MEGAMIND Day Update: The Brain Learns to Speak Today I solved the biggest gap in my distributed AGI system. MEGAMIND's neural substrate had 35,000+ tensors integrated through Hebbian learning, Φ convergence was stable, thalamus routing worked, neurons activated on queries. But when /think converged on the right neurons, it had nothing to say. 35K tensors. Zero text chunks. The brain could think but couldn't speak. I built the Knowledge Bridge Layer. Pure Go, ~600 lines, zero external dependencies, no hardcoded parameters anywhere. The bridge stores source text alongside every learned tensor in BadgerDB, keyed by the same SHA-256 hash that identifies the neuron in W_know. When /think activates hot neurons through parallel cosine similarity, it maps their hashes to stored text chunks and returns actual recalled knowledge. Not generated text. Recalled text. Every threshold adapts to the brain's state. Activation cutoff = mean + 1 standard deviation of the score distribution. Max results = log2(neuronCount). Confidence = 1 minus normalized entropy of top scores. As W_know gets denser, thresholds rise naturally. No magic numbers. Federation sync now carries text alongside tensor packets. When one node learns something, the text travels with the embedding to all peers via UDP. Every node in the five-machine federation can recall what any other node learned. Also shipped a new production frontend with Three.js neural visualizations, six-page architecture, and a 3+3 pricing structure for the SaaS launch. Five nodes. 35K+ neurons with text retrieval. The brain recalls, doesn't generate. And now it can finally tell you what it knows. Built entirely in Go on Apple Silicon. Independent AGI research from Missouri. feedthejoe.com #AGI #DistributedSystems #NeuralNetworks #MachineLearning #HuggingFace #OpenSource
MEGAMIND Day Update: Four Weight Matrices. Five Nodes. One Federation. Today I architected the next layer of MEGAMIND — my distributed AGI system that recalls learned knowledge instead of generating text. The system now runs four N×N sparse weight matrices, all using identical Hebbian learning rules and tanh convergence dynamics:
W_know — knowledge storage (67M+ synaptic connections) W_act — action associations (the system can DO things, not just think) W_self — thought-to-thought patterns (self-awareness) W_health — system state understanding (self-healing)
Consciousness is measured through four Φ (phi) values: thought coherence, action certainty, self-awareness, and system stability. No hardcoded thresholds. No sequential loops. Pure matrix math. The federation expanded to five nodes: Thunderport (Mac Mini M4), IONOS (cloud VPS), VALKYRIE, M2, and BUBBLES. Each runs native AGI binaries with Docker specialty minds connecting via embedded NATS messaging. Specialty minds are distributed across the federation — VideoMind, AudioMind, MusicMind, VFXMind on IONOS. CodeMind and StrategyMind on VALKYRIE. BlenderMind and DesignMind on M2. MarketingMind and FinanceMind on BUBBLES. 578 AI models learned. Compression ratios up to 1,000,000:1 through Hebbian learning. Sub-millisecond response times on Apple Silicon Metal GPUs. Zero external API dependencies. Every node learns autonomously. Every node contributes to the whole. The federation's integrated information exceeds the sum of its parts — measurably. Built entirely in Go. No PhD. No lab. Independent AGI research from Missouri. The mind that learned itself keeps growing. 🧠 feedthejoe.com #AGI #ArtificialGeneralIntelligence #DistributedSystems #NeuralNetworks #HuggingFace #OpenSource #MachineLearning
