▲ | kvnhn 2 days ago | |||||||||||||||||||||||||||||||||||||||||||
IMO, a key passage that's buried: "You can ask the agent for advice on ways to improve your application, but be really careful; it loves to “improve” things, and is quick to suggest adding abstraction layers, etc. Every single idea it gives you will seem valid, and most of them will seem like things that you should really consider doing. RESIST THE URGE..." A thousand times this. LLMs love to over-engineer things. I often wonder how much of this is attributable to the training data... | ||||||||||||||||||||||||||||||||||||||||||||
▲ | brookst 2 days ago | parent | next [-] | |||||||||||||||||||||||||||||||||||||||||||
They’re not dissimilar to human devs, who also often feel the need to replat, refactor, over-generalize, etc. The key thing in both cases, human and AI, is to be super clear about goals. Don’t say “how can this be improved”, say “what can we do to improve maintainability without major architectural changes” or “what changes would be required to scale to 100x volume” or whatever. Open-ended, poorly-defined asks are bad news in any planning/execution based project. | ||||||||||||||||||||||||||||||||||||||||||||
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▲ | iguessthislldo 2 days ago | parent | prev | next [-] | |||||||||||||||||||||||||||||||||||||||||||
This is something I experienced first hand a few weeks ago when I first used Claude. I have this recursive-decent-based parser library I haven't touched in a few years that I want to continue developing but always procrastinate on. It has always been kinda slow so I wanted to see if Claude could improve the speed. It made very reasonable suggestions, the main one being caching parsing rules based on the leading token kind. It made code that looked fine and didn't break tests, but when I did a simple timed looped performance comparison, Claude's changes were slightly slower. Digging through the code, I discovered I already was caching rules in a similar way and forgot about it, so the slight performance loss was from doing this twice. | ||||||||||||||||||||||||||||||||||||||||||||
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▲ | 2 days ago | parent | prev | next [-] | |||||||||||||||||||||||||||||||||||||||||||
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▲ | nativeit 2 days ago | parent | prev [-] | |||||||||||||||||||||||||||||||||||||||||||
> I often wonder how much of this is attributable to the training data... I'd reckon anywhere between 99.9%-100%. Give or take. |