Thread

1. fishfasell · 1789905692 · focus · HN ↗

   I think there's a lot of setup and context required for an AI agent to consistently write good code. Once the agent has these guard rails in place I usually get great quality- far better than what I would write in most cases.

   I think where things get dicey is being able to write in any language. I write and review code in many languages and frameworks I'm not fluent in, so it's hard for me to distinguish between working code and great code. I can spot when the fundamental logic is wrong, but when it comes to "best fit" choices I'm clueless.

   1. this_user · 1789906931 · focus · HN ↗

      The issue is that in order to have the agent write good code, you need to implement standard SWE best practices. But that also means a lot of manual intervention in terms of writing specs, checking acceptance criteria, and reviewing code. So you end up spending a lot of time on managing your agent, which means you won't get a 1000% productivity gain, you get maybe 50 or 100, possible less in some areas and with some issues.

      1. user43928 · 1789907672 · focus · HN ↗

         A 1000% productivity gain is quite possible on solo greenfield projects.

         At work, with a team and code reviews, the 50%-100% figure seems much more likely.

         This can probably move towards the more spectacular productivity gains as the AI's output becomes more reliable, people realize this, and less time is spend on code review and cleaning up the output.

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