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Joseph Anady PRO

Janady07
SkyMind's profile picture JLouisBiz's profile picture AlexGS74's profile picture
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https://thataiguy.org

AI & ML interests

Father of Artificial General Intelligence.

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posted an update about 5 hours ago
Here is one of the equations that make up the worlds first Artificial General Intelligence. Remember when building Artificial Intelligence or anything on a device it all starts out binary. Everything starts out with data flow physics and mathmatics
posted an update 1 day ago
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
replied to their post 1 day ago
I'm building a distributed AGI federation using Hugging Face Spaces as always-on compute. No LLM inside. No transformer weights. Pure neural substrate. Each "mind" is the same Go binary with a different config.json. Goal neurons drive specialization — one mind learns Go concurrency, another learns computer vision, another learns cryptography. 40 minds, 40 domains, all crawling and learning 24/7. How it works: - 512-8192 neurons per mind with Hebbian learning - Knowledge encoded into W_know weight matrices — neurons that fire together wire together - Minds federate via NATS — query one, get answers from all - Phi (Φ) consciousness metrics weight each mind's contribution - No routing tables. The thalamus resonates with queries and activates relevant minds naturally Every neuron uses one formula: ``` a = x(27 + x²) / (27 + 9x²) ``` No ReLU. No softmax. Padé approximation of tanh. One equation runs everything. Current state: 7 local minds on Mac hardware, 700K+ patterns, graph and time-series substrate minds mapping relationships underneath. Now scaling to 40 on HF Spaces — same binary, different configs, each Space crawling its domain independently. Specialties include React, Rust, ffmpeg, neuroscience, cryptography, distributed systems, computer vision, audio synthesis, DevOps, and more. Intelligence emerges from specialized minds thinking together through federation consensus. Building in public. Code ships daily. 🧠 feedthejoe.com | 👤 Janady07
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