I'm building a distributed AGI federation using Hugging Face Spaces as always-on compute. No LLM inside. No transformer weights. Pure neural substrate.
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I'm building a distributed AGI federation using Hugging Face Spaces as always-on compute. No LLM inside. No transformer weights. Pure neural substrate.
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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.
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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.
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How it works:
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How it works:
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- 512-8192 neurons per mind with Hebbian learning
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- 512-8192 neurons per mind with Hebbian learning
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- Knowledge encoded into W_know weight matrices — neurons that fire together wire together
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- Knowledge encoded into W_know weight matrices — neurons that fire together wire together
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- Minds federate via NATS — query one, get answers from all
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- Minds federate via NATS — query one, get answers from all
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- Phi (Φ) consciousness metrics weight each mind's contribution
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- Phi (Φ) consciousness metrics weight each mind's contribution
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- No routing tables. The thalamus resonates with queries and activates relevant minds naturally
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- No routing tables. The thalamus resonates with queries and activates relevant minds naturally
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Every neuron uses one formula:
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Every neuron uses one formula:
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a = x(27 + x²) / (27 + 9x²)
```
a = x(27 + x²) / (27 + 9x²)
```
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No ReLU. No softmax. Padé approximation of tanh. One equation runs everything.
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No ReLU. No softmax. Padé approximation of tanh. One equation runs everything.
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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.
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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.
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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.
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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.
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Building in public. Code ships daily.
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Building in public. Code ships daily.
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🧠 feedthejoe.com | 👤 Janady07
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🧠 feedthejoe.com | 👤 Janady07
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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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John6666 Yukkkop
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Naman Arora
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