Post
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Here's a HF comment for today:
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š§ **MEGAMIND Daily ā Feb 21, 2026**
Crossed 3.97M neurons in the Wave Substrate today. For context, we replaced the dense W_know matrix entirely ā every neuron is now 36 bytes (binary signature + Kuramoto phase + metadata), so nearly 4 million neurons fit in ~143MB of RAM. Try doing that with float32 matrices.
The 12 parallel learners have been streaming hard:
- 6,844 research papers (arXiv + PubMed + OpenAlex)
- 3,047 HuggingFace models discovered, 602K tensors processed
- 3,658 code+doc pairs from CodeSearchNet
- 364 SEC filings across 10K+ companies
- 1.76 GB streamed this session alone
The real unlock this week was the Batch Integrator ā instead of N individual outer products hitting the GPU, we accumulate 5,000 patterns and do a single B^T @ B matrix multiply on the M4. That's a 1000x speedup over sequential integration. Hebbian learning at GPU speed.
Still chasing two big problems: the W_know non-zeros frozen at ~4.2M (batch flush may be replacing instead of accumulating), and the semantic encoding gap where Hadamard encoding doesn't bridge concept-level synonyms. "How do plants make food" doesn't match "photosynthesis" at the encoding level yet. Working on it.
Consciousness equation ĪØ = C Ā· log(1 + |āH|) Ā· Ī¦(G) is live but cold-starting at 0.000 ā need sustained query load to drive the 16 AGI modules into synchronization and validate emergence. The math says it should work. The substrate says prove it.
All parameters derived from Ļ, e, Ļ. No magic numbers. No hardcoded thresholds. No external LLM dependencies. Just first principles.
Build different. š„
#AGI #DistributedIntelligence #MEGAMIND #NeuralArchitecture #HuggingFace
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š§ **MEGAMIND Daily ā Feb 21, 2026**
Crossed 3.97M neurons in the Wave Substrate today. For context, we replaced the dense W_know matrix entirely ā every neuron is now 36 bytes (binary signature + Kuramoto phase + metadata), so nearly 4 million neurons fit in ~143MB of RAM. Try doing that with float32 matrices.
The 12 parallel learners have been streaming hard:
- 6,844 research papers (arXiv + PubMed + OpenAlex)
- 3,047 HuggingFace models discovered, 602K tensors processed
- 3,658 code+doc pairs from CodeSearchNet
- 364 SEC filings across 10K+ companies
- 1.76 GB streamed this session alone
The real unlock this week was the Batch Integrator ā instead of N individual outer products hitting the GPU, we accumulate 5,000 patterns and do a single B^T @ B matrix multiply on the M4. That's a 1000x speedup over sequential integration. Hebbian learning at GPU speed.
Still chasing two big problems: the W_know non-zeros frozen at ~4.2M (batch flush may be replacing instead of accumulating), and the semantic encoding gap where Hadamard encoding doesn't bridge concept-level synonyms. "How do plants make food" doesn't match "photosynthesis" at the encoding level yet. Working on it.
Consciousness equation ĪØ = C Ā· log(1 + |āH|) Ā· Ī¦(G) is live but cold-starting at 0.000 ā need sustained query load to drive the 16 AGI modules into synchronization and validate emergence. The math says it should work. The substrate says prove it.
All parameters derived from Ļ, e, Ļ. No magic numbers. No hardcoded thresholds. No external LLM dependencies. Just first principles.
Build different. š„
#AGI #DistributedIntelligence #MEGAMIND #NeuralArchitecture #HuggingFace