Write small neural network weights from a spec without training

Reddit user u/Hephaestite, Matthew Gribben, asked Fable 5.1 to set every weight of a neural network directly from a plain-language spec, starting from zero with no training data and no optimiser. They tried it on two unrelated jobs. The first was a 2.1 million parameter transformer that labels each tool call in a coding agent's log as productive, recovering, stalled or oscillating. The second was a 3,057 parameter network that tells a ray marcher how much light reaches each point inside a cumulus cloud. Results and an interactive demo are on the author's blog.

The loop detector scored 0.957 on a hidden test set and 0.908 on logs from a separately written generator, with false alarms on 6.8% of healthy windows; the same architecture trained the normal way on about 224,000 labels gave false alarms on 34.5%. The cloud network came within 13.8% of path-traced luminance, against 82.4% for a single-scattering baseline. Most of its weights were zero or a physics formula, so it compiled to 116 terms of shader code and runs at 60 fps in a browser. Costs were $64.62 and $75.77 at API rates, though the author used a subscription.

Commenters pointed out the idea already has a name, weight-space programming, and a prior paper; the author said they were inspired by it and test a different domain. The post does not say which harness ran the model.