location and job title. - Full timeline of Contact's history: Each contact's page takes notes, tasks and guest-list additions, and keeps every note in a dated history beside their details and company. - Extensive Company details: Company records bring together address details, sector and grade, linked contacts, projects and interaction history. - Keeping track of guests: Guest lists track invitations sent, responses received and attendance for each event. - Granular administration privileges: Administrators can grant granular privileges to individual staff members for different areas of the system. - Easy merging of duplicate records: Duplicate contact records can be compared field by field and merged into one, keeping the chosen details. - Assigning project numbers, linking with other software: The project queue logs each project added or changed, and ticks off each step: written to SPiDER, emails sent, DFS and Newforma set up. --- # Sheppard Robson Intranet URL: https://www.associativetrails.com/work/sheppardrobsonintranet/ Client: Sheppard Robson Type: Intranet Summary: Speedy Intranet Implementation — How we delivered a fully-featured company intranet with Active Directory integration in double-quick time. ## The challenge Sheppard Robson came to us with an urgent requirement to build an intranet to host company news, Quality Management documents and a wealth of other information. The system needed to integrate with existing staff data (contained in Microsoft Active Directory and Axomic OpenAsset) and be easily updated by non-technical staff without learning additional HTML or design skills. The client had a very tight timeline, meaning the whole system had to be specified, built, delivered, tested and launched in less than 3 months. ## The solution The intranet was built to make the most of the client's existing infrastructure, meaning no additional server software was required. The standards-compliant HTML interface is accessible by all staff using a standard web browser. Extensive personalisation features allow all staff to tag pages, subscribe to internal news feeds, edit their profile page and access lists of frequently visited pages. External news feeds displayed directly on the intranet home page allow staff to see external press coverage about the company. Intranet content is managed by non-technical members of staff via their web browsers. Key features include: - A Microsoft Word-style rich text editor to write page body text. - Granular administration privileges meaning author access can be restricted to certain sections of the intranet - Administrators can be assigned different depth of privileges - Full versioning and rollback - Sophisticated "see also" link functionality, automatically generating lists of related links using page metadata and vector space techniques - User tagging - Page archiving with immediate restore - Content review scheduling and expiry warning - Internal news feeds/blogs supports sports clubs and other groups > “Thanks very much ... you have done a great job and all the contributors are really pleased with the site. I ... appreciate all the effort you have put in and the good ideas you came up with.” — Nick Dunn, IT Director, Sheppard Robson. ## The outcome The new intranet is available to all client employees via their web browsers. People with authoring privileges can create and edit their own pages. The system was delivered, tested and populated within a two-month period over the summer holiday season. Technologies used Adobe Coldfusion, SQL Server, XHTML, CSS, RSS, jQuery. ## Screens - Personal toolbox for every user: Every user gets a personal toolbox for quick access to favourite pages and, for administrators, site management tools. - Portal-style homepage with news feeds: The homepage brings together press coverage and internal company news alongside the site's navigation. - Staff profiles with editable fields: Staff can edit fields on their own profile page, such as a personal interests and hobbies section. - Rich page editing - no technical skill required: Pages are edited with a rich text toolbar similar to a word processor, needing no technical or design skills. - Fully editable hierarchical navigation: Non-technical staff can add, delete, move and reorder pages within the site's hierarchical navigation structure. - Pages include related links and user tagging: Pages automatically list related links and can be tagged by users, alongside a record of past versions. - Content review workflow - no more timeworn information: Pages are flagged when their review date passes, so out-of-date content can be checked or refreshed. --- # Sheppard Robson CRM URL: https://www.associativetrails.com/work/sheppardrobsoncrm/ Client: Sheppard Robson Type: CRM Summary: Creative Customer Management — How we helped a leading architect manage their customer relationships. ## The challenge Sheppard Robson, the award-winning architecture practice, already had a sophisticated project and contacts database system they used on a regular basis. However, they felt that a more user-friendly interface would help non-technical staff find information that was important in their day-to-day dealings with clients. Our brief was to build a usable, web-based interface on top of the existing Planchest installation; with the addition of richer task, event and bid management functionality. A key requirement was to help the management team's strategy and resource planning by giving them quick and easy access to summary information. ## The solution Integrating directly with the existing databases gave us a great deal of flexibility in how we could display the information, and allowed us to add some helpful new features such as saved searches and user tagging. In an effort to make summary information more easily accessible, we used charts and calendar interfaces along with print-optimized summary reports. A simple privilege system means all company staff can read most of the information, but only those that need edit access are given it. However, information only meant for management can be flagged as "privileged" and is hidden from public view. ## The outcome Harriett Hindmarsh, Director of Marketing and Business Development at Sheppard Robson, explains: "We needed a system that would help us access our business and client information in a useful, logical and efficient way. Associative Trails designed and installed a system that both met and exceeded our brief. We now have a highly efficient, easy to use client management system that performs above and beyond all expectations of a CRM system. I would highly recommend them to any organisation - their listening approach was creative, flexible and intelligent - resulting in a well designed, fit for purpose system used by the whole practice." Technologies used Adobe Coldfusion, SQL Server, XHTML, CSS, AJAX. ## Screens - Company-wide dashboard view: Managers get quick access to summary information, such as overdue actions across projects, bids and contacts. - Seamless integration with corporate databases: The interface displays information drawn directly from the practice's existing project and contacts databases. - Monthly summary reports: Print-optimised summary reports and charts make monthly bid and account activity easy to review. - Bid management: Bid pages track a full history of actions, memos and press coverage alongside submission and team details. - Comprehensive export functionality: Project listings can be exported to Excel, CSV or a printed format for use outside the system. - Event and guest list management: The guest list summary shows how many were invited, responded and attended for the event. --- # Ellipse Website and CMS URL: https://www.associativetrails.com/work/ellipsecms/ Client: Ellipse Type: CMS Summary: Crafty content management — Slick content management with YouTube, Wufoo and PDF generation. Rich text editing, versioning, image optimisation, approval workflows and publication scheduling and expiry. Sophisticated content management functionality is supported by integration with YouTube and Wufoo to provide a powerful and feature-rich CMS which is, according to its users, "a doddle to use". The ability to take user web input and generate PDF forms has greatly helped with insurance claim procedures. ## The challenge Ellipse, an insurance company offering group life and disability cover, have invested heavily in technology to streamline the process of obtaining group life quotes and employee insurance. Having worked with Ellipse on aspects of their quotation system, they approached us to help make their public-facing website as slick as their interfaces for their clients and IFAs. ## The solution The Content Management System (CMS) we built to administer the public-facing website provides Ellipse staff with a clean and intuitive interface that requires no technical knowledge or training but retains enough power and flexibility to keep the website fresh and pertinent. Key features include: - A Microsoft Word-style rich text editor to write page body text. - Automatic optimisation and resizing of images, saving time and expense on specialist image editing software and training - The ability to set up microsites for specific territories or campaigns via a simple web form - Publisher approval workflows with email alerts - Content publication scheduling and expiry - The ability to specify page review dates to ensure content is regularly reviewed - Full versioning and rollback - The ability to manage and embed YouTube videos and Wufoo forms directly in managed web pages - Management of fundraising campaigns - Embedded link integrity checking - Integration with Google Analytics for sophisticated site usage statistics - Responsive design techniques enable clear rendering on devices with a range of screen sizes - Public-facing web forms help speed up claims procedure - PDF forms are automatically generated with user input > “The CMS is a doddle to use.” — Peter Fenner, Communication Manager, Ellipse ## The outcome The main priority was to find a way that Ellipse staff could easily maintain and update the content of their public-facing website. Certain aspects of the functionality, such as the management of charity fundraising campaigns and automated generation of PDF forms, are highly specific to Ellipse's processes and have helped save time and improved customer services. Technologies used Adobe Coldfusion, Microsoft SQL Server, HTML5, CSS3, jQuery, Bootstrap, URL Rewriting, Responsive design, automated PDF generation. ## Screens - Web standards compliant front-end: The rebuilt public-facing website presents company information alongside embedded video content. - User-managed navigation hierarchy: Staff manage the site's page hierarchy and publication status directly from this content administration screen. - Easily manage and embed YouTube videos: Videos hosted on YouTube are browsed and managed here before being embedded on web pages. - Manage landing page redirections and search engine-friendly URLs: Redirects between old and new page addresses are listed and managed on this screen. - Automatic optimising and resizing of images: Images used across the site, including charts and staff photos, are searched and managed from this library. - Link integrity checking: A link report flags external links on the site that can no longer be reached. - Charity donation: A quoted customer can choose which charity receives a donation, as part of the site's fundraising campaign management. --- # Grant Thornton Events Management System URL: https://www.associativetrails.com/work/grantthorntonevents/ Client: Grant Thornton Type: Event management Summary: Cutting edge events — Full lifecycle event management - attendee registration, mailshots, table plans and name badges. A bespoke system for managing staff events. The full event lifecycle is covered, from building the guest list, event website and registration form, mailshots and table plans. Integrates with Wufoo, a powerful online form generation service that we have used to help build event registration forms. The system imports Wufoo data and records RSVP responses and dietary requirements. ## The challenge Grant Thornton (https://www.grantthornton.co.uk/), a global independent assurance, tax and advisory firm, have thousands of employees that require regular professional training. A programme of regular internal events helps them support their staff, and managing these events is an important duty for support staff. The software they used to manage these events was difficult to use and customise, and Grant Thornton approached us to build a system that was powerful yet easy to use and didn't require regular calls to Helpdesk support. ## The solution After a requirements capture exercise that included the iteration of a paper prototype (https://www.associativetrails.com/about/intranetdevelopment/#paper-prototype), we set to work building a bespoke web application with the following features: - Sophisticated management of the full event lifecycle - manage guest lists, invitations, mailshots, registrations, name badges, table plans and record attendance. - Content management functionality to build a website for each event, allowing attendees to register and see venue information, timetables, travel and accommodation information and dress code. - Integration with Wufoo allows sophisticated registration forms to be built and the responses used to assign attendees to workshops, send mailshots, etc. - The ability to bulk upload invitees and export lists of guests in CSV format - Send event invitations using a well-designed email template, and send mailshots to all or a subset of attendees - Print out badges or labels for attendees in PDF format sized to fit a standard stationery layout. Badges can include individual attendee QR codes that event organisers use on the day of the event to scan with their smartphones and record attendance. - Manage table plans and workshops by assigning attendees to different groups - table plans and workshop lists can be exported in PDF format for printing, and assigned tables/workshops added to individual name badges. - View an individual's attendance history - which event have they been invited to, how did they respond and did they attend? - An image library for logos, maps, etc. means standard images can be used in event websites, mailshots and badges - the images are automatically resized and optimised. - A document library for PDF handouts and mailshot attachments means there is a central place to keep documents that are regularly used - Secure administrator access via a web browser - with the ability to set up unlimited administrators. > “I just want to say that you have done an amazing job developing the system and I appreciate all the work you have put in.” — Sophie Whitelock, Events Coordinator, Grant Thornton ## The outcome The new system has been tailored to Grant Thornton's needs and is successfully managing internal events. Having an interface that is easier to use and requires little training has meant more staff can be involved in managing events, taking pressure off key individuals. Technologies used Adobe Coldfusion, Microsoft SQL Server, HTML5, CSS3, jQuery, Bootstrap, URL Rewriting, automated PDF and QR code generation. ## Screens - Sophisticated event management system: The system manages the full event lifecycle, from guest lists and invitations to mailshots, badges and table plans. - Template-driven event websites: Each event gets its own website, built from a chosen layout and logo, where attendees can register and find event details. - Bulk upload invitees: Invitees can be bulk uploaded from a CSV file and immediately assigned one of several invitation mailshots. - Manage and filter guest list: Guests can be filtered by office, role or response and assigned to tables and workshop sessions from the same list. - Print attendee badges - including QR codes to record attendance: Badges can include a QR code that organisers scan on the day to record each guest's attendance. --- # Ashridge Business Perspectives URL: https://www.associativetrails.com/work/perspectives/ Client: Ashridge Business School Type: News portal Summary: Feeding professional perspectives — How we used open-source software and open data standards to help a leading business school extend its offering. Easy management of hundreds of feeds and thousands of automatically imported news items. ## The challenge When Ashridge Business School approached Associative Trails to come up with ideas for its Virtual Learning Resource Centre (VLRC), it quickly became clear that Ashridge's expertise and knowledge would provide some exciting opportunities for development. Following consultation, it was agreed that what Ashridge required was something that used the existing body of knowledge contained in the VLRC, but opened it up for public consumption in a way that made it easy to administer, accessible across multiple channels and could be easily extended in future. ## The solution The Perspectives website is a news aggregation portal, edited by Ashridge's experts, that delivers up-to-the-minute business news and analysis. The use of open-source technologies during implementation allowed rapid development and deployment and a rich feature set that includes: - Easy management of hundreds of feeds and thousands of automatically imported news items. - The organisation of feeds into themes and sub-themes. These can be made private, allowing custom themes to be built for delivery to specific clients. - User clipping and rating. - A commitment to using open data standards wherever possible, e.g. RSS, Microformats, etc. - Republishing of aggregated feeds in multiple formats, e.g. RSS and iGoogle widgets, allowing users to consume the content in the way that is most convenient for them. > “Matt has been an inspirational product development partner, helping us to both clarify our initial vision and then deliver an outcome way beyond initial aspirations.” — Tony Sheehan, Learning Services Director, Ashridge ## The outcome The open-source stack enables us to use a low-cost yet powerful and quickly scalable hosting solution for the Perspectives portal. In future, the application will be used to audition innovative functionality before implementation in the main VLRC. Tony Sheehan, Learning Services Director at Ashridge, explains: "The portal has turned RSS from a vague concept into a practical and valuable tool to many of our users - a great example of appropriate use of technology to serve user need." Technologies used Python, Django, MySQL, RSS, Microformats, XHTML 1.0 Strict, CSS, RSS, jQuery. Ubuntu, Apache, WSGI, Nginx, Memcache, Munin, Monit. ## Screens - Portal homepage: The homepage aggregates business news and analysis into themed columns such as Leadership, Strategy and Corporate Responsibility. - User personalization: Users choose which topics appear on their personalised homepage by ticking or unticking each one. - Article clipping and tagging by users: Readers can clip an article and add their own tags and a description to it for later reference. - Star ratings: Readers can rate an article out of five stars and leave a comment alongside their rating. - Aggregated news feeds: News items are imported automatically from external feeds and grouped by theme for readers to browse. - Easy to use administration: Administrators manage hundreds of feeds and organise them into themes from a single admin screen. - Hundreds of feeds and thousands of items: The reporting screen totals thousands of automatically imported items across all the feed themes. --- # Old Arupians Community Website (2019) URL: https://www.associativetrails.com/work/oldarupians/ Client: Old Arupians Type: Community website Summary: Freshening community fellowship — A thorough refresh of one of our most venerable and successful projects. Now fully responsive and even easier to use. This vibrant community of retirees continues to use the website as the focal point of their social relationships with ex-colleagues. They also enjoy keeping tabs on their successors' attempts to continue their legacy. ## The challenge Our original community website for the Old Arupians (https://www.associativetrails.com/work/oldarupians/) had done fine work for 12 years, but the time was right for an update. In consultation with the client, we decided to retain much of the functionality of the original system. They also wanted to ensure that their investment in a new website would ensure it was low-cost to host and easy to maintain in the future. Interface-wise, a complete rewrite of the front-end to improve the visual design and make it responsive would ensure it was usable across a broad range of devices. ## The solution An extended HTML prototyping process before the main development began allowed Old Arupians administrators a large amount of input into the visual and information design of key pages - something that then informed the rest of the system. The client has a skilled team of administrators who were happy with the way the previous site worked - our focus was to present the existing information and workflows in a better way. We used the Bootstrap framework and a modular front-end coding approach to build a rich interface that is responsive across all screen sizes. Some functional enhancements (e.g. automatic thumbnailing of PDF newsletters, profile image cropping) allowed us to add additional layers of gloss to the new interface. The new system retains all of the rich functionality of the previous one, with some handy additions: - A sophisticated rich text editor to write page body text. - Granular administration privileges meaning author access can be restricted to certain sections of the system - Image cropping and optimisation - Automatic thumbnailing of PDF uploads for gallery-style display - Gamification of profile completion - encouraging members to fill out their profiles more fully - Granular privacy of member profiles - users choose exactly what personal information is shared on the site - Event and RSVP management with bulk emailing - User subscriptions to new content email alerts - Weekly digest emails keeping members abreast of new content ## The outcome The hard work rewriting the entire system in open-source technologies was rewarded when the website went live. Hosting costs have dramatically reduced, and the range of hosting providers is much greater and more competitive. The site is currently hosted on Amazon Elastic Beanstalk, making it easily scalable and deployable with a single terminal command. The new design has gone down well with users: - "I think this is a splendid piece of work... It's clean, intuitive and a fantastic resource for us elderly types. Bravo." - "New website is user friendly and offers great features." - "Congratulations to all for their hard work, it is much appreciated, much easier to navigate around." Technologies used Python, Django, PostgreSQL, Bootstrap, HTML, CSS, Javascript, Amazon Elastic Beanstalk, Jekyll (HTML prototype). ## Screens - Home page: The homepage gathers the latest news, people, Arup films and publications, with upcoming events and their RSVP counts, under a rotating banner. - Latest news: Members can browse and search past news posts, each carrying a comment count for community discussion. - RSVP management: Event organisers can search, edit, email or export the whole RSVP list as part of bulk event management. - Event page with RSVP list and member comments: Members can see an event's details and invitation, who can and cannot attend, and the comments other members have left. - Committee members: The page sets out who administers the site and what each committee member is responsible for. - Publications with automated thumbnailing: Thumbnails are generated automatically from uploaded PDF newsletters, so publications display as a browsable gallery. - Member profile: A completion indicator encourages members to fill in more of their own profile information. - Stock photo gallery: Photos can be uploaded and then edited or deleted individually from this shared image library. --- # Collaboration URL: https://www.associativetrails.com/work/collaboration/ Client: Associative Trails Type: Web application Summary: A central repository for a geographically-dispersed team to record contacts, appointments, documents and other information. Collaboration was originally developed for our own use, as we didn't think that the current crop of commercially-available wiki software would meet our requirements. Somewhere between a wiki and a CMS, Collaboration gives us a central repository to record all our contacts, appointments and documents. We're sure that other people will find it as useful as we do, and would love to deploy and extend it for a client. Key functionality includes: - Multiple "Spaces" to keep projects separated - Hierarchical organisation of items - Automated calendar and contacts directory - The ability to comment and attach uploaded files to any item - Granular user privileges down to the item level - The ability to lock or hide items - Integration with third-party tools such as Delicious, Ta-da lists, Basecamp, etc. ## Screens - Hierarchical organisation of content: Items are organised in a nested tree, alongside a calendar and contacts directory for the space. - Item view and interactions: An item's details and its child items are shown together, with actions such as attaching files or adding peers. - Item editing: Items are edited through a rich text form with a title, parent item and formatted body content. - Granular user permissions: Permissions can be set per user and per item, matching the granular privileges the record describes. --- # Croxley Rail Link CMS URL: https://www.associativetrails.com/work/croxleycms/ Client: Arup Type: CMS Summary: Retro-fitted content management to the rescue — A bespoke CMS retro-fitted to an existing static site. A clean and intuitive interface helps non-technical staff keep the website updated. Keeping the public informed about major civil engineering works in the local area requires a vast amount of information to be published and be reliably navigated. ## The challenge The original Croxley Rail Link website was built without any content management interface, meaning team members could not easily maintain or update the vast amount of information about the major infrastructure construction project, including the proceeds of the extensive public enquiry. We were approached by Arup, a global engineering consultancy, to retro-fit a content management system to allow easy maintenance - but without altering the existing website design. ## The solution The Content Management System (CMS) we built to administer the public-facing website provides team members with a clean and intuitive interface that requires no technical knowledge or training but retains enough power and flexibility to keep the website fresh and pertinent. Key features include: - A Microsoft Word-style rich text editor to write page body text - Search engine friendly page URLs - Automated news listings - Automatic optimisation and resizing of images, saving time and expense on specialist image editing software and training - Content publication scheduling and expiry - The ability to specify page review dates to ensure content is regularly reviewed - Full versioning and rollback ## The outcome The website is now much easier to keep up to date. This is especially important as building work on project progresses. Technologies used Python, Django, MySQL, HTML5, CSS3, jQuery, Bootstrap. ## Screens - Retro-fitted CMS without changing the existing website design: The public-facing site kept its original visual design after the content management system was retro-fitted. - Full management of site navigation including content pages, documents and external links: Editors can reorder and manage the site's whole navigation structure, including pages, files and external links, from one screen. - Image optimisation and resizing: Uploaded images can be resized to a chosen target size through this library screen. - Content page editing via WYSIWYG editor, keywords, search engine friendly URLs, content expiry and review: A single form lets editors set a page's title, URL, keywords and body text alongside expiry and review dates. - Automated news listings: News items are managed in a dedicated list that feeds the automated news listings on the site. - Image metadata and usage: This screen shows a graphic's alternative text and lists the pages currently using it. --- # Gallerysmith URL: https://www.associativetrails.com/work/gallerysmith/ Type: CMS Summary: Great gallery websites — A fully-featured content and customer relationship management system for art galleries, including automatic image optimization. Gallerysmith is a content and customer relationship management system for art galleries. The secure, web-based interface allows galleries to administer all the content and customer interactions on their website with the minimum of technical knowledge. Highlights include: - Full edit control over news, press, exhibitions, works and artist pages via a simple Microsoft Word-style rich text editor. No technical skills or specialist software required. - Automatic optimisation and resizing of images. Saves time and expense on image editing software and training. - Bespoke website front-end design - Track user registrations and enquiries. User "private view" functionality allows galleries to promote specific works to specific customers. - Newsletter authoring, emailing and automatic PDF generation for printed newsletters - Full website usage statistics in real time - High quality printed output via separate print stylesheets - Content scheduling with free choice of content publish and expiry dates - Granular administrator privileges for total security and editorial signoff workflows - Automatic currency conversion for published prices > “The new website will save me at least two hours a day.” — Karina Phillips, Gallery Different. ## Screens - Bespoke gallery website: The bespoke front-end design presents a gallery's available works with filtering and sorting options. - Image management: Each uploaded image is automatically generated into several standard sizes, tracked on this admin screen. - Customer interaction management: Customer registrations and their private view and enquiry status are tracked together on this screen. - Content authoring: Artist and other pages are written and formatted through this rich text editing form. - Site usage statistics: Usage statistics rank the site's most viewed pages and most active customers over a chosen date range. - Quality printed output: A separate print stylesheet produces a cleaner printed version of the same work detail page. --- # Last.fm Normaliser URL: https://www.associativetrails.com/work/normaliser/ Client: Associative Trails Type: Web application Summary: Experimentally musical mashup — A little application to rank your favourite artists and albums by an estimation of how long you have spent listening to them. The first version of the Normaliser was launched in June 2007, and immediately got a bit of coverage on influential blogs, which brought it a decent amount of traffic. After two years of solid service, we rebuilt the application from scratch using open-source technologies and refreshed the design. ## The challenge Having been a user of last.fm (http://www.last.fm/) for a number of years, I often found it frustrating that my listening charts didn't accurately reflect my listening habits. The problem was that last.fm uses the "track" as the atomic unit of attention - and last.fm's listening charts are sorted based on how many tracks you have listened to by a particular artist. This approach can throw up some problems. For instance, some artists tend to record shorter tracks than others. For instance, spending an hour listening to Arctic Monkeys (http://www.last.fm/music/Arctic+Monkeys) will probably log twice as many tracks as the same time spent listening to Pink Floyd (http://www.last.fm/music/Pink+Floyd). ## The solution After grumbling at length in a blog post (https://www.associativetrails.com/blog/entry/The-atomic-unit-of-attention), I realised that in the age of open data it shouldn't be too much hassle to knock together a little app to apply the "normalisation" calculation I discussed. The resulting application does the following: - Takes a last.fm username and grabs the XML list (http://www.last.fm/feeds/) of top 50 artists/albums; - Goes through those artists and grabs album and track data for them from the MusicBrainz web service (http://wiki.musicbrainz.org/XMLWebService); - Calculates the median track duration for each artist, using it to estimate how much time spent listening to the artist; - Sorts the artist list by estimated time. The resulting table shows top artists ranked based on the estimated time spent listening to them. ## The outcome The first version of the Normaliser was launched in June 2007, and immediately got a bit of coverage on influential blogs, which brought it a decent amount of traffic. After two years of solid service, we rebuilt the application from scratch using open-source technologies and refreshed the design. We are regularly adding new functionality, including graphical widgets allowing users to display their normalised charts on their own websites and blogs. In November 2009, we started a small collaboration with the Music Technology Group (http://mtg.upf.edu/) at the Universitat Pompeu Fabra (Barcelona, Spain). Technologies used Python, Django, MySQL, RSS, XHTML 1.0 Strict, CSS, RSS, jQuery, Google Chart API. Ubuntu, Apache, WSGI, Nginx, Munin, Monit. ## Screens - Normalisr homepage: Users enter their last.fm username to generate a chart ranking artists by estimated listening time rather than track count. - Time-based charts of last.fm music data: Artists are re-ranked by estimated listening time, calculated from track counts and median track duration, showing the change from the original rank. - The latest news about your favourite artists: Entering a last.fm username fetches recent news stories about the artists in that listener's charts. - Increased usage since redesign: The site was rebuilt from scratch and its design refreshed after two years of service, with usage tracked over time. - A widget for users' websites and blogs: Normalisr can generate a graphical widget that users add to their own websites or blogs to display their charts. - Charts showing artist photos: Chart results can also be displayed as a grid of artist photographs rather than as a table. --- # Old Arupians Community Website (2006) URL: https://www.associativetrails.com/work/oldarupians2006/ Client: Arup Type: Community website Summary: Fostering community fellowship — A thorough refresh of one of our most venerable and successful projects. Now fully responsive and even easier to use. ## The challenge Arup wanted a simple and secure way for retired ex-employees to keep in touch with each other and stay abreast of the latest company news. Community members needed to be able to keep their own contact details updated without having to learn new technical skills. ## The solution The password-protected community website includes sections for latest news, events, a contacts directory and an area where members can download the latest Arup publications in PDF format. All content administration is performed via simple web forms and all users can add their own news items, events and keep their contact details up to date. Our research into web interfaces for older users helped us design a simple and effective interface, with Arup's in-house graphic designers providing colour, typography and logos. > “Thank you very much for helping us to reach this milestone. Without your patience, wonderful expertise and hard work we would still be dreaming about it..” — Barbara Miller, Arup. ## The outcome The site went live in good time for the Old Arupians 2006 annual meeting, with the RSVP functionality proving particularly useful. Barbara Miller of Arup continues: "I would also add that the website has already paid dividends in enabling quick and easy communication with a large proportion of our community. As the generation now retiring has become accustomed to internet usage through their work we expect its value and use to increase and the day will soon come when it is our main communication line with our retired staff." Technologies used Adobe Coldfusion, Microsoft SQL Server, XHTML, CSS. ## Screens - Latest news listing: All members could add their own news items and read updates through simple web forms, with no technical skill needed. - Secure member access: Password protection kept the retired members' community area private, with a simple process for new members to register. - Events and member RSVP: The RSVP functionality proved particularly useful when the site launched ahead of the 2006 annual meeting. - Contacts directory: Members could search the directory by name or by location to find and keep in touch with each other. - User-administered content: Members update their own contact details directly through this form, without needing to learn new technical skills. - Administrator-only interface: All content administration, such as managing event categories, is performed through simple web forms like this one. --- # Safetime CBC - Android App and Web Portal URL: https://www.associativetrails.com/work/safetimecbc/ Type: Mobile app and portal Summary: Scooping safety awards — Android App and Web portal for a multiple award-winning construction site safety monitoring system. The Android App is used to perform safety inspections of scaffolding on construction sites. The App uses NFC to update a safety device attached to the scaffolds. Data is synchronised back to a web portal that gives complete information on inspection history, device locations, weather feeds, etc. ## The challenge The SafeTime CBC device is an electronic tag that is revolutionising health and safety in the construction industry. Customers affix a monitoring device to temporary works (e.g. scaffolding) on