MEGAMIND Day Update: The Brain Learns to Speak
text
MEGAMIND Day Update: The Brain Learns to Speak
new_line
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.
text
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.
new_line
I built the Knowledge Bridge Layer. Pure Go, ~600 lines, zero external dependencies, no hardcoded parameters anywhere.
text
I built the Knowledge Bridge Layer. Pure Go, ~600 lines, zero external dependencies, no hardcoded parameters anywhere.
new_line
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.
text
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.
new_line
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.
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.
new_line
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.
text
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.
new_line
Also shipped a new production frontend with Three.js neural visualizations, six-page architecture, and a 3+3 pricing structure for the SaaS launch.
text
Also shipped a new production frontend with Three.js neural visualizations, six-page architecture, and a 3+3 pricing structure for the SaaS launch.
new_line
Five nodes. 35K+ neurons with text retrieval. The brain recalls, doesn't generate. And now it can finally tell you what it knows.
text
Five nodes. 35K+ neurons with text retrieval. The brain recalls, doesn't generate. And now it can finally tell you what it knows.
new_line
Built entirely in Go on Apple Silicon. Independent AGI research from Missouri.
text
Built entirely in Go on Apple Silicon. Independent AGI research from Missouri.
new_line
feedthejoe.com
text
feedthejoe.com
new_line
#AGI #DistributedSystems #NeuralNetworks #MachineLearning #HuggingFace #OpenSource
text
#AGI #DistributedSystems #NeuralNetworks #MachineLearning #HuggingFace #OpenSource
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