The proving ground
MEGAMIND is the federation I run on my own hardware
Before I build a private AI federation for anyone else, I run one for myself. MEGAMIND is a distributed AI system spread across a cluster of machines I own, with state shared over my own network and nothing sent to an outside model to think.
A research property, not a product
MEGAMIND is intentionally a research property. I do not sell access to it. I learn from operating it: where federated training breaks, where private inference actually holds up, and what failure modes look like at small scale, long before a client system ever has to face them. That experience is what carries into every federation I design for a client.
Where a client needs fully on premises AI inference, work with strict privacy requirements in medical, financial, or legal settings, the MEGAMIND architecture is the reference point. I have already lived with the failure modes on my own iron, so the deploy starts ahead of the curve instead of guessing.
What is running today
The substrate, in numbers
The architecture, briefly
MEGAMIND runs as a Go binary called MADDIE on every node, and the nodes share state with each other over NATS messaging rather than through any outside service. The W_know substrate is a sparse weight matrix that encodes learned patterns, built from a 256 dimensional intermediate representation expanded to 8192 dimensions inside the substrate. Sparse top K activation, mild decay, and periodic desaturation keep it from saturating as it learns.
Two smaller sub brains run alongside the main substrate: BlenderMind, with 4096 neurons, and FrankMind, also 4096 neurons. Each one trains on a topic specific corpus rather than a general one, which is closer to how a client federation is actually shaped around a single organization's data. See infrastructure for the fleet that hosts all of it.
Why it matters for your build
What MEGAMIND proves before your deploy starts
Full data residency
When compliance means inference cannot leave the building, MEGAMIND is where I already tested that boundary, not where I test it on your system first.
Fine tuning open models
When a client wants a model tuned narrowly to their own vertical instead of a general purpose one, this is the substrate that experience comes from.
Distributed inference
When cost or latency call for spreading inference across more than one machine, the node and messaging pattern here is the one I bring to your architecture.
Honest documentation
The same rigor I hold MEGAMIND to, naming what it does and does not do, is the rigor applied to SEFERIM, my other research property, and to every client system.
Begin
Want the same discipline on your data?
The readiness assessment maps what a federation would look like for your organization, built with the same standards MEGAMIND runs on.