141–150 of 235 posts
Kimi is capable model but it needs a very good harness. With a good harness it is a very capable model. But it can get into all kinds of issues (loops and such) something that frontier models do not. As I said, you can blame the model, but it is nothing that the harness cannot take care of more deterministically.
The initial models were corrected by programmers which gave a very high quality feedback signal. Whereas with vibe coding on the rise, you’ll lose that signal.
Earlier quoted context omitted.
> for American economy. There is more to American economy than big tech. And that's precisely why this has started: https://www.wired.com/story/super-pac-backed-by-openai-and-p...
>There is more to American economy than big tech. Most of the stock market valuation is big-tech, and most of people's retirements are the stock market, so... if the AI bubble bursts a lot of the US will be affected.
Kimi is capable model but it needs a very good harness. With a good harness it is a very capable model. But it can get into all kinds of issues (loops and such) something that frontier models do not. As I said, you can blame the model, but it is nothing that the harness cannot take care of more deterministically.
Which harness do you recommend using then for a model like kimi 2.6, Opencode or something else?
It is a lot trickier to use kimi compared to sonnet - hence why it seems that sonnet is more powerful while I think it is down to the harness.
Earlier quoted context omitted.
It absolutely does matter. The enshittification will go unnoticed at first but I'm already finding my favourite frontier models severely nerfed, doing incredibly dumb stuff they weren't in the past. We need open weight models to have a stable "platform" when we rely on them, which we do more and more.
Most people won't roll out their own K2 deployment across rented GPUs, so in that sense it doesn't matter that much, they'll be using a paid service which is just as much of a black box as Claude or ChatGPT. For example, on OpenRouter you can select a provider which state they use a given open model, but you have no idea what actually goes on behind the curtains, which quantization levels they use and so on. That sai…
Earlier quoted context omitted.
Which harness do you recommend using then for a model like kimi 2.6, Opencode or something else?
We have our own... so I was making the comment form our own experience working with the model. It is a lot trickier to use kimi compared to sonnet - hence why it seems that sonnet is more powerful while I think it is down to the harness.
If someone were to not use your harness and rather use some stock harness though, what is the one that you would recommend? I am curious about that too.
Kimi is nowhere near GPT or Opus unfortunately. I really wish it was. I’m running evals where models have to generate code that produces 3D models and it’s obvious that it lacks spatial understanding and makes many more code errors before it succeeds. Maybe it’s better in one particular case here and there and I think this blog post is example of that.
This has already happened.
I have downloaded both the big Pro model and the smaller but multimodal MiMo-V2.5.
https://huggingface.co/XiaomiMiMo/MiMo-V2.5-Pro
https://huggingface.co/XiaomiMiMo/MiMo-V2.5
https://huggingface.co/XiaomiMiMo/MiMo-V2.5-Pro-Base
https://huggingface.co/XiaomiMiMo/MiMo-V2.5-Base
The download of MiMo-V2.5-Pro takes 963 GB, while that of MiMo-V2.5 takes 295 GB.
For comparison, the download of Kimi-K2.6 takes 555 GB.
These posts are going to be a constant for the next year, because there's no objective way to compare models (past low-level numbers like token generation speed, average reasoning token amount, # of parameters, active experts, etc). They're all quite different in a lot of ways, they're used for many different things by different people, and they're not deterministic. So you're constantly gonna see benchmarks and test…