Introduction
brainz isn’t some wannabe chatbot crap. it’s a full-stack, self-learning llm beast for devs who are done playing nice with closed apis. you get raw control over every layer — no middlemen, no throttling, no “terms of use” handcuffs.
this thing is built to train, adapt, and evolve while running, straight from your own data. think:
✅ live fine-tuning on the fly ✅ semantic memory that actually remembers (vector search, top-k retrieval) ✅ autonomous agents that rewrite, retrain, and optimize without you babysitting ✅ one stack: backend, web ui, cli – all talking natively ✅ docker-native setup, runs anywhere – local, private, prod, whatever
built for:
web3 crews hacking custom ai copilots
ml nerds training domain-specific monsters
privacy-maxis who don’t trust anyone’s cloud
infra devs who’d rather grep logs than click dashboards
why brainz?
llm land is broken. closed endpoints, vendor chokeholds, “memory” that resets like goldfish, and no self-learning unless you beg for api credits.
brainz flips that. it’s built to evolve, with:
self-learning feedback loops baked in
full observability at runtime
nothing hidden — you own the stack
what you get
training pipelines: fine-tune any supported model live – user input, logs, or cli. no restart, no bullshit.
inference api: local, filtered, controlled generations.
memory engine: semantic embeddings + similarity search for long-term context.
agent layer: self-healing loops, optimizers, retrainers.
unified interface: fastapi backend, react + vite frontend, cli tools.
full access: postgres for storage, transparent logs, extensible config.
privacy-first: no telemetry, no vendor lock, just your db + containers.
open source. yours.
MIT license. host it, fork it, break it, sell it – whatever. no subscriptions, no quotas, no “pay per token”. just code, containers, compute.
official site: https://brainz.monster
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