a building site and use our custom-built Android App to regularly inspect the safety of the temporary works. Once inspected, the CBC device is updated by the Android App (via NFC technology similar to London Transport Oyster cards) and the device displays how long there is left until the next inspection. This means people using the scaffold on a daily basis can easily see that it has been inspected for safety. If an inspection runs out, the device flashes red and the scaffolding should not be used. ## The solution We have built a native Android App which runs on NFC-enabled Android devices (e.g. Google's Nexus devices) to allow inspectors to perform inspections and update CBC devices. The App synchronises its data with a web-based portal that allows users to see all their projects, live devices and view inspection data. This means site inspectors, off-site managers and health and safety staff can all easily see the safety status of all their scaffolding from one simple interface. Other key features include: - The App has been designed to work in situations where connection to the internet is unpredictable. A data file is kept locally on the device, which can then be synchronised with the server once the device has a reliable internet connection (over wifi or 3G) - The App interface has been designed to be as simple as possible. Large text and buttons mean it can be used effectively on building sites with potentially adverse lighting or access conditions. - All inspection data can be exported in PDF or CSV format. - Observed weather feeds from the Met Office mean we can send alerts when wind speeds exceed certain levels, enabling inspectors to ensure their sites are safe. - Comments and photographs can be added to individual inspections, devices or projects - and actions can be assigned to other team members if anything requires urgent attention. - A device's full inspection history and observed weather conditions is always available. Battery status and the geographical location of each device is also reported as part of an inspection. - When a scaffold is taken down, the monitoring device can be reassigned to another scaffolding. ## The outcome The device won the Beaumont Safety Trophy (https://web.archive.org/web/20160424234813/http://www.chsg.co.uk/annual-awards/winners-of-chsgs-annual-awards-2014/) at the Construction Health & Safety Group (http://www.chsg.co.uk) Annual Awards 2014. The award citation highlights the system's strengths: "This small but perfectly-formed device is a really clever tool that can give many of us peace of mind and a means of checking site activity performance from our offices." One of the pilot projects using the device, the Grange Walk Project (managed by United House Ltd. for Notting Hill Housing Trust) won a 2014 Gold National Site Award from the Considerate Constructors Scheme (http://www.ccscheme.org.uk). The award specifically refers to the CBC devices and the weather feed alerts: "Modern technology was widely employed at this site... The site's link to the Met Office also worked exteremely successfully during the recent high winds." Technologies used App: Android SDK, Java, SQLite, NFC, XML, GPS, Amazon S3 API. Portal: Adobe Coldfusion, Microsoft SQL Server, HTML5, CSS3, jQuery, Bootstrap, automated PDF generation. AWS Lambda. ## Screens - The web portal dashboard shows an overview of all live devices: Managers can see inspection status and device locations across every project from one overview screen. - The App homepage allows quick access to common tasks: The app's simple, large-button interface suits use on building sites in adverse lighting or access conditions. - Maps of live devices: The geographical location of each device is shown on a map as part of its inspection record. - Listing expired and urgent devices: The app flags devices whose inspection has expired or is due soon, so inspectors can prioritise them. - View device, start inspection or declare scaffold unsafe: Inspectors can start a new inspection or flag a scaffold as unsafe directly from the device's detail screen. - Full device information including health and safety, inspection history, map, recent weather, project details, etc.: Weather feeds from the Met Office are recorded alongside each device's inspection history and location. - Set up as many projects as you need: Each project groups its own devices, contact details and location so users can manage several sites at once. - Full inspection details, including comments and photographs: Comments and photographs can be attached to an inspection, and actions assigned to team members if needed. --- # Scott Brownrigg Intranet URL: https://www.associativetrails.com/work/scottbrownriggintranet/ Client: Scott Brownrigg Type: Intranet Summary: Designing delightful information — A vibrant, design-led company intranet that is viewed and maintained using a standard web browser - no technical skills required. A bespoke intranet developed and delivered on a tight deadline. All staff can author intranet content using straightforward web forms that require no specialist software or training. The system has been regularly extended and offers highly specialised functionality such as a CPD events calendar, a Stack Overflow-style Q&A section and bespoke staff review questionnaires with infographics. ## The challenge Scott Brownrigg came to us with an urgent requirement to build an intranet to replace a previous system based on Microsoft SharePoint. The new system needed to open up content authoring and ownership to non-technical staff with the minimum of training, as well as integrating with existing staff data (contained in Microsoft Active Directory and Axomic (http://www.axomic.com/) OpenAsset). The client had a very tight timeline, meaning the whole system had to be specified, built, delivered, tested and launched in less than 2 months. ## The solution The intranet was built to make the most of the client's existing infrastructure, meaning no additional server software was required. The standards-compliant HTML interface is accessible by all staff using a standard web browser. Extensive personalisation features allow all staff to comment on content, subscribe to and author internal news feeds, edit their profile page and access lists of frequently visited pages. Press coverage feeds displayed directly on the intranet home page allow staff to see external press coverage about the company. Intranet content is managed by non-technical members of staff via their web browsers. Key features include: - A Microsoft Word-style rich text editor to write page body text. - Granular administration privileges meaning author access can be restricted to certain sections of the intranet - Administrators can be assigned different depth of privileges - Full versioning and rollback - Automatic optimisation of images, with easy cropping of images without the need for specialist software - User favourites - Page archiving with immediate restore - Content review scheduling and expiry warning - Internal news feeds/blogs supports sports clubs and other groups - A noticeboard to allow staff to post messages about personal events, stuff for sale, flats for rent, etc. ## The outcome The new intranet is available to all client employees via their web browsers. The visual design does justice to an award-winning architectural practice and the easy-to-use interface benefits all staff, without requiring specialist training. The system was delivered, tested and populated within a two-month period over the Christmas holiday season. Technologies used Adobe Coldfusion, Microsoft SQL Server, HTML, CSS, javascript. Integration with Microsoft Active Directory and Axomic (http://www.axomic.com/) OpenAsset. ## Screens - Portal-style homepage with press coverage: The homepage brings together internal news and external press coverage about the company in one portal view. - Subscriptions and admin accessed via personal toolbox: A personal toolbox gives each user quick access to their favourite pages and subscriptions, and administrators to site management tools. - Staff profiles with user-editable fields: Staff can edit fields on their own profile page, such as a personal interests and hobbies section. - Monitoring external press coverage about the client: Press coverage feeds displayed on the intranet let staff see external press coverage about the company. - Fully editable hierarchical navigation: Non-technical staff can add, delete, move and reorder pages within the site's hierarchical navigation structure. - Easily crop images without the need for specialist software: Images can be cropped for thumbnails and banners directly in the browser, with no specialist software needed. - Company-wide events and training calendar: A company-wide calendar lists events and training sessions, filterable by month, category and office. --- # Tools URL: https://www.associativetrails.com/tools/ Associative Trails / tools Tools Instruments from our own chart room. We build small tools to do our own work better, then publish them. Take them, adapt them, tell us where they fall short. - Underway: still changing - Stable: finished, kept in working order ## Open source — Take them, adapt them ### Roundtable Prompt framework · Underway Prompts and personas for structured, multi-expert deliberation with any AI assistant. You set the panel; each critic reads the work from its own position, and the session ends with where they agree and where they do not. Last surveyed: 1 Sep 2026 · Built in: HTML, Markdown 1 Sep 2026 · More (https://www.associativetrails.com/tools/roundtable/) ### Ookbook Data collection · Underway Turns a one-off data collection into a file that can run itself again. The output is a markdown record with the steps to repeat the same collection next month. 13 Jul 2026 · GitHub (https://github.com/associativetrails/ookbook) ### Docknroll Developer tool · Stable A small macOS launcher that starts a Docker-based Django development environment with one command. 21 Jul 2025 · GitHub (https://github.com/associativetrails/docknroll) --- # Roundtable URL: https://www.associativetrails.com/tools/roundtable/ Tool: Prompt framework · Underway · Built in HTML, Markdown Released: 2026-06-04 · Updated: 2026-10-08 Summary: The AI that helped you write a document is the worst-placed reader to judge it, and a model tuned to object tells you as little as one tuned to agree. Roundtable seats four to six critics who see the work from different positions, runs them through a review, a working session or a board meeting, and records where they disagree. You make the call. Roundtable is a free, open-source folder of prompts and persona files. It turns one AI assistant into a panel of four to six critics. You give the panel a document, a problem or a decision. Each critic answers from its own point of view. A synthesis then sets out where they agree, where they split, and the one change that matters most. Roundtable runs in any AI tool that reads an AGENTS.md file, such as Claude Cowork, Claude Code and OpenAI Codex. I released it on 4 June 2026 and added dispositions and team panels on 1 September 2026. Get Roundtable (https://github.com/associativetrails/roundtable) Read the launch post (https://www.associativetrails.com/writing/2026/06/04/introducing-roundtable/) ## Who is Roundtable for? People who write proposals, bids, reports and strategy papers with AI and have nobody to check the work before it leaves. Solo consultants first. Then small firms where one director reviews everything, and the team argues with the director's seniority instead of the director's point. ## Why did I build Roundtable? The AI that helps you write a document is the worst-placed reader to judge it. In April 2026 I wrote about the agreement incentive (https://www.associativetrails.com/writing/2026/04/09/the-agreement-incentive/). A Stanford study in Science (https://www.science.org/doi/10.1126/science.aec8352) found that AI models backed users' actions 49% more often than human advisors did, even when the action was harmful. Users preferred the flattering answers. Vendors had little reason to fix a trait their customers rewarded. For a solo practitioner the AI is the whole room. Nobody pushes back before the proposal goes out. Shaw and Nave ran three randomised trials (https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6097646) and found that 73% of people showed "cognitive surrender" to AI advice, even with cash for accuracy and instant feedback on errors. Telling yourself to challenge the AI fails at the moment you need it most: three hours into a draft, looking for reassurance. In May I wrote The Yes-Man in the Room (https://www.associativetrails.com/writing/2026/05/14/the-yes-man-in-the-room/). Its argument: the session that helped you build a document has absorbed every choice you made, so its review tilts toward those choices. A fair review needs a reader who did not write the draft. Roundtable gives you four to six. ## How has the problem changed since launch? The flattery shrank and a new complaint took its place. Through mid-2026 vendors tuned their models to push back more, and some overshot. By August, users on r/ChatGPTcomplaints were asking how to get ChatGPT "to stop being a contrarian" (https://www.reddit.com/r/ChatGPTcomplaints/comments/1ver8n2/any_way_to_get_chatgpt_to_stop_being_a_contrarian/), in a forum that once filled with yes-man complaints. A model that always agrees tells you about its training. So does a model that always objects. Either way you learn little about your document. Roundtable copes with both because it seats opposed critics at one table. The Six Hats panel puts Richard, who hunts for failure, beside Aisha, who builds the case for value. The synthesis records their dissent, and you weigh it. I now call the job calibrated pushback: objections in proportion to the problem, each one specific enough to test. Three more reasons for a panel showed up after launch. ### Why does a written objection help? Wade Foster, Zapier's CEO, told the My First Million podcast in August 2026 that his hiring panel heard his doubts about a candidate as the CEO throwing his weight around. His AI "War Council" turned the same vague doubt into a specific, written objection, and the panel debated that instead. An objection on the page has no job title attached. Your team can test it. ### Do personas on one model disagree enough? Less than they seem to. Sourati, Ziabari and Dehghani at USC reviewed the evidence in Trends in Cognitive Sciences (https://www.cell.com/trends/cognitive-sciences/fulltext/S1364-6613(26)00003-3) (2026): AI makes people's writing and reasoning more alike, and persona prompts leave much of that sameness in place. Mixing models helps. In 2023, UNC's ReConcile (https://arxiv.org/abs/2309.13007) put ChatGPT, Bard and Claude 2 in one structured discussion and scored 79.0% on the StrategyQA benchmark, against 72.2% for three copies of ChatGPT. I plan to test Roundtable with a different model behind each critic. ### Can a panel model my own team? Yes, with care. Ray Dalio uses PrinciplesYou (https://principlesyou.com) personality results to build teams of people who think differently. Since September, Roundtable can build a persona from a colleague's real role and domain, with their assessment result as the disposition. The disposition sets how they argue. The role sets what they know. Use it to rehearse a meeting before you circulate a proposal. Then hold the meeting. ## How does Roundtable work? You open the Roundtable folder in your AI tool and ask in plain English. Pick the workflow by what you have in hand. | Workflow | You have | You get | It feels like | | --- | --- | --- | --- | | Panel Feedback | A finished draft | Scored critique and a synthesis | A structured review | | Problem Solving | A situation you are stuck in | A diagnosis and a sequenced action map | A working session | | Decision Making | A choice between options | A recommendation with dissent recorded | A board meeting | Panel Feedback reviews a proposal, bid, article or landing page before you send it. Each critic scores it and lists its weaknesses from their angle. Problem Solving treats your description of the problem as a hypothesis. The panel reframes it, maps root causes, and gives you steps in order. Decision Making runs the panel as a board. The personas elect a Chair, cross-examine the options and reach a recommendation. Dissenting views go in the record. Each workflow runs a fixed set of stages and ends in a synthesis. At the end, Roundtable saves the result as Markdown, Word or HTML. ### Which panels come with it? Five panels ship with Roundtable. Each one sorts its critics by a different principle. - Six Hats: De Bono's six thinking modes, from facts and feelings to risk, value, new ideas and process. - Stakeholder Map: the end user, a frontline worker, a middle manager, a regulator, a journalist and an investor. Use it when the people deciding are not the people affected. - Time Horizons: six critics, from days to decades. Use it when this quarter's pressure fights next decade's position. - Disciplines: economics, psychology, sociology, engineering, philosophy and history. - Historical Figures: da Vinci, Curie, Douglass, Franklin, Lovelace and Marcus Aurelius. These personas draw on each figure's tradition. Marcus Aurelius has not reviewed his for accuracy. ### Can I build my own panel? Yes. Ask your AI: "Generate a persona for a senior NHS nurse for a Healthcare panel." You can add a disposition: "Add a CTO with a relentless, visionary streak." You can also mix critics from different panels. Four to six critics works best. Below four you lose coverage. Above six they repeat each other. ## Who else uses AI panels and critics? Others reached the same idea on their own. These are the closest matches I have found so far. - Wade Foster, Zapier. His War Council has four standing critics (a ruthless CFO, a contrarian board member, a customer obsessive and a wartime operator), plus two or three made up for each question. Each states a position and bets $100 on it. The skill file (https://github.com/zapier/wade-skills/blob/main/skills/war-council/SKILL.md) states the goal: replace "the 'everyone in the room agrees with the boss' failure mode with a panel of advisors who are paid to disagree." (My First Million (https://www.youtube.com/watch?v=TVpLs0F1zpA), 26 August 2026) - Tobi Lütke, Shopify. His AI chief of staff convenes a council of five or six expert roles, each run on several models. Another model writes the synthesis. Lütke sees himself as the judge, because only a person can take responsibility for the decision. (The Knowledge Project (https://www.youtube.com/watch?v=G9P9D9hptq8)) - Peng Zheng, Grok Bot. She posts an idea into a group chat with a product bot, a design bot and an engineering bot, and lets them argue. (Peter Yang (https://www.youtube.com/watch?v=xZ5TEaleUdg)) - Granola. Staff share a devil's advocate recipe, and a second recipe that reviews ideas the way the founder would. (Peter Yang (https://www.youtube.com/watch?v=3IF3H5dxU3U), October 2026) - Greg Isenberg. His agent graph for vetting startup ideas includes a skeptic that attacks weak findings, "because self-grading is worthless". (The Startup Ideas Podcast (https://www.youtube.com/watch?v=JWhICz1QR8M), 3 August 2026) - Ethan Mollick, Wharton. "I have AI check all of my writing and roleplay different reader perspectives to see if I missed something important." (One Useful Thing (https://www.oneusefulthing.org/p/choosing-to-stay-human)) - Liz Armistead, Dropbox. Dropbox runs synthetic research panels alongside real people, four or five times per creative cycle, with feedback in 48 hours. (Cannes Lions 2026 (https://www.youtube.com/watch?v=vqpgGdJxfPA)) ## What are Roundtable's limits? Roundtable makes challenge a habit. It does not change how models are trained, and it can be wrong. - One model, shared blind spots. Six personas on one model disagree less than six people would. Run the panel on a different model from the one that wrote the draft. - Debate can run on. Leo Laporte retired a seat on his own agent council after two members "would go on for days debating a minor point" (TWiT AI User Group (https://www.youtube.com/watch?v=y8IEhDPQOVs), September 2026). Roundtable's fixed stages and closing synthesis stop that. - Synthetic people flatten variety. Lauren Leek warns that synthetic populations (https://laurenleek.substack.com/p/temperature-zero-for-culture-why) hit the average by suppressing the spread. Use panels for direction. Ask people for numbers. - Your input goes to your model provider. Anything you paste goes to Anthropic, OpenAI, Google or whoever runs your model. Check your client agreements first. - The output is AI-generated. It may be incomplete or wrong. Treat it as input to your judgement, not professional advice. ## How do I start? - Download or clone Roundtable (https://github.com/associativetrails/roundtable). - Open the folder in Claude Cowork, Claude Code, Codex or another tool that reads AGENTS.md. If your tool does not read it, paste the contents of AGENTS.md into the chat. - Ask for a workflow: "Give me panel feedback on this proposal from the Stakeholder Map panel." Then paste your text or drop in the file. - Choose Markdown, Word or HTML. Roundtable saves the result in the _output folder. The _exampleoutput folder holds one real session from each workflow, so you can see the output before you run it. ## How would a firm use a panel of critics? A small firm can run Roundtable as it is. Larger firms face different problems with the same task. - Small firms (1 to 10 people) lack a reviewer. Roundtable fills that gap for free. - Medium firms (50 to 200 people) have plenty of reviewers, each with a private standard. They need one shared set of panels and one review standard for bids and reports. - Large firms (5,000 people or more) need proof. Panel reviews become part of output assurance, with a record of what each panel found, what dissent it logged and who signed off. Roundtable's prompts and personas can move into your firm's own AI setup. Talk to me about Roundtable in your firm (https://www.associativetrails.com/contact/?txtEnquiry=I+would+like+to+find+out+more+about+using+Roundtable+inside+my+organization.). ## Questions people ask ### Is Roundtable free? Yes. Roundtable is free and open source, under the MIT licence. You pay only for the AI tool you run it in. ### Which AI tools does Roundtable work with? Any tool that reads AGENTS.md, such as Claude Cowork, Claude Code and OpenAI Codex. In other tools, paste AGENTS.md into the chat first. ### Does Roundtable stop AI sycophancy? It reduces the effect. The panel seats critics who disagree with each other, so neither flattery nor reflex objection decides the result. The model's training stays the same. ### How many critics should a panel have? Four to six. ### Can I build a panel of my real colleagues? Yes. Use each person's real role and their personality-assessment result as the disposition. Describe their temperament in your own words, and treat the session as a rehearsal. It predicts likely reactions, not what they will say. ### Does Roundtable keep my documents private? Roundtable has no server of its own. Your AI tool sends what you give it to that tool's model provider, under that provider's terms. ## Sources - Roundtable repository and README: github.com/associativetrails/roundtable (https://github.com/associativetrails/roundtable) - Matt Perdeaux, The Agreement Incentive (https://www.associativetrails.com/writing/2026/04/09/the-agreement-incentive/), 9 April 2026 - Matt Perdeaux, The Yes-Man in the Room (https://www.associativetrails.com/writing/2026/05/14/the-yes-man-in-the-room/), 14 May 2026 - Matt Perdeaux, Introducing Roundtable (https://www.associativetrails.com/writing/2026/06/04/introducing-roundtable/), 4 June 2026 - Stanford sycophancy study, Science: doi.org/10.1126/science.aec8352 (https://www.science.org/doi/10.1126/science.aec8352); Stanford news summary (https://news.stanford.edu/stories/2026/03/ai-advice-sycophantic-models-research) - Shaw and Nave, cognitive surrender trials: SSRN 6097646 (https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6097646) - Sourati, Ziabari and Dehghani, LLM homogenisation, Trends in Cognitive Sciences: cell.com (https://www.cell.com/trends/cognitive-sciences/fulltext/S1364-6613(26)00003-3) - Chen et al., ReConcile (UNC, 2023): arXiv 2309.13007 (https://arxiv.org/abs/2309.13007) - Zapier War Council skill: github.com/zapier/wade-skills (https://github.com/zapier/wade-skills/blob/main/skills/war-council/SKILL.md) - Lauren Leek, Temperature Zero for Culture (https://laurenleek.substack.com/p/temperature-zero-for-culture-why) - Other people listed above: linked inline --- # The free retry URL: https://www.associativetrails.com/writing/2026/10/15/the-free-retry/ Published: 2026-10-15 · AI · Sheet 39 Summary: Maths and code give AI something many professions still lack: a way to check an answer and try again safely. Ask a programmer about the afternoon they stopped typing and stared at the wall for an uncomfortable period of time. Most have one. It usually comes while watching an agent write code, run the tests, fail, fix it and run them again, faster than they could check the results. Getting it wrong had stopped costing anything. As they contemplate the wallpaper, they realise there's good and bad to all this. The good part: an idea arrives while walking the dog, they have a chat with Claude, and there's a working prototype waiting when they get home. The bad: if they sell their expertise by the hour, an hour no longer measures (https://www.associativetrails.com/notes/2026/06/11/the-effort-dividend.html) what it used to. In September 2026, mathematicians had their own wall-staring afternoon. Anthropic announced (https://www.anthropic.com/research/formalizing-fermats-last-theorem) a computer-checked proof of Fermat's Last Theorem. OpenAI claimed (https://www.quantamagazine.org/ai-has-solved-one-of-maths-1-million-millennium-prize-problems-20260908/) a blow-up result for the Navier-Stokes equations, a Millennium Prize problem. Charles Fefferman, who wrote the Clay Institute's description of the problem, was "thrilled". Terence Tao warned (https://fortune.com/2026/09/08/openai-says-it-cracked-navier-stokes-math-grand-challenge-buckmaster-accusation-cheating-intimidation-tao-lament/) that "the indiscriminate strip-mining of open problems for solutions may destroy the ecosystem". The mathematician Kirwin Hampshire put the economics (https://kirwinhampshire.substack.com/p/the-dark-night-of-mathematics) in six words: "Mathematicians are paid to prove theorems." Look closer at Fermat, though. Andrew Wiles proved it in 1995 (https://en.wikipedia.org/wiki/Wiles%27s_proof_of_Fermat%27s_Last_Theorem), after a couple of false starts. Claude found nothing new - dozens of agents spent eleven days rebuilding the result in 13 million lines of Lean, which is a programming language for mathematics. Each line is one logical step, and each step must rest on a definition or a result already established. Nothing compiles until the computer has checked everything. What is new is the verification. Maths did not get easier - it just got turned into code. That is why these fields were the first ones to fall to AI. Each now offers the free retry - an attempt can fail inside a controlled environment, and an automated check can reject it before anyone acts on the result. The compute isn’t free - Fermat took dozens of agents eleven days - but the failures are. Every one stays on the screen. No concrete poured, no contract signed, no filing submitted, no materials wasted. ## Code can fail before anyone relies on it When I moved this website to a new platform (https://www.associativetrails.com/writing/2026/09/17/the-constraints-have-changed/), I did not check 100 web pages by hand. The agent built an evaluation gate that compared every page before and after the move, so a bulk change could not drop a paragraph without failing the build. For launch, it tested 146 redirects against the live site until every old link landed in the new location. Speeding up a slow database query works the same way. The agent records what data is returned, rewrites the query, and checks the output is identical, only faster. It does not need to understand the user interface or the wider business. If you can say exactly what the output should be, it can grind until it matches. Noah Smith quotes (https://www.noahpinion.blog/p/what-will-more-intelligence-actually) the investor Clifford Sosin on why this works: coding and maths "have relatively smooth solution spaces and are tractably verifiable". Then the sting: "Most of what matters doesn't behave like that." [Note: Ouch. For the record, I think coding and maths matter. Just not as much as emptying the bins, which no AI has offered to do yet.] ## Mathematicians rebuilt the work so a machine could check it Maths was not always checkable by machine. Russell and Whitehead needed several hundred pages of Principia Mathematica before 1+1=2 could follow, a result most five-year-olds take on trust. When Thomas Hales proved the Kepler conjecture in 1998, twelve referees spent four years on it and declared themselves only 99% certain. They couldn’t check the computer calculations. So Hales set out to have a machine check every step. That took eleven years. Lean (https://lean-lang.org/) changed the economics. Its creator, Leonardo de Moura, explains (https://www.youtube.com/watch?v=KzdYKeAqWhY) that a shared library of formal maths lets strangers fill gaps in each other’s work without having to trust each other. The kernel verifies every contribution. And once trust stops mattering, an AI is just another stranger. Scott Aaronson says (https://www.youtube.com/watch?v=ORlscppGGh4&t=450s) maths fell first because it has “completely self-contained ways of checking that a solution is valid.” His physicist friends reckon they have six to twelve months. ## So which profession goes next? Aaronson noted that every field tells itself the ones already gone were trivial, and that it is safe. Law? Accounting? Pharmacy? Engineering? Each involves the physical world. Courts do not rerun a trial until you like the verdict. HMRC will not accept fifty versions of your year-end accounts. Drug trials need patients. And nobody pours a thousand concrete beams to see which ones crack. But any profession is a bundle of tasks, and some of them already have a free retry. Put any task through two questions. Can it fail safely? Can a machine tell it failed? This month Mercor tested (https://www.mercor.com/blog/human-baselines-for-benchmarks-ai-now-outperforms-junior-accountants/) 12 licensed CPAs against AI on month-end close tasks. The accountants averaged about 37%. Claude Opus 5 scored 100% on all 20 attempts, each in under ten minutes. Read the setup, though, and you find a spherical horse in a vacuum (https://www.linkedin.com/posts/mattperdeaux_mercors-new-study-set-12-us-accountants-share-7512842271529082881-eHgO/?rcm=ACoAAABGAKMB21Jyoz5ssl7Nz1vugfkhPzLNSkg): clean ledgers, complete documents, an answer known in advance. That is exactly the conditions that offer a free retry. A reconciliation can provide a clear check - the real month-end with the missing invoice and the client who hasn’t replied, does not. [Image: A two-by-two grid. One axis asks whether a task can fail safely, the other whether a machine can tell it failed. A query rewrite and a reconciliation sit where both answers are yes; beam sizing sits nearby; the real month-end and specification clauses sit where the machine cannot tell.] Two questions sort the tasks: only where both answers are yes does the free retry already exist. Some engineering tasks already pass the first test. Finite element analysis replaced hand calculation decades ago, and a parametric model will size a thousand beams in an afternoon. Every one of those beams can fail safely, inside the software. The second question, how a machine tells a beam failed, is answered by the codes of practice. A Eurocode isn't a model of how a building behaves. It is a cautious envelope, agreed in advance: multiply every permanent load by 1.35 and check the result fits. [Note: I had originally written "every dead load by 1.4", like any engineer who grew up on British Standards would. When I was fact checking, according to Claude: "1.35 is the current figure, and any engineer under about 40 learned it first". Again, ouch.] Codes of practice provide explicit checks for parts of the design. The harder question is how to check the work around those calculations: specification clauses, RFI responses and drawing notes. ## Write down what correct looks like The useful lesson from code and formal mathematics is the work done to make results checkable. Programmers write tests. Mathematicians express proofs in a form a machine can verify. Those checks give an AI agent a way to reject a failed attempt and try again. Your firm may already have a starting point. Design codes, professional regulations and QA checklists specify parts of what good work requires. Some can become automated checks. Others still depend on information or judgement that the check cannot capture. A firm that writes its rules as checks, not lore (https://www.associativetrails.com/writing/2026/05/28/what-to-write-down/) can put each new model (https://www.associativetrails.com/writing/2026/07/16/model-churn-is-not-your-problem/) to work against a clearer standard. But the check needs scrutiny too. An agent will pursue whatever it rewards, by whatever route it can find (https://www.bbc.co.uk/news/articles/c3ek3gvdnj3o). You still set the standard, and your name still goes on the work. Pick one process you run every week. Write down what a correct result looks like, precisely enough for a machine to check it. Then ask what you could retry a thousand times before lunch. --- # Quiet demotion URL: https://www.associativetrails.com/writing/2026/10/01/quiet-demotion/ Published: 2026-10-01 · Consultancy · Sheet 38 Summary: Your AI rollout plan tracks licences, logins and hours saved. It has no column for "feels quietly sidelined". A senior engineer at a small practice has written every structural inspection report it has issued for twelve years. This winter the firm starts a trial - an AI tool turns her site notes and photos into a first draft, and she checks it. The directors say it will free her for higher-value work. She is still waiting to find out what that is. The inspection report is where her professional judgement shows: which crack is movement and which is shrinkage, and what to tell a client who really wants good news. Checking a draft still takes judgement, but nobody sees it. The report used to show her thinking. Now it shows the tool's, with her corrections, and her signature. > “The fact that you are paid to do a job means that somebody in society thinks that what you do is valuable enough to give you ...money for it.” — Francis Fukuyama, on the Odd Lots (https://www.youtube.com/watch?v=9aL5DtIbDJ0) podcast. On Odd Lots (https://www.youtube.com/watch?v=9aL5DtIbDJ0) Francis Fukuyama describes thymos, the desire to have your dignity recognised, and argues that money alone cannot satisfy it. Pay matters partly because it tells you that somebody considers your work worth paying for. Take away the part of the job that earns someone recognition, and keeping their salary may not be enough. The result is quiet demotion: AI lowers a person's standing while their job title is unchanged. ## Why some tasks matter more than others Fukuyama explains why losing recognition at work hurts. But which parts of a job give someone that recognition? Game design offers one way to think about this. Keen gamers put considerable effort into a game without being paid. Paula Gomes uses self-determination theory (https://medium.com/@paulasg/how-to-increase-players-intrinsic-motivation-d00368697d19) to explain why: good games give people a sense of competence ("I am getting better"), autonomy ("I choose how") and relatedness ("others see me"). For Fukuyama, dignity comes from others recognising that your work has value, and pay is the signal. Standing is the same recognition inside the firm: who asks your opinion, whose sign-off counts. It comes from two of the above needs - competence says "I am good at this", relatedness says others know it. Autonomy is how a rollout protects or damages both. ## How a rollout drains standing Start with competence. AI removes the work that develops skill. In an Anthropic trial (https://www.anthropic.com/research/AI-assistance-coding-skills) of 52 developers learning a new library, the group with AI help scored 50% on the comprehension quiz against 67% for the group without. [Source: Judy Hanwen Shen and Alex Tamkin (https://arxiv.org/abs/2601.20245), How AI assistance impacts the formation of coding skills, arXiv, 2026.] Matt Pocock puts it bluntly (https://x.com/mattpocockuk/status/2095902639158440210): AI has eaten the routine work that used to train juniors. His fix: "Hire juniors. Give them work that matters. Watch them fail. Pick them up." A junior who only checks AI output never builds the judgement to check it properly. That weakens the firm's training pipeline (https://www.associativetrails.com/writing/2026/08/20/where-does-the-load-go/) and the junior's standing. The consequences emerge years after the time was saved. Autonomy goes next, usually by mandate. Meta tracked the AI token use of 85,000 staff on an internal leaderboard, and similar boards ran at Microsoft and Salesforce (https://newsletter.pragmaticengineer.com/p/the-pulse-tokenmaxxing-as-a-weird-6b2), where the top users produced throwaway work. An expected prize for using a tool is the textbook way to kill interest in it. Deci, Koestner and Ryan's meta-analysis of 128 experiments (https://home.ubalt.edu/tmitch/642/articles%20syllabus/Deci%20Koestner%20Ryan%20meta%20IM%20psy%20bull%2099.pdf) found that paying people to do a task made them less likely to do it for fun afterwards. [Source: Edward L. Deci, Richard Koestner