Github copilot is utter garbage. The diffing crawls along at a snail’s pace. I think it’s coming up on two years and this must criticised aspect of it still isn’t fixed—-even with all the reverse engineering of how cursor did it. I wish I could find an alternative to cursor (which has other issues). Honestly, that company just threw away a golden opportunity as the first mover.

Nah, these are much smaller models than Qwen3 and GLM 4.5 with similar performance. Fewer parameters and fewer bits per parameter. They are much more impressive and will run on garden variety gaming PCs at more than usable speed. I can't wait to try on my 4090 at home.

There's basically no reason to run other open source models now that these are available, at least for non-multimodal tasks.

So far none of these models can write even a slightly complicated function well for me. I tried Mistral, ChatGPT, Qwen Coder 2, Claude, ... they apparently all fail when the solution requires to make use of continuations and such. Probably, because they don't have enough examples in their training data or something.

Example: Partition a linked list in linear time. None of these models seems to be able to get, that `reverse` or converting the whole list to a vector are in themselves linear operations and therefore forbid themselves. When you tell them to not use those, they still continue to do so and blatantly claim, that they are not using them. Á la:

"You are right, ... . The following code avoids using `reverse`, ... :

[code that still uses reverse]"

And in languages like Python they will cheat, because Python's list is more like an array, where random access is O(1).

This means they only work well, when you are doing something quite mainstream, where the amount of training data is a significantly strong signal in the noise. But even there they often struggle. For example I found them somewhat useful for doing Django things, but just as often they gave bullshit code, or it took a lot of back and forth to get something useful out of them.

I think it is embarrassing, that with sooo much training data, they are still unable to do much more than going by frequency in training data when suggesting "solutions". They are "learning" differently than a human being. When a human being sees a new concept, they can often apply that new concept, even if that concept does not happen to be needed that often, as long as they remember the concept. But in these LLMs it seems they deem everything that isn't mainstream irrelevant.

No, it's unhealthy. Folks already have day jobs, families, other responsibilities.

Having to tack on top of that 2-4h of work per day is not normal, and again, it's probably unhealthy.

Qwen has previously engaged in deceptive benchmark hacking. They previously claimed SOTA coding performance back in January and there's a good reason that no software engineer you know was writing code with Qwen 2.5.

https://winbuzzer.com/2025/01/29/alibabas-new-qwen-2-5-max-m...

Alibaba is not a company whose culture is conducive to earnest acknowledgement that they are behind SOTA.

You can get a used 5 year old Xeon Dell or Lenovo Workstation and 8x64GB of ECC DDR4 RAM for about $1500-$2000.

Or you can rent a newer one for $300/mo on the cloud

Any significant benefits at 3 or 4 bit? I have access to twice that much VRAM and system RAM but of course that could potentially be better used for KV cache.

Ye the model looks extremely powerful! I think they're also maybe making a small variant as well, but unsure yet!