mudkipdev

HuggingFace has a nice UI where you can save your specs to your account and it will display a checkmark/red X next to every unsloth quantization to estimate if it will fit.

Why is the assumption that they trained for a pelican on a bicycle, rather than running RL for all kinds of 'generate an SVG' tasks?

The GLM coding plan price increased dramatically

I'm getting a "failed to verify your browser" error on this article

Why do you need an API key to tokenize the text? Isn't it supposed to be a cheap step that everything else in the model relies on?

Grayish dark themes are underrated

The Claude prompt is already quite bloated, around 7,000 tokens excluding tools.

If anyone has a better workflow for creating lots of captions in kdenlive please let me know. I had to duplicate each title to the media library and drag it into the timeline, because if I simply copy/pasted then the text content/styling would be shared across instances

Re-read that

Does the large system prompt work fine for this model? If needed, you could use a lightweight CLI like Pi, which only comes with 4 tools by default

I built a Claude-inspired UI for Ollama/llama.cpp

https://github.com/mudkipdev/chat

People have made toki pona translation models before, not exclusively trained though

It's strange that my iPhone 14 is at regular temperature when using the E2B model. But also it's a lot slower (not sure how to measure the exact tokens per second, ~12 if I had to guess)

This is probably a consequence of the training data being fully lowercase:

You> hello Guppy> hi. did you bring micro pellets.

You> HELLO Guppy> i don't know what it means but it's mine.

If you use the 'run' command, it pulls automatically for you

Under 15 is too slow for conversation personally. I guess 5 tokens per second is nice if you're one of the people who likes letting coding agents run overnight

Can't wait for gemma4-31b-it-claude-opus-4-6-distilled-q4-k-m on huggingface tomorrow

There can't be any interesting discussion about AI programming. Every conversation boils down to what skill files you use, or how Opus 4.6 compares to Codex, or how well you can manage 16 parallel agents.

Not to be confused with Gemini 3.1 Flash Lite

It simply means the tokenizer's training corpus may have included a massive amount of German literature or accidentally oversampled a web page where that word was frequently repeated. Look up "glitch tokens" to learn more.

Yes, I agree that the value of Apple's chips is hard to beat, but there's still a massive bottleneck with hardware accessibility. The 128 GB MacBook described costs over $5,000 on their website, and in the consumer space, even the most often recommended GPU which is a 3090 with 24 GB VRAM, you can find used going for $700 at minimum. This effectively prices out all non-professional users who don't have money to spend to simply have parity with the $20 subscription on their phones. (and even for those who do, they have to cope with the fact that the model will always be dumber than ChatGPT, and that their hardware will grow outdated very quickly)

Also a noticeable disconnect between the hardware we have and the primary focus of open-source labs (scale up and cater to their enterprise customers, just look at GLM-5's increase to 744B parameters, double from GLM-4.5's 355B). We really just need some kind of Cambrian explosion in cheap hardware for local models to be feasible.

This page without exaggeration reduced my browser to 5 frames per second.

This is a bot

Are you saying you're learning go because you've freed up time elsewhere or is AI helping?

This is an AI slop bot

I would recommend using the ropey crate for easy performance gains. A string buffer is quick to implement but you will hit a wall as soon as you need to edit large files.

I think vote rigging detection might be based on the length of your session

Probably refresh the api models list every couple minutes instead. No one could have guessed the name of GPT-Codex-Spark

Or remove "is"

This got renamed right in front of my eyes