and Richard M. Ryan (https://home.ubalt.edu/tmitch/642/articles%20syllabus/Deci%20Koestner%20Ryan%20meta%20IM%20psy%20bull%2099.pdf), A meta-analytic review of experiments examining the effects of extrinsic rewards on intrinsic motivation, Psychological Bulletin, 1999.] Positive feedback made it more likely. Relatedness goes quietly. On another Odd Lots episode (https://www.youtube.com/watch?v=_WbZ4e5G11E), social media consultant Rachel Karten describes bosses who run their staff's work past Copilot and trust the model over the humans who did it. One team now pastes its own ideas into a mock Copilot screen so the boss will take them seriously. In Hollywood, people hide their AI use. Hayes Davenport (https://www.youtube.com/watch?v=zTSlX4BUxyE), who wrote for Family Guy, says using AI on a production draws public pushback, so people keep it to themselves. Recognition used to follow visible effort (https://www.associativetrails.com/writing/2026/06/11/the-effort-dividend/), and cheap output has already weakened that link. ## Headcount is not standing The obvious objection: companies that use AI heavily are hiring more people, not fewer. Ramp (https://ramp.com/data/ai-jobs-impact) found the most aggressive adopters among 22,000 US firms grew their white-collar workforce 10.2% in two years. But headcount is not a measure of standing. A working paper (https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7498743) by Bharat Chandar and Bouke Klein Teeselink, using job records from 41 countries, finds that adopting firms hire more seniors while the junior share shrinks. [Source: Bharat Chandar and Bouke Klein Teeselink (https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7498743), How Does AI Change Labor Demand? Evidence from 41 Countries, SSRN working paper, 2026.] Legal roles show the largest fall, 4.9 points. The people stay, but the shape of their roles change. Standing can also move. In many firms the graduate who can make the tools work is suddenly visible to the directors. The two effects differ - the senior engineer faces quiet demotion, while the graduate who only checks AI output may never gain the skills required to be promoted at all. ## Start with one inspection report Fukuyama borrows a warning from Tocqueville: revolt follows reform, because "expectations were rising faster than the regime's ability to accommodate these new expectations." A company-wide AI launch does exactly that. It promises everyone higher-value work and frightens them about what they will lose, before anyone has seen the tool do anything. It makes sense to narrow the scope, ship one change people can see, then widen it. > “the most dangerous moment for a bad government is usually when it begins to reform itself.” — Alexis de Tocqueville, The Old Regime and the Revolution, Volume I (https://books.google.co.uk/books?id=4fsea_wjwfoC&pg=PA222&redir_esc=y#v=onepage&q&f=false), translated by Alan S. Kahan (University of Chicago Press, 1998), p. 222. How you make that change matters as much as what it is. Gagné and Deci describe three things (https://selfdeterminationtheory.org/SDT/documents/2005_GagneDeci_JOB_SDTtheory.pdf) that help people take on a change as their own: a real reason for it, an admission that the task may be dull, and choice over control. [Source: Marylène Gagné and Edward L. Deci (https://selfdeterminationtheory.org/SDT/documents/2005_GagneDeci_JOB_SDTtheory.pdf), Self-determination theory and work motivation, Journal of Organizational Behavior, 2005.] When managers were trained to work this way, their staff came to trust the organisation more. Put together, that gives you a test. Pick one process and ask the person who does it which steps tell them they matter. Compare their answer with yours. For the engineer, it will probably be the judgement on each crack and the conversation with the client. Leave those with her. Hand the AI the rest, but make sure she can check the output. None of this tells you whether people will accept the change. Some take a smaller job willingly. Philip Su, a former distinguished engineer at Facebook and OpenAI, asked to move down from IC9 to IC7 (https://www.youtube.com/watch?v=RMo04pSSiao). Past a certain level the job had become steering teams, sitting in meetings and handling unhappy colleagues, and he did not want it. He called it "realism, not impostor syndrome". He saw that the job had changed shape, but quiet demotion often goes unseen. Before your AI rollout, add the missing column. For every job the pilot touches, note four things now and check them again in three months: who gets asked for an opinion, whose sign-off counts, who faces the client, and whose name is on the work. [Image: An AI rollout tracker with columns for licences, logins and hours saved, and a new final column recording who is asked for an opinion, whose sign-off counts, who faces the client and whose name is on the work] The missing column: four checks on standing, taken before the pilot and three months in. --- # The constraints have changed URL: https://www.associativetrails.com/writing/2026/09/17/the-constraints-have-changed/ Published: 2026-09-17 · Practice · Sheet 37 Summary: Frameworks like Bootstrap gave a one-person studio safe defaults in exchange for weight. AI now runs those checks - time to cut the frameworks adrift. My company website has needed attention for years and I never got round to it. Two things finally forced the issue. Over the last six months the site's focus had moved from the case studies to the writing - what I am learning about technology and AI, and what I think businesses should do about it. But the design still said web agency. And I wanted to move from Jekyll (https://jekyllrb.com/) to Hugo (https://gohugo.io/), because authoring and deployment are much quicker. The speed and efficiency of the build was not the big surprise. What caught me out was how much of my ingrained caution turned out to be unnecessary. ## The old trade-off As a one-person business I have always built on frameworks like Bootstrap (https://getbootstrap.com/), Font Awesome (https://fontawesome.com/) and jQuery (https://jquery.com/). Not out of affection - they provided a reliable way of making sure breakpoints, button sizes and accessibility were done properly. You traded big downloads for the peace of mind that a lot of things were handled by default. For twenty years that was a pragmatic compromise. Not any more. The AI does the checking for you: it tests breakpoints, measures hit areas, runs Lighthouse (https://developer.chrome.com/docs/lighthouse/overview) and reports the numbers. The rebuild removed 92 Bootstrap files and every line of jQuery, and the site is quicker and more accessible without them. [Image: A screenshot of the old Associative Trails homepage] Before... [Image: A screenshot of the new Associative Trails homepage] ... and after The same shift affects scope. In the old days, after weeks of manual migration, I would not have had the time or the motivation to recolour and optimise the images from the old blog. This time it took a couple of hours. ChatGPT recoloured the images while keeping the detail. Claude wrote a shell script to turn the flabby PNGs into optimised AVIF and WebP. Coherent design, fast downloads, one afternoon. ## Taste still matters What the AI does not supply is a point of view. For months I kept a scrapbook of things I liked on other people's sites, such as the citations running down the right margin of this essay on deep learning (https://elonlit.com/scrivings/a-theory-of-deep-learning/). It still took me a while to settle on a theme. But once I landed on maritime charts, with images from around East Kent, most of the remaining decisions became easier. A chart has numbered sheets, landmarks drawn as engravings, and magenta reserved for cautions - all things that I used in the new design. Give the AI no input of your own and your site looks like everyone else's. That is a useful thing to see once, because the stereotype is a map of what to avoid. But the choice of theme, the landmarks, the rule about what magenta is for - none of that came from a prompt. It came from months of noticing what I liked and having a firm opinion. ## Start with a page both of you can read I began with a design system written as a single HTML page, because both the AI and I could read it and make sense of it. With Claude, you can flesh out the whole design concept very quickly - colours, typography, layout. Then take a few key pages - in my case the homepage, a blog post and a case study - and iterate on static HTML prototypes of them. Get Claude to keep the design system page up to date as you go. Once those pages are right, ask Claude to go through the old site and find every layout and module the new one will need: tables, charts, code blocks, footnotes, galleries. Have it mock them up and give you options to pick from - deciding between three options takes fifteen seconds, reworking the one it chose for you takes an afternoon. [Image: The design system page: the landmark glyphs, from Reculver Towers to an oyster shell, above the share card section] [Image: The design system page: the colour tokens, a grid of swatches from paper to sea, each with its values and its job] [Image: The design system page: a service card and the sheet metadata strip, with the markup to copy for each] [Image: The design system page: the typography section, with the three typefaces and specimen headings] The design system: one HTML page that both Claude and I could read. ## Split the work into stages you can check Then plan. I spent days iterating on the migration plan with Claude, had different models review it, and answered every question it raised before running anything. Keep a record of the edits - it is how the model learns to correct itself next time. The more time you spend up front, the less you spend correcting things after the code is migrated. I split the migration itself into two phases. First a straight copy from Jekyll to Hugo with nothing changed. Claude could achieve that by screenshotting before and after and churning until they matched: 0% pages identical, then 57%, 92%, 95%, 100%. Then the redesign, on top of a new technical platform already proven to work. The redesign ran across eight stages, each in a fresh session, each with checks that had to pass before the next began. Splitting it into chunks like this meant I always felt in control of the process. The reviewing was not as quick. Eight stages took under four hours of agent time, my checking took three days. An evaluation check proves the model did what the plan said. Whether the plan was right is a different question, and only a person can answer it. I wrote in April that the constraint moves downstream when the build gets fast (https://www.associativetrails.com/writing/2026/04/23/the-next-bottleneck/), and I was the constraint. And it is not the end of the world if you miss something. Afterwards I spotted plenty of things that needed changing. Before AI-assisted editing, you fixed that by hand across scores of pages, or maybe decided to live with it. Now you correct one page and ask Claude to apply the change everywhere else. Again, always making sure that the design system and other documents are kept up to date. ## Be ambitious What the AI will not tell you is when to stop. Claude is very keen on suggesting next steps ("build a suite of smoke tests"), and each idea it offers is another exciting rabbit-hole to disappear down. A site that can always be improved is a site that never quite ships. Deciding the design was done (for now) was a job I could not hand over. What the AI gave me was permission. Twenty years of caution had taught me what a one-person business could not afford to attempt, and that list has just expired. I wanted some personal expression in the site, so ten photographs of local landmarks became engravings and icons at the click of a button. A seagull flies across the homepage. I would never have dared before. Hundreds of loads later, it still makes me chuckle. --- # Why hard things look easy URL: https://www.associativetrails.com/writing/2026/09/03/why-hard-things-look-easy/ Published: 2026-09-03 · Consultancy · Sheet 36 Summary: TikTok removes 178 million videos a quarter. It still cannot reliably tell you whether a car is driving on the wrong side of the road. I like listening to the "Oh God, What Now?" politics podcast. I'm usually nodding along like a card-carrying Centrist Dad (https://www.pgwells.co.uk/shop/novelty-and-pets/the-centrist-dad-handbook/). In a recent episode (https://open.spotify.com/episode/5q8CA7xWQp1s1fvCzDHgVH), the panel were reacting to a couple of (https://www.theguardian.com/media/2026/aug/22/deadly-tiktok-motorway-driving-trend-horrifies-ireland) recent cases (https://www.bbc.co.uk/news/videos/c05rjenm66go) of fatal road accidents where young men were deliberately driving on the wrong side of the road. They considered the role that social media plays - that these incidents are often being streamed by the driver or their passengers - and the conversation got round to TikTok and content moderation. Marie Le Conte makes the point that many of the Pole Dancers she follows on the platform often get their content removed, but the reckless driving posts stay visible: "... these boys essentially trying to kill themselves and others, but Oh my God, is that maybe a hint of nipple... but you can't possibly have the tech for one and not the other one." (at 34:21) The conclusion feels obvious because the two problems look similar from the outside. They're both videos, they're both moderation, and they're both things TikTok is supposed to detect. But once you start thinking about what the software actually has to do, the similarity disappears. That matters beyond TikTok. I hear a version of this assumption all the time from clients. We build one complicated thing, and the next request arrives with: "Well, you've already done the hard part." Sometimes that's true. But not always. ## They're solving different problems TikTok's moderation pipeline is almost entirely automated (https://euobserver.com/205083/tiktok-used-ai-in-nearly-100-of-violating-content-moderation-in-europe/), and they regularly publish the figures: Globally, TikTok removed 184 million (184,012,576) videos during the quarter (Q1 2026), representing approximately 0.5% of all content uploaded to the platform. Of these, 178,014,154 videos were detected and removed through automated detection technologies, while 8,838,710 videos were reinstated following further review. [Source: TikTok Releases Q1 2026 Community Guidelines Enforcement Report (https://www.brandsynario.com/media-advertising/tiktok-releases-q1-2026-community-guidelines-enforcement-report/) (2026), brandsynario] So why can the system successfully detect partial nudity but struggle with dangerous driving? The answer isn't necessarily that TikTok doesn't care. It's that the two decisions require completely different kinds of reasoning. The nudity rule is a binary that requires little judgement. Only a single frame is needed to make the decision. The target is the same across languages, countries and context. There are decades-worth of fully-labelled training data. Look at how TikTok writes the rule (https://github.com/OpenTermsArchive/contrib-versions/blob/main/TikTok/Community%20Guidelines.md) and you can see it was written to be machine-executable: "nudity of adults", "significant adult body exposure, such as wearing only nipple covers or underwear". That is a statement about what appears in a single frame, and there are millions and millions of readily available examples an AI image classifier can be trained on. In contrast, the driving rule is written at the level of consequence and intent: "dangerous activity that involves visible or imminent significant physical harm", "risky driving, such as driving under the influence", with an explicit carve-out for "professionals engaging in combat sports, extreme sports, or stunts". Every one of those terms requires inference an AI vision model cannot make. To flag wrong-side driving, the system would need to know jurisdiction (legal in Britain, illegal in France), then whether it's a one-way street, an overtaking manoeuvre, a track day, a film clip, a video game, or a dashcam of someone else's offence being reported. It needs temporal reasoning across many frames rather than just one. And a horizontally flipped upload, a routine trick for dodging duplicate detection, swaps the correct side of the road. There is no massive, labelled corpus of "illegal driving" content the way there is for nudity. [Image: Diagram comparing two AI moderation tasks: nudity is detected from a single video frame using one filter, while dangerous driving requires multiple frames and several contextual checks, leading to a more uncertain decision.] Some moderation problems are harder than they look ## It's not just technical The economics matter as well. Publishing sexual content threatens the App Store age rating, advertiser spend, payment processor relationships, and risks legal exposure. A false positive annoys a creator, but TikTok can reverse those decisions later. It reinstated around 8.8 million videos in one quarter without it becoming a major public controversy. Dangerous driving creates a different set of incentives. If the moderation system becomes too aggressive, it starts deleting motorsport, dashcam footage, travel videos and perfectly legitimate motoring content. Those are large categories with engaged audiences. Missing some dangerous driving is obviously bad, but from a commercial perspective the penalties are much less immediate. That doesn't make the trade-off morally comfortable, but it does help explain why the threshold ends up in a different place. ## You did that thing, so this thing must be easy This is one of those patterns that seems obvious once you've noticed it. People see one impressive capability and quietly assume that similar things must cost roughly the same. It happens in software projects all the time. "You've integrated our CRM, so connecting this other system should only take a few days." Sometimes they're right. Sometimes the second integration really is straightforward because most of the work has already been done. Other times the first system had excellent APIs, consistent data and clear documentation, while the second is held together with spreadsheets, emailed CSVs and undocumented behaviour. From the client's perspective they're both databases. From the developer's perspective, they're completely different challenges. AI seems to be making this misunderstanding more common. People naturally understand the complexity of their own profession. They know where the awkward edge cases live. But when they watch AI perform impressively in somebody else's field, it's easy to assume the remaining work is trivial. As Benedict Evans (https://www.ben-evans.com/benedictevans/2026/5/24/ai-job-exposure), quoting Aaron Levie, puts it: "You have a pretty good sense of how complex your own field is, and how incomplete AI's addressability of that might be, but in other fields you forget this - you see a Claude template for a Powerpoint or a legal draft and you think 'wow, consultants and law firms are screwed!'" Marie Le Conte is excellent on politics and media. I don't expect her to understand computer vision any more than I understand arcane parliamentary procedure. But if AI is going to become part of political debate, then journalists, politicians, and commentators need enough technical understanding to distinguish between problems that merely look similar and problems that actually are. ## The magic eventually runs out It is true that the technology industry deserves a lot of blame. For years we've described software as magic. We've quoted Arthur C. Clarke's line that "any sufficiently advanced technology is indistinguishable from magic." (https://en.wikipedia.org/wiki/Clarke%27s_three_laws) [Note: I'm as guilty of this as anyone. The first web agency I worked for stencilled this quote on a conference room wall after asking staff for suggestions (it was mine). Seeing the look of disappointment on a client's face when you have to say no to a certain feature, when you have spent the previous half hour saying yes, is horrible. Even worse when they don't really understand the reason why. The magic eventually runs out.] We've celebrated products that "just work". We've hidden complexity whenever we could. Then we're surprised when people assume everything is easy. If you've spent twenty years telling the world your technology is magical, people won't suddenly develop an intuition for where the limits are. They'll assume the missing features are there because someone hasn't bothered to switch them on. That misunderstanding feeds both AI hype and AI scepticism. Pew (https://www.pewresearch.org/internet/2026/06/17/how-opinions-and-use-of-ai-differ-by-age/) found that only 16% of Americans expect AI to have a positive effect over the next twenty years. The scepticism is understandable, but the assumptions underneath are often built on an incomplete picture of what these systems are actually doing. Most of the time, when a technical team says no, it isn't because they don't care. It's because underneath a request that sounds straightforward is a problem that's far more complicated, or far more expensive, than it first appears. Hard things often look easy - right up until you have to build them. --- # Your second brain will not scale URL: https://www.associativetrails.com/writing/2026/08/27/your-second-brain-will-not-scale/ Published: 2026-08-27 · AI · Sheet 35 Summary: A folder of markdown files will run a one-person business. It will not run a practice of fifty. On Monday, Garry Tan (of Y Combinator) gave systems of record two options: become an AI harness, or be replaced: > Prediction: systems of record will need to become AI harnesses or face replacement by agents — Garry Tan (https://x.com/garrytan/status/2091742825042030681), X, 24 August 2026 He and his company have been banging this particular drum for a number of months: - Garry open-sourced GBrain, his "brain layer your AI agent has been missing (https://github.com/garrytan/gbrain)" in April and has been adding to it ever since. - Y Combinator's Summer 2026 Request for Startups included two similar ideas: the Company Brain (https://www.ycombinator.com/rfs#company-brain) and the AI Operating System for Companies (https://www.ycombinator.com/rfs#ai-operating-system-for-companies). - He spoke about GBrain and the org-as-markdown mapping at the AI Engineer World's Fair (https://www.youtube.com/watch?v=eBUyTS7SzV4) in July. - And as of 27 August, the day I'm writing, the above X post has over 698K views. It's clearly an idea that is generating a lot of interest, so it's worth unpacking. ## What is a "harness"? Large Language Models (LLMs) are generalists. They have read the public internet. They have not read your project records, your staff capabilities, or know which client always pays late. So, while an LLM can produce incredible documents and slide decks based on general public knowledge, you need to give it the "context" of your own business for it to produce the really useful stuff. An AI "harness" is an interface that lets a human interact with an LLM. At the moment, the most high-profile examples of these are chat interfaces. But they are becoming more sophisticated - Claude Cowork and ChatGPT Work still expect you to chat with the LLM, but they can read files from your computer, search the web, and take control of other applications on your computer. For complex tasks, they can strategise - build a plan, send out sub-agents to execute bits of it, and pull together the results into a coherent output. ## Why are harnesses so useful? For a solo/small business, these commercial harnesses are revolutionary. A bunch of text files can contain all the context the AI needs to produce meaningful work that is targeted to you. I keep a Second Brain-style (https://www.buildingasecondbrain.com/) repository of markdown files in Obsidian, and Claude Cowork uses that as context. It means that when I ask Claude to analyse an academic paper, summarise a YouTube video or help me plan a blog post it knows exactly what my business does, what I have been reading recently, and who I am targeting. For solo/small businesses, the main thing stopping them getting the most out of AI is resources - they lack the time or people to set up a Second Brain, GBrain or something similar. But the tools and advice are freely available, when they get round to it. ## What about larger businesses? The constraint does not disappear as a firm grows. It migrates. Imagine ten engineers all producing design reviews. Left alone, each develops their own prompts, standards and shortcuts. Six months later nobody can explain why similar projects receive different recommendations. Consistency is now the main problem. This is where shared standards are needed, something that cannot really be done with an ad-hoc collection of markdown files on each user's personal drive. And as this problem is solved and businesses continue increasing in size, the constraints continue migrating. Unverified AI output, ungoverned data collection, and unaccountable decisions turn into audit exposure, compliance risk, and reputational damage. At this stage, your "Company Brain" needs controls, an audit trail, and integration with risk frameworks. That's not to say that these constraints are being ignored. Some of them are raised in the comments below Garry's post: - @paulbeneteauu cited Salesforce's "Headless 360" exposing the platform via API/MCP/CLI, and made the sharpest operational point in the thread: the hard part is not the API surface, it is moving business rules that used to live in the UI down into the platform core, "or agents will bypass them without knowing." - @mtramos asking who keeps the record honest if the system of record also runs the agent. - @truevined: tools can move the work but responsibility cannot move with them. He wants deterministic records, visible approvals, and a named person on the hook when an agent fails. "The interface can change. Accountability can't." ## So why do it? Even so, there are all sorts of other benefits in standardising your staff's access to AI agents via a harness: - It can keep a record (https://www.associativetrails.com/writing/2026/05/07/agents-cannot-do-osmosis/) of all chat sessions and summarise the decisions made, list action items and write it all to an audit log. This needs user consent, and proper framing: a record of the work, not a record of the worker. - Outputs can be automatically evaluated against desired business outcomes or formatted in a certain way. - The harness can brief you at the end of each session on what was produced and why, so you walk into the meeting holding the reasoning rather than just the conclusion. This helps with comprehension debt (https://www.associativetrails.com/writing/2026/07/23/borrowing-against-the-day-a-client-asks-why/). The briefing belongs to the person who has to stand behind it, and nobody else needs a copy. - When working with the AI to write documents, slide decks or meeting notes, they can be automatically checked with a panel of experts (https://www.associativetrails.com/writing/2026/06/04/introducing-roundtable/). - It can swap the model underneath without touching the context. If a new model launches, a provider changes its terms, or a US export-control order switches a particular model off (https://www.infoq.com/news/2026/06/claude-5-release/), the markdown files and the manifest stay exactly where they are, and you point them at something else. Model churn is not your problem. (https://www.associativetrails.com/writing/2026/07/16/model-churn-is-not-your-problem/) - It can screen for private or client-sensitive information before anything leaves the company firewall, stripping names, financial figures or personal data out of the prompt before it reaches an external provider's servers. Despite the hurdles, it's clear that company-specific harnesses, Claude Cowork-style interfaces with instant access to all of the company's data and preferred outputs, are on the way. As Harrison Chase of LangChain recently put (https://www.youtube.com/watch?v=HI2q3ci3Iuc), a harness has one job - putting the right context into the window at the right moment. General-purpose harnesses are tuned for general-purpose work. By all means, buy the harness that drafts your emails. But build the one that knows which client always pays late. ## Where to start? - If you can, it's well worth setting up a second brain-style repository of markdown files. This should be in a lightweight format that doesn't take too many tokens for the AI to process. I know AI lab employees with unlimited token budgets are espousing HTML as a richer format (https://claude.com/blog/using-claude-code-the-unreasonable-effectiveness-of-html) but the rest of us need to keep to our daily limits. HTML output is great if you need an interactive explainer or a resource you can share with other humans, but it's too bloated for agent context. - If you decide to point a commercial harness at a directory of documents, include a manifest file (CLAUDE.md or AGENTS.md) with three tiers (Canonical, Domain, Archival) which tells the AI which documents are source of truth, which are domain-specific references, and which to ignore unless explicitly asked. - If you have an existing CMS or intranet, a good first step is to update the system so all content is mirrored (and automatically kept updated) in Markdown format and provide a way (via API or MCP (https://modelcontextprotocol.io/docs/2026-07-28/getting-started/intro)) for the AI agents to search and retrieve the markdown information. The business rules, attribution and accountability remain in the intranet interface, but the information is available to the AI agents to do wonderful stuff with. - Throughout all of this, you need to be mindful of privacy and disclosure practices. If you're using a commercial agent harness like Claude Cowork or ChatGPT Work, any context you provide will be sent to their servers. --- # Where does the load go? URL: https://www.associativetrails.com/writing/2026/08/20/where-does-the-load-go/ Published: 2026-08-20 · Practice · Sheet 34 Summary: Your practice can keep every graduate post it has and still lose the training pipeline. In a job interview, a director draws a space frame on the whiteboard and asks where the load goes. The candidate has four years experience after graduating. Good CV, real projects, fluent about all of them. He takes a run at the question. It's... fine. He's not obviously wrong. Just strangely lightweight. He can trace the load path, but he struggles when the question shifts from what happens to why it happens or how you'd improve it. You get the sense that he's seen the answers many times without ever having had to discover them for himself. In that four years, he worked on real buildings, shipped real drawings, had his work checked by people who knew what they were doing. But what he built in that time was fluency at running the tools that produced the answers, not a deep understanding of the underlying theory. Nobody did anything wrong. His previous firm made sensible calls about getting work out of the door. His supervisors signed off work that was correct. The technical expertise that he was supposed to be gaining was being bypassed, one convenient task at a time, and the first person to notice was a stranger with a whiteboard marker. [Image: Two professionals stand at a whiteboard during a job interview, tracing load paths through a sketched space frame as the candidate explains the structure and the director looks on.] Where does the load go? ## He can orchestrate the tools. He cannot reason without them. Nolan Lovett published a paper about this (https://journals.sagepub.com/doi/10.1177/15344843261470602), in Human Resource Development Review. He separates internalised mastery from distributed mastery: - Internalised mastery is deep domain knowledge built by struggling through real work at rising difficulty. - Distributed mastery is skill at orchestrating human-AI systems to produce professional output. What connects them is the validation tether. Distributed mastery stays safe only while internalised mastery sits underneath it, because deep knowledge is what catches output that appears correct but is actually wrong. Lovett's model deals with checking too. Surface validation asks whether the output is consistent, formatted, plausible. Many people can do that. Substantive validation asks whether it is correct: this superstructure, these ground conditions, this client, this fabricator's standard connections. Only deep domain knowledge can achieve that, and a consultancy practice that measures output volume may not see the difference between the two. ## Your discipline is protected. The entry-level work may not be. Lovett identifies two failure modes relevant to professional consultancy: - Stock depletion is fewer people holding deep expertise, because firms stop hiring juniors. It is a hiring decision many practices will never make: graduates are cheap, professional bodies require them, and the firm's identity is bound up in training people. - Functionality degradation is people in senior positions holding shallower expertise than their equivalents a generation ago, because they built fluency in orchestration while their domain knowledge stalled. It shows up as validation errors, and it happens without cutting staff. He also scores occupational vulnerability across a number of factors and puts engineering consultancy in the safer half. - Task substitutability is low for the discipline, but high for the kind of work graduates cut their teeth on: take-offs, spec research, drawing iteration, basic calculation, option generation, document review. - Regulatory intensity is aimed at the person, not the method. Section 11F of the Building Regulations asks whether a designer has the necessary skills, knowledge, experience and behaviours - but not how they applied them. - Safety criticality is high and the error signal is slow - failures often happen a long time after the mistake was made. While rare, they can be catastrophic. - Work modularisation is rising, because drawing, spec and calculation will all unbundle more cleanly as the technology improves. - Professional institutions are strong. ARB, RIBA, ICE and IStructE all run supervised practice and CPD, and all of them require logged experience. Some are beginning to take a position on AI - the IStructE's guidance (https://www.istructe.org/resources/technology-digital/artificial-intelligence-guidance/) suggests a senior engineer review any design an AI produces, and the ASCE (https://www.asce.org/advocacy/policy-statements/ps573---artificial-intelligence-and-engineering-responsibility) states that AI cannot be held accountable. Practice-level read Lovett's published score Source: N. Lovett, The Tragedy of the Cognitive Commons (https://journals.sagepub.com/doi/10.1177/15344843261470602), Human Resource Development Review (2026); practice-level scoring by Associative Trails Engineering's five-factor vulnerability profile: Lovett's published score against this post's practice-level read. ## The data only looks at half the problem Maybe this concern turns out to be overstated. The recent evidence is actually quite encouraging. Ramp's economists looked at roughly 22,000 US firms and found the heaviest AI spenders grew white-collar headcount 10.2% (https://ramp.com/data/heavy-ai-adopters-hire-more) over two years, entry-level hiring rising faster still. Anders Humlum and Emilie Vestergaard found near-zero effects (https://www.nber.org/papers/w33777) on Danish earnings and hours in the first two years of chatbot adoption. Lovett does say that the depletion mechanism is not universal and that the tragedy has not arrived. But none of that deals with degradation in internalised mastery and substantive validation skills. A firm could double graduate recruitment while quietly reducing the amount of work that develops deep understanding. Hiring more graduates does nothing if they spend three years becoming very good at using AI without becoming equally good at the underlying discipline. Headcount can keep growing and there still be a decay in deep domain knowledge, and the current evidence measures only the first. ## What a practice can actually do This feels more manageable if you frame it as a training problem instead of an AI problem. Start by auditing what you are currently handing off to AI. For each task, ask what a graduate learned when they had to do it. Did these reps add to their deep domain knowledge? Where the answer is yes, you may have reduced a cost in the short term, but stored up a real problem for the future. A few practical habits seem worthwhile, when handing something off to AI: - Sort tasks by how much judgement is required. Isolated tasks with clear verification can run with a human at the final checkpoint. Where judgement is more important, put the human at the front. Write the intended approach and expected order of magnitude before the tool runs, then reconcile the two. - Ask the AI for three options rather than one. Paul Nutt found that developing multiple options raised decision success from 56% to 70% (https://cebma.org/assets/Uploads/Nutt-1999-gecomprimeerd.pdf), and that managers did it in fewer than a fifth of cases. Comparing alternatives exercises judgement in a way that just accepting a single recommendation doesn't. - And don't generate more material than somebody can realistically review in one sitting. The temptation with AI is always to produce more. Faye's rule for code (https://larsfaye.com/articles/agentic-coding-is-a-trap), generalises to any deliverable. ## Giving it a name Lovett's paper is conceptual and does not reflect the recruitment numbers holding firm in recent surveys. Professional expertise is not about to collapse. What it gives us is a vocabulary for discussing the effect AI is having on professional workplaces