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ako | on: Postgres 19 is late because so many more bugs are being found now
I think ai is more rigorous with testing. It’s not so much about what an llm “knows”, but more about how well it does plan-do-check-act.
jacquesm | parent
AI is as good as the prompt. It's a simple as that. If you don't foresee everything and anything that can go wrong AI isn't not going to magically fill in the blanks and save you. So in projects that already had good test discipline, well written specs and tickets and very clear expectations, coding guidelines and documentation it is relatively easy to fit an AI in for guided work. You still need to check up but chances are good that it will usually work as expected.

But any kind of mistake, any kind of ambiguity and you'll be in for a wild ride. It usually takes a while for the rot to set in. I've had to revert - more than once - to many commits prior to undo a bunch of work that had me completely puzzled as to what the reason that particular fork was taken to begin with. Usually I end up figuring it out but definitely not always. In some cases this kind of wild modification fed back on itself until the situation was beyond rescue. I'm a bit quicker with such reverts now than in the past, for the first year or so I would try to save what could be saved, now I just restore and try again.

But if you're not sharp when evaluating what gets done there is a good chance that you'll miss it and then it can pop up way later.

What I've noticed as correlation (but I'm not sure if that is the root cause) is that non-local effects and stack effects are particularly hard as is everything that requires a working model of the world.

edit: this comment seems to make people uncomfortable.

ako | root | parent | next
I think the environment you set up for the agents is just as important as the prompt. I've been working full time on a claude code developed project for the last 10 months, and it seems to become better and better at developing the right thing. It has the tools to research whatever context it needs, test it's output in a relevant way, etc. It doesn't by itself suggest complete refactoring, or big performance optimizations, so that's something you have to prompt for, but it will collect all the evidence and context to make a proposal, implement it, and test it.
grey-area | root | parent | next
I disagree that the prompt is the most important thing, I'd say other things like the model you use, a clean codebase, actually reading code and using discretion about which changes are accepted are far more important if you're using AI for work that needs to be correct. LLMs still make significant mistakes in design especially.

Harness, prompt etc etc are all peripheral IMO, though people like to think their super long prompt, custom harness and MakeNoMistakes.md skills are what contributes to their special skill with AIs, but really models are just improving.

You can just trust the AI to generate a perfect answer I suppose (and why are humans slowing it down at all if so?), but if you don't expect that yet you have to put in the work.

aa-jv | root | parent
Every single case of AI slop code I've encountered in a shipped product is a result of a complete lack of proper, professional workflow on the part of the human who decided to ship it. People are using AI as a reason to continue to justify their lack of responsibility for what it is they're doing.

And on the other side, I have seen AI produce exceptionally efficient, productive and effective code - the opposite of slop - when it is treated like a junior developer who will do any idiot thing, except when it is mentored by an actually experienced, professional developer.

The source of slop in AI code is not AI, its not the training data - its in the actual workflow of the human involved in its production.

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