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My automated doubt development process

89 points by aself101

by docheinestages

4 subcomments

Most writings about the spec-driven development I see start with a product requirements document that is assumed to be valid. But I doubt that's the case. If so, you would've written about it, and probably would've involved agents in the research that goes into it. My gut feeling tells me there's much more emphasis on implementing the feature than on questioning if it's relevant, feasible, and based on valid assumptions.

by watersb

0 subcomment

Strangely reminiscent of an Electric Monk:
The Electric Monk was a labor-saving device, like a dishwasher or a video recorder. Dishwashers washed tedious dishes for you, thus saving you the bother of washing them yourself, video recorders watched tedious television for you, thus saving you the bother of looking at it yourself; Electric Monks believed things for you, thus saving you what was becoming an increasingly onerous task, that of believing all the things the world expected you to believe.
-- Douglas Adams, "Dirk Gently's Holistic Detective Agency"

by m12k

1 subcomments

I've stumbled on the same workflow. Except for one thing: If I just do as OP does, Claude Code will tend to overengineer. For example it'll build complex solutions to super rare race conditions that have trivial fallout. But I've found that all it takes is a "skeptical pass". Here's how it goes: After having a bunch of specialist subagents review the (plan/implementation), after doing the deduplication/synthesis of their findings, the main agent will bucket them into A) Trivial/obvious fix B) there's multiple possible resolutions, but the LLM had a strong lean, so it went with it on its own C) Genuine ambiguity, where it asks me what to do (and presents its lean) and D) Wontfix. Crucially, after doing this, I have it run a "skeptical pass" where it takes a hard look at these findings and see if maybe some of them deserve to be downgraded. Generally, a lot of things make their way into wontfix this way. I find, I don't need to push back against overengineering, I can have the LLM do so itself, and it'll actually do a decent job of it.

by aself101

2 subcomments

This has been my attempt at wrangling the new A.I. assisted development that seems to be overtaking the software engineering profession. I jumped head first into LLM development after observing the trends from the last year and it appears this process might be a viable path forward.

by marcus_holmes

0 subcomment

I have a similar skill that assesses project drift (how far the current project state is from the original spec and brief) and looks for artifacts of that - orphaned code or features that are no longer relevant, design decisions that made sense at the time but are now dubious, or parts of the design that no longer fit the current project state.
I found it really useful taming the spaghetti that claude tends to generate by itself.

by HappySweeney

0 subcomment

I think its common to develop an adversarial-collaborative approach to getting some semblance of quality out of AI. I personally favour using multiple models for different roles, having a bunch of continuity documentation maintained, and having the plan surface human-verifiable deliverables as soon as feasible. It does involve more attention than most people would tolerate probably.

by tuo-lei

0 subcomment

review agents have the same training biases as the one writing the code. you get 30 findings about error handling and edge cases, but wrong domain assumptions slip right through.

by dbgrman

1 subcomments

Enjoyed it until the first emdash (was half expecting it to arrive anyway). Sorry.

by agnitripathi

0 subcomment

by claudiosilva

0 subcomment

by yudidnmkating

0 subcomment

by victorkulla

0 subcomment