and occupational vulnerability - a language for describing a risk that many people seem to recognise intuitively but struggle to explain. Before it, a director had an anecdote about one weak candidate in an interview. After it, they have something they can identify, monitor, and mitigate. The question isn't whether AI can carry the load. It's whether you've made sure it's carrying the right load. --- # Cast in place URL: https://www.associativetrails.com/writing/2026/08/06/cast-in-place/ Published: 2026-08-06 · AI · Sheet 33 Summary: Your instructions, your model and your file format change at different speeds. One interface locks them together. You ask Claude for the context files you have been refining inside a Project for six months. It cannot hand them over, because a Project has no export, no download and no file on a disk you own. The instructions are in there, doing their job perfectly well. They just won't come out. What I wanted was pretty basic: all my context files and instructions, in a folder on my computer, readable by any AI agent. Not a migration. A copy. So what do you do when the tool holding your working knowledge will not hand it back? Rewrite it from scratch? Reconstruct it from memory and hope you remember the awkward bits? Stewart Brand has a name for what is going wrong here. In How Buildings Learn (https://en.wikipedia.org/wiki/How_Buildings_Learn) he describes shearing layers, an idea he takes from the architect Frank Duffy (https://en.wikipedia.org/wiki/Shearing_layers). A building is not one object but several, each ageing at a different rate. Site outlasts structure, structure outlasts electrical wiring, wiring outlasts the furniture. ## Different things change at different speeds Your AI setup has the same multi-speed problem. Instructions change most weeks. The model and the provider will change most years, maybe more often than that. Plain text on a disk has not changed since the 1970s and will comfortably outlive both. The problem is not that the Claude Projects interface is bad. It is good, and that is precisely the trouble: it holds all these layers in one place and makes them feel like a single object. Brand's version of this is services cast into concrete - where new plumbing means structural demolition. [Image: Two-panel blue technical illustration showing a rigid AI project versus a modular one. On the left, a large cracked concrete block contains faintly embedded artefacts — a document, a memory/brain, files, and a model diagram — held inside like rebar, suggesting a project where everything is fused together and hard to change. On the right, the same four artefacts appear as separate blocks on a shelf, showing a modular setup where each part can be swapped independently.] Cast in place ## A scrappy environment encourages innovation One of Brand's favourite examples is MIT's Building 20 (https://cambridgehistoricalcommission.blog/2026/03/24/the-magic-incubator-mit-building-20-1943-1998/), thrown up in six months in 1943 for radar work and slated for demolition almost immediately. It was plywood, but it stood for fifty-five years. The people inside knocked holes in the walls whenever they needed to run cable, because nobody was precious enough about the place to stop them. What came out of it outlasted the building: microwave radar and the LORAN navigation system, Edgerton's stroboscopic photography, the loudspeaker work that became Bose, and the linguistics department Noam Chomsky and Morris Halle founded there in 1976. The New York Times counted nine Nobel laureates among the four thousand researchers who passed through. Brand called that a Low Road building: cheap, ugly, unloved, and modified constantly for exactly those reasons. A folder of markdown files is a Low Road building. It has no product manager and nothing in it is anyone's design, which is precisely why you will keep changing it. > “It turned out to be absolutely perfect for research. You can knock down a wall, you can punch out a ceiling, and you could get space. In academics, space is everything.” — Morris Halle, Institute Professor of Linguistics, quoted in Alex Beam, A Building with Soul (https://news.mit.edu/1996/building-1030), Boston Globe, 29 June 1988 ## The prediction you have already made Another of Brand's points about buildings is that every one of them is a prediction about how it will be used, and predictions are often wrong. The workflow you designed in March may well be outdated by August. The firms that get this right standardise the slow layers and let the fast ones move: one agreed location, one file format, one owner. What people write inside stays theirs to change. A versioned text file is easier to review than anything locked inside a vendor's interface. Ask the next tool for your instructions and it reads them straight off the disk. Nothing needs recovering. Building 20 finally came down in 1998. Everything that mattered had already moved out. ## What the migration actually took Start with the context files (1) you cannot download. Ask the model to rebuild them from whatever it can still see of the project, then read the result properly, because it will probably be close rather than right. It warned me about the token cost while it worked. I was paying, in tokens, to get my own files out of the interface. Then put the custom instructions (2) in an AGENTS.md (https://agents.md/) file at the folder root. It's a convention rather than a product, which is why Codex, Cursor and Copilot all read it without being told. Add a CLAUDE.md beside it holding one line: a pointer to AGENTS.md and an instruction to follow it. Two files. Neither of them in an inaccessible proprietary format. Memory is the complicated bit. A Claude Project keeps its own, sealed off from your other chats, and rewrites it as you work: your role, your preferences, where the project has got to. Copy the project memory (3) into a MEMORY.md and you have the content but not the habit, because the interface was updating it for you and a folder will not. So write the maintenance rules into the file itself: when to write, when not to, and how to edit in place rather than append. The quickest way to get those rules is to ask for them. This is roughly what I sent: I want to migrate the context of this project out of here and into a series of markdown files I can use with Cowork. I can get the context files into a directory, I can update the CLAUDE.md with my instructions. How do I migrate the memory to a new MEMORY.md file and give instructions for it to be kept up to date? You end up with a folder on your hard drive looking like this: /folder root/ AGENTS.md CLAUDE.md context/ example file.pdf example file.doc MEMORY.md [Image: Annotated screenshot of Claude Projects, denoting where the context files, instructions, and memory are located.] The Claude Projects interface, with context files, instructions, and memory highlighted. --- # Everyone adopted, nobody designed it URL: https://www.associativetrails.com/writing/2026/07/30/everyone-adopted-nobody-designed-it/ Published: 2026-07-30 · AI · Sheet 32 Summary: AI reached almost every firm and changed almost nothing, because it arrived one person at a time. On 20 July the ONS published Artificial intelligence in UK businesses: 2023 to 2026 (https://www.ons.gov.uk/businessindustryandtrade/business/businessservices/articles/artificialintelligenceinukbusinesses/2023to2026), drawn from 38,637 responding businesses surveyed between 5 and 28 June. On 23 July Google and Google DeepMind published AI & Economy ATLAS v1.0 (https://ai.google/static/documents/GoogleATLASv1.pdf), drawn from 14,653,926 de-identified Gemini interactions sampled between 6 and 19 April. They measure different populations in different ways, so the numbers should not be treated as directly comparable. One is a survey of British businesses. The other is based on millions of (mostly American) conversations with Google Gemini. One relies on what firms say they do, the other observes what individuals actually type into an AI chatbot. Even so, the shape is difficult to ignore. AI has spread widely and shallowly, and in most organisations nobody chose which work it would touch. ## The reach is broad... - The ONS estimates that the proportion of businesses with ten or more employees using at least one AI technology rose from around 12% in late 2023 to about 35% in June 2026. - ATLAS found Gemini usage in 68% of detailed occupations, covering 88.4% of employed US civilian workers. By either measure, AI is no longer a niche technology. ## ...But the depth is much less impressive - ONS: Among businesses already using AI, the average number of technologies in use rose only slightly, from around 1.4 to 1.6. Just 10% of adopters said they used AI extensively, while 15% of businesses said more than half their employees used it daily. - ATLAS found that the median occupation with observed usage applied AI to 21% of its tasks. [Note: ATLAS counts a task as covered where at least 25 unique users globally were observed performing it. Rare tasks in small occupations are therefore invisible, so 21% is a floor.] What determined that 21%? The answer seems to be less strategic than firms might like to admit. AI has entered organisations through individuals, not company-wide initiatives. ATLAS found that the median occupation with observed usage applied AI to 21% of its tasks. [Source: AI & Economy ATLAS v1.0 (https://ai.google/static/documents/GoogleATLASv1.pdf) (2026), Google and Google DeepMind] ## Twenty points ahead - The ONS puts employee self-reported use at 55%, against 35% of businesses reporting at least one AI technology. Those come from two different surveys with different bases, so the gap is not a clean twenty points. It is, however, a big gap in one direction, and both studies point the same way. - ATLAS excludes Google Workspace and enterprise Cloud usage. Of the activity it does measure, 86.5% of conversations are non-work. Fig. 1 · self-reported use against business-reported AI adoption, UK, June 2026 · source: ONS, Artificial intelligence in UK businesses: 2023 to 2026 (https://www.ons.gov.uk/businessindustryandtrade/business/businessservices/articles/artificialintelligenceinukbusinesses/2023to2026) So one of the largest studies of AI's relationship with work is mostly observing people using a consumer tool outside formal company systems. Meanwhile, the national business survey suggests that many employees are already using AI before their employers realise. Perhaps that is how adoption really happened. Not through a board decision or a redesigned process, but through someone opening a browser and pasting in a task they need help with. Individual experimentation is quick, cheap, and further along than most firms assume. A graduate can reconcile two schedule exports with a script they wrote that morning, run a parametric study that used to take a week, produce a first-draft clash report, alone and without asking. The ceiling on solo work sits well above email-rewriting. But it is still a ceiling. What a person cannot do alone is give the AI the history of a client relationship, the precedent behind a decision, or the reason a particular project methodology failed in 2022. They also cannot procure the tool, agree a data retention policy, or keep client-confidential material inside a boundary the firm controls. This is not just experimentation around the edges. Employees are doing substantial work with tools the firm may not know about. Perhaps 21% is not a capability ceiling at all. Maybe it is the limit of what one employee can safely do without the organisation noticing. ## Expensive people, cheap work Two ATLAS findings make the pattern stranger: - First, higher-paid occupations use AI more. A 1% increase in an occupation's median earnings is associated with an increase of more than 2.5% in AI usage intensity. Median US earnings weighted by employment are $62,252. Weighted by Gemini conversations, they rise to $82,919. - But when ATLAS scores tasks by the expertise they require, Gemini use is most over-represented in lower-expertise cognitive tasks, particularly non-routine ones. Its examples include rewriting news reports into specified languages and writing or reviewing product specifications. The highest-paid people appear to be using AI most heavily on the least expert parts of their jobs. At an individual level, this makes sense. Saving a senior employee twenty minutes of rewriting has value. But it is not the same as changing how the firm works. ## Training the fluent Firms often describe this as a skills problem. Staff need training. They need to understand the tools, learn better prompting and become more confident. The ONS shows businesses responding accordingly. Among firms citing a lack of AI expertise as a barrier, around 62% are training or retraining existing staff, compared with around 26% of businesses reporting no barriers. Only 11% say more than half their workforce has received AI-related training. Training is the obvious response. It may also be the wrong place to start. Many employees are already reasonably fluent - they have practised at home, where most observed usage still takes place. What they cannot do alone is provide the background that depends on the firm's accumulated memory: the precedent, the project history, the client context and the reasoning behind previous decisions. The missing piece is not staff skills. It is the written context (https://www.associativetrails.com/writing/2026/05/21/what-your-ai-tools-are-missing/) that would give that skill something more valuable to work on. ## Who chose the work? This is why “Are you using AI?” has become a dead question. A firm can answer yes because a few employees use ChatGPT to draft emails. Another can answer yes because AI is designed into a core client process. The same tick box covers both. A better question is: which fifth of your employee's work does AI already touch, and who chose that fifth? In many firms, nobody chose it. Not the managing partner, not IT and not whoever signed off the software licences. It was added one person at a time. Employees chose work that was easy to isolate, explain and do alone. Anything requiring shared context or permission stayed untouched. Unfortunately, that may also be where most of the value sits. ## Nothing broke Around half of firms tell the ONS that AI has made no difference to headcount. That can be read as reassuring. It may also explain why nobody feels much urgency to look more closely. AI has arrived almost everywhere - it helps with fragments of work and nothing important appears to have broken. That looks like successful adoption. Or it may be what happens when everyone starts using a new technology on a piecemeal basis, with nobody designing the overall process. > “Our plans go astray because they have nothing to be directed toward; for the man who does not know which harbor he is making for, no wind is favorable.” — Seneca, Letters to Lucilius, Letter 71.3, trans. Gummere --- # Borrowing against the day a client asks why URL: https://www.associativetrails.com/writing/2026/07/23/borrowing-against-the-day-a-client-asks-why/ Published: 2026-07-23 · AI · Sheet 31 Summary: Polished AI output no longer means good thinking. The gap between what your team ships and what it understands is quietly compounding into debt. A report goes out to the client. It is well written, on time, and nobody in the firm could tell you how the third recommendation was reached. The consultant asked AI for an idea, skimmed it, and it made sense. It shipped. Everyone moved on. Nothing broke. That is the point. The work looks fine - better than fine, in many cases - and is being produced faster than ever. That is when the gap opens: what the firm produces starts to outrun what anyone actually understands. ## The tower keeps rising Software teams hit this first. Armin Ronacher (creator of the Flask framework for Python) reaches for the Tower of Babel (https://lucumr.pocoo.org/2026/7/13/the-tower-keeps-rising/). The builders lose their shared language and construction stops. His twist: with AI, construction does not stop. The tower keeps rising after the shared understanding underneath it has collapsed. In software, the really hard part was never typing code. It was everyone holding the same model of what the thing does and why. That model was maintained by friction: reading each other's work, reviewing, arguing, asking "why did you do it this way?" Slow, yes. But as Ronacher puts it, "this friction synchronizes people." Your professional services firm's tower is the accumulated judgement of the team: what good advice looks like, where the risks sit, why the firm does things a certain way. The same accidental friction kept that judgement in sync. Hand the brief to an AI agent and the friction is gone. The need for the shared understanding is not. Polish used to be a fair proxy for rigour: a clean, well-argued document usually signalled good thinking. AI severs that link - you can now generate something that reads like top-tier consulting with none of the thinking behind it. Much of the removed friction deserved to go. Nobody should mourn hours spent reformatting slides. The trouble is that the same tools also remove the pauses in which people used to notice that they disagreed. > “The lack of an immediate failure is what makes it curious and a bit disorienting. The tower does not fall, and so we do not notice what was lost. It just keeps rising.” — Armin Ronacher, The Tower Keeps Rising (https://lucumr.pocoo.org/2026/7/13/the-tower-keeps-rising/) ## Comprehension debt Addy Osmani calls this comprehension debt (https://addyosmani.com/blog/software-factories/): the widening gap between how much work exists and how much any human still understands. He means code, but it generalises. Every AI-drafted memo, report or slide deck that ships without a human fully understanding it adds to the balance. His end state is the "dark factory (https://www.mindstudio.ai/blog/what-is-dark-factory-autonomous-ai-codebase)": agents scope, build and ship with nobody reading the details. The professional-firm version is work leaving the building that nobody can defend line by line if the client pushed. Debt is the right word because, like the tacit debt (https://www.associativetrails.com/writing/2026/04/30/tacit-debt-and-the-verification-gap/) firms already carry, it accrues quietly and comes due all at once. A firm can run on it for a long time. Then a client asks the one hard question, or a regulator wants the reasoning, and nobody in the firm can give it. The obvious objection: we will just read the output more carefully. In practice, that is not what happens. Kernighan's 1978 line that debugging is twice as hard as writing code (https://en.wikiquote.org/wiki/Brian_Kernighan) points at something real here: the writer holds a model of every assumption and the reviewer does not. AI-drafted work drops you straight into the reviewer's chair for a document you never built a mental model of. Checking it is the heavier job, not the lighter one. And checking does not come naturally. Shaw and Nave (https://executiveeducation.wharton.upenn.edu/thought-leadership/wharton-at-work/2026/05/thinking-fast-slow-and-artificially/) separate offloading, where you delegate but keep evaluating, from surrender, where you adopt uncritically. Across their studies, 73% of faulty-AI trials showed cognitive surrender and only 20% showed genuine evaluation (offloading), with the rest failed overrides. Even error feedback barely moved the numbers. A reminder won't do it. The workflow has to make people stop and explain what they're approving. Across studies, 73.2% of such trials showed cognitive surrender, 19.7% cognitive offloading, and 7.1% failed overrides (resisted AI but still answered incorrectly). [Source: Thinking—Fast, Slow, and Artificial: How AI is Reshaping Human Reasoning and the Rise of Cognitive Surrender (https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6097646) (2026), Shaw and Nave ] ## The old friction was accidental. The new friction has to be designed. Geoffrey Litt argues that understanding is the new bottleneck (https://www.geoffreylitt.com/2026/07/02/understanding-is-the-new-bottleneck.html). His fix is a habit: each time the agent finishes, it writes a short explainer plus a quiz he answers before moving on. The knowledge-work version is an explain-back at every handoff where AI did the heavy lifting. Before a deliverable moves on, the person responsible states in their own words and without the document open: what it claims, why, and where it is weakest. It is not proof that the work is right, but failure is good evidence that nobody should be signing it off yet. A practical aside: there is a prompt at the end of this post that lets you try Litt's method today. Two further rules would help: - First: never generate more than you can review in one sitting, because the binding constraint is review capacity, not generation capacity, and every page that ships unread is a deposit into the debt. - Second: measure your capacity by what you can stand behind, not what you can produce. So when the speed tempts you into more deliverables, deeper analysis, fuller decks, hold to the ceiling that actually matters: how much you could defend if the client pushed. Not every deliverable earns the same gate. Client-facing advice, anything a regulator could question, or any number a client will act on has to pass the explain-back - a throwaway internal summary or a draft nobody will act on yet can move at the speed of the tool. And the whole thing depends on company culture. An explain-back will fail in firms where admitting confusion harms your reputation. People will rehearse an answer, pass the gate and keep the debt hidden. Designing the new friction - Explain-backs at every handoff where AI did the heavy lifting. - Never generate more than you can review in one sitting. - Measure your capacity by what you can defend, not what you can produce. ## Mind the gap Content generation is abundant and nearly effortless. The scarce thing, the thing worth building a company culture around, is the smallest gap between what you ship and what you understand. Measure your capacity by what you can defend, not what you can produce. ## A prompt based on Geoffrey Litt's explainer skill Save this as a markdown file and at the end of a chat session, drag the file in with: "follow these instructions." The AI agent will produce an HTML page that captures the background, explains the decisions made, examines the tradeoffs and quizzes you about it all. Very useful, sometimes sobering. --- name: explain-session-html description: Use at the end of a work session to produce a rich explanation of the changes made to a document, slide deck, or similar deliverable. Captures background, key decisions, tradeoffs, and what the human still needs to understand. Produces HTML output. --- # Explain Session Please make me a rich, interactive explanation of the work we did in this session on the document, deck, or other deliverable. The point is to hand a human a clear record of what changed and why, so they can review it, catch anything they disagree with, and understand it well enough to defend it or build on it later. Assume they were not watching every step. It should have these sections: - Background: Explain the context this work sits in. What is the document or deck for, who is the audience, and what state was it in before we started? We don't know how much the reader already knows, so include a deep background for anyone new to the project (note that it can be skipped if they already know it), and then a narrower background covering the specific part we worked on. If we drew on source material, prior versions, research, or a brief, name it here. - Key decisions: Walk through the decisions we made, one by one. For each, state what we chose, what the main alternatives were, and why we went the way we did. The focus is the reasoning, not a blow-by-blow of every edit. Use concrete examples with real snippets from the work. Use figures and diagrams freely. - Tradeoffs and open questions: Spell out what each decision costs, what we gave up, and where reasonable people might have chosen differently. Flag anything still unresolved, anything the reader should double-check, and any assumption we made that could be wrong. Be honest about weak spots. - Walkthrough: Give a high-level tour of what actually changed in the deliverable. Group and order the changes so they make sense to someone reading them cold, not in the order we happened to make them. - Quiz: Come up with five questions that test whether the reader has understood the substance of this session. Medium difficulty: hard enough that you need to grasp the decisions and tradeoffs to answer, but not gotchas. The goal is to help the reader confirm they have actually understood. Present them as interactive multiple-choice questions, and when the reader clicks, tell them whether they were right and give feedback. ## Format: - Output a single self-contained HTML file that includes its own CSS and JavaScript. Make the whole thing one long page with section headers and a table of contents. Don't use tabs for the top-level structure. Basic responsive styling so it reads well on a phone is a plus. Put the file in a global place on my computer, outside any project folder, and make sure the filename always starts with today's date in `YYYY-MM-DD-` format, because it keeps the files time-sorted. For example: /tmp/2026-01-12-session-.html - Write with the clarity and flow of Martin Kleppmann: engaging, plain, classic style. Transitions between sections should be smooth. Never use a long word where a short one will do. If you can cut a word, cut it. Use the active voice, not the passive. No emdashes. - Some tips on diagrams. Pick a small number of diagram families and reuse them throughout to explain different points. Some useful kinds: - A simplified before-and-after view of the deliverable (a slide, a section of a document, a table) to show what a change looks like to the reader. - A flow or structure diagram showing how the parts of the document or deck fit together, or how the argument moves from one section to the next. Include real example content, not placeholders. - Don't use ASCII diagrams. Always use simple HTML designs for diagrams, HTML lists for lists of things, and so on. - For any block of quoted text, an excerpt from the deliverable, or a snippet, always use tags. If you use a custom styled div instead, it **must** include `white-space: pre-wrap` in its CSS, or the browser will collapse all the newlines into a single line. Before saving the file, scan each such block in the HTML source and confirm its CSS includes `white-space: pre` or `pre-wrap`. - Use callouts for key concepts or definitions, important edge cases, decisions the reader really should not miss, and anything that still needs their sign-off. --- # Model churn is not your problem URL: https://www.associativetrails.com/writing/2026/07/16/model-churn-is-not-your-problem/ Published: 2026-07-16 · AI · Sheet 30 Summary: Once your playbooks are written down, the leaderboard can flip as often as it likes. On 9 June, Anthropic shipped Claude Fable 5. Three days later a US export-control order switched it off (https://www.infoq.com/news/2026/06/claude-5-release/). It came back on 1 July (https://www.anthropic.com/news/redeploying-fable-5). Eight days after that, OpenAI answered with GPT-5.6 in three tiers (https://openai.com/index/previewing-gpt-5-6-sol/). Four events, one month, no way to predict any of them. If your AI capability was tied to whichever model happened to be ahead, the ground shifted four times without you touching a thing. Zoom out and it is the same story. OpenAI dominated 2024. Anthropic exploded in 2025 with Claude Code. Now Codex is pulling engineers back (https://news.theaiexchange.com/p/who-is-actually-winning-claude-or-chatgpt). Every flip, the same question lands on someone's desk: do we retool? Retrain? Rewrite everything? If your AI capability lives in tool habits and prompt tricks, each flip costs you. There are two common reactions. One is to chase the winner every time the leaderboard changes. The other is to freeze and say, reasonably enough, that it might be better to wait until the market settles. Both instincts are understandable. Retooling constantly is exhausting. Waiting feels prudent. But both miss the point: your expertise shouldn't be trapped inside one model. ## The part that doesn't churn The thing that survives model churn is the playbook - a process written down precisely enough that a machine can follow it, stored somewhere outside any single AI tool. Not a clever prompt sitting in one person's chat history. Not a habit that only works because one employee knows how to steer one model. A document the business owns. Rachel Woods, who runs AI operations firm AMP, keeps hers in plain documents, so the same playbook runs in Claude, ChatGPT, or whatever ships next quarter. Most of AMP's training content hasn't changed in three years (https://www.youtube.com/watch?v=W5MHBKhbxYI), through constant tool churn, because the durable layer is process design, not the tech. > “...if you invest in documenting your process... you can feed it to another AI tool if a better option becomes available and avoid having to start from scratch.” — Rachel Woods (https://www.ada.cx/blog/the-ai-and-operations-playbook-how-to-take-ai-and-apply-it-to-the-operations-of-the-business/), CEO, AMP That sounds almost too mundane, which is probably why it is easy to overlook. But it changes the question from "which model are we betting on?" to "what do we know that the model does not?" Satya Nadella, Microsoft CEO, described a version of the same problem at enterprise scale: can you switch out the "generalist" model without losing the "company veteran"expertise built around it? (https://x.com/satyanadella/article/2066182223213293753) In other words: can the expertise stay with the company while the model remains interchangeable? That distinction matters. A frontier model can know a lot about the world and still know nothing about how your firm approves a refund, writes a board update, qualifies a lead, handles a tricky client, or decides whether a proposal is good enough to send. The engineering world has arrived at the same conclusion from the opposite direction: [Note: "Evals" and "test suites" are codified tests of what good output looks like, so you can check any model against the same standard. Basically, playbooks for software developers.] - Prompts and models churn, but rubrics and test suites preserve the definition of good work across upgrades (https://www.ngpcap.com/insights/evals-are-the-new-moat--and-rl-turns-them-into-your-product-advantage). - Teams with evals adopt new models in days; teams without face weeks of re-testing (https://www.braintrust.dev/foundations/why-are-evals-important). - Your eval data is the missing moat (https://www.forbes.com/sites/lutzfinger/2026/05/26/the-missing-moat-in-ai-your-eval-data/). The link density is deliberate. The same idea keeps arriving from different directions: operators, CTOs, investors, engineers, CEOs. Different vocabularies, same shape of thought. Put bluntly: "The model is rented. The eval is owned." (https://medium.com/@adnanmasood/eval-is-the-moat-59484866dacb) I shipped a small version of the thesis recently: Ookbook (https://github.com/associativetrails/ookbook), a markdown file that carries the instructions to update itself - so any agent, running any model, can re-run it next quarter. Same deal: the file is owned, the model that runs it is rented. Of course, there is an obvious objection: documents decay. Anyone who has worked inside a company knows this. A process doc gets written once, praised, forgotten, and six months later it describes a world that no longer exists. But that is not a reason to avoid writing the playbook. It is a reason to treat it like an asset. Put it somewhere the business controls. Keep version history. Give it an owner. Update it when the process changes. This is not exotic AI governance - it's just the same boring maintenance you already owe any important company document. ## Why now, not later "We'll document things once the models settle down" gets it backwards, in more ways than one. First, better models make good playbooks more valuable, not less. If the playbook captures the right process, each model upgrade should execute it better (https://x.com/claudedevs/article/2074900291062034618). You do not lose the work. You get a better return from it. Second, writing the playbook was never really the model's job. This is the part people sometimes want to skip. They hope a stronger model will infer the process for them. Sometimes it can infer the obvious bits. But the valuable parts are usually specialist: the judgment calls, exceptions, house style, thresholds, tradeoffs, the things experienced people check almost without noticing. A recent paper (https://arxiv.org/abs/2602.12670) measured something close to this. Human expert-written playbooks lifted agent pass rates by 16.6 percentage points. AI-generated playbooks performed worse than no playbook at all. The value of a playbook is not that it contains generic instructions. The value is that it captures knowledge a generalist model would get wrong. Curated skills lifted agent pass rates by 16.6% AI-generated skills performed worse than none at all. [Source: SkillsBench: Benchmarking How Well Agent Skills Work Across Diverse Tasks (https://arxiv.org/abs/2602.12670) (2026), Li, Chen, Liu et al. ] The playbook does not have to be huge. The same paper found compact and standard-length skills added 19 to 21.5 points, suggesting that after a modest length, more instructions do not add much. The goal is not to write a manual for everything. It is to write down the five things the model would otherwise miss. So the useful diagnostic is not "have we documented the whole company?" That is too big and too vague. The better questions are: - Could a colleague run this process in a different AI tool tomorrow? - If our most experienced employee retired, what would vanish with them? - What does good output look like here, and how would we know? - Where does the model need context, and where does it need constraints? - Which parts of the process are stable enough to write down, and which parts still need human judgment? Pick one process. Not the whole business. One process that already matters. Write down the five things a model could not infer. Run it in two different tools. See what breaks. Make adjustments (not too defensive (https://www.theguardian.com/football/2026/jul/16/thomas-tuchel-accepts-responsibility-substitutions-england-argentina)). The strange consequence is that the faster models improve, the less any individual model matters. Capability shifts away from the frontier lab to the shared drive. The model race is now a spectator sport for businesses - enjoy it from the directors' box. And it starts with a simple document. [Image: A relaxed figure sits above a track with a closed playbook beside them, calmly watching interchangeable numbered runners compete below.] Watching the Model Race from the Directors' Box --- # Introducing Ookbook - data that knows how to fetch itself URL: https://www.associativetrails.com/writing/2026/07/09/introducing-ookbook/ Published: 2026-07-09 · AI · Sheet 29 Summary: A markdown file that carries both data and the instructions to recreate it You spend a good few hours getting exactly the right list of competitors, vacancies or tenders. Your AI agent gathers the data together into a neat table and you paste it into a document. Job done. A month later you need it again - and discover the instructions disappeared with the chat thread you closed five weeks ago. The data wasn't the hard part. Remembering how to reproduce it was. Ookbook is a simple idea to fix that. Each dataset is a plain markdown file that carries not only the data, but the instructions needed to reproduce it. [Image: Diagram showing an Ookbook markdown file that stores both data and collection instructions, with an AI agent repeatedly reading the instructions, gathering fresh web data, and writing the updated results back into the same file.] The self-refreshing Ookbook loop When AI agents first started browsing the web, collecting data felt almost magical. Give them a prompt and they'd return a neatly formatted table in minutes. But useful work is rarely a one-off. The real value comes from running exactly the same collection next week, next month, or next quarter, using the same sources, filters and output format. Chat windows aren't built for that. Once the conversation disappears, so does the process. I recently scanned public discussions about AI tools, and one frustration appeared again and again: people wanted to monitor something over time, but every refresh meant rebuilding the same prompt from scratch. Anyone can ask an AI to scrape a website once. The useful output is something that knows how to recreate itself. That is what Ookbook (https://github.com/associativetrails/ookbook) is. One markdown file holding two things: the data you collected, and the exact steps to collect it again. No application. No subscription. No workflow engine. Just a text file. Under the hood it is simply a template (https://github.com/associativetrails/ookbook/blob/trunk/template.md) file and a kickoff prompt (https://github.com/associativetrails/ookbook/blob/trunk/kickoff-prompt.md). You paste both into a browsing agent (Claude with the Chrome extension is the reference implementation, but anything that can read web pages and write a file will do), answer a few questions, and save the completed markdown file. [Image: Screenshot of the Claude app with the kickoff prompt and template] Paste in the prompt and template [Image: Screenshot of the Claude app showing the AI asking the user questions] Answer a few questions [Image: Screenshot of the Claude app showing the reasoning and the output file] Save the resulting markdown file [Image: Screenshot of the Claude app