221–230 of 400 posts
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I've been running Opus and GLM side-by side for a couple weeks now, and I've been impressed with GLM. I will absolutely agree that it's slow, but if you let it cook, it can be really impressive and absolutely on the level of Opus. Keep in mind, I don't really use AI to build entire services, I'm mostly using it to make small changes or help me find bugs, so the slowness doesn't bother me. Maybe if I set it to make a…
> The big kicker for GLM for me is I can use it in Pi, or whatever harness I like. Yes, but... isn't the same true for Opus and all the other models too?
So you're either paying $1000's for Opus in Pi, or $30/month for GLM in Pi. If the results are mostly equivalent that's an easy choice for most of us.
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I was watching Qwen3.6-35B-A3B (locally) doing the same dance yesterday. It eventually finished and had a reasonable answer, but it sure went back and forth on a bunch of things I had explicitly said not to do before coming to a conclusion. At least said conclusion was not any of the things I'd said not to do.
That is essentially what the reasoning reinforcement training does. It is getting the model to say things that are more likely to result in the correct final answer. Everything it does in between doesn't necessarily need to be valid argument to produce the answer. You can think of it as filling the context with whatever is needed to make the right answer come out next. Valid arguments obviously help. but so might exp…
Also, thanks for pointing me at that specific paper; I spend a lot more of my life closer to classical control theory than ML theory so it's always neat to see the intersection of them. My unsubstantiated hypothesis is that controls & ML are going to start getting looked at more holistically, and not in the way I normal see it (which is "why worry about classical control theory, just solve the problem with RL"). Control theory is largely about steering dynamic systems along stable trajectories through state space... which is largely what iterative "fill in the next word" LLM models are doing. The intersection, I hope, will be interesting and add significant efficiency.
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Change layer size and you have to retrain. Change number of layers and you have to retrain. Change tokenization and you have to retrain.
None of that is true, at least in theory. You can trivially change layer size simply by adding extra columns initialized as 0, effectively embedding your smaller network in a larger network. You can add layers in a similar way, and in fact LLMs are surprisingly robust to having layers added and removed - you can sometimes actually improve performance simply by duplicating some middle layers[0]. Tokenization is probab…
What if you still have to obtain the best result possible for given coefficient/tokenization budget?
I think that my comment express general case, while yours provide some exceptions.
Everybody's out here chasing SOTA, meanwhile I'm getting all my coding done with MiniMax M2.5 in multiple parallel sessions for $10/month and never running into limits.
For serious work, the difference between spending $10/month and $100/month is not even worth considering for most professional developers. There are exceptions like students and people in very low income countries, but I’m always confused by developers with in careers where six figure salaries are normal who are going cheap on tools. I find even the SOTA models to be far away from trustworthy for anything beyond thro…
Also not everyone wants to use Claude Code, so if they're paying API pricing it's more likely thousands of dollars a month. If you can get the same results by spending a fraction of that, why wouldn't you?
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For actually serious work, it's a stark difference if your proprietary and security relevant code is sent abroad to a foreign, possibly future hostile country, or is sent to some data center around the corner. It doesn't even need to be defence related.
AFAIK all these companies have SOTA or near-SOTA models available under enterprise licenses. AI companies are not interested in your secret sauce, they are trying to capture the SDLC wholesale.
On a related note, I really need to try some local models (probably starting with qwen), since, at least in 2026, the Chinese models are way better at protecting democracy and free speech than the US models.
https://www.alibabacloud.com/help/en/model-studio/context-ca... I’ve also been testing models like Opus, Codex, and Qwen, and Qwen is strong in many coding tasks. However, my main concern is how it behaves in long-running sessions. While Qwen advertises large context windows, in practice the effectiveness of long-context usage seems to depend heavily on its context caching behavior. According to the official document…
Ok I find it funny that people compare models and are like, opus 4.7 is SOTA and is much better etc, but I have used glm 5.1 (I assume this comes form them training on both opus and codex) for things opus couldn't do and have seen it make better code, haven't tried the qwen max series but I have seen the local 122b model do smarter more correct things based on docs than opus so yes benchmarks are one thing but realit…
The only reason I'm stuck with Claude and Chatgpt is because of their tool calling. They do have some pretty useful features like skills etc. I've tried using qwen and deepseek but they can't even output documents. How are you guys handling documents and excels with these tools? I'd love to switch tbh.
Ok I find it funny that people compare models and are like, opus 4.7 is SOTA and is much better etc, but I have used glm 5.1 (I assume this comes form them training on both opus and codex) for things opus couldn't do and have seen it make better code, haven't tried the qwen max series but I have seen the local 122b model do smarter more correct things based on docs than opus so yes benchmarks are one thing but realit…
Nowadays, I'm working on a realtime path tracer where you need proper understanding of microfacet reflection models, PDFs, (multiple) importance sampling, ReSTIR, etc.. Saying that mine is a somewhat specific use case. And I use Claude, Gemini, GLM, Qwen to double check my math, my code and to get practical information to make my path tracer more efficient. Claude and Gemini failed me more than a couple of times with…