☆ Yσɠƚԋσʂ ☆

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Joined 7 years ago
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Cake day: January 18th, 2020

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  • Using LLMs to vomit out all of your code into one commit is vibecoding. It’s clearly vibecoding when there weren’t even multiple commits. No trial and error, no refinement, just one giant dump.

    Yeah, I completely agree there. Just like with regular coding, you have to go slowly, implement functionality in small focused chunks, review, and iterate.

    And I’d also wait and see whether the project continues to be maintained before considering it. I just posted it because I thought it was neat, and might be worth watching to see how it develops, not that I’d use it over a more mature option at the moment.

    I think we’re actually in complete agreement here.





  • Again, I don’t really see the difference here. Whether the authors continue to maintain the project long or not has little to do with what tools were used to build the project. And calling anything where LLMs are used for development vibecoding is just silly to be honest. There’s a huge variation in quality of code both in hand written code and that where LLMs are used. What you should be looking at in both cases is how much usage project gets, how good the tests are, whether authors built useful things before, and so on.

    If a project is useful then I really don’t care how it’s coded. Existing projects are in no way diminished by the fact that this project exists.











  • People really need to stop parroting this line uncritically. The process takes time because even when you’re distilling answers, you still need to actually do reinforcement training on the model. And given that Fable and GPT 5.6 just came out there simply hasn’t been much time to do that. However, models like Kimi also do better than Fable or GPT on a lot of tasks, which means it’s not just distillation but also difference in architecture. You can watch this talk from Kimi founder to see how Kimi was actually trained and why it performs well.

    It’s also absolutely hilarious that people think only Chinese companies use distillation, as if Anthropic or OpenAI are above that or something. Not to mention that they basically ignored copyrights on all the data the siphoned and are now crying that people aren’t respecting their terms of use.

    The reality is that China tops the world in artificial intelligence publications today. Chinese labs have come up with a bunch of genuine innovations: GRPO, auxiliary loss free MoE load balancing, MLA, muon optimizer, and a bunch of other ones. The Deepseek papers are really well written, this isn’t just sneaking a peek at a peer. Anybody who thinks China is simply distilling glorius American models is not engaging with reality.

    This whole narrative has just been a massive cope.




  • Not really, while it’s supposed to be released on the 27th, you need massive hardware to run it. So, it won’t be useful to individuals. The problem from Anthropic perspective is that other providers will be able to run Kimi at that point. Moonshot has already closed subscriptions because they’re at capacity, so they actually benefit from more companies running and offering Kimi since they can only serve so many requests themselves. On the other hand, Anthropic has to foot the bill for every instance of Claude themselves.
















  • The process takes time because even when you’re distilling answers, you still need to actually do reinforcement training on the model. And given that Fable and GPT 5.6 just came out there simply hasn’t been much time to do that. However, models like Kimi also do better than Fable or GPT on a lot of tasks, which means it’s not just distillation but also difference in architecture. You can watch this talk from Kimi founder to see how Kimi was actually trained and why it performs well.

    It’s also absolutely hilarious that people think only Chinese companies use distillation, as if Anthropic or OpenAI are above that or something. Not to mention that they basically ignored copyrights on all the data the siphoned and are now crying that people aren’t respecting their terms of use.