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0 pointsewoodrich2mo ago0 comments

Right, I am a daily user of agentic LLM tools and have this exact problem in one large project that has complex business logic externally dictated by real world requirements out of my control, and let's say, variable quality of legacy code.

I remember when Gemini Pro 3 was the latest hotness and I started to get FOMO seeing demos on X posted to HN showing it one shot-ing all sorts of impressive stuff. So I tried it out for a couple days in Gemini CLI/OpenCode and ran into the exact same pain points I was dealing with using CC/Codex.

Flashy one shot demos of greenfield prompts are a natural hype magnet so get lots of attention, but in my experience aren't particularly useful for evaluating value in complex, legacy projects with tightly bounded requirements that can't be easily reduced to a page or two of prose for a prompt.

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swat5352mo ago

To be fair, you're not supposed to be doing the "one shot" thing with LLMs in a mature codebase.

You have to supply it the right context with a well formed prompt, get a plan, then execute and do some cleanup.

LLMs are only as good as the engineers using them, you need to master the tool first before you can be productive with it.

ewoodrichOP2mo ago

I’m well aware, as I said I am regularly using CC/Codex/OC in a variety of projects, and I certainly didn’t claim that can’t be used productively in a large code base.

But that different challenges become apparent that aren’t addressed by examples like this article which tend to focus on narrow, greenfield applications that can be readily rebuilt in one shot.

I already get plenty of value in small side projects that Claude can create in minutes. And while extremely cool, these examples aren’t the kind of “step change” improvement I’d like to see in the area where agentic tools are currently weakest in my daily usage.

gf0002mo ago

I would be much more impressed with implementing new, long-requested features into existing software (that are open to later maintain LLM-generated code).

ewoodrichOP2mo ago

Fully agreed! That’s the exact kind of thing I was hoping to find when I read the article title, but unfortunately it was really just another “normal AI agent experience” I’ve seen (and built) many examples of before.

j / k navigate · click thread line to collapse

0 pointsewoodrich2mo ago0 comments

0 comments

swat5352mo ago

To be fair, you're not supposed to be doing the "one shot" thing with LLMs in a mature codebase.

You have to supply it the right context with a well formed prompt, get a plan, then execute and do some cleanup.

LLMs are only as good as the engineers using them, you need to master the tool first before you can be productive with it.

ewoodrichOP2mo ago

I’m well aware, as I said I am regularly using CC/Codex/OC in a variety of projects, and I certainly didn’t claim that can’t be used productively in a large code base.

But that different challenges become apparent that aren’t addressed by examples like this article which tend to focus on narrow, greenfield applications that can be readily rebuilt in one shot.

gf0002mo ago

I would be much more impressed with implementing new, long-requested features into existing software (that are open to later maintain LLM-generated code).

ewoodrichOP2mo ago

j / k navigate · click thread line to collapse