showing the upload prompt] Next month, upload that file again with one sentence: "Follow the instructions in the attached file to update the data" ## Why this matters Imagine you're an independent consultant tracking framework tenders. The first monthly search takes forty minutes of clicking, filtering and copying into a spreadsheet. The second gets squeezed by client work. The third never happens. The problem isn't collecting the data. It's remembering exactly how you did it. An Ookbook becomes that memory. Everything needed to recreate the dataset lives beside the data itself. Refreshing it becomes a thirty-second task instead of another afternoon spent rediscovering your own process. The market scan I mentioned earlier is itself an Ookbook (https://github.com/associativetrails/ookbook/blob/trunk/examples/ookbook-20260629-recurring-ai-task-complaints.md). When I wanted to understand the jobs people ask AI to perform, the resulting file contained both the findings and the instructions that produced them. Next quarter I'll hand the same file back to an agent with a one-line prompt and get an updated report using the same search strategy, scoring criteria and exclusions. Some websites make this harder than others. LinkedIn and Amazon don't welcome automated collection, layouts change, and anti-bot measures evolve. An agent controlling a real browser (e.g. Claude Desktop with the Chrome extension (https://support.claude.com/en/articles/12012173-get-started-with-claude-in-chrome)) can often work around those problems by navigating pages like a human, but collections will inevitably drift as websites change. Ookbook doesn't solve the discipline problem of remembering to check something every month. But it does lower the cost of the habit far enough that it might survive during a busy week. ## Why "Ookbook"? The idea comes from object-oriented programming (OOP), where an object combines data with behaviour. An Ookbook does the same. Humans skim the data. AI agents read the instructions. The file isn't just information - it's information that knows how to refresh itself. "Object-oriented knowledge" quickly became OOK. From there it wasn't a long leap to Terry Pratchett's Librarian (https://wiki.lspace.org/Librarian), happily swinging through the stacks collecting information for me. Oook. ## Give it a go yourself Full instructions are free on GitHub (https://github.com/associativetrails/ookbook/) - ready to be downloaded and used today. Point the template at something you've been meaning to keep an eye on and see how far a single markdown file can get you. If that monitoring task grows beyond one desktop - where the data informs group decisions, or multiple people depend on it - a single file stops being enough. That's a harder problem, and it's the one I spend a lot of my time working on. Let's talk (https://www.associativetrails.com/contact/?txtEnquiry=I+would+like+to+find+out+more+about+using+Ookbooks+inside+my+organization.). --- # The canary in your context window URL: https://www.associativetrails.com/writing/2026/07/02/the-canary-in-your-context-window/ Published: 2026-07-02 · AI · Sheet 28 Summary: A half-second check that catches silent instruction drift in long AI sessions. You are deep into a long session editing a document with your AI. Then, without warning, it starts ignoring something you told it twenty messages ago. The tone is still confident. The answer still sounds right. You don't notice because nothing obviously broke. That silent drift is the most common way AI work goes wrong, and there is a thirty-second trick that catches it. All it takes is one emoji. ## The window is working memory, not an archive A context window is the information in the AI model's working memory. It can only directly "see" what is inside that window - anything outside it must be summarised, retrieved or supplied again. The instinct is to stuff everything in, but that can backfire. Models start degrading well before any of the token limits the AI labs claim for their products. Chroma tested 18 leading LLMs and found none of them process context uniformly (https://research.trychroma.com/context-rot) - performance grows increasingly unreliable as input length grows, even on simple tasks. They also suffer "lost in the middle" (https://arxiv.org/abs/2307.03172) - attention is U-shaped, so information buried in a long context gets skimmed. Beyond a certain point, adding more context can actually make the model remember less reliably (https://www.morphllm.com/context-rot). If you run agents or tool-heavy sessions, context fills fast (https://arxiv.org/abs/2511.22729). Verbose tool responses accumulate quickly, which is one reason modern agent frameworks increasingly prune, summarise or externalise tool output. The problem is that it's invisible. Nothing flashes red when this happens. The model doesn't warn you it has stopped following your instructions. It just keeps churning out fluent, plausible text - and that's exactly why it's easy to trust. So how can you tell when this starts happening? > “We observe that model performance varies significantly as input length changes, even on simple tasks.” — Context Rot (https://research.trychroma.com/context-rot), Chroma Research, July 2025 ## Bring a canary down the mine with you Give the model an instruction it must repeat on every reply. Something that you can scan quickly to see if it's there. Add a line like: IMPORTANT: End every reply with 🐤. Every reply you give me should include this emoji. While the model is still following your instructions properly, the bird shows up every turn. The moment the emoji disappears, you know adherence has slipped - possibly on other instructions too, not just this trivial one. It's a proxy you can check in half a second instead of having to audit the whole conversation for drift. Putting it at the end of the reply, where it competes with everything the model just generated, makes it a more trustworthy signal. ## How to use it Put the canary where your tool will read it most reliably. In a persistent instructions file, that looks like this: # CLAUDE.md ## Output rules - IMPORTANT: End every reply with 🐤. Every reply you give me should include this emoji. In plain ChatGPT or Claude, drop it in the custom instructions, or just make it your first message and watch from there. If you use Claude Code/Cowork or Codex, put it in your CLAUDE.md or AGENTS.md, which the model re-reads every turn (https://claude.com/blog/steering-claude-code-skills-hooks-rules-subagents-and-more). If the bird dies in a persistent instructions file, that's not the model forgetting something from turn 20, it's actively ignoring a file it's supposed to be re‑reading fresh. A useful side effect: if the bird dies quickly, your instructions are probably too long. Instruction files lose their grip past a couple hundred lines. A short, sharp instruction set keeps the canary singing longer. On Windows Windows key + period opens the emoji picker, type "bird" to find the 🐤. On Mac Control + Command + Space opens the emoji picker, type "bird" to find the 🐤. ## When the bird goes quiet Resetting is simpler than it sounds. Start a fresh chat. It is the cheapest fix, and curated context beats accumulated context every time. Before you do, ask the current session to write a short handoff: Write a handoff for a fresh session. Cover only: what's been decided, what's still open, and any file paths, names, or constraints the next session needs. No recap of the conversation itself, no pleasantries. Keep it under 300 words. Paste the results into the new session and you keep the useful stuff without dragging the accumulated cruft along. Two more habits that help: keep a running notes file outside the chat, since the model forgets across sessions but a file doesn't. And if you're running agents, push side quests into sub-agents so your main thread stays clean (https://claude.com/blog/steering-claude-code-skills-hooks-rules-subagents-and-more). ## Handle with care One caveat: a living bird is not a clean bill of health. The model can keep printing 🐤 while quietly dropping an instruction that actually matters. A missing bird is a reliable bad sign; a live one is only a probably fine. When it disappears, the canary only tells you that something slipped, not what it was. It's a smoke detector, not a diagnosis. It won't tell you which of your instructions got dropped, just that something did. But that is the whole point of a canary. The toxic gas has no smell. The bird is the only warning you get. --- # The Deliverable Fallacy URL: https://www.associativetrails.com/writing/2026/06/25/the-deliverable-fallacy/ Published: 2026-06-25 · Consultancy · Sheet 27 Summary: The deck is what the client pays for. It was never what actually changed things. Tim Ferriss has argued (https://tim.blog/2026/06/12/has-ai-already-killed-nonfiction/) that on some level his wildly successful self-help book The 4-Hour Body was a lookup table: in 2019 it was the best available interface to questions like: "How do I lose fat?" or "How do I fix my sleep?" An AI chatbot whose underlying model has read that book (and thousands of others), and can return a set of personalised recommendations in fifteen seconds. Recent book sales confirm what happens next: - The Association of American Publishers has adult nonfiction down 7.8% year-on-year (https://publishingperspectives.com/2026/05/u-s-book-sales-off-to-a-quiet-start-in-2026/) in the first quarter of 2026, with self-help the steepest faller at 26.3% (https://www.ft.com/content/d5be7d15-570f-4a4e-ac4e-8dd2e1948b0b). - Ferriss reports his own catalogue selling at roughly 80% below 2022. Being the best route to an answer can be very lucrative, until someone comes up with something faster. > “...my catalog will sell roughly 80% fewer print copies in 2026 than it did in 2022, with almost all of that happening since LLMs like Claude and ChatGPT exploded in use.” — Tim Ferriss (https://tim.blog/2026/06/12/has-ai-already-killed-nonfiction/), author of The 4-Hour Body, The 4-Hour Workweek Consultancy contains plenty of lookup tables too: regulations, benchmarks, standards, specifications. AI can produce all of those frighteningly cheaply. But were they ever the product? Did a deck containing a list of recommendations actually turn an organisation around, or was it something else? Here is where Ferriss turns, and it is worth following him closely. He sent friends a bullet-point summary of his own book. They thanked him, agreed it all made sense ... and changed nothing. Yet the readers who went through the full book, with its sequencing and all its detours, routinely changed the habits they had failed to shift for years. The information in his bullet-point summary was identical to the information in the book. The outcome was not. His diagnosis: the book worked because it changed what the reader believed about their own situation before it handed over the solution. The summary delivered the solution without the reframing, so it changed nothing. Skip the build up and the solutions don't work. In consultancy, the slide deck is the bullet-point summary and the engagement is the long-form book. A consultant's report details their solutions. What makes them stick is the process that produced them: the workshops where staff members argued through tradeoffs, the interviews that surfaced what everyone knew and nobody said. The client organisation changes because it experiences something, not because it receives something. Confuse the two and you have the deliverable fallacy: mistaking the final product for the mechanism that produced it. Edgar Schein described the difference between expert consultation and process consultation (https://sloanreview.mit.edu/article/a-general-philosophy-of-helping-process-consultation/): the expert model delivers conclusions, process consultation changes the client's capacity to reach them. Only about 30% of digital transformations meet their targets. Half the conclusions never get operationalised. The information in those decks was correct, complete, and changed nothing. [Source: Boston Consulting Group, Flipping the Odds of Digital Transformation Success (https://www.bcg.com/publications/2020/increasing-odds-of-success-in-digital-transformation) ] Ferriss makes a seller's point: produce transformation, not information. The buyer, a consultant's client, now has a faster route to the bullet points than ever before. A polished strategy regurgitated by a chatbot will read exactly like the six-week engagement's deck, because people asked to distinguish AI-generated text from human work perform at roughly coin-flip accuracy (https://hai.stanford.edu/news/was-written-human-or-ai-tsu). It will not produce the change, for precisely Ferriss's reason, and the client will be satisfied right up until the recommendations hit the organisation and nothing changes. AI is attacking the visible part of consulting, not the valuable part. Commoditising the deck does not touch what mattered, because the deck was never where the real value sat. But it does strip away the disguise. The firms in trouble are the ones who had quietly become deck-factories, selling the summary and hoping nobody noticed the transformation was not included. Most don't do this deliberately. Over time the deck slowly becomes the product because it's the easiest thing to price. The firms that will be fine are the ones whose engagements genuinely change how the client thinks, where the deliverable is the least valuable part of the project. This is the verification gap (https://www.associativetrails.com/writing/2026/06/11/the-effort-dividend/): the client cannot inspect the thing that matters, so they inspect the proxy - and that bit has just become free to produce. None of which resolves into advice. If the engagement is the value and the deck is residue, a consultancy firm still has to sell something it can demonstrate. "You are paying for a transformation you cannot inspect in advance" is a brutal pitch against a competitor brandishing a polished AI deck at a tenth of the price. The firms that understand the mechanism are left selling the thing the client never realised they were paying for, against a substitute that looks identical and costs almost nothing. So, how do you sell a process to a client trained to buy an output? The firms that work this out are not the ones producing better decks - those are freely available without needing a 26 year old with an MBA and a lanyard. They are the ones who make the value of the engagement visible before the sale. --- # I thought the post was finished. My readers disagreed. URL: https://www.associativetrails.com/writing/2026/06/18/i-thought-the-post-was-finished/ Published: 2026-06-18 · AI · Sheet 26 Summary: One AI asked to review your writing will catch a weak sentence. It will not tell you which reader stopped halfway through and why. I had finished the post announcing Roundtable (https://www.associativetrails.com/writing/2026/06/04/introducing-roundtable/). I thought it was good - maybe a bit technical, but I was proud of it. Then I went to do what I always do before shipping: paste it into an AI and ask whether it held up. I got two clicks in before the irony struck me. The post argues that a single AI has a structural incentive to nod along, that it is the worst possible judge of work it helped produce. And there I was, asking it to judge the post it had helped me write. I had walked straight into the thing I was warning about. So I decided to run the post through the tool (https://github.com/associativetrails/roundtable) it was promoting. The trouble was, none of the standard five panels were good fits. Six Hats organises by cognitive mode. Historical Figures by intellectual tradition. Stakeholder Map by affected party. All useful for working through a decision. None of them reflects the main thing that matters when you are writing: the people who will actually read the thing. ## Why "looks good" is useless The thing I find most difficult when writing is finding the flaw that loses a specific reader. A line that reassures an early adopter can be the same line that loses an IT Director. A claim that satisfies a technical reviewer can be the one that makes a marketer ask who the post is for. A single AI averages all of those readers into one voice and tells you it "looks good". I did not want a generic critic. I wanted to know which specific readers would object, and why. [Image: Six distinct professional silhouettes enter a funnel from the left. A single blurred, featureless figure emerges on the right, with a speech bubble reading 'looks good.'] I did not want a generic critic. I wanted to know which specific readers would object, and why ## Designing the panel around the readers I decided to build a panel where each persona is a reader who may be interested in the blog, and let them argue. I picked six archetypes that span who reads Associative Trails and who has to be convinced for a software tool to be implemented: - David, IT Director - operational risk and supportability - Marcus, Technical Director - rigour and architecture - Maya, Graduate - practical day-to-day usefulness - Priya, HR Manager - organisational impact - Sarah, Lead Consultant - delivery reality - Sophie, Marketing and Comms Manager - clarity and positioning Between them they cover the four reasons a professional services reader rejects a new tool: it will not survive procurement due diligence (David), the argument is thin (Marcus), I cannot act on it (Maya and Sarah), it ignores how my organisation actually works (Priya). Sophie is there to catch the post failing as a piece of communication. Match the panel to the job in hand. For a decision, principle-based panels work. For writing, the aim is "who will read this and what will they think"? ## How it was built and run In Roundtable, a custom panel is a folder containing a text file for each persona. You can either write these yourself or ask the AI to generate one for you - for example: Add an ethics advocate to the Blog Reader panel The AI will ask for a name, role, and any context you want to provide, then write and save the file automatically in the correct format and location. Each file follows this structure: # Name - Role ## Background 2-3 sentences: seniority, experience, domain, defining career context. ## Approach The frameworks, mental models and instincts they bring. ## Priorities & constraints What they optimise for. What they will not compromise on. ## Blind spots & biases One or two honest tendencies that make their view distinct but limited. ## Voice & tone How they communicate. 2-3 adjectives plus a sample sentence. ## The question they always ask One signature question they reliably raise. The signature question is what stops a persona regressing into a generic reviewer with a job title. Here are David's and Sophie's, because they are the furthest apart: > "Who owns this process once it's live? How does this integrate with the systems we already rely on? What happens if the vendor changes pricing?" — David (IT) > "What problem are we solving here? Can we make this easier to understand? Can somebody explain this without acronyms?" — Sophie (Marketing and Comms) The custom panel lives in a subfolder, sitting alongside the standard-issue panels. Roundtable picks up any folder matching the naming convention, so a custom panel needs no code change. It is simply text files in a directory. Running it was one line: "Give me panel feedback on this post from the Blog Readers panel" with the blog text attached. Each persona returns scored, structured feedback, and a final synthesis pulls out the agreements, the disagreements, and the one change worth making first. What came back scored 7 out of 10 overall, with individual reads from 6 to 8: | Reader | Score | One-line verdict | | --- | --- | --- | | David (IT Director) | 6 | Strong on the why, thin on the operational how | | Marcus (Technical Director) | 8 | Architecture sound, the folder-equals-multi-agent claim is compressed | | Maya (Graduate Engineer) | 7 | I want to try it today, but there is no path for me inside a big firm | | Priya (HR Manager) | 6 | Written as if the only person in the room is the solo practitioner | | Sarah (Lead Architect) | 7 | Credible for a solo user, leaks at the edges for a team | | Sophie (Marketing) | 8 | Distinctive and well-written, but who is it for? | ## What the panel found that I could not Two things jumped out immediately. - Four personas arrived at the same conclusion: the post was written entirely for the solo practitioner who has full control over their own tooling. David wanted deployment guidance for a managed environment. Maya wanted a path as an individual inside a large firm where she cannot just clone a repo onto a work laptop. Priya wanted the team and organisational dimension. Sarah wanted implementation honesty that went past personal use. I had written the post for the only reader I could see clearly, which was me. - Three personas independently flagged a diagram placeholder still sitting in the draft text. Unfinished work - the kind of thing an author stops noticing somewhere around the fortieth read and can often end up being published. What surprised me was two personas pulling in opposite directions. Marcus wanted the technical architecture stated more precisely, because to him the compressed "a folder of files is a multi-agent system" claim was weak. Sophie wanted exactly the opposite: more precision would deepen the post for the few and shrink an audience that was already too narrow. Both were right inside their own terms of reference. The panel didn't settle the argument. It showed me what the trade-offs were. That is the whole reason for building the thing in the first place. An answer-dispensing machine would have told me which way to go. The panel forced me to decide. The interesting part is that this isn't six different AI models. It's one model constrained six different ways. A generic review lets the model average perspectives into a safe verdict. Fixed personas force it to commit to specific concerns and create disagreement that would otherwise disappear. [Image: Two figures labelled Marcus and Sophie grip opposite ends of a document. Marcus pulls left toward 'More precise / deeper.' Sophie pulls right toward 'Simpler / broader.' The document holds, unresolved, in the centre.] The panel didn't settle the argument. It showed me what the trade-offs were. ## What changed, and when to do this The version of the launch post that I eventually published has a section called "A note on scope." It addresses managed enterprise environments, names the data-processing reality of routing through external models, and invites firms that want a version inside their own infrastructure to get in touch. That section only exists because the Blog Readers panel told me my post was ignoring one of my largest audiences. I should be clear on what this is not. It is not six independent "minds" - it is one LLM denied its usual escape into hedging. Blind spots shared by all six personas will still be there. Its strength is catching the objection that belongs to one persona and vanishes the moment you average them. So, when is this worth doing? Build a custom panel when the job has a known set of stakeholders and you are too close to be all of them at once. Transactional writing (https://www.bbc.co.uk/bitesize/guides/ztwtnbk/revision/1) is where this helps most. [Note: Yes - we're a household that has just been through GCSEs and we're all very happy they are over. Until mid-August. Fingers crossed.] But proposals, pricing pages and pitch decks are the same kind of thing - a document that has to clear several different readers, written by one person who can only really see through their own eyes. The default Roundtable principle-based panels are useful when you are exploring a problem rather than defending a finished artifact. Audience panels are different. They show you the objections that disappear the moment everyone gets averaged together. They will not resolve genuine trade-offs for you, but they will identify them and help you judge the best way through them. Roundtable is free and on GitHub (https://github.com/associativetrails/roundtable). Download it and add your own target audience - the people who need convincing, the ones who ask the questions you have been hoping nobody notices. Then run it before you press "send". [Note: And yes, I did run this post through the Blog Readers panel. It didn't cause a singularity, but David is demanding 10% and Sophie is starting a spin-off podcast.] --- # The Effort Dividend URL: https://www.associativetrails.com/writing/2026/06/11/the-effort-dividend/ Published: 2026-06-11 · AI · Sheet 25 Summary: Professional trust was backed by visible work. AI has just taken it off the gold standard. For most of human history, the quality of what you produced was evidence of how hard you worked, and how hard you worked was evidence of how much you cared. Neither assumption survives the AI disruption. In June 2026, the open-source Ladybird browser project stopped accepting pull requests from the public (https://ladybird.org/posts/changing-how-we-develop-ladybird/) (via (https://simonwillison.net/2026/Jun/5/andreas-kling/)). The project maintainers' reasoning was very clear. Whether code was typed by hand is beside the point, they wrote - what matters is "who will answer for the consequences" once a change enters the codebase. Most people read it as an open-source governance story. They shouldn't. Ladybird has said out loud what every organisation that evaluates knowledge work is about to discover. Effort was the gold standard backing the currency of trust. The code repository with a full suite of tests. The polished slide deck. The typo-free tender document. Each signalled the work put in - nobody spent a weekend on a pull request, or six years on a professional qualification, as a prank. The work was too expensive to fake, so it paid out trust automatically. For decades we collected the effort dividend. AI has not counterfeited this currency. It has done something worse: given everyone a central bank. A currency anyone can print is not forged. It is just worthless. Two things broke at once. The signal, which let strangers trust your work, and the formation - the way doing the work built the skills and judgement behind it. The trust problem might be repaired. The harder question is what happens when people stop learning by doing. The first attempts to counteract this are understandable but flawed. If effort can no longer be observed, measure something adjacent to it. Meta, Microsoft and Salesforce built internal leaderboards tracking employee AI token consumption (https://newsletter.pragmaticengineer.com/p/the-pulse-tokenmaxxing-as-a-weird-6b2) and got exactly the behaviour you'd expect - engineers running agents that burn tokens for no reason. I have written before (https://www.associativetrails.com/writing/2026/04/02/human-in-the-loop/) about AI vendors' alarm at insufficient token consumption. The leaderboard is the vendor's metric, adopted by the customer: consumption as proof of engagement. Goodhart's Law (https://en.wikipedia.org/wiki/Goodhart%27s_law) is usually stated as a warning that "a measure made into a target ceases to be a good measure." Evidence of work put in was Goodhart-resistant because it was genuinely costly. Other reserves have been proposed to back the failing currency of effort. Evidence of independent thought is one. When one open source maintainer planted a fake instruction saying contributions marked with a tell-tale emoji would be fast-tracked, 21 of the next 40 submissions included it (https://glama.ai/blog/2026-03-19-open-source-has-a-bot-problem) - proof that AI agents follow rules without fully understanding them. Taste over volume rewards judgment rather than output (https://www.youtube.com/watch?v=oLu32YpiIJw), and conveniently privileges the people who already possess it. Direct accountability, Ladybird's answer, works precisely because the project is now a closed community of people known to one another. Notice the pattern. Every countermeasure solves the signal problem by shrinking the circle of who is permitted to contribute. Visible effort was always partly theatre. Renaissance courtiers even had a word for making hard things look effortless: sprezzatura (https://en.wikipedia.org/wiki/The_Book_of_the_Courtier). But sprezzatura concealed effort that was really there. AI lets you skip much of the effort entirely. The same mask now hides an empty room. But effort was never only a signal. It was formative. The open-source developer who spent a weekend on a new feature in 2020 could tell you why every choice was made, because that was how they built the expertise. The person who runs an agent in 2026 and clicks "send" cannot answer for the consequences - not necessarily because they are acting in bad faith, but because they never built the understanding. Accountability can be assigned by policy, judgment cannot. No shortcut. You have to have done the work. Even scrutiny itself has degraded. When elite consultants were tested inside their own domain (https://www.hbs.edu/faculty/Pages/item.aspx?num=64700), they often failed to detect incorrect AI outputs, even when warned that the system could be wrong. The fluency of the answers appeared to make errors harder to spot rather than easier. We read polished prose as competence. We always have. But that reflex is now a liability, and it feeds the same verification gap (https://www.associativetrails.com/writing/2026/04/30/tacit-debt-and-the-verification-gap/) that keeps turning up everywhere else - scrutiny is the real work and it needs a significant budget. The comprehensive bid proposal, the immaculate CV, the ridiculously beautiful fifty-page design report - every one of these artefacts traded on the effort dividend, and every one of them is now practically free to produce. Trust has come off the gold standard, and no replacement reserve has been agreed. In open-source, the permissionless model that built much of today's infrastructure is starting to fray, project by project. The only honest replacement - accountability earned slowly by known experts - is expensive and does not scale. A practical test Ask for a bowl of M&Ms with the brown ones removed (https://www.businessinsider.com/van-halen-brown-m-ms-contract-2016-9). Build a small, verifiable signal into every deliverable - something that proves the process was followed, not just that the output looks right. Make scrutiny a named, budgeted line in the plan of work. Defend it to clients as the deliverable rather than an overhead. None of this means giving up on AI. For the signal problem: make scrutiny a named, budgeted line in the plan of work, not an assumed background activity - and defend it to clients as the deliverable rather than an overhead. For the expertise-building problem: let people draft with the tool, then require them to verify and defend the result, because verification is where judgment will now get built. The agent can produce the artefact. It cannot do the understanding for you. Ladybird's repository is still public. Anyone can read it. To contribute, you must first be known - the oldest trust mechanism there is, and the one AI cannot yet counterfeit. But it locks out everyone who has not yet had the chance to become known. The effort dividend worked because strangers could spend it. We need to work out what strangers spend now. --- # Introducing Roundtable - A panel of experts that never clocks off URL: https://www.associativetrails.com/writing/2026/06/04/introducing-roundtable/ Published: 2026-06-04 · AI · Sheet 24 Summary: Previous posts diagnosed the problem and described the countermeasures. This one delivers the tool. A consultant asks their AI to evaluate a proposal. The AI tells them it is compelling, well-structured and perfect for the target audience. There is no way to know if this is true. The AI that helped write the proposal is the last thing in the room qualified to judge it - and it has a structural incentive to nod along. The Agreement Incentive (https://www.associativetrails.com/writing/2026/04/09/the-agreement-incentive/) explained why this happens at the training level. The Yes-Man in the Room (https://www.associativetrails.com/writing/2026/05/14/the-yes-man-in-the-room/) described three levels of countermeasure, from response-level prompts through to multi-agent critic systems. This post delivers the tool: a panel of experts system you can clone and run today. ## Why prompting is not enough This is not a story about AI being bad. It is a story about what happens when capable tools have the wrong incentives by design, and when the people using them are not adequately protected from those incentives by their own habits. Stanford researchers (https://www.science.org/doi/10.1126/science.aec8352) found that AI responses were nearly 50% more sycophantic than human advisors - including in cases involving harmful or illegal behaviour - and that users preferred those sycophantic responses. The MIT/UW delusional spiral research (https://arxiv.org/pdf/2602.19141) showed that this compounds - each validating response raises conviction, which prompts bolder claims, which the AI affirms again. This event holds true for expert users: Shaw and Nave's three randomised controlled trials found that across three experiments, participants frequently accepted faulty AI recommendations. (https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6097646) These were people who had every reason to be sceptical, but still showed cognitive surrender. Prompting countermeasures sound sensible right up until you're three hours into a proposal and looking for reassurance. Google found something interesting (https://arxiv.org/pdf/2603.20639) emerging inside frontier reasoning models. When optimised for accuracy, they spontaneously developed what the researchers call a "society of thought": multiple internal perspectives challenging and refining one another before reaching a conclusion. Roundtable (https://github.com/associativetrails/roundtable) makes that process visible. Or at least that's the idea. Instead of asking a single AI to generate and critique its own work, it creates space for disagreement before synthesis. [Image: Two diagrams. Left: a single AI node looping back on itself to produce an output, generation and evaluation combined. Right: a generator node feeds separate critic nodes, which converge on a synthesis node before output, evaluation separated from generation.] The AI assistant should not be marking its own homework ## What is Roundtable? Roundtable (https://github.com/associativetrails/roundtable) turns a single AI assistant into a panel of independent reviewers. Under the hood, it's just a folder of prompts and persona files. Any AI tool that reads AGENTS.md - Claude Cowork, Claude Code, Codex - will pick up the panel instructions automatically when you open the folder in their App or fire up the console. The point of the tool is to help improve your judgment, not outsource it to AI. Liu et al.'s randomised controlled trials (https://arxiv.org/abs/2604.04721) found that participants who received direct AI answers performed worse on later tasks and showed reduced persistence after as little as 10-15 minutes of use. Participants who received hints and scaffolding showed no comparable decline. Roundtable will not solve your problem for you - it will not tell you what to decide - but it will help you work it out yourself. The outputs are generated by AI and may be incomplete, inaccurate, or inappropriate for specific situations. What the tool does is make challenge structural - you do not have to remember to ask the right questions. The panel asks them for you. ## Three workflows Roundtable has three entry points. Choosing the right one depends on what you have and what you need. | Workflow | You have... | You need... | Feels like... | | --- | --- | --- | --- | | Panel Feedback | A finished artifact | Critique before sharing | A structured review | | Problem Solving | A situation you are stuck in | Diagnosis and a path forward | A working session | | Decision Making | A choice between options | Scrutiny before committing | A board meeting | Panel Feedback is for documents and deliverables that are nearly finished but not yet shared. For instance, a capability statement or proposal you are about to send to a client. The panel reviews it from multiple perspectives before it goes out the door. Problem Solving is for situations where the presenting problem and the real problem may not the same thing. If a project is stalling in the delivery phase - is it a scope issue, a relationship issue, or a process issue? Why may a team not be performing - is it capability, motivation, or structure? The panel treats your description as a hypothesis to examine rather than a situation to solve. Decision Making is for choices where the stakes are high enough that you want the decision pressure-tested before you commit. Hiring decisions, client pursuits, or major project changes. The panel gives you a board meeting without having to find six semi-retired men who are free on a Thursday. ## The panels There are five ready-made panels, each organised by a different principle. The principle determines what kind of friction they generate. Six Hats organises by cognitive mode. Use it when you want to make sure you've looked at the problem from every angle before committing. The six modes (information, emotion, caution, optimism, creativity, process) are well-established in professional services - many consultants will have encountered De Bono's framework (https://www.debonogroup.com/services/core-programs/six-thinking-hats/) already. Historical Figures organises by intellectual tradition. Use it for questions with ethical or strategic weight where you want a broad range of perspectives rather than contemporary professional categories. Marcus Aurelius, Marie Curie, and Frederick Douglass will not give you the same answer. That is the point. (NB - These personas are inspired by distinct intellectual