Thread

1. the_duke · 1790703020 · focus · HN ↗

   The GPT 6 release was ... not great.

   Sol 6 was so bad that I switched over to Opus 5.5 exclusively.

   Huge regression compared to Sol 5.6, often doing really dumb things. Same for Luna.

   Even Astra is very unreliable for coding. Brilliant for vision, sometimes just great, but it also often does very stupid things.

   I'm a bit sour on OpenAI right now and skeptical that 6.1 will be much different.

   (Note: this is after preferring and shilling Codex/OpenAI models for the last half year)

   1. nxc18 · 1790703303 · focus · HN ↗

      How does this jive with the exponential growth claims? Theoretically sol models are better than the 4 series models I was using at the beginning of the year, but in practice the results don’t seem to be much better. They always nerf the models over the course of the release so it _looks_ like the next version is better but I haven’t seen actual capability growth since ~January, and I’m pretty sure that was all tooling/harness improvements.

      1. sigbottle · 1790706523 · focus · HN ↗

         How large of codebases are you working on? The models have gotten good enough to 1 shot stupid "trivial" throwaway integration projects with 0 handholding (was having RL'd garbage in late 2025), and I'm actually enjoying designing bounded greenfield personal software from scratch with Astra, in my experience. It's quite slow - 2 weeks of credits and constant talking and back and forth with Astra, but it doesn't feel annoying to talk to and is like an intelligent colleague maybe 70% of the time? Which is great. Just push back when it's dumb.

         I'm by no means an AI booster, but given 2022 - 2026 progress I'd say it's "exponential" in the sense of, "holy shit, every year I can do more and more genuinely different things", not "RSI mind reading intelligence can do anything is here".

         I don't think Navier-Stokes level intelligence translates over to my projects, unfortunately. Yet? Who knows.

         > I haven’t seen actual capability growth since ~January, and I’m pretty sure that was all tooling/harness improvements.

         Even if that were the case, I'd say that it's improved in practice. And just from a philosophy perspective, if you're trying to imply some kind of mind dualistic way of viewing things, uh, I disagree with those theories of intelligence strongly (which also incidentally also disagrees with AIT-style theories of intelligence on one axis, though I have many bones to pick with the culture there).

         1. moshegramovsky · 1790712296 · focus · HN ↗

            I work on a very large code base (millions of LOC) and I've had lackluster results with autonomous work and 1 shotting. AI is definitely fantastic at working on many programming problems but I am not seeing amazing results at refactoring. In fact, I am seeing very poor results, even with Astra, even with extensive planning docs. All the recent models I've used can definitely get that refactor done, but not autonomously. It needs to be small slices. I've yet to see it 1 shot anything really complicated.

            Here's a good example with some assumptions on my part: I work in C++ and it really feels like the models are trained so hard to keep everything compiling all the time. That's a huge negative in my opinion because what happens is that the AI will do things like use wrappers to keep things compiling, even when that basically results in creating or hiding abstraction leaks. Or they get sneaky and include a header they shouldn't. Or they actually do see that there should be a layer boundary and they write some kind of abstraction to cross it but the abstraction itself is garbage or doesn't follow existing API patterns.

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