Thread

1. brink · 1791054618 · focus · HN ↗

   I also have found that AI has not lived up to many of its promises and have dialed back what AI gets control of. My projects were turning into unmaintainable messes. The people who say coding is solved aren't paying attention.

   1. lucianmarin · 1791055784 · focus · HN ↗

      Yes. I started two projects with AI from scratch. Both abandoned, complete mess. Projects without AI are so easy to manage, maintain, add/remove features, etc. I use AI as a search engine on my projects instead of Google. I ask what's wrong with my code and change or improve it myself based on my experience.

      1. fasterik · 1791057584 · focus · HN ↗

         I've been a vibe-coding skeptic for years, but because of the math breakthroughs of the past few weeks I decided to experiment with the latest models on some test projects. They're a lot more capable than I thought they would be. I agree that it's easy to create an unrecoverable mess, especially when you're one-shotting a lot of features without detailed instructions. But I find that as long as I'm strict about the API boundaries and force the agent to work in small chunks, it's pretty effective. As one example, I got it to write an SVG renderer in a few hours (not the whole spec, but most of the path features and text rendering), which would have taken me at least a week just for the coding part, plus extra time to learn the algorithms.

         1. globular-toast · 1791059921 · focus · HN ↗

            You could have also copied an SVG renderer that implements the whole spec from whatever open source project the model copied it from.

            1. fasterik · 1791063651 · focus · HN ↗

               It didn't copy any source code from any external projects. I had it write a stratified sampling renderer for ground truth, then had it implement feature by feature by matching the pixels. Unless you mean it "copied" it in the sense of third-party code being part of the training data. I don't think that definition of "copy" makes any sense given how these models represent embeddings. It would also imply that humans are "copying" the things they've learned from.

               1. sashank_1509 · 1791093096 · focus · HN ↗

                  I don’t think we should hold humans and models to the same standards. Humans have a very small working memory. Most humans cannot reproduce code they wrote even a year back exactly.

                  A model can reproduce large swaths of its training data exactly. It’s a different algorithm that powers its learning process (it’s why it needs trillions of tokens to even learn basics of language).

                  If there was a spectrum from copying on one end to creative production inspired from something else on the other end, the human generally lies heavily on the right end, while the model is much more on the left, that gap is large enough, that yes the model is in some sense “copying”.

                  1. XajniN · 1791099673 · focus · HN ↗

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