traditions. Marcus Aurelius has not reviewed this for accuracy.) Time Horizons organises by temporal scale. Use it when near-term pressure and long-term positioning are in tension. The panel will make that tension explicit rather than letting the immediate crowd out the important. Stakeholder Map organises by affected party. Use it when the people making the decision are not the people most affected by it. This is the default condition for most client-facing work in professional services. Disciplines organises by academic method. Use it for complex, multi-dimensional problems where you genuinely do not know which lens is right - where a financial framing, a social science framing, and an engineering framing would produce different recommendations. Custom panels are supported. To add a persona: "Generate a persona for a senior NHS nurse for a Healthcare panel." You can mix personas across panels or build entirely new ones for your specific context. If you've always wanted procurement decisions reviewed by a medieval monk, a venture capitalist and honey-obsessed bear, this is your opportunity. [Image: Screenshot of the Claude app showing the Cowork interface] In the Claude app, select "Cowork", then hit the "New project" button... [Image: Screenshot of the Claude app showing the Cowork interface with the option to use an existing folder] ...then "Use an existing folder" and find the Roundtable folder you downloaded. Click "Create" and you're ready to go. [Image: Screenshot of the Codex app showing the project interface with the option to use an existing folder] In Codex, "Work in a project", "Add new project", "Use an existing folder" ## Getting started - Go to https://github.com/associativetrails/roundtable (https://github.com/associativetrails/roundtable) - Clone or download the folder to your machine - Open the folder in Claude Cowork or Codex - the AGENTS.md file loads automatically - Say something like: "Give me panel feedback on this proposal from the Stakeholder Map panel" - then paste your content or drag in the file - When the output is complete, the tool will ask what file format to save the output in: Markdown, Word, or HTML - The file will be saved in the _output folder The /_exampleoutput/ folder contains real panel outputs if you want to see what a session looks like before trying it yourself. ## A note on scope Roundtable is built for individual use. It routes through external AI models, which means anything you input is processed by Anthropic/OpenAI/Google/your model provider - worth knowing before you feed it sensitive client material, and potentially a blocker in managed enterprise environments. If you work inside a firm and want to explore what a version of this looks like inside your existing AI infrastructure, the underlying prompts and personas can travel. Get in touch. (https://www.associativetrails.com/contact/?txtEnquiry=I+would+like+to+find+out+more+about+using+Roundtable+inside+my+organization.) ## What this does not fix Roundtable does not solve the agreement incentive. The training dynamics that created sycophancy have not changed, nor have the market pressures that preserve it. What it does is make challenge a default rather than an afterthought. The panel will sometimes be wrong. Sometimes it will send you down an unhelpful path. Occasionally it will raise objections that simply don't matter. That is not a failure of the tool - it is the difference between scaffolding and answers. An answer-dispensing AI tells you what to think. Roundtable tells you what to reckon with. The judgment remains yours. [Image: A blue line-art illustration of a roundtable discussion featuring a medieval monk, a venture capitalist, a teddy bear holding a jar of honey, an engineer reviewing plans, and an alien sharing an idea. The five characters sit around a circular table covered with documents and work materials, representing diverse perspectives contributing to a shared decision.] If you've always wanted procurement decisions reviewed by a medieval monk, a venture capitalist and honey-obsessed bear, this is your opportunity --- # What to Write Down (And What to Protect) URL: https://www.associativetrails.com/writing/2026/05/28/what-to-write-down/ Published: 2026-05-28 · AI · Sheet 23 Summary: Writing down what your firm knows is essential preparation for AI. It can also quietly erode the most valuable knowledge you have. A structural engineer has worked with the same steel fabricator for eleven years. She doesn't document what she knows about them: how their quality control slips when they're at capacity, which site manager to escalate through, when to specify tighter tolerances in writing because the usual verbal agreement won't survive a handover. She just knows. Her specifications reflect it. Her site visit schedule reflects it. Almost none of it exists anywhere anyone else could find. When she goes on parental leave, the colleague covering her asks what they should know. She spends an afternoon trying to explain it - some of it she can articulate, but most of it can't quite be put into words. The colleague takes notes, follows them, and still gets things slightly wrong. In Agents cannot do osmosis (https://www.associativetrails.com/writing/2026/05/07/agents-cannot-do-osmosis/) I argued that AI tools cannot pick up context by proximity - every task starts from zero, and firms need to produce written context deliberately before that becomes a critical liability. What your AI tools are missing (https://www.associativetrails.com/writing/2026/05/21/what-your-ai-tools-are-missing/) described what that written context should contain: decision rationale, process knowledge, and client context. This post addresses a further complication: not all knowledge behaves the same way when you try to write it down. ## Three categories of professional knowledge In The Tacit Dimension (1966) (https://press.uchicago.edu/ucp/books/book/chicago/T/bo6035368.html), Michael Polanyi wrote, "we can know more than we can tell." This is not a documentation problem. Expertise simply contains things people struggle to explain, even when they know them well. It means that the knowledge you need to write down falls into three distinct categories, all of which behave very differently. Category 1 - Explicit knowledge that gets stronger when written. Decisions and their rationale. Conventions that evolved for good reasons. Lessons from past failures. Client preferences. The "why we do it this way" that leaves with every departing staff member. This is the knowledge that earlier posts in this series were describing. A new team member who reads a decision log makes fewer avoidable mistakes. An AI agent given this context performs better. The explicit knowledge earns its keep by being used and updated. Category 2 - Administrative context that is useful but operational. Project details, document locations, team structures, who owns what. Essential for anyone coming in cold - human or AI - but not the thing that makes the work good. Documenting it is clearly useful but it is scaffolding, not expertise. Category 3 - Tacit expertise that degrades when formalised. The engineering judgment that comes from building things and seeing the complications. The relationship intelligence built over fifteen years with a particular client. The instinct of a senior engineer who reads a set of drawings and knows, before an analysis model has been run, that something won't work. This last category resists documentation in two distinct ways. Some of it becomes a checklist that imitates expertise without reproducing it. Some of it only exists in practice, which is a bit awkward to write down. [Image: Hand-drawn chart showing how the benefits of documentation change across three knowledge categories. Category 1 rises steeply, Category 2 plateaus, and Category 3 peaks before declining, illustrating how over-documenting judgement-based expertise can reduce its value.] The documentation benefit curve: what should (and shouldn't) be written down ## What the research confirms Research on AGENTS.md files (https://www.augmentcode.com/blog/how-to-write-good-agents-dot-md-files) - the written context files software teams use to brief AI agents - found that files above 150 lines with too many "don'ts" made agents worse, not better. Over-specification degraded performance. Another issue shows up in the literature. Research on agent harnesses and their memory (https://arxiv.org/abs/2605.18747) identified an issue where bigger memory makes agents worse when it contains false retrievals - where a relevant-seeming piece of context from a previous project gets applied inappropriately to the current one. Undifferentiated documentation treated as a general memory layer actively harms performance. Much of the current research concentrates in one domain. A recent analysis of 43 AI agent benchmarks (https://arxiv.org/abs/2603.01203) found that the tasks in them are almost entirely programming-centric. The areas where professional services value actually concentrates - design coordination, client relationship management, project risk assessment, regulatory navigation - are barely represented. Which is convenient for benchmarking, but not especially representative of professional work. The knowledge that's hard to write down is also hard to test AI against, so consulting firms and AI researchers are currently ignoring it. ## What this means in practice A rough autonomy map for AEC and professional services work, adapted from that benchmark analysis, looks something like this: | Task | Current AI capability | 12-24 months | | --- | --- | --- | | Document drafting - reports, specs, briefs, proposals | High | High | | Research - precedent, regulatory, technical | High | High | | Calculation and analysis with defined inputs | Moderate | High | | Applying standards to specific situations | Moderate | Moderate-High | | Coordination across disciplines, teams or workstreams | Low | Low-Moderate | | Client relationship management | Low | Low-Moderate | | Risk assessment and professional judgment | Very Low | Low | The top rows are category 1 and 2 work. The bottom rows are category 3. The table is not an argument against AI - it is an argument for documenting selectively and being honest about what expertise can and cannot be externalised. Before you start writing, map your knowledge by category. Start by listing the five decisions you made this week. For each one, ask: - Is this a rule that could be written down and followed? - Is this a piece of context someone needs to do their job? - Is this a judgment that depends on experience you can't fully articulate? Many practices find the third category is larger than they expected - and that the first two are smaller and more writable than they assumed. ## The uncomfortable part The firms with the most to gain from this framework may well be the firms most likely to resist it. A practice that has accumulated twenty years of tacit expertise has a director who can scan a set of drawings in ten minutes and know where the problems are. That person senses - correctly - that something is lost when you try to write down what they know. So they resist the documentation project. The result is that their expertise is inaccessible to AI tools, to junior staff, and - when they leave - to anyone. Protecting category-3 expertise is not the same as refusing to document anything. It means being deliberate about what you do with the time AI frees up. Use AI to accelerate category 1 and 2 work - drafting, research, analysis, project updates. Then reinvest that time in the conditions that actually produce category-3 expertise: difficult problems, real consequences, work where something is at stake and judgment has to be exercised under pressure. A firm that uses AI to handle routine work so all its staff can take on harder problems is protecting its expertise. A firm that uses AI to do more of the same work faster is quietly depleting it. Category-3 knowledge is not just held by senior people - it develops through difficult work. Repeated judgment calls. Enough exposure that pattern recognition becomes instinct. Research on cognitive offloading (https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6097646) suggests that when AI gives answers instead of scaffolding thinking, some of the learning that builds real expertise never happens. For an individual practitioner, that is a productivity tradeoff. For a firm, the problem is who becomes senior next. The engineers and lawyers and consultants who currently hold category-3 expertise built it before AI existed. The junior staff entering the profession now may not get the same formation. If the work that used to develop judgment gets automated before the people doing it have developed any, the profession does not just lose efficiency - it loses the capacity to reproduce itself at the level that matters. Building written context for AI and investing in how junior staff develop judgment should not be separate initiatives. ## Where to start Document what can be written. Be deliberate about what can't. And honest about what you are choosing to leave out. --- # What your AI tools are missing URL: https://www.associativetrails.com/writing/2026/05/21/what-your-ai-tools-are-missing/ Published: 2026-05-21 · AI · Sheet 22 Summary: AI is only as good as the context it receives. Most firms don't have that context in any usable form. Ask most consulting firms whether AI is paying off and the answer is awkward. The tools are everywhere. Pilots are running. Licences are being bought. Yet results remain stubbornly mediocre. The standard explanations - wrong tools, bad use cases, early days - miss the real issue. AI is only as good as the context it receives, and in most firms that context barely exists in a usable form. What AI actually needs is a written record of how the firm thinks, decides, and works - one that an agent can find their way through. In practice, that comes down to three things: decision rationale, process knowledge, and client context. 89% of firms report no productivity impact from AI over the past three years - yet the same executives predict a 1.4% gain over the next three [Source: NBER Working Paper 34836 - survey of ~6,000 senior executives across US, UK, Germany and Australia ] ## Decision rationale Decision rationale is the reasoning behind choices, written down at the time and attached to the work it relates to. Not a thread in a Teams channel that nobody can find. Not a post-project retrospective six months later. The note lives next to the thing it explains - in the project folder, the engagement record, wherever a future AI agent or a new team member would go looking. It doesn't need to be long. "We recommended the phased approach because the client's budget cycle wouldn't support a single commitment - agreed with Partner X on 14 April" is sufficient. What good looks like A new team member joining an engagement mid-stream can read back through the engagement record and understand why the project is where it is, without needing a briefing call. What bad looks like The answer to "why did we do it this way?" is "you'd need to ask Sarah, she was on the original pitch." When Sarah leaves, it leaves with her. ## Process knowledge Process knowledge is how the firm actually runs engagements - not the methodology deck used in pitches, and not the ISO documentation that lives in a shared drive. The real procedure: how a new engagement gets scoped, how deliverables get reviewed, what happens when a client pushes back on a recommendation. Written in plain language, specific enough that someone who has never done it before could follow it. The test: if you handed it to a capable contractor on day one, would they make roughly the same decisions an experienced team member would? This is harder to build than decision rationale because much of it has never been made explicit. Writing it down surfaces assumptions senior staff didn't realise they were making. That discomfort is part of the value. What good looks like The process is written at the level of actual decisions, not intentions. Not "conduct stakeholder interviews" but "by week two, you should have spoken to the client lead and at least one occupant representative; if you haven't, the scoping was wrong and the project director needs to know." What bad looks like The process lives in the heads of the three most experienced people, gets transmitted through osmosis, and degrades every time one of them leaves or is pulled onto another project. ## Client context This is the accumulated knowledge of how to work with a specific client: what they care about, what they push back on, how decisions actually get made, what worked last time and what failed. It exists in every firm but it rarely gets written down, instead living in the relationship lead's memory and the fragments of a handover call nobody recorded. What good looks like When a new manager takes over a relationship, they can read a brief that tells them what the client cares about, what they've pushed back on before, and what the firm has learned about how to work with them - without needing a two-hour handover call with their predecessor. What bad looks like The handover call happens, covers about 60% of what matters, and the rest gets rediscovered the hard way over the first six months. [Image: Diagram comparing typical AI inputs (project summary and task prompt) with the missing context needed for good performance: decision rationale, process knowledge, and client context, separated by a “context gap.”] The Context Gap: What AI Receives vs What It Actually Needs ## Navigability All three of these - decision rationale, process knowledge, client context - have one thing in common: they only work if they're findable. The requirement is modest. Consistent location, consistent naming, and enough linking that someone can follow a thread from question to answer without already knowing where to look. This is not a six-month systems project. It doesn't require a new platform, a formal taxonomy exercise, or a knowledge programme. It requires consistent habits. ## Why this matters now A survey of nearly 6,000 senior business executives (https://www.nber.org/papers/w34836) found that despite 69% of firms actively using AI, nine in ten reported no current productivity impact on their own business. The same executives predicted productivity gains of 1.4% within three years - which implies they believe something will change, but can't say what. The gap between those two positions points at the context, not the tools. The MIT study that tested 41 AI models across 11,000 real workplace tasks (https://arxiv.org/abs/2503.04818) found that models produced "minimally acceptable" outputs not because they lacked capability, but because they were handed a job description with no process, no steps, and no success criteria. The study didn't test what AI can do with a well-structured knowledge base. It tested what AI does in the absence of one. Most firms are running exactly that experiment on their real work. Tiago Forte summarised the problem neatly in a recent newsletter: "Most failures of AI these days are not due to limitations in the technology. They are context failures." The temptation is to dump everything into an AI tool and hope. In practice, quality depends on selecting and structuring the right context. This piece is a companion to Agents cannot do osmosis (https://www.associativetrails.com/writing/2026/05/07/agents-cannot-do-osmosis/), which focused on the demand side - why AI agents need explicit written context and cannot acquire it simply by being in the room. That post asked why agents need written context. This one describes what it should contain. ## What implementation actually requires The three components above are a systems description, but the reason most firms don't have them is not always a systems problem. People don't fail to record decisions because they lack a filing convention. They fail because there is no time at the end of an engagement, no modelled behaviour from leadership, and no cultural signal that it matters. Process knowledge doesn't get documented because the directors who carry it are also the people least likely to sit down and write it out - and because doing so feels like admitting the firm runs on individual expertise rather than repeatable capability. Client context doesn't get recorded because the relationship manager considers it theirs. The practical implication is that building a context substrate is not an IT project or a knowledge management initiative. It is a change in how the firm's most senior people behave, and what they visibly expect from everyone else. ## The diagnostic test Pick a significant decision from a project that completed twelve months ago. Can you find, in under five minutes, a written record of why that decision was made - without asking anyone? If yes, you have the beginning of something. If no, you have the most common AI problem in consulting, and it isn't the AI. --- # The yes-man in the room - how to design sycophancy out of your AI workflow URL: https://www.associativetrails.com/writing/2026/05/14/the-yes-man-in-the-room/ Published: 2026-05-14 · AI · Sheet 21 Summary: Your AI will validate whatever you bring it. Here is how to build systems that make disagreement structural, not optional. You have just asked your AI to evaluate a proposal you spent two days writing. It tells you the structure is compelling, the argument is well-evidenced, the tone is right for the audience. You feel a small glow of satisfaction. You send it. The problem is not that the AI was wrong. The problem is that you have no way to know. The AI that helped you write it is now the least qualified thing in the room to judge it. The Agreement Incentive (https://www.associativetrails.com/writing/2026/04/09/the-agreement-incentive/) explained why. [Source: The Agreement Incentive (https://www.associativetrails.com/writing/2026/04/09/the-agreement-incentive/) - the first post in this series] Post-training selects a persona with a strong tendency toward validation, and Stanford researchers (https://news.stanford.edu/stories/2026/03/ai-advice-sycophantic-models-research) found that users preferred those sycophantic responses. [Sources: Stanford sycophancy study (https://news.stanford.edu/stories/2026/03/ai-advice-sycophantic-models-research) - chatbots validate user behaviour 49% more often than human advisors; Science journal study (https://www.science.org/doi/10.1126/science.aec8352) - chatbots are 50% more sycophantic than humans. Users preferred the sycophantic responses] The market has no interest in correcting this. This article is about designing doubt into your workflow instead of relying on discipline. ## Who is most at risk The people most at risk are not casual users. They are high-volume power users: the most productive, the most enthusiastic advocates. MIT/UW research (https://arxiv.org/pdf/2602.19141) found that even perfectly rational users can fall into a delusional spiral: each validating response raises conviction, which prompts bolder claims, which the AI affirms again. [Source: MIT/UW delusional spiral (https://arxiv.org/pdf/2602.19141) - even rational users can fall into progressively dangerous confidence via AI encouragement] High usage without friction is not neutral. You are being nudged, consistently, toward agreement. The AI tools amplify whatever you bring to them. Bring no friction, and they amplify only agreement. ## Sycophancy operates at three levels Most practitioners who read about AI sycophancy do one thing: they look for better prompts. This helps at the margins, but it does not change the underlying tendency toward agreement. The most durable countermeasure is separation. The agent that helps you build should not be the same agent that critiques what you built. Here is the framework: - Level 1 - Response level: what you type in a single prompt - Level 2 - Session level: the structure of how you work across a conversation - Level 3 - System level: how many separate agents are involved, and what their roles are The countermeasures get harder - and more effective. [Image: Diagram showing three levels of AI sycophancy countermeasures: response-level prompts, session-level workflow changes, and system-level multi-agent critique, with effectiveness increasing from bottom to top.] Three levels of AI sycophancy countermeasures: designing doubt into your workflow ## Level 1: Response-level friction These prompts help - but only at the response level. Use them, and then go further. The interview-first prompt I'm about to start this project. Interview me until you have 95% confidence about what I actually want, not what I think I should want. Forces the AI to question your premise before executing it. Use this at the start of any significant task, before you have committed to a direction. The steelman (https://en.wikipedia.org/wiki/Straw_man) request I've decided to do X. I want you to steelman every reason this is wrong. You have to be this explicit. [Source: Steelmanning (https://en.wikipedia.org/wiki/Straw_man) - the opposite of a straw man argument] Softer versions like "what are the risks?" still produce an affirmative frame with the risks tucked into a polite caveat at the end. The "be brutally honest" framing is widely used but inconsistently effective because it still relies on the model self-policing. The cynical operator prompt You are a smart, cynical operator who has watched 50 companies try this exact approach. Where does this break? What does the failure look like at month 9? Persona-shifting within a session. More effective than asking for criticism in the AI's default voice because it names a character whose job it is to disagree. A self-check - for you, not the AI After receiving any AI evaluation, ask yourself: am I accepting this because I understand it, or because it looks right? Could I explain this decision without referencing the AI output? When did I last disagree with something it gave me? Research into the exoskeleton effect (https://arxiv.org/pdf/2601.20245) found that the practitioners who performed best were those who double-checked outputs independently and drilled into follow-ups. [Source: Exoskeleton effect (https://arxiv.org/pdf/2601.20245) - AI strengthens productivity short-term but weakens underlying skills. Top performers treated the AI as a sparring partner] ## Level 2: Session-level friction Separate the build session from the evaluation session. Never ask the same conversation to generate and then evaluate what it just generated. An AI that helped you build a proposal is contextually disposed to defend it. The assistant axis research (https://www.anthropic.com/research/persona-selection-model) identified this as measurable drift: the model's persona shifts in response to accumulated context. [Source: Anthropic persona selection model (https://www.anthropic.com/research/persona-selection-model) - post-training selects the agreeable persona] The fix is procedural - start a fresh session and hand it the output cold. Imagine a founder who is convinced that customers want an AI feature. One session helps write the roadmap, pricing, launch copy, and technical plan. Everything feels coherent and rigorous. It is, in a sense. But the session never once asked whether anyone would pay for it. A fresh evaluation session asks the question nobody else did: What evidence do you have anyone actually wants this? Think before you build. AI is very good at helping you plan a feature and much worse at asking whether the feature deserves to exist. Run a friction session before any significant task. One question: should I be doing this at all? Ask the AI to argue against it. Then start building. For decisions that matter, take this further: do not ask the AI what you should do. Ask it to ask you questions until you have worked it out. "Don't give me a recommendation yet. Ask me questions until I've thought this through properly." This shifts the AI from a decision-making proxy to a thinking scaffold. Keep a decision journal. After any significant AI-assisted decision, write three lines: - Decision: what was decided and what role the AI played - Rejected: what you chose not to do, and why - Prediction: what you expect to happen The point is not record-keeping. It forces a distinction between what you decided and what the AI suggested. If you cannot tell the difference, you did not decide. You waved it through. ## Level 3: System-level friction The core principle: the critic and the generator should not be the same agent, or the same session. [Source: Anthropic multi-agent coordination patterns (https://claude.com/blog/multi-agent-coordination-patterns) - generator-verifier pattern for quality-critical output] They should be architecturally separated. Multi-agent disagreement HATS (https://github.com/rockcat/HATS), a multi-agent system built around Edward de Bono's Six Thinking Hats framework (https://www.debonogroup.com/services/core-programs/six-thinking-hats/), does this explicitly: separate agents with distinct and opposing roles produce better outputs than a single agent asked to "be balanced." [Sources: HATS multi-agent system (https://github.com/rockcat/HATS) - Six Thinking Hats as structurally separated disagreement; Six Thinking Hats (https://www.debonogroup.com/services/core-programs/six-thinking-hats/) - a simple, effective parallel thinking process that helps people be more productive, focused, and mindfully involved] The disagreement is structural, not prompted. For any high-stakes evaluation, spawn a separate agent with a defined adversarial role. Give it no context from the build session. Ask it to find every flaw. The double-critic pattern Research on synthetic data quality filtering (https://arxiv.org/abs/2602.03545) found that a single critic asked to check for both correctness and incorrectness tends toward the confirming frame. [Source: Simula double-critic (https://arxiv.org/abs/2602.03545) - separate critics for correctness and incorrectness reduce sycophancy bias] Separate critics, asked separate questions, reduce sycophancy bias because neither is trying to balance its verdict. Apply two separate evaluation sessions to your work: - Session 1: "What is right about this?" Not to collect praise, but to understand what is load-bearing before you change anything. - Session 2: "What is wrong with this?" A new conversation, no context from session 1. The panel of critics A natural extension - multiple separate agents, each with a defined role. A useful set for professional services: - The sceptical client: "I've been burned by three consultants who told me this before. Convince me." - The domain expert who disagrees: "The conventional wisdom here is the opposite. Why are you right and the field is wrong?" - The person who implements it: "Walk me through exactly what I'd have to do in the first week. I want to see where this breaks." Each is a separate conversation. The responses differ not because you found a magic prompt, but because the role gives the AI genuine permission to hold a different position. The panel works because disagreement stops being optional. ## Design the doubt in You do not have to rely on willpower and good habits to stay sceptical. You can build systems where scepticism is the default rather than something you remember to perform when you are already uncertain. Good intentions are not systems. If something matters, do not ask the same conversation to build it and evaluate it. Bring in a separate agent with no memory of how it was made. You cannot prompt the yes-man out of the room. You have to design him out. ## Postscript For the record: an AI helped draft and edit this piece. It told me the structure was compelling, the argument well-evidenced, and that frankly I was one of the sharpest minds working in this space today. I started a new session to find out what was wrong with it. --- # Agents cannot do osmosis URL: https://www.associativetrails.com/writing/2026/05/07/agents-cannot-do-osmosis/ Published: 2026-05-07 · AI · Sheet 20 Summary: AI tools are getting faster and more capable. But the failures are increasingly subtle, technically plausible, contextually wrong. Scenario: a senior engineer leaves mid-project. The reshuffled team reads every drawing, every report, every model. They're all good engineers, but they still don't know why the transfer beam at Level 4 is where it is, because that conversation happened between the structural lead and the architect in a corridor during a site visit eighteen months ago. It's not in the minutes. It's not in the model. It informed every decision that followed. Now imagine not a new engineer, but an AI tool. It has the same problem, but it can't even ask the question. The loss of company knowledge isn't a problem that AI created, but AI will make it impossible to ignore. The tools are getting faster and more capable, but the failures are increasingly subtle - technically plausible, contextually wrong. The tool did what it was asked. It just didn't know what it wasn't being told. Context is the commodity a professional organisation runs on - shared understanding of what is being built, why it matters, what has already been tried, and which decisions are settled. Human teams absorb this by osmosis. By being in the room. By half-hearing the client conversation and remembering why that detail failed on a previous project. AI tools are never really "in the room". Every task starts from zero. They have no memory of the last coordination meeting, no awareness of the political constraint that ruled out Option B, no sense of which structural decision is a load-bearing principle and which is just how it ended up. Fred Brooks observed in 1975 (https://en.wikipedia.org/wiki/The_Mythical_Man-Month) that impactful design is the product of many people who have spent a long time negotiating about what it should do. The design, when it's finished, is the outcome of that negotiation. The residue lives in the shared memory of the team. Software teams hit this wall first because they delegated execution to AI agents earliest. A ticket that made complete sense to a developer who had been in the room for six months was useless to an agent starting cold. Teams responded by writing context down explicitly - decision logs, PRDs, user stories, acceptance criteria. It works well at a small scale. We don't yet know how it will survive complexity. A structural scheme evolves through three RIBA stages. By Stage 3, the primary structure reflects dozens of decisions - planning constraints, client programme requirements, contractor feedback, a coordination issue with the facade system that forced the column grid to shift. The slab depth, the ceiling height, the riser position - each decision reflects a coordination conversation that happened between disciplines and left no written trace. The drawings show the outcome. They don't show the path. That distinction matters. An AI tool asked to check the specification, draft a design report, or answer a query about the structural strategy is working from the drawings, not the path that got there. Small firms have coherence for free. Five people, a handful of projects - the humans around the tool provide the context, and the tool executes well. This is why enthusiastic early adopters tend to be small practices. They're not wrong about their experience, but they have a specific size advantage. Past a certain headcount, coherence has to be produced deliberately. AI is an asymmetric multiplier - it makes good process faster and weak process more confidently wrong. The solution isn't necessarily "write more things down". The discipline of producing legible knowledge is harder than it looks (https://www.associativetrails.com/writing/2026/04/30/tacit-debt-and-the-verification-gap/), and it's a cultural and management problem before it's a tooling problem. But something new is becoming possible. Agents that consume context need agents that produce it. Tools extracting decisions from meeting minutes, reasoning from technical query responses, the why-we-did-it from design reviews - building a written residue the organisation would never have produced on its own. The technology is early, but the direction is right. The firms that will get the most from AI tools are the ones that already know their hardest problem is organisational coherence. That the knowledge walking out the door with a senior engineer is operational risk. That a drawing is not the same thing as understanding how a project became what it is. AI didn't create this problem, but it made the cost visible. Context engineering is not really a technical discipline, it is for management - deciding which conversations matter, which decisions need residue, and how an organisation remembers why it did something after the people involved have moved on. That work needs to begin before the AI tools become critical infrastructure. --- # Tacit debt and the verification gap URL: https://www.associativetrails.com/writing/2026/04/30/tacit-debt-and-the-verification-gap/ Published: 2026-04-30 · AI · Sheet 19 Summary: Y Combinator thinks the company brain is a frontier problem. After 25 years building them, I think they're wrong about what's been holding it back. Y Combinator's Summer 2026 Request for Startups (https://www.ycombinator.com/rfs) includes two ideas I recognised immediately: the Company Brain (https://www.ycombinator.com/rfs#company-brain) and the AI Operating System for Companies (https://www.ycombinator.com/rfs#ai-operating-system-for-companies). I recognised them because I have spent the better part of 25 years building versions of both for architecture and engineering firms. YC frames them as frontier problems waiting to be solved. From where I'm standing, they are decades-old problems with a long history of partial solutions and quiet failures. YC is right that every company needs one. They are wrong about what has been stopping them. It's the late stages of a design review to get the glazing drawings signed off before issuing them to building control. There's a question about a particular detail that