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Keeping all the specifications and user discussion does create more context, which is useful for AI. However, I'd bring in Brooks discussion on essential and a

Keeping all the specifications and user discussion does create more context, which is useful for AI. However, I'd bring in Brooks discussion on essential and accidental complexity. In other words, there being No Silver Bullet https://www.cs.unc.edu/techreports/86-020.pdf https://www.cs.unc.edu/techreports/86-020.pdf The problem with specification and user discussion is they still have errors that code has. But unlike code, there are no tests to confirm correctness. So now we have a definition of the system in a non-exact language with no ability to test to confirm it's correctness. The code holds the essential complexity and now we are adding accidental complexity on top to manage. Again agree the specifications and user discussion provides context for the AI. However, a well written test suite provides similar context that can actually confirm correctness of the system. However, saying all the above. Focus of ImpactGate ( https://impactgate.officefloor.net https://impactgate.officefloor.net ) is about erosion of the code, not correctness.

visarga 19d ago
You usually don't know what you want upfront, in real life it is a stream of specification and steering.
- sagenschneider 19d ago
  Yes, agree. It's a learning process. I tend to find when I build systems that at some point you need to stop analysis and just start building things to explore the problem. As you do, you prototype, refactor and possibly throw out ideas in favour of understanding the problem and discovery the real solution. The code becomes a reflection of that. I'm interested in your experiences of capturing specifications and user discussion on whether this captures the end intentions? Or whether it keeps you focused on earlier dead end directions?
  - visarga 19d ago
    I think agents are pretty capable of reading a log when given the explicit task of extracting the latest version of what the user wants. In general, they work well for direct tasks like this. They don't forget and do something else the way they do when they are deep into development work or debugging. Besides intent, I also mine signs of "user friction," which I use as input for the agent to come up with new tests. What I complain about is one of the signals driving testing.
    - sagenschneider 18d ago
      Yes, agree very capable of consuming large amounts of information I'd be interested to see what happens: - to token counts after a year of so of changes, as the specification list grows? - how it goes with concurrent changes in teams? Plus whether asking AI to add good commenting to the code could achieve the same thing?

visarga 19d ago

You usually don't know what you want upfront, in real life it is a stream of specification and steering.

sagenschneider 19d ago
Yes, agree. It's a learning process. I tend to find when I build systems that at some point you need to stop analysis and just start building things to explore the problem. As you do, you prototype, refactor and possibly throw out ideas in favour of understanding the problem and discovery the real solution. The code becomes a reflection of that. I'm interested in your experiences of capturing specifications and user discussion on whether this captures the end intentions? Or whether it keeps you focused on earlier dead end directions?
- visarga 19d ago
  I think agents are pretty capable of reading a log when given the explicit task of extracting the latest version of what the user wants. In general, they work well for direct tasks like this. They don't forget and do something else the way they do when they are deep into development work or debugging. Besides intent, I also mine signs of "user friction," which I use as input for the agent to come up with new tests. What I complain about is one of the signals driving testing.
  - sagenschneider 18d ago
    Yes, agree very capable of consuming large amounts of information I'd be interested to see what happens: - to token counts after a year of so of changes, as the specification list grows? - how it goes with concurrent changes in teams? Plus whether asking AI to add good commenting to the code could achieve the same thing?