simedw

very good question, but during training it gets the actually characters as input, and then * as what gets rendered, so I think it should understand the difference.

It's only in the models context.

That's a very cool idea, maybe a bit expensive to scale.

Exactly, I wonder if I'm not hitting the right WebGPU path for Firefox?

Thank you.

The base model already have a good grasp of language, so that man pages look like coherent sentences. It also knows python and similar language well, which means that things like 1+5 or print(["hi" for _ in range(5)]) work.

I did try to train from scratch, but it was much weaker (also added a lot of aux losses).

The first part of the context is the window width:

``` BOS SCREEN_START 32 96 ```

I trained it on three different resolutions, but it should kind of work with in between ones too.

Normal only keypresses invoke the next ”frame”. There is a sleep token that the runner could use to indicate that time is moving.

I haven’t run extensive tests yet, but larger models also get better at memory.

Thank for posting it, happy to answer any questions!

It shows a good improvement on my COCO object detection benchmark: 0.504 mAP zero-shot.

Gemini 3 Flash was around 0.403, 2.5 Pro was around 0.34.

For some reason, in almost every test I did with Gemini 3.5 Live, it classified my Swedish (native) as Dutch. There are similarities between the languages, but still.

It’s pretty good at understanding my broken Mandarin, though.

This was neat, well done!

The only alternative version of Go I’ve played before was magnetic Go, where all the rules are the same except that, along the axis lines where you place a piece, like colours attract and opposite colours repel.

On Voronoi, I did feel like the continuous space made it really hard to judge whether there was enough room, whether the opponent could get in, etc. Maybe it would work better if it were discretized a bit? Or maybe I just need to play it more.