Thread

1. axegon_ · 1789905896 · focus · HN ↗

   Ah, the "skill issue" argument again. Same crap aswhen everyonewas worshiping Musk 5-6 years ago, this time it's dario and altman with a claude/chatgpt mask. Crash can't come soon enough.

   1. skybrian · 1789906594 · focus · HN ↗

      And why wouldn’t writing software be a skill issue? Yes, it’s an annoying meme, but we should expect that there are better and worse ways to write software. It would be weird if everyone got the same results regardless of experience.

      I’m doubtful that the author’s recommendation always work, but I do some similar things and they do seem to help.

      1. malfist · 1789907364 · focus · HN ↗

         Anyone who thinks they can produce high quality code from an LLM is mistaken about how to judge code. Trust me, I've seen enough PRs to last a life time. A lot of professionals wouldn't know good code if it slapped them in the face.

         1. preg_match · 1789925545 · focus · HN ↗

            You can most definitely produce high-quality code via an LLM, particularly if you test your code aggressively and review it. Yes there are a lot of shoddy PRs, but that's nothing too new. The problem with LLMs is the amount of code they produce. More code = more garbage. But, that code velocity can be leveraged to increase quality. Through careful design and testing.

            Ultimately, I would take LLM code + high-quality multi-strategy testing over human code with little to no tests. And some would say "well that's a false dichotomy". I disagree, before LLMs engineers didn't have the time or incentives to aggressively test. The tests either would not exist, or would be shitty unit tests intended to get an arbitrary coverage percentage. Now, we can write high-quality tests, differential testing, fuzzing, and more, in much less time.

            1. rented_mule · 1789930839 · focus · HN ↗

               Going much deeper on tests has been transformative for me. In a solo project started from scratch, I'm 6-7 weeks in, and it's up to ~90K lines. ~60K of those are tests. Those tests have now found (and then the agent has correctly diagnosed) multiple bugs in broadly used libraries that I'm using in my project. That's because those bugs surfaced as occasional issues in my project. Especially powerful are all the property-based tests (perhaps what you are calling fuzzing? I'm using the Python package called Hypothesis for this).

               Another spectrum that I've found useful to explore is the scope of what I ask the coding agent to do in one turn. I see some people trying to do one massive prompt that the coding agent works on for a day or more. I find a large boost in overall quality if I do 10-20 prompts per day (not counting the prompts where I'm just trying to understand things). It's still much less of my time than hand-coding, but the resulting architecture looks like my own. The quality of the overall system is great. There are certainly issues here and there in the code, but it's always that way once a project gets large enough. Now it's easier to address any particular issue throughout the code base in one go.

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