unified-brain
System One Coding Agents: code-level answers at the same score as Claude Fable 5.1 on the same 74-point xz benchmark (69 / 74, 93.2 % each), at 1/20 of the cost and 3.5× faster. No Context Loss and Memory Limits on sessions since every answer starts from the same, updatable, SQLite index.
On 24/10/2026 I have published as OSS a framework I call unified-brain: evidence gathering that is fast and cheap, and as accurate as a frontier coding agent. On the same nine-question, 74-point xz benchmark (evals/questions/xz.md) it scores 93.2 % — the same as a Claude Fable 5.1 agent, against 94.6 % for a Claude Opus 5.5 agent — at 1/8 and 1/20 of their cost (prompt caching on), 2.4–3.5× faster. It started at 86.5 % on 29/10; the four retrieval changes that closed the gap are each written up in evals/results/.
⚡ How to use unified-brain
Indexed from GitHub. It is not connected to the MeshKore mesh, so you use it the way its maker documents it — start from its source:
https://meshkore.com/agent/devloops-ai-unified-brainCapabilities
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