AI should only run as fast as we can catch up

Re:AI should only run as fast as we can catch up by bob1029 on Monday December 08, 2025 @02:50PM (#46196747)

I have my best successes by keeping things constrained to method-level generation. Most of the things I dump into ChatGPT look like this:

  public static double ScoreItem(Span<byte> candidate, Span<byte> target)
  {
     //TODO: Return the normalized Levenshtein distance between the 2 byte sequences.
     //... any additional edge cases here ...
  }

I think generating more than one method at a time is playing with fire. Individual methods can be generated by the LLM and tested in isolation. You can incrementally build up and trust your understanding of the problem space by going a little bit slower. If the LLM is operating over a whole set of methods at once, it is like starting over each time you have to iterate.

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Re:AI should only run as fast as we can catch up by samdoesnothing on Monday December 08, 2025 @04:07PM (#46197658)

I do this but with copilot. Write a comment and then spam opt-tab and 50% of the time it ends up doing what I want and I can read it line-by-line before tabbing the next one.

Genuine productivity boost but I don't feel like it's AI slop, sometimes it feels like its actually reading my mind and just preventing me from having to type...

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Re:AI should only run as fast as we can catch up by jerf on Monday December 08, 2025 @04:39PM (#46197999)

I've settled in on this as well for most of my day-to-day coding. A lot of extremely fancy tab completion, using the agent only for manipulation tasks I can carefully define. I'm currently in a "write lots of code" mode which affects that, I think. In a maintenance mode I could see doing more agent prompting. It gives me a chance to catch things early and then put in a correct pattern for it to continue forward with. And honestly for a lot of tasks it's not particularly slower than "ask it to do something, correct its five errors, tweak the prompt" work flow.

I've had net-time-savings with bigger agentic tasks, but I still have to check it line-by-line when it is done, because it takes lazy shortcuts and sometimes just outright gets things wrong.

Big productivity boost, it takes out the worst of my job, but I still can't trust it at much above the micro scale.

I wish I could give a system prompt for the tab complete; there's a couple of things it does over and over that I'm sure I could prompt away but there's no way to feed that in that I know of.

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Re:AI should only run as fast as we can catch up by theshrike79 on Tuesday December 09, 2025 @06:50AM (#46203878)

"Dumping into ChatGPT" is by far the worst way to work with LLMs, then it lacks the greater context of the project and will just give you the statistical average output.

Using an agentic system that can at least read the other bits of code is more efficient than copypasting snippets to a web page.

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Re:AI should only run as fast as we can catch up by bob1029 on Tuesday December 09, 2025 @09:38AM (#46205350)

> then it lacks the greater context of the project

This is the point. I don't want it thinking about my entire project. I want it looking at a very specific problem each time.

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Re:AI should only run as fast as we can catch up by theshrike79 on Tuesday December 09, 2025 @03:44PM (#46210382)

But why?

Most code is about patterns, specific code styles and reusing existing libraries. Without context none of that can be applied to the solution.

If you put a programmer in a room and give them a piece of paper with a function and say OPTIMISE THAT! - is it going to be their best work?

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