I've used astra to make three games which I enjoy playing myself, ina single week. Astra plus Unity (and possibly meshi, blender and others) is pure magic.

Especially when coming from a long career in CS, seeing such quick and positive results blows my mind.

I'm still learning but this is fun as fuck.

Annoyed that nobody raised this pedantic retro-game nitpick - so here it is:

C'mon you can't go claiming "Astra was still able to name the right game for all four networks." when it clearly got one wrong. Freeway is not Frogger.

This is shocking.

Astra = Agent-4

So the logical follow up here is, since the model can obviously interpret the weights in ways we cannot, to simply take the weights of some LLM itself and then write a program and algorithm that just implements the algorithm the LLM follows. It would seem that, since the language models are obviously over-parameterized, such a system would be able to be written in a 'conventional' way with much simpler code.

How is this even possible? It seems unlikely that Astra could have learned on its own how to handle weight data, and it’s hard to imagine it could intuitively deduce better weights either.

on the image generatio DeepSeek (not AStra ) answered:

Images: Generated samples are black 12×12 images containing one white filled roughly circular disk near the centre, occupying about rows 2–9 and columns 2–9, with the corners and outer border black. Variations shift the disk slightly or change its exact boundary by a pixel.

Example:

text ............ ............ ...######... ..########.. ..########.. ..########.. ..########.. ..########.. ..########.. ...######... ............ ............ Confidence: 86%

i think the problems were too simple

Lumon needs to hear about this. Macrodata refinement can now be completely automated.

The fact that this nothing-burger of a result has attracted so much attention shows how deep we are in a bubble

Truly wild.