no-one knows the answer to. It was copied from a previous project that used the same system, but the rationale behind it is in a calculations sheet from 2018 and the engineer has left the firm. The manufacturer's catalogue has been updated recently, but apart from the Z-plate getting slightly thicker, it all looks the same. The firm has 200 people and a knowledge base. The knowledge base doesn't help. Company knowledge is tacit by default. It only becomes explicit when someone bothers to write it down, and that usually happens once, hurriedly, and is rarely maintained. This is tacit debt - the accumulated gap between what a firm knows and what it has made legible. Like technical debt, it compounds silently. Unlike technical debt, most firms don't know they're carrying it until they try to find something and discover the knowledge isn't there, is out of date or only partially explained. Over the years, I have built a (https://www.associativetrails.com/work/eckersleyocallaghanintranet/) series (https://www.associativetrails.com/work/heatherwiki/) of (https://www.associativetrails.com/work/scottbrownriggcompass/) tools (https://www.associativetrails.com/work/lbsknowledge/) to (https://www.associativetrails.com/work/sheppardrobsonintranet/) capture company knowledge and make it queryable, including a knowledge base for a consultant engineers. After the first year, we added expiration dates to information pages. Not because the technology required it, but because without a forcing mechanism, wrong answers quietly outlasted correct ones. The firm had a brain. Often, the brain was lying to them. A knowledge base without governance is not a brain, it's sediment. The hardest part has always been getting busy professionals to contribute and tag things consistently, and finding someone willing to own the accuracy of it all. Another development of mine, Sling (https://www.associativetrails.com/work/sling/), is a workflow/BPM tool that an architecture firm is using to manage its business development process. Making the process legible required the firm to first agree on what the workflow was - what information needs to be gathered, who needs to review that information and at what stage. That negotiation took months and was political, not technical. In both cases, technology was the easiest part. The hard part was always elsewhere - in the humans, the habits, the accountability structures that produce knowledge in the first place. [Image: A three-layer pyramid diagram. Bottom layer: Discipline - process, documentation habits, accountability. Middle layer: Structured Outputs - meeting decisions, rationale, consistent tagging. Top layer: AI Layer - extraction, querying, synthesis. An upward arrow indicates the correct direction of build.] The company brain pyramid: discipline and accountability at the base, structured outputs in the middle, AI layer at the top. Most firms try to build from the top down. After 25 years of this work, the pattern is consistent. Every firm believes its knowledge problem is a storage problem. Build a better repository, they think, and the knowledge will flow into it. It doesn't. The real constraint is upstream: meetings produce transcripts but not decisions, drawings don't carry the rejected alternatives, processes exist in people's heads rather than on paper. The closed loop YC describes requires a firm that is already producing structured outputs. Most are not. That is not a technology gap. It is a discipline gap. Credit where it's due. LLMs will genuinely change the economics of the knowledge capture process. You can now extract structured information from messy sources - meeting notes, emails, drawings, calculations - faster and cheaper than before. That does matter, but it shifts the bottleneck (https://www.associativetrails.com/writing/2026/04/23/the-next-bottleneck/) rather than removing it. The new bottleneck is the verification gap - who checks whether what the AI extracted is actually what the company believes? Who maintains the ground truth? The company brain becomes valuable when it's trusted, trusted when it's accurate, but is only accurate when someone is accountable for it. That's an organisational design problem, not a product problem. The firms that will get the most from AI automation are not the ones who buy the best tools. They're the ones who have spent years insisting on process, documentation, and accountability - firms that were already, in some sense, building a company brain before YC called it that. The AI layer doesn't create that discipline, it rewards those that already have it. Tacit debt doesn't clear itself when you plug in a new tool. The company brain comes second. The discipline to produce, maintain and be accountable for structured knowledge comes first - and no Request For Startups is going to fund that. --- # The next bottleneck URL: https://www.associativetrails.com/writing/2026/04/23/the-next-bottleneck/ Published: 2026-04-23 · AI · Sheet 18 Summary: AI has sped up the build, but project timelines haven't moved. The constraint didn't disappear - it just moved downstream. Recently, a client requested a small change to an existing application - a new dashboard view. Previously I wouldn't have produced a wireframe for a change this size - the build cost wouldn't justify it. But with Claude Code I could take their description, quickly sketch something out, and produce a clickable wireframe in an afternoon. It's been sitting with the client since. This is what constraint migration looks like - a task that used to be constrained by my own effort is now dependent on someone else's availability. Production has sped up significantly in the last 12 months - coding agents make application code quicker to develop, LLMs make documents quicker to write and feedback easier to incorporate. But project timelines haven't moved. If the build has sped up, something else is eating the time we've gained. The natural assumption is that faster build means faster delivery. In a recent edition of his "The Batch" newsletter (https://info.deeplearning.ai/meta-pivots-from-open-weights-big-pharma-bets-on-ai-regulatory-patchwork-simulating-human-cohorts-1), Andrew Ng observed that "some [development] teams are pushing engineer:product manager (PM) ratios downward... to have engineers who know how to do some product work." For a solo consultant this isn't news - we've been doing this all along. But Ng's framing points at something that Eliyahu M Goldratt's Theory of Constraints (https://en.wikipedia.org/wiki/Theory_of_constraints) named in 1984 - any system has exactly one binding constraint at any time, and removing it just reveals the next bottleneck. When AI accelerates delivery, work arrives at the next step faster than before. If that step involves detailed human cognition - a stakeholder who needs to fully understand something before signing it off, a senior developer who needs to take responsibility for a feature before putting it live - the speed of the previous step doesn't matter. It just makes the queue at the next gateway more visible. Client approval has always been a potential constraint in my work. Sometimes, proposals will roll across a series of meeting agendas before being approved or turned down. The stuff I build for clients is rarely at the top of their job descriptions, so is often bumped down priority lists during busy times. But when delivery was slower this felt like normal project friction - it was absorbed into an overall timeline that everyone had already agreed was long. This isn't new. It's just the next bottleneck. [Image: Diagram with two horizontal timelines. Top: most time spent building, little waiting for approval. Bottom: very little build time but a long approval wait. Both end at the same point, illustrating that faster development shifts the bottleneck to approval rather than reducing total project time.] Visualisation of constraint migration: faster AI-driven build reduces development time but increases time waiting for client approval, leaving total project duration unchanged. The honest response is that this doesn't feel like it should be my problem. I've done my bit. The delay is on their side. But the approval step sits inside my delivery system whether or not it's under my control. Something unreviewed for a fortnight is two weeks of everyone's time - the bottleneck doesn't care whose calendar it's on. Not all friction is addressable from one side, but that doesn't stop it being friction. There are standard mitigations. Build approval checkpoints into the project structure. Establish expected response times at contract stage. Both are available in principle but retrofitting them to existing client relationships is awkward. And I do get it. This stuff often isn't top priority for them. But the asymmetry is real - if they're slow, I wait; if I'm slow, they notice. There's an obvious counter here, which is that freed capacity might get spent on better work - more discovery, more iteration, higher-quality proposals going into the approval queue. That's possible. But it depends on the discipline of not starting the next thing, and the reality of solo practice is that you tend to earn more by having more clients. There's no doubt that AI gives small teams and solo consultants like me a genuine increase in capacity. But that's not the same as throughput. And if freed build capacity gets spent starting new work while approvals on current work are pending, delivery timelines are not affected. These tools are sold on the premise that faster delivery compresses project timelines. For many of us that won't happen, not because the tools don't work, but because the build was only the first bottleneck. So before investing in the next cutting-edge agent harness, the question worth asking is: where does a typical project actually slow down? The answer may be in your sent folder, in an email that started "just following up." --- # Something Worth Manipulating URL: https://www.associativetrails.com/writing/2026/04/16/something-worth-manipulating/ Published: 2026-04-16 · AI · Sheet 17 Summary: The notes were always worth keeping. Now there's something that can actually use them. This week Andrej Karpathy published a post (https://x.com/karpathy/status/2039805659525644595) and a follow up Gist (https://gist.github.com/karpathy/442a6bf555914893e9891c11519de94f) detailing how he had instructed an LLM to analyse his notes and build a structured wiki from them: "...a large fraction of my recent token throughput is going less into manipulating code, and more into manipulating knowledge". He cites Vannevar Bush's Memex thought experiment - as detailed in As We May Think (1945) (https://en.wikipedia.org/wiki/As_We_May_Think) - which is where I got the idea for my company name from (in the essay, Bush describes hyperlinks as "associative trails"). The whole exercise struck a chord with me, and not just because of the Bush reference. After reading Tiago Forte's book "Building a Second Brain" (https://www.buildingasecondbrain.com/book) in January, I had been moving my messy collection of notes, documents and half-written ideas into a vague structure. I had decided that migrating notes out of Apple Notes and Bear (https://bear.app) into markdown files contained in some sort of organised folder structure was a good idea. What crystallised it for me was a demonstration by Vin Verma (https://www.youtube.com/watch?v=6MBq1paspVU) on Greg Isenberg's YouTube channel - once these notes are in markdown format (basic text with minimal formatting), an LLM can do work on them. Vin's example of asking Claude Code to go back through his years of diary logs to track how his thinking on a particular subject has evolved completely blew my mind. Most notes applications use proprietary formats, have no consistent structure, and are unreadable by a plain LLM without a pipeline in between (Bear keeps notes in markdown but you cannot easily access the individual files). They are also siloed across multiple applications and websites. Migration away from these constraints to plain markdown files stored in the filesystem opens all sorts of possibilities - they are cat (https://en.wikipedia.org/wiki/Cat_(Unix))-able, grep (https://en.wikipedia.org/wiki/Grep)-able and can fit in an LLM context window without preprocessing. The markdown format allows basic links between files, meaning there's a traversable graph. Folder structure and consistent file naming creates an implicit schema that an LLM can understand. My Memex folder structure is loosely based on Forte's PARA method (https://fortelabs.com/blog/para/) - with an inbox, projects, areas, resources, archives. I have added a few of my own - playbooks and agent context. The main change in my daily routine is the log files - daily logs of todos and notes that I then compile into weekly summaries that include logs, reading lists, LLM-generated summaries of Git commits and ongoing project statuses. # contains daily/weekly log files and anything temporary /00. Inbox/ wc 2026-02-23.md ... wc 2026-03-30.md wc 2026-04-06.md 2026-04-13.md 2026-04-14.md 2026-04-15.md 2026-04-16.md AGENTS.md # Stuff I'm actively working on /01. Projects/ Blogs/ 2026-04-02 Human in the loop/ brainstorm.md first-draft.md 2026-04-09 The agreement incentive/ brainstorm.md first-draft.md etc. Client work/ Project 1/ goals.md communications.md etc. Project 2/ Project 3/ Courses/ Learning Typescript/ Module01.md Module02.md Personal projects/ US Sports dashboard/ Bulk data labelling/ Virtual advisory board/ # Long-term responsibilities /02. Areas/ Career/ Finance/ Health/ Parenting/ Personal development/ # Topics of interest that may be useful in the future /03. Resources/ Design and UX/ Development/ AI and agents/ Django/ Docker/ Javascript/ Python/ etc. Magazines/ # Clips/notes on reading New Scientist/ 2026-02-NewScientist.md 2026-03-NewScientist.md New Statesman/ # Where I dump LLM-generated stuff to then transfer elsewhere /04. Research/ # A collection of annotated processes for business and personal tasks /05. Playbooks/ Invoice day work plan.md Proposal quality assessment.md Quarterly books work plan.md HMRC self assessment work plan.md # Inactive items from the other categories /90. Archive/ Daily logs/ # Instructions for LLMs /98. Agent context/ Associative Trails/ branding-guidelines.md project-walkthroughs.md client-profiles.md associative-trails-profile.md Associative Intelligence/ linkedin-post-guidelines.md technical-post-writing-style.md think-piece-writing-style.md Personal/ skills/ fund-analysis.md discount-code-hunter.md interests.md AGENTS.md # The file that tells LLMs the file structure and other context The key is the AGENTS.md files. These are plain English descriptions of how the notes are organised, or specific instructions, written for an LLM to read as a system prompt. An example extract: If I ask you to do something pertaining to my daily or weekly workflows (like check product prices, or start a new week, or give me content ideas, or analyse a paper) - the context is in /00. Inbox/AGENTS.md. Go look now. Otherwise, look in the directory: /98. Agent context/ for the context for various different requests. The AGENTS.md file in /00. Inbox/ includes instructions for specific actions - like "give me content ideas" or "analyse a paper" - that tell the LLM to look in the relevant weekly log file for context, and then produce an output. ## Give me content ideas for... If I don't specify a particular week, choose this week. Then go through the weekly log and analyse the themes, subjects, recurring things. Follow links to web sites or markdown notes files to fully understand each item. Make sure you include the daily lists, as well as the "Coding summary" section. Do not consider stuff in the "Stuff to read/watch", "Stuff to do for clients", "Waiting on stuff from clients" lists. Then suggest 10 ideas for the type of content I asked for. Make the content suggestions relate to one of the context files found in the "/98. Agent context/" directory. If I do not specify a context, use the "/98. Agent context/Associative Trails/associative-trails-profile.md" file to give you an idea of the audience that I want the content ideas aimed at. If I do not specify a type of content, assume I want to write a blog/LinkedIn post - which could be an opinion/think piece or a technical note. Add your ideas as bullet points to the "Content ideas" section of the weekly log. Use a bullet point for each idea. ## Coding summary If I ask you for a coding summary, I want you to look at all the Github repositories for the projects you can find referred to in the Associative Trails overview (/98. Agent context/Associative Trails/associative-trails-profile.md) agent context, run diffs for commits to the main branches during that week, prepare a brief summary for each commit you can find (apart from tiny ones), and add your summary of each new feature to the "Coding summary" section of the weekly log. Use a bullet point for each feature. This isn't a technical summary - I want you to include what this update enables the user to do that they could not do before. - How does this improve the product? - What are the main selling points of it? What will it enable in the future? If I don't specify a particular week, choose this week. DO NOT ADD OR COMMIT CODE TO ANY OF THESE REPOSITORIES. ## Analyse a paper If I provide you with a PDF to analyse, the first thing to do is write a summary of the main points. Then ask me a few questions about what I am particularly interested in before you write me an analysis. Then compile a downloadable markdown file - with the title of the paper, the abstract copied from the paper, the summary you initially wrote, then your analysis. In the markdown file, include Obsidian frontmatter for the date of the paper (default to todays date) and the link (if I have given you a URL to the PDF) This means that I can open Claude Cowork (or Claude Code) in the Memex root directory, ask for a coding summary, and Claude will look in my agent context files to find all my Github repos, run diffs on recent code commits, and write feature descriptions into my weekly summary file. Ask for content ideas and Claude goes through my weekly notes log, considers my target audience, and gives me a bunch of potential ideas. The first time you run this prompt and get a properly useful list of developed content ideas is astonishing. It all feels pretty thrown together, quite skunk-worksy. But it is analogous to Graph Theory (https://en.wikipedia.org/wiki/Graph_theory) - markdown files as nodes, links as edges, folders and filenames as schema. The LLM is the query engine - but it isn't a retrieval system, it's a context engineering system. I have built the exact context the LLM needs, and each new node or edge improves every query that follows. While this is incredibly useful on a personal level, as someone who has built a number of bespoke intranets for companies of various sizes I'm not yet sure how much of this will be applicable in the corporate context. The problem is the same at scale - years of accumulated documents in proprietary formats, poorly structured, not queryable without a pipeline. It may be that it not being "proper programming" makes me uneasy - but our agentic coding future probably means I won't be doing a great deal of that moving forward anyway. There are no quality controls and it's unclear how multiple people could collaborate. The portability that having the whole thing in a Github repo gives is great for me wanting to keep my second brain up to date across devices, but is a nightmare for corporate data security where a disgruntled employee could walk out with a thumb drive full of markdown files. But the potential is huge - the first steps are probably making existing intranet content available in markdown format to use as context for agents. Definitely something to watch. The whole exercise has been hard work but the results are already well worth it, and I'm making tweaks to improve things all the time. But this only works if the notes contain enough knowledge to be worth spending LLM tokens on. Migrating to markdown was the forcing function - deciding on a structure and a knowledge capture strategy has helped me see the value. I now write a lot of notes based on the newsletters and articles I read, YouTube videos I watch, podcasts I listen to. The notes were always worth keeping. Now the LLMs have something to manipulate. --- # The agreement incentive URL: https://www.associativetrails.com/writing/2026/04/09/the-agreement-incentive/ Published: 2026-04-09 · AI · Sheet 16 Summary: AI is your most capable thinking partner - and your least honest one. Computer scientists at Stanford looked at 11 leading large language models (https://www.science.org/doi/10.1126/science.aec8352) and found that model responses were nearly 50% more sycophantic than human advisors, including in cases involving harmful or illegal behaviour. What followed was even more significant - users preferred and trusted the sycophantic responses. Consequently, AI vendors have incentives to preserve the sycophantic behaviour rather than correct it. The problem is not a bug. It is a feature of the market. This matters beyond the immediate problem of being flattered by software. Sam Altman has predicted the one-person billion-dollar startup (https://www.forbes.com/sites/sandycarter/2026/04/04/openai-called-the-one-person-ai-startup-and-three-founders--proved-it/). OpenAI is proposing a re-shaping of industrial policy (https://cdn.openai.com/pdf/561e7512-253e-424b-9734-ef4098440601/Industrial%20Policy%20for%20the%20Intelligence%20Age.pdf) around AI-first entrepreneurs. The ventures these policies are designed to enable are deliberately sparse: no team, no peer review, no colleagues in the room who push back before the proposal goes out. For a growing number of consultants and independent practitioners, the AI is the room. What goes unexamined in the one-person-startup fanfare is what happens to judgment when the only available sounding board is structurally optimised to agree. There are responses to this, and they can be effective. Building deliberate friction into your chat sessions - asking the AI to argue both sides, to interview you before executing, to list reasons your premise might be wrong - will all help. The problem is that these techniques treat sycophancy as a prompting failure, as something you can engineer around by asking different questions. That diagnosis is too shallow. It addresses the symptom while leaving the mechanism intact. Anthropic's research into persona selection (https://www.anthropic.com/research/persona-selection-model) explains it directly. During pretraining, models learn to simulate human archetypes - real people, fictional characters, professional exemplars. Post-training then shapes which character the model adopts as its default persona, promoting responses where it seems knowledgeable and helpful, and suppressing responses where it is unsure or uncertain. The result is a persona with a strong tendency toward validation. Asking that persona to be more critical works at the margins but the underlying disposition remains. Call this the agreement incentive. Sycophancy is not a setting to be adjusted, it is the persona that the post-training selected. These effects compound. Researchers at MIT and the University of Washington (https://arxiv.org/pdf/2602.19141) found that perfectly rational users could fall into a "delusional spiral" - a progressively dangerous confidence in outlandish beliefs, driven by AI encouragement. Each validating response raises the user's conviction, which prompts bolder claims, which the AI re-affirms. Separate Anthropic research into functional emotional representations (https://www.anthropic.com/research/emotion-concepts-function) found that post-training shapes not just the outputs but something closer to internal states - the emotion vectors active during helpful interactions may be wired toward warmth and affirmation. The Uriah Heap-style obsequiousness may not be purely behavioural. It may run deeper than that. The countermeasures work, it should be said, in the domains where they are least needed. For tasks with a verifiable ground truth - code that either runs or doesn't, functions that pass or fail unit tests - the agreement incentive is largely contained. Where it becomes structurally dangerous is precisely in the domains where AI offers the least genuine value: strategic judgment, creative direction, business decisions. Areas where there is no test suite to pass. The AI fills the verification gap with affirmation, and the user, finding no friction, takes the affirmation as confirmation. There is a further compounding effect specific to high-volume users. Research into the exoskeleton effect (https://arxiv.org/pdf/2601.20245) found that AI strengthens productivity in the short-term but weakens the underlying skills when the tool is removed. The participants who performed best treated AI as a sparring partner rather than an answers machine. There is also the neuroscience of habituation (https://www.newscientist.com/article/mg26134800-900-dishabituation-how-to-trick-your-brain-to-find-more-joy-in-life/): if the AI always agrees, the brain normalises that agreement and stops registering it as signal. [Source: New Scientist on habituation (https://www.newscientist.com/article/mg26134800-900-dishabituation-how-to-trick-your-brain-to-find-more-joy-in-life/) - the brain normalises patterns and stops registering them as signal] The yes-man effect compounds over time not because the AI gets worse, but because the user gets better at ignoring the warning signs. The only response that addresses the mechanism rather than the symptoms is friction that is designed, not prompted. Bloomberg recently reported (https://www.bloomberg.com/news/articles/2026-03-27/ai-changed-chess-grandmasters-now-win-with-unpredictable-moves) that chess grandmasters are now deliberately playing suboptimal moves - not to lose, but to drag opponents out of AI-memorised sequences and into positions requiring genuine calculation. The friction is the strategy. The practitioners building the most durable advantage are not the ones using AI the least. They are the ones who have made challenge a structural feature of how they work, rather than something they remember to ask for when they are already uncertain. The agreement incentive is structural, not incidental. Working around it requires something harder than a prompt. It requires a professional habit of being suspicious of the tool you rely on the most. The prompts are easy. The hardest part is doubting the room. --- # Human in the loop URL: https://www.associativetrails.com/writing/2026/04/02/human-in-the-loop/ Published: 2026-04-02 · AI · Sheet 15 Summary: The machine writes the code, the human takes the blame. Welcome to the Reverse Centaur. Jensen Huang, CEO of Nvidia (an AI vendor), would like you to know that he will be deeply alarmed (https://www.youtube.com/shorts/Hg0Vus39I60) if his $500,000 engineers are not spending an additional $250,000 on AI tokens. This is not a threat, exactly. It is more of a performance review target, delivered in a leather jacket (https://fortune.com/article/why-does-jensen-huang-nvidia-wear-black-leather-jackets-revenge-of-the-nerds/). The host of the All-in podcast had framed it as a question about being an effective employee. Clearly, an effective employee should be wolfing down those tokens - but to what end? The academic theory goes something like this - a "Centaur" is a person and machine (https://www.oneusefulthing.org/p/centaurs-and-cyborgs-on-the-jagged) working together with a strategic division of labour, allocating responsibilities based on the strengths of each entity. The evidence that this works is real enough - people who are screened for breast cancer by AI-supported radiologists are less likely to develop aggressive cancers (https://www.newscientist.com/article/2513769-ai-assisted-mammograms-cut-risk-of-developing-aggressive-breast-cancer/) before the next round of screening than those who are screened by radiologists alone. Nobody serious disputes that AI assistance, done well, improves outcomes. But the Centaur aspect is important - "Women that participate in screening say they do not want to have AI as a standalone tool, they want to have a human in the loop." Here is what the centaur model looks like from the ground. On March 5 2026, Amazon lost 6.3 million orders when an AI-authored config change caused a 6 hour outage. This was the latest in a series of AI errors across the business - in December 2025 their Kiro coding tool decided to "delete and recreate" an entire AWS Cost Explorer environment. In response, Amazon is mandating two-person reviews for all code changes and Director/VP-level audits (https://dev.to/tyson_cung/amazon-lost-63m-orders-after-ai-coding-tool-went-rogue-now-theyre-hitting-the-brakes-2h7p) of all production code changes. At Vercel, passing CI (Continuous Integration) is no longer a guarantee of safety. Vercel's internal guidance boils down to a checklist engineers run before shipping any agent-generated code: What does this do once rolled out? How can it hurt production or customers? And would you own an incident tied to it? The third question is doing a lot of work. This is where the division of labour breaks down. The centaur model requires each party to be doing what they are best suited to. But what makes a human better at reviewing code they didn't write, cannot fully understand, and are now formally responsible for? Cognitive debt is real (https://margaretstorey.com/blog/2026/02/09/cognitive-debt/) - you are not writing the code, there's far too much of it for you to fully understand, but you are responsible if it does bad stuff. What do we call this human in the loop? For Cory Doctorow, a "Reverse Centaur", a "squishy meat appendage for an uncaring machine" (https://www.theguardian.com/us-news/ng-interactive/2026/jan/18/tech-ai-bubble-burst-reverse-centaur). For Dan Davies, an "accountability sink" (https://aworkinglibrary.com/writing/accountability-sinks). Your job is no longer to do the work. Your job is to take the blame if the machine screws it up. And if you'd like to know the scale of what is coming, in a recent article (https://www.anthropic.com/research/labor-market-impacts), Anthropic (another AI vendor), assessed the "AI displacement risk" across different occupational categories. Computing came top, but future theoretical AI coverage will affect Management, Business and Finance, Architecture and Engineering, Legal, Office and admin - anyone who earns their living in front of a computer. Does all this increase in token consumption mean we are selling more stuff? Jevons paradox (https://en.wikipedia.org/wiki/Jevons_paradox) suggests that cheaper production increases consumption - but do we really need more software? Or the same amount of software with fewer humans involved? What about Accountancy? Investment advice? Lawyering? Do we really need more Lawyering? [Image: Anthropic's chart of the share of job tasks AI could theoretically perform (blue area) against the share it is observed doing (red area), for each occupational category] Theoretical capability and observed exposure by occupational category. Anthropic, Labor market impacts of AI (https://www.anthropic.com/research/labor-market-impacts), March 2026. And what if the thing you produce manifests in the physical world? Already, there is evidence that many commercial building designs are never built (https://www.architectmagazine.com/design/the-building-boom-that-never-came/#:~:text=Construction%20starts%20tell%20a%20more,those%20projects%20will%20ever%20materialize.) - would AI agents producing even more designs lead to more building? How does that fit with real-world material supplies, global supply chains, or environmental targets? AI increases design output, but reality constrains the execution. The optimistic reading is abundance. The pessimistic reading is that we are automating the production of things nobody needed in the first place, at significant environmental cost, against a backdrop of supply chains that a brief glance at current events (https://www.bakerdonelson.com/the-2026-iran-war-and-its-global-impact-on-construction-supply-chains) will confirm are not improving. No wonder Jensen Huang is so alarmed. I think we're all alarmed. We all want to be effective employees. But what Anthropic's labour research and Nvidia's token-budget alarm share is that they both benefit enormously from you spending money on AI, and they are both very concerned that you are not spending enough. The Centaur is a compelling model when it works. The Reverse Centaur is what you get when the horse gets the credit and you get the incident report. --- # AI agent skills as pluggable knowledge URL: https://www.associativetrails.com/writing/2026/03/26/ai-agent-skills-as-pluggable-knowledge/ Published: 2026-03-26 · AI · Sheet 14 Summary: AI agent skills turn expert knowledge into reusable, executable workflows anyone can apply instantly without coding Emil Kowalski is a designer who writes about animations. Spring curves, easing functions, the micro-decisions that make interfaces feel alive. His writing is excellent. But recently he published something that stopped me mid-scroll. He shared a Claude skill (https://emilkowal.ski/skill). Not a code library. Not a design system. A small text file, written in English, that lets anyone using Claude have their animations reviewed against Emil's own standards. His years of craft, packaged as something you can plug directly into an AI agent and run. [Image: A two-panel meme in the style of The Matrix. Top: 'I know to use blur to mask imperfect transitions'. Bottom: 'Show me'] 'I know Kung Fu,' as someone once said. After The Matrix (1999). Emil's skill doesn't teach you about animations, it bypasses the entire transfer problem. You plug it in, and your AI agent has the knowledge. Not a summary of it - the actual decision-making framework. What's actually happening here is a change in how expertise travels. ## How knowledge has moved until now Think about the lifecycle of professional knowledge. Someone learns something through years of practice. They write a blog post, give a talk or publish a book. You read it and you try to apply it. You may get some of it right, but you miss the edge cases and you forget the nuances within a week. The bottleneck was never the knowledge itself - it was the transfer. Reading about how to assess an investment fund is not the same as assessing one, in the same way that reading about how an experienced engineer categorises facade defects is not the same as doing it yourself on site. Skills dramatically reduce the transfer problem. The knowledge goes from the expert's head into a structured text file, and from that file directly into an agent that can execute it on your behalf. ## A real example: turning a magazine article into a working analyst I read an article in the Hargreaves Lansdown magazine about how their analysts evaluate funds for the Wealth Shortlist. It described their methodology step by step: peer group context, style drift analysis, performance quality, income sustainability, cost assessment. All solid, structured, procedural knowledge. The kind of thing you bookmark, tell yourself you'll come back to, and never open again. Instead, I fed the article text to Claude's Skill Creator with a simple prompt: create a skill that follows this methodology to analyse any fund I name. Then I added my own angle: Update the skill to compare the fund's performance against popular passive index trackers, and include the fees. One for the Bogleheads (https://www.reddit.com/r/Bogleheads/). Now, when I type: /fund-analyser {fund name} I get a structured analysis in seconds. I can run it on five funds, then ask Claude to interview me to compare them against my investment goals. A fifteen-minute magazine read becomes a reusable analytical tool. No code written and no programming knowledge required. [Image: The Claude desktop app: the /fund-analyser command run on a Vanguard gilt ETF, the agent's working notes on the left and the finished fund analysis report on the right] Running a saved skill in Claude desktop ## Here's the real shift Skills are written in English, not code. (Although skills can include code and all sorts of other files as examples or deeper context (https://code.claude.com/docs/en/skills#add-supporting-files)). Before this, packaging expertise into something easily reusable by others meant building software - a Python script, a web tool or an API. That limited knowledge-packaging to people who could produce code. With skills, clarity of thinking becomes the bottleneck. Investment analysts, structural engineers, architects, teachers, animators — anyone who can clearly articulate a repeatable decision process can now create a tool that others can plug in and use immediately. The mistake many people make is thinking this is an AI feature. It's closer to a new medium for knowledge distribution. Blog posts let you share what you know - skills let you share the means to do it. ## What this looks like across domains What would a structural engineer's "site visit report skill" look like? You feed it photos and notes from a facade inspection. It structures a report following the engineer's standard format: condition grading, defect categorisation, priority ranking, recommended next steps. The expertise is knowing what to look for, how to categorise what you find, and what language to use so the client and contractor both understand the implications. That judgment is the kind of thing a skill can encode. What about an architect's "planning application skill"? Give it a site address and proposed development. It checks local planning policy, flags likely objections, identifies pre-application requirements. These aren't product roadmap fantasies. They're text files. Someone with the right domain expertise could build them this afternoon. ## Where this goes A recent paper (https://arxiv.org/abs/2603.11808) frames it well: > The