TokenRhythm's bet: train models on lessons from your agent's own runs
PrajwalTomar_ · x · 2026-09-16
The author highlights a common pain point: an agent can find a bug, try three bad fixes, and finally solve it—yet make the same mistake next week. The chat remembers; the model itself doesn't get smarter, wasting useful work.
The proposed stack has three parts: OpenSquilla runs jobs, the TokenRhythm API connects them to different models, and NeoHorse-1 is trained using lessons distilled from those agent runs. The last part is the big bet: turning agent work into real model-level improvement.
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