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Depending on your definition of context, my attempt to solve just the foundational context of what words mean in a given domain, and sharing it cleanly, quickly and reliably with both new people in the team and AI agents, is the humble concept map stored in plain text. Talk here: https://www.infoq.com/presentations/concept-map/ Product here: https://thinkingtools.software/concepticon

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Interesting.is this product of yours? Have you used it on a larger engineering team? I'd be interested to know whether it actually reduces onboarding time or improves AI-generated code in practice.

Yes it's a product of mine, but it's actually a recent rebuild in desktop app form of an app I built as a web app well over a decade ago. And yes, I used it to get hmans on the same page at some scale, which you can hear me describe in talk I did at the time like this one: https://www.infoq.com/presentations/concept-map/

I haven't done formal evals in this AI era about how having a concept map alongside the codebase changes what the agents do, but informally I know mine read them because I tell them to and they seem to take it into account, and I seem to have less trouble than the average geek I talk to keeping my code base adequately coherent. It's just plain text context, after all so there's no magic, just very concise and accurate context.

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