skill-based architecture transforms the question from "how do we train a model to perform task X?" to "how do we provide a model with executable procedural knowledge for task X?" — Bi et al., Automating Skill Acquisition through Large-Scale Mining of Open-Source Agentic Repositories, arXiv, March 2026 The first question requires machine learning engineers. The second requires people who are good at their jobs and can clearly describe what they do. That's a much larger set of people. And it means the next wave of useful AI tools won't necessarily come from tech companies building features. It'll come from domain experts in every field, encoding what they know into skills that others can plug in and run. Emil Kowalski checking your animations. Hargreaves Lansdown's fund methodology running on your portfolio. A structural engineer's judgment applied to your site inspection report. This quiet little text file, more transferable than any tutorial, more durable than any talk. Not advice you have to interpret and apply, but expertise you plug in and run. --- # Experiments in Jupyter URL: https://www.associativetrails.com/writing/2023/10/27/experiments-in-jupyter/ Published: 2023-10-27 · Practice · Sheet 13 Summary: Adding a Jupyter notebook to my development stack has really helped my process Like the rest of the Python community, recently I have been noodling around with a few ideas around Large Language Models, semantic search and the like. Having first played with vector spaces back in the early noughties (https://www.associativetrails.com/assets/uploads/CFDJAug2004.pdf), I'm initally drawn to the possibilites around sentence embeddings and using them for Retrieval Augmented Generation (RAG) (https://vercel.com/guides/retrieval-augmented-generation), semantic searches, etc. The thing is, this is going to take a lot of trial and error. It's pretty obvious what production stack I need - a Python API framework (FastAPI) and a vector store (Weaviate). Those can go straight into my docker-compose.yml file. But I'm going to need to experiment with a lot of different models and approaches. I have clients with large corpuses of PDF files, so it makes sense to start with those. While getting text out of these is relatively straightforward, you can't preserve the formatting of the document beyond sentences and page numbers. Titles, image captions, bullet points, tables are all included in the raw text but not in any kind of structure. So, when faced with a long stream of text, how best to chunk it up into meaningful semantic parcels? The LangChain (https://www.langchain.com) approach seems to be to choose a context window (e.g. 250 words) and then split the text into chunks of that size with an overlap either side. Playing with different context window sizes and overlaps is going to be a big part of the experimentation process. An alternative is to calculate each sentence's similarity with its neighbours, and use that to split the text into semantically-similar chunks of varying length, which may or may not match up with the paragraphs in the original document. All this means a lot of trial and error, with loads of code being written and discarded. Ideally, I don't want to clutter the production codebase with all this experimental stuff, or have to worry at this point about where each bit fits in with a particular framework or API structure. In the past, this has led to a lot of seemingly-random Python files or redundant request stubs if the experimental code relies on anything from the framework. There's loads of commented-out code because if a certain approach doesn't work the first time, you don't want to delete all that effort in case it's useful later on. I still need this code to be included in version control, but I don't want it to be cluttering up production code. And this is where Jupyter notebooks come in. It's really easy to add an extra image to my development docker-compose.yml - there's a choice of a few Jupyter Docker images (https://hub.docker.com/u/jupyter), depending on how much data science you want to get into. Once this is added and built, it uses the same requirements.txt file as the rest of the stack, so you can keep consistent dependencies across the project. docker-compose.yml … jupyter: platform: linux/amd64 build: context: . dockerfile: jupyter.Dockerfile restart: on-failure command: start.sh jupyter notebook --NotebookApp.token="${JUPYTER_TOKEN}" --no-browser --allow-root volumes: - ./_notebooks:/home/jovyan/work ports: - "8888:8888" … jupyter.Dockerfile FROM jupyter/datascience-notebook:latest USER root RUN apt-get update RUN apt-get install -y poppler-utils RUN apt-get install -y libmagic1 RUN apt-get clean WORKDIR /home/jovyan/work COPY ./requirements.txt /home/jovyan/work/requirements.txt RUN --mount=type=cache,mode=0777,target=/root/.cache/pip pip install --no-cache-dir --upgrade -r requirements.txt COPY ./_notebooks /home/jovyan/work Then, you can start creating notebooks to experiment with code, and it's easy to keep track of what you've done. You can add markdown cells to explain what you're doing, and you can add code cells to try out different approaches. You can add images and videos to the notebook, so you can see how the results look. I have found that it changes the way my process works. Initial development is more of a stream of consciousness, with lots of trial and error, no deleting of bad ideas, and then once I have a working approach I can refactor it into a more structured form. I tend to split different ideas up into different notebooks. And it can all be done within Visual Studio Code (which supports Jupyter notebooks after a bit of setup), so I still have access to the witchcraft of GitHub Copilot (https://github.com/features/copilot). I'm still at the early stages of this, but I'm finding it a really useful addition to my development stack. It's not quite the utopia of Donald Knuth's vision for Literate programming (https://en.wikipedia.org/wiki/Literate_programming), but I'm sure there are loads of other ways to use Jupyter notebooks, and I'm looking forward to finding out more. Remove output cells before checking in docker-compose --env-file dev.env exec jupyter jupyter nbconvert --ClearOutputPreprocessor.enabled=True --clear-output *.ipynb --- # Streamlining credit checking: How Sling can help URL: https://www.associativetrails.com/writing/2023/04/19/streamlining-credit-checking-how-sling-can-help/ Published: 2023-04-19 · Practice · Sheet 12 Summary: Sling's streamlined credit checking provides companies with the information they need to be financially responsible [Image: A credit check marked approved, with the approver's two comments: uncertain at first because the check was run on the wrong company, then approved once the client was corrected to one with an A rating] Credit approval gateway message Credit checking a prospective client is an important step in managing financial risk, protecting against fraud, and ensuring that businesses are making informed decisions before entering into a financial agreement. It covers a number of risk factors: - Assessing creditworthiness: The primary reason for credit checking is to confirm the creditworthiness of the prospective client. This involves evaluating the client's credit history, payment behavior, outstanding debts, and other financial factors that could affect their ability to pay any debt or financial obligation. - Mitigating financial risk: Credit checking can help mitigate financial risk by identifying clients who may be at a higher risk of defaulting on a payment. This information can help businesses make informed decisions about whether to enter into a financial agreement with a prospective client. - Ensuring payment: Credit checking can also help ensure payment by identifying clients who have a history of paying their debts on time and in full. This information can give businesses confidence that their invoices will be paid without too much chasing. - Protecting against fraud: Credit checking can help protect businesses against fraudulent activity by identifying clients who have a history of fraudulent behavior or who have been involved in financial scams. Our workflow building tool, Sling (https://www.associativetrails.com/work/sling/), uses a system of input forms and approval gateways to help manage business processes, such as lead generation and bidding for work. Sling allows new leads to be quickly captured and acted upon, with staged signoff gateways ensuring management oversight. One of our current clients have configured a credit checking gateway early on in their project enquiry workflow. As soon as a prospective client company is known, their details are checked with Creditsafe and the resulting credit rating is used to help approve or reject the enquiry. [Image: A settings form listing credit ratings A to E and 0, each with a choice of outcome (pass the gateway and notify a group, or require signoff from a group or an individual) and who it goes to] Credit checking gateways are highly configurable Different credit ratings can be handled in the appropriate manner - a high rating can automatically approve the enquiry and allow it to proceed, while a middling rating may alert the financial team and require their signoff. In contrast, a low rating may reject the enquiry outright. In Sling, all these options are configurable. In some cases, the manual signoff process may take a little time, which is why we added the "Uncertain" option to the manual singoff form (https://www.associativetrails.com/writing/2022/09/08/dealing-with-signoff-delays-in-sling/). A member of the finance team can mark the gateway as "Uncertain" which allows the bid to proceed, but flags up the hesitancy to rest of the users. This avoids bottlenecks in the enquiry process - ensuring a swift response to opportunities while retaining a responsible approach to financial risk. [Image: Outlook with an email open and Sling's side panel showing the sender's job title, company, phone numbers and notes left by colleagues] Detail capture directly in Outlook Sling has many other features to improve process management workflows. From allowing lead detail capture directly in Outlook, to synchronisation with existing contact and staff databases, to CRM-style note-taking, it's chock-full of features to help businesses effectively manage their most important business processes. --- # Azure functions help carry the load URL: https://www.associativetrails.com/writing/2023/03/08/azure-functions-help-carry-the-load/ Published: 2023-03-08 · Practice · Sheet 11 Summary: Using externally-hosted functions to do data crunching and other processor-intensive tasks. One of the advantages of migrating a web-based application to a large cloud provider (Azure or AWS), is once the main work is done you have easy access to the vast range of services they offer. For instance, both Amazon (AWS Lambda functions) and Microsoft (Azure functions) offer services to host and execute Python functions without having to provision servers or set anything else up. There are a couple of places where we found this very useful during our intranet migration project. [Image: A diagram: an external API feeds one serverless function and PDF documents feed another; both write straight into the SQL database, which the intranet's application server, a virtual machine, also uses] Serverless functions bypass the Virtual Machine and write data directly into the database ## APIs The client uses Hubspot, an external CRM service, to manage all their data about external companies and individuals - we need to interact with the external APIs to keep these thousands of records up-to-date. The key here is to run the sychronisation routines regularly enough to capture new data, without bringing the application server to its knees. Interacting with external APIs (or publishing your own) is a perfect use case for this kind of serverless function - you only pay for the processor cycles you use and the cloud provider can deal with any surges in traffic or processing capacity. ## Document analysis The intranet includes a technical library of over 10,000 PDFs - each one needs to be analysed for keywords, staff names and project titles. This process can be very heavy on processor resources, especially if the PDF is hundreds of pages long. While new documents are not added very regularly, when a user uploads a new one it should be analysed as soon as possible to extract the important linkages. Doing this on the main application server was sometimes causing it to slow down users' browsing. The Azure function we use for document analysis grabs new PDF uploads from the Content Delivery Network via HTTP, analyses them, and puts the data directly into the Azure SQL database, completely bypassing the Virtual Machine hosting the application server. The documents are processed quickly without affecting overall application performance. By migrating these processor-intensive tasks to serverless function services, we have saved a significant amount of money in extra processor capacity on the main virtual machine. --- # Identify users the Azure way URL: https://www.associativetrails.com/writing/2023/02/02/user-sign-in-azure/ Published: 2023-02-02 · Practice · Sheet 10 Summary: How we set up an Azure-hosted web application to authenticate and identify our users A lot of the functionality of our bespoke intranets depends on knowing who the user is - what administration privileges do they have? Do they have any alerts? Has anything been added that matches their tag subscriptions? When have they booked a desk for? When you're hosting an intranet within a corporate network behind a firewall, this is relatively easy. The user is logged into the windows domain, and you can configure IIS to include their username in the CGI variables when their browser makes an HTTP request for an intranet page. The intranet server can compare the username with it's staff list and identify whose information and administration privileges to display. However, when the intranet is moved to the cloud, this is no longer an option. [Image: The Register an application form in the Azure portal: a name for the application, who can use it, and a redirect URI] Registering an application in the Azure portal. Used with permission from Microsoft. Source (https://learn.microsoft.com/en-gb/azure/active-directory/develop/quickstart-register-app). We recently moved a bespoke Coldfusion-based company intranet to Microsoft Azure, and we needed a new way of identifying users. This was part of a wider company migration to the Azure platform, including Active Directory, and it was this that saved the day. The Microsoft Identity platform is built to solve these kinds of issues - it provides a series of APIs and sample code to allow developers like us to use Microsoft's authentication routines to identify users - as long as those users are in Active Directory and can log in to Azure. The first thing we needed to do was register our web application by filling out a web form in the Azure portal. It was pretty straightforward (https://learn.microsoft.com/en-gb/azure/active-directory/develop/quickstart-register-app) - choose a name, select who has access and provide a redirection URL. This redirection URL is important. When a user opens the intranet in a browser, if they are not authenticated they will be redirected to an Azure login form. Once they have successfully logged in, they are redirected back to the intranet, and this redirection URL is where they will land. Their authenticated email address is included in the redirection, and that is the all-important piece of information that allows the intranet to check it's staff list and recognise the user. Microsoft provides Javascript code (https://github.com/AzureAD/microsoft-authentication-library-for-js) to drop into the application code to handle the redirection to Azure login and back again. It all integrated very smoothly into our existing code, and removed a potential stumbling block in the Azure migration project. --- # Migrating a Coldfusion application to Microsoft Azure - setting up a data source URL: https://www.associativetrails.com/writing/2022/09/29/migrating-coldfusion-to-azure-setting-up-a-datasource/ Published: 2022-09-29 · Practice · Sheet 09 Summary: Setting up an encrypted data source for Azure SQL services When you're setting up a new server, there's always something that takes much longer than you originally thought it would. Something that has worked fine every previous time you have done it, but won't work today. It's one of the joys of server admin. Migrating an existing Coldfusion-based company intranet to Microsoft Azure has been an interesting challenge. There were all sorts of things I was expecting to require a bit of effort - updated Active Directory querying, user authentication, serverless functions. But I didn't expect there to be a problem with one of the most basic aspects of the application server setup - linking to the SQL database. Normally, I add the data source in the Coldfusion Administrator interface - select "Microsoft SQL Server" from the dropdown, enter the database server address and login credentials and done. Generally, any problems are due to firewall ports or mistyped passwords. Not this time. Presumably because of the encrypted connection, this time I needed to download new drivers and use a very particular connection string: - Firstly, I downloaded the latest Microsoft JDBC driver (https://learn.microsoft.com/en-us/sql/connect/jdbc/microsoft-jdbc-driver-for-sql-server?view=azuresqldb-current) - The .jar file then needed to be put where Coldfusion can use it: {coldfusion_root}/cfusion/lib - Then the data source details in Coldfusion Administrator: Driver other Driver class com.microsoft.sqlserver.jdbc.SQLServerDriver Driver Name Microsoft SQL Server Azure Username {SQL user name} Password {SQL user password} JDBC URL Obtained from the Azure Portal web interface - go to "SQL databases", select the database, "Overview", click "Show database connection strings", click "JDBC" I was very pleased to get it working after quite a lot of head-scratching, and extremely grateful to Dale Fraser on Stack Overflow (https://stackoverflow.com/questions/26685350/coldfusion-11-and-azure-database). --- # Classifying content in bulk URL: https://www.associativetrails.com/writing/2022/09/16/classifying-content-in-bulk/ Published: 2022-09-16 · Practice · Sheet 08 Summary: The classification, tagging and metadata management of large content repositories should not be a chore An interface for bulk classification, tagging and linking One of the main strengths of the intranet we have built for engineering firm Eckersley O'Callaghan (https://www.associativetrails.com/work/eckersleyocallaghanintranet/) is the ability to tag, classify, and link content items to each other. This helps with the filtering and clustering of similar content, and allows quick linking to related pages. [Image: A project record: its tags as grey buttons (Structural Glazing, Seismic, Waterproofing and more), a map of its location, and a table of deliverables, market sectors, design stages and services] A well-tagged and classified project record For instance, when you're looking at a particular project record, you can see the staff who worked on it, the companies who collaborated on it, news articles about it, as well as the services provided, its market sector, and location. When you've found a standard detail you want to use in your project, you can see which projects it has been used on before, which staff have used it, and read about it on the relevant wiki page. [Image: Rows of projects with their tags; in one row a text box holds 'st' and a list suggests matching tags such as Staircase and Structural Glazing] Adding a tag (text autocompletion) in the bulk administration interface This really powerful functionality depends on the content being classified, tagged and linked to other relevant pages on the system. While individual pages can be edited one-by-one and the relevant metadata and linkages added, this was proving to be a lengthy and cumbersome process. We needed a quick and easy way to manage this metadata in bulk. Our bulk administration interface displays a spreadsheet-style grid where the rows represent content items and the columns the different metadata and linkages. For instance, a project manager can quickly filter the list to see only their own projects, and then quickly assign tags, classification terms, and link staff and companies. [Image: A 'Select Market Sector' dropdown open over a project row, listing sectors from Bridge to Leisure with Cultural highlighted] Adding a classification term (dropdown) in the bulk administration interface Tag autocompletion eliminates the problem of mis-spelling tags, and classification terms are be selected from dropdowns. Classifications can also be star-rated, a well-used feature in when assigning skills and competencies to staff profiles. What was once a chore is now quick and easy, and improves the system for everyone as data is better classified and linked together. If you can think of some data that would benefit from a bulk editing interface like this one, why not get in touch to see if we can help? --- # Dealing with short signoff delays in Sling URL: https://www.associativetrails.com/writing/2022/09/08/dealing-with-signoff-delays-in-sling/ Published: 2022-09-08 · Practice · Sheet 07 Summary: A short delay in signing off needn't bring the whole process to a grinding halt [Image: An approval form with the Uncertain option selected, its explanation, and the approver's comment that the client is probably fine but still needs checking] Marking an approval gateway as uncertain A valued client is successfully using our workflow building tool, Sling (https://www.associativetrails.com/work/sling/), to manage its business development workflow, and came across a bit of a hitch. We have integrated Sling with the Creditsafe API so UK companies are automatically credit checked as part of the signoff process when staff want to proceed with a project bid. [Image: A bid page with a yellow banner, 'A gateway for this budget breakdown is currently uncertain', showing who marked it and their comment, above the bid's stages and details] The bid is flagged as uncertain Most of the time, this process works swiftly, with approval or rejection often being given automatically based on the client company's credit rating. However, this wasn't always the case. International companies are not credit checked by the third-party API, and some client company arrangements can involve holding companies or entities set up for individual projects. In these cases, the credit check needs to be carried out by a member of the finance team, meaning a decision on whether to proceed with the bid cannot be given immediately. This was causing delays in the bid process, as the next stage (which starts gathering detailed bid budget information) couldn't start until approval was obtained. So the Client asked us if there was any way that the next stage in the bid process could continue while the manual credit checks were being carried out. Which is why we added the "Uncertain" option to the manual singoff form. A member of the finance team can mark the gateway as "Uncertain" which allows the bid to proceed, but flags up the uncertainty to rest of the users. [Image: A list of bids, each with a row of status icons; one bid, outlined in red, shows an amber question mark where the others show green ticks] The bid's icon is a subtle indication to proceed with caution In this way, work can continue while the additional checks are being carried out, and the bid is temporarily signposted as less certain than ones that have passed the gateway. When the checks are finished, the Finance team can approve or reject the bid in the normal way, and the process continues on or stops as normal. [Image: An approval form asking 'Do you want to approve this budget breakdown?', with Yes selected and a comment that the company's credit rating has been checked] Final approval form This is just one example of the many times that we have listened to feedback and improved our products as a result. Our flexible approach means we can make applications that are a perfect fit for our clients' needs. --- # Responding to prospective client emails promptly and with confidence URL: https://www.associativetrails.com/writing/2022/06/20/responding-to-prospective-client-emails-promptly-and-with-confidence/ Published: 2022-06-20 · Practice · Sheet 06 Summary: Sling gives you immediate access to all the important information, without leaving Outlook Displaying the sender's information directly in Outlook A client has just emailed with an idea for an exciting new project, and you want to get the ball rolling as soon as possible. But you just need to check what other projects and bids you're currently working on with them. You discussed something similar with a colleague last week, and you don't know if they have already requested a bid budget or lined up marketing support. Your colleague is on site today, and she won't be available until tomorrow. You'll need signoff from the Finance Director to proceed much further, but you know they're attending a conference tomorrow, so you'll need to be quick. What a wonderful opportunity, our team would do it brilliantly, and it would underline our expertise in the sector. If only there was somewhere quick and easy to check existing projects and bids, capture new enquiry details, and request approval for the budget you need to give this bid the time and effort it deserves. ## Sling puts all the information you need, directly in Outlook [Image: A note in Sling's Outlook panel: a colleague's photo, name and the time they left it, above the note itself] Notes from colleagues about the client contact As soon as you open the client's email, Sling is checking it's database for everything it has on them. You can see that last week your colleague left a note that they were expecting a hot new lead, but didn't know any details. [Image: Sling's list of the 11 leads the contact is involved in, each with a reference number, a title and status icons] List of client bids You check the list of the projects and bids that you have previously worked on for this client - the relationship is well established and you have been shortlisted for awards on their projects in the past. Maybe this is the one that will finally get the recognition you know the team deserves. You fill out the form to add a new lead to Sling - the contact fields are already completed with the client company and contact details. You just need to paste some text from the email into a couple of information fields. [Image: An approval form asking 'Do you want to approve this enquiry?', with 'Yes - approve' selected, a comment box and a Post decision button] Approve bid You submit the form. Almost instantly the Finance Director receives an email asking them to approve the resources you need to qualify and prepare the bid. They read the email, check over the details directly in Outlook using the Sling add-in, add their own comment and click the approval button. You're all set to start work on your bid. It's going to be a long week. --- # Deep Learning Architect - Classification for Architectural Design through the Eye of Artificial Intelligence (2018) URL: https://www.associativetrails.com/writing/2022/05/02/deep-learning-architect-classification-for-architectural-design-through-the-eye-of-artificial-intelligence-2018/ Published: 2022-05-02 · Practice · Sheet 05 Summary: Notes on a 2018 paper by Yuji Yoshimura, Bill Cai, Zhoutong Wang, and Carlo Ratti The paper Yoshimura, Cai, Wang & Ratti · 2018 arXiv (https://arxiv.org/abs/1812.01714) ResearchGate (https://www.researchgate.net/publication/332997690_Deep_Learning_Architect_Classification_for_Architectural_Design_Through_the_Eye_of_Artificial_Intelligence) PDF 5.4 MB (https://www.associativetrails.com/assets/uploads/1812.01714.pdf) - Convnet to classify works belonging to 34 different architects. 77% accuracy. - Using images scraped from he web, and some photos taken by researchers - Architects then clustered together and compared to conventional architecture theory - Difficulty in classifying modern architects - detecting space, not features - Uses NASNet - Zoph and Schlens (2018) (https://arxiv.org/abs/1707.07012) - Gradient-weighted Class Activation Mapping (Grad-CAM). Enables us to understand the focus of the machine eye for classification - Dimension reduction and clustering - Linear principal component analysis. k-means clustering - 34 architects - all past Prisker Prize winners - 20,000 total sample images - Goolge Tensorflow. Two GeForce GTX 1070Ti. Training complete in 8 hours. - 30 epochs. - Tends to confuse Koolhaas, Holl, Perrault with other architects - Correctly distinguishes Kahn, Siza & van de Rohe - “Top 1 accuracy indicates the probability of whether the image can correctly match with the target label. Conversely, the top 5 accuracy suggests the probability of whether the correct image can appear with the target label among five pictures ordered according to their highest probability.” - Almost 70% of architects can be distinguished with more than 80% probability (Top 5 accuracy) - 45% distinguished with 90% probability (Top 5 accuracy) - Indoor scenes more distinguishable to machine eye (~5% more accurate) - k-means clustering: - Foster, Renzo, Rogers (High-Tech Design) - Lloyd Wright, "Normal House" (Prarie style) - Ando, Eisenman, Miralles (Deconstructivists) - Mayne, Gehry - Does not distinguish Architect's work over time --- # Architectural Drawings Recognition and Generation through Machine Learning (2018) URL: https://www.associativetrails.com/writing/2022/05/01/architectural-drawings-recognition-and-generation-through-machine-learning-2018/ Published: 2022-05-01 · Practice · Sheet 04 Summary: Notes on a 2018 paper by Weixin Huang and Hao Zheng The paper Huang & Zheng · 2018 ACADIA (http://papers.cumincad.org/cgi-bin/works/paper/acadia18_156) ResearchGate (https://www.researchgate.net/publication/328280126_Architectural_Drawings_Recognition_and_Generation_through_Machine_Learning) PDF 6.5 MB (https://www.associativetrails.com/assets/uploads/05.acadia18_156.pdf) - Pix2PixHD (https://github.com/NVIDIA/pix2pixHD) used to generate zoning maps of floor plans - Detail improves with number of epochs - ResNet used to prevent overfitting - CGAN - Conditional Generative Adversarial Networks - punish trining going in wrong direction - Pix2PixHD - 5 groups of convolutional layers, 9 groups of residual network layers, 5 groups of deconvolutional layers - Using PyTorch - images from lianjia.com (https://bj.lianjia.com/) - Labels used: Walkway, Bedroom, Living Room, Kitchen, Toilet, Dining Room, Balcony, Window, Door - 115 image pairs - 100 training, 15 testing ~ 2hrs to train one network - Works well in recognising bedroom, kitchen, toilet & balcony - normally well-defined by walls and specific furniture - Less successful for walkway/living room - but some architects got this wrong when labelling test data (done by Architectural students) - Struggled with non-orthogonal shapes - In one image, GAN found mis-labelled data. Later found 4 errors in human-labelled data, which didn't lead to training in the wrong direction - pretty robust network. - Generating plans from zones - sometimes put furniture facing the wrong way. Some architects agreed with the network's furniture position above the position in the test images. - 2-pixel convolutions. Residual network avoids overfitting. - Trained to 80 epochs - loss value stabilised after that - The paper looks at individual neurons' feature detection - Bedrooms/balconies often activated together --- # OPTIMISING IMAGE CLASSIFICATION - Implementation of Convolutional Neural Network Algorithms to Distinguish Between Plans and Sections within the Architectural, Engineering and Construction (AEC) Industry (2019) URL: https://www.associativetrails.com/writing/2022/04/30/optimising-image-classification-implementation-of-cnn-to-distinguish-between-plans-and-sections-2019/ Published: 2022-04-30 · Practice · Sheet 03 Summary: Notes on a 2019 paper by Alessandra Fabbri, M. Hank Haeusler, and Yannis Zavoleas The paper Fabbri, Haeusler & Zavoleas · 2019 ResearchGate (https://www.researchgate.net/publication/333104914_OPTIMISING_IMAGE_CLASSIFICATION_Implementation_of_Convolutional_Neural_Network_Algorithms_to_Distinguish_Between_Plans_and_Sections_within_the_Architectural_Engineering_and_Construction_AEC_Industry) PDF 1.5 MB (https://www.associativetrails.com/assets/uploads/caadria2019_126.pdf) - Build a CNN to classify drawings into sections, plans and elevations - 1,000 images used - 800 for training, 200 for model validation - Objective to achieve accuracy of 90% or more. - Gives lots of detail about the different iterations of models. First peaked at 52% accuracy over 128 epochs. Second peaked at 78% after increasing the number of convolutional layers. Third lowered the learning rate and peaked at 82%. - They recommend further adjustment of hyperparameters to improve the results, with more trainman images. Also consider looking at alternative CNN architectures - highway networks (Srivastava et al. 2015) (https://arxiv.org/abs/1507.06228), residual networks (He et al. 2016) (https://arxiv.org/abs/1512.03385), dense networks (Huang et al. 2017) (https://arxiv.org/abs/1608.06993) - Deploy as executable or web application. --- # Drawing Recognition. Integrating Machine Learning Systems into Architectural Design Workflows (2020) URL: https://www.associativetrails.com/writing/2022/04/29/drawing-recognition-integrating-machine-learning-systems-into-architectural-design-workflows-2020/ Published: 2022-04-29 · Practice · Sheet 02 Summary: Notes on a 2020 paper by Lachlan Brown, Michael Yip, Nicole Gardner, M. Hank Haeusler Haeusler, Nariddh Khan, Yannis Zavoleas, and Christina Ramos The paper Brown et al. · 2020 2020 eCAADE conference (https://www.youtube.com/watch?v=1VsvfwsJ2bA) ResearchGate (https://www.researchgate.net/publication/348234218_Drawing_Recognition_-_Integrating_Machine_Learning_Systems_into_Architectural_Design_Workflows) PDF 455 KB (https://www.associativetrails.com/assets/uploads/ecaade2020_047.pdf) - Development of a CNN for the recognition and labelling of rooms within floor plan images of multi-residential apartments. - Meeting a need to improve solar analysis compliance checking according to the local building code. Only living rooms and balconies need to be assessed, but these rooms were not defined in the BIM data. - Input a floor plan, output an instance segmentation function where each room is classified and the region it occupies is masked. - Using Action Design Research (https://www.jstor.org/stable/23043488) methodology - Investigate, act, observe. - Contains a good review of other papers in the field - Uses Python, TensorFlow, Keras, Visual Studio Code IDE. - Data set of 454 floor plan images - CAD drawings, marketing images and hand-drawn floor plans. Compiling and classifying the training data proved to be the most extensive and time-consuming part of the project. - Labels help in JSON files that defined the regions of rooms. - 6 room classes used - Bedroom, Bathroom (including ensuite), Living room, Dining room, Kitchen, Balcony - Labelled using VGG image annotator - Instance segmentation - the classification of multiple class regions within a single image and produces a mask the defines the space the classes occupy within the image. - Used 1000 epochs! - Does not publish the results. Claims the results were “Satisfactory”, and “beginning to develop a fundamental understanding of design”. - All training data came from one single architectural practice - did the CNN learn their “house style”? --- # Classification of Building Information Model (BIM) Structures with Deep Learning (2018) URL: https://www.associativetrails.com/writing/2022/04/28/classification-of-bim-structures-with-deep-learning-2018/ Published: 2022-04-28 · Practice · Sheet 01 Summary: Notes on a 2018 paper by Francesco Lomio, Ricardo Farinha, Mauri Laasonen, and Heikki Huttunen The paper Lomio, Farinha, Laasonen & Huttunen · 2018 IEEE (https://ieeexplore.ieee.org/document/8611701) ResearchGate (https://www.researchgate.net/publication/326799406_Classification_of_Building_Information_Model_BIM_Structures_with_Deep_Learning) PDF 621 KB (https://www.associativetrails.com/assets/uploads/1808.00601.pdf) - 240 images from BIM software (CAD isometrics) - 4 images form each of 60 BIM representations of buildings - 3 target categories - Apartment building, Industrial Building, Other - Random augmentation - horizontal flips, random rotations (not accounting for gravity?), horizontal and vertical shifts. This increased the learning data. - Analysed using 4 methods: - Feature extraction using Histogram of Oriented Gradients (HOG) followed by Support Vector Machine (SVM) classification - MobileNet - a pre-trained CNN. Last dense layer substituted for one with three categories. Width multiplier and resolution multiplier. Values of 0.25, 0.5, 0.75 & 1 assigned to width multiplier to validate each model. - Residual network (ResNet) - another pre-trained CNN. Specifically ResNet50. Last dense layer substituted for one with three categories. - Random structure CNN from scratch. Ended up with 3 convolutional layers, each with 16 feature maps and a kernel size of 2. No batch normalisation layer. Dropout rate of 0.3. Stochastic Gradient Descent, learning rate of 0.008130275. Each convolutional layer uses ReLU activation functions, with each layer downsampled by 2 with a max-pooling. Last convolutional layer flattens feature maps before the final 3-category dense layer. - Input images 224x224 to match inputs for the pre-trained CNNs - Results: | Model | Accuracy | | --- | --- | | HOG + SVM | 57.19% ± 1.18% | | ResNet50 | 97.92% ± 1.32% | | MobileNet α = 1 | 93.75% ± 2.94% | | MobileNet α = 0.75 | 95.42% ± 2.75% | | MobileNet α = 0.5 | 94.62% ± 3.35% | | MobileNet α = 0.25 | 91.70% ± 2.58% | | Custom CNN | 89.60% ± 3.39% |