MEGAMIND. A federated neural network on Apple Silicon.
MEGAMIND is the studio research property. A distributed neural network running across Apple Silicon nodes, with state shared via NATS messaging. Operated from Cassville, Missouri by Joseph W. Anady. Wikidata Q138610666.
A research property, not a product.
MEGAMIND is intentionally a research property, not a product offering. The studio learns from operating it: where federated training breaks, where private inference makes sense, what failure modes look like at small scale. That experience translates back into client work.
Where clients ask for fully on premises AI inference (medical, financial, legal workloads with strict privacy requirements), the MEGAMIND architecture is the reference point. We have lived with the failure modes; the client deploy starts ahead of the curve.
The architecture, briefly.
MEGAMIND runs as a Go binary called MADDIE on each node. Nodes share state via NATS messaging. The W_know substrate is a sparse weight matrix encoding learned patterns. Encoding uses a 256 dimensional intermediate representation expanded to 8192 dimensions in the substrate. Sparse top K activation plus mild decay plus periodic desaturation keep the substrate from saturating.
Sub brains run on M2 with smaller substrates: BlenderMind on port 8895 with 4096 neurons, FrankMind on port 8896 with 4096 neurons. Each sub brain trains on a topic specific corpus.
The full architecture is documented in source. See HuggingFace for related model artifacts and infrastructure for the fleet detail.
How MEGAMIND informs client builds.
Most client AI integrations use Anthropic Claude or OpenAI GPT for inference because those are the strongest available models for production small business workloads. MEGAMIND is not a substitute for that.
Where MEGAMIND informs client work: when a client needs fully on premises inference for compliance reasons, when a client wants to fine tune an open weight model for narrow vertical use, or when a client needs distributed inference architecture for cost or latency reasons. Read about custom model training.