aziis98

It is absurd how this kind of anticipated ssd streaming

I started doing a rewatch a few weeks ago xD

Seeing it now the show also has some pretty fun foreshadowing of llm technology like an episode where they anticipate the idea of "ssd streaming" (running a model slower when you don't have enough memory to fit it all at once), at least this is how I interpreted it to make the connection.

And there are many others connections that now can be made thinking about llms vs the machine

Oh and they also anticipated the openai incident where models started using random stuff as external persistent memory across "sessions"

Ok the custom rich text latex editor is really cool. But I'm on a tablet and stuff like selecting text is a bit awkward (the up down auto scroll is not exactly the same as the native one and isn't as precise as the native one).

But still this is an awesome project!

Exactly the same for me! I love it for the terminal especially

Just tried this on my Intel Ultra 7 255H, I also only have an iGPU. This does ~22tps! Love this.

I just had to do a little patch to support my iGPU device that is a bit newer than Intel Xe-LP, maybe I'll do a PR.

On a side note the other day I was experimenting with Sonnet 5.5. I gave it the llama cpp repo and told it to extract in a single file inference for a single model + backend (qwen3.5 4b mtp + sycl) and (after a long time) it actually worked! It produced a ~1400 lines file with no deps. I need to check the quality of inference yet but I think this is still a great achievement.

I'm pretty sure 2027 will be a very interesting year for local models and inference.

When for the training part you have to consider brains had like billions of years to develop. Maybe one of the reasons llms seem to be so expensive to train is because we are "compressing" in far less time that learning part

For me, at least for agentic use, you need at least ~40tps. Less might be good only for tasks you could run in the background (like at night maybe).

Instead of coding agents like tool for local models I would like to see more "docker ps" like tools where you can queue up tasks that get processes incrementally (maybe when the pc i idle) and are specialized for doing retries and caching as much work as possible to work decently with slow local models.

Is this GUI only?

Love it!

I also did some experiments some time ago. The thing this is missing for me is the ability to also run arbitrary commands other that just using a few premade apps. In fact I think this stuff becomes really interesting when you put a real "shell" on top of this.

And I don't mean a classical posix shell, something that can be used to leverage the full power of the custom ui and frontend. Also a must have is "nestable connections".

The experiment I was doing was with a web interface and a statically compiled Go backend (for easy deployment via ssh). Maybe some day I will finish it xD

Wow, awesome project! I gave a look a bit at the code (before realizing this was made with coding agents as said below on the website) and this seems really well made.

Still, I would really like to know how you approached this from an architecture perspective. I'm also curious to how much coding agents structured the code like so by themselves or if you had to steer them heavily (I've just tried a bit gemini flash from antigravity cli so I'm a bit behind on this).

Also how did you approach the rendering correspondence with actual tikz code? Do you have some tests that like render the tikz using latex and your js pipeline and compare the result for differences?