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Comment by roblaszczak

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Comment by roblaszczak

> The prototype is working software. And the improvement and testing of that prototype is further enabled by more improvement loops with the AI. It gets better with more testing and verification, not through human code review, but through usage and testing.

I would love it if it was that simple :-) Agents can one-shot this prototype and that feels like they are "almost done". The hard part starts when we try to make it production-grade.

I'm not saying that it's not possible, but it requires more effort than productionizing a PoC that we used to create "by hand". We start to discover shortcuts taken by the coding agent and we spend 80% of the time on getting the last 20% right (but to be clear, it's still less time than writing it by hand).

It looks like the author may not have hit this reality check yet:

> Of course, neither of these things is currently shipped, or supported and isn’t what I’d call production ready software. So you could say what many say about AI, which is that it helps you ship the prototype faster. But that isn’t really giving enough credit here.

For AI-generated code, finding "the last 20% rough edges" is much, much harder than creating PoC. Often the rough edges are buried in the code that we don't know. Fixing those problems requires big refactors that, for wrongly architected code, can break other things.

Tests help, but that's not enough for models to autonomously fix them. If they make one error, it compounds over multiple iterations.

It's especially true for problems where we don't have a clear oracle. It's impressive that models can brute-force problems with a clear oracle. But many problems where a clear oracle doesn't exist (a lot of business software that I know) will still require a lot of product engineering.

Maybe it will improve? I'm not sure if it can happen with hallucinations around (from what I see, they are still a big deal for domains that agents are not trained on).

Replies roblaszczak · 2026-08-26
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