61–70 of 819 posts
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…
LiteLLM is your friend.
Price is playing a big role in my AI usage for coding. I am using Grok Code Fast as it's super cheap. Next to it GPT-5 Codex. If you are paying for model use out of pocket Claude prices are super expensive. With better tooling setup those less smart (and often faster) models can give you better results. I am going to give this another shot but it will cost me $50 just to try it on a real project :(
I'm glad they at least gave me the full $100 refund.
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I never understood the point of the pellican on a bicycle exercise: LLMs coding agent doesnt have any way to see the output. It means the only thing this test is testing, is the ability of the LLMs to memorise. Edit: just to show my point, a regular human on a bicycle is way worse with the same model: https://i.imgur.com/flxSJI9.png
Memorise what exactly?
I bet their ability to form a pellican result purely because someone already did it before.
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pelican on a bicycle benchmark probably getting saturated... especially as it's become a popular way to demonstrate model ability quickly
But where is the training set of good pelicans on bikes coming from? You think they have people jigging them up internally?
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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.
Maybe we’re entering the Emo Claude era.
Per the system card: In 250k real conversations, Claude Sonnet 4.5 expressed happiness about half as often as Claude 4, though distress remained steady.
It’s a simple substitution request where I provide a Lint error that suggests the correct change. All the models fail. I could ask someone with no development experience to do this change and they could.
I worry everyone is chasing benchmarks to the detriment of general performance. Or the next token weight for the incorrect change outweigh my simple but precise instructions. Either way it’s no good
Edit: With a followup “please do what I asked” sort of prompt it came through, while Opus just loops. So theres that at least