NeuronAI is a controlled, autonomous multi-troop AI for commodity Linux hardware — no GPU, no cloud. A commander deploys the right specialist model for each mission, grounds answers in your own knowledge, remembers across sessions, and improves with every task — all under governance you control.
Built end-to-end on a Sacred Layer of identity and rules above a working runtime.
A CEO-style commander auto-selects and deploys the best-fit specialist “troop” — each its own local model — for the mission. A second, always-warm reasoner gives an independent opinion.
A neural memory and knowledge graph — remember, recall, link and explore — plus cross-session memory and decision logs. Context that compounds, entirely on-device.
A lessons-and-feedback loop captures what worked and feeds it back; per-troop after-action reviews make the next run better than the last.
TurboQuant compression runs capable models inside a laptop’s memory — benchmarked, estimated and validated — so commodity hardware punches above its weight.
Red-team challenges, approval gates, an audit trail, policies and a taxonomy sit under a Sacred Layer of fixed identity and golden rules — autonomy with a leash.
Git, Postgres, SQLite, the filesystem, the shell, Kubernetes and HTTP — NeuronAI does real dev and ops work, not just conversation. It profiles the hardware and picks the right model for the job.
Retrieval-augmented generation over an offline corpus, with each persona paired to its own scoped knowledge — specialists that actually know their field, anchored to your documents.
A daily brief and an operator cockpit; project goals, progress and next actions are tracked so the system stays accountable to the mission.
On CPU-only hardware, a “fast” query took ~90 seconds. The obvious suspect is token generation — and it’s the wrong place to look.