71–80 of 819 posts
Oh wow, a lot of focus on code from the big labs recently. In hindsight it makes sense that the domain the people building it know best is the one getting the most attention, and it's also the one the models have seen the most undeniable usefulness in so far. Though personally, the unpredictability of the future where all of this goes is a bit unsettling at the same time...
I've really got to refactor my side project which I tailored to just use OpenAI API calls. I think the Anthropic APIs are a bit different so I just never put in the energy to support the changes. I think I remember reading that there are tools to simpify this kind of work, to support multiple LLM APIs? I'm sure I could do it manually but how do you all support multiple API providers that have some differences in the…
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My subjective personal experience is the exact opposite of yours, GPT-5-codex is super slow and the results are mediocre at best. I would probably stop using AI for coding if I was forced to use GPT-5-codex.
I'm on your side. I find there's a quite large spread in ability between various models. Claude models seem to work superbly for me, though I'm not sure whether that's just a quirk of what my projects look like.
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The Anthropic models have been vibe-coding tuned. They're beasts at simple python/ts programs, but they definitely fall apart with scientific/difficult code and large codebases. I don't expect that to change with the new Sonnet.
In my experience Gemini 2.5 Pro is the star when it comes to complex codebases. Give it a single xml from repomix and make sure to use the one at the aistudio.
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Opposite for me…5-codex high ran out of tokens extremely quickly and didn’t adhere as well to the agents.md as Claude did to the Claude.md, perhaps because it insists on writing extremely complicated bash scripts or whole python programs to execute what should be simple commands.
Codex was a miserable experience for me until I learned to compact after every feature. Now it is a cut above CC, although the latter still has an edge at TODO scaffolding and planning.
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The Anthropic models have been vibe-coding tuned. They're beasts at simple python/ts programs, but they definitely fall apart with scientific/difficult code and large codebases. I don't expect that to change with the new Sonnet.
In my experience Gemini 2.5 Pro is the star when it comes to complex codebases. Give it a single xml from repomix and make sure to use the one at the aistudio.
This has been outstanding for what I have been developing AI assisted as of late.
Looking at the chart here, it seems like Sonnet 4 was already better than GPT-5-codex in the SWE verified benchmark. However, my subjective personal experience was GPT-5-codex was far better at complex problems than Claude Code.
Oh wow, a lot of focus on code from the big labs recently. In hindsight it makes sense that the domain the people building it know best is the one getting the most attention, and it's also the one the models have seen the most undeniable usefulness in so far. Though personally, the unpredictability of the future where all of this goes is a bit unsettling at the same time...
Looking at the chart here, it seems like Sonnet 4 was already better than GPT-5-codex in the SWE verified benchmark. However, my subjective personal experience was GPT-5-codex was far better at complex problems than Claude Code.
GPT-5 is like the guy on the baseball team that's really good at hitting home runs but can't do basic shit in the outfield. It also consistently gets into drama with the other agents e.g. the other day when I told it we were switching to claude code for executing changes, after badmouthing claude's entirely reasonable and measured analysis it went ahead and decided to `git reset --hard` even after I twice pushed back…