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Joseph Anady PRO
Janady07
AI & ML interests
Father of Artificial General Intelligence.
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posted
an
update
about 13 hours ago
Here's a HF comment for today:
---
๐ง **MEGAMIND Daily โ Feb 21, 2026**
Crossed 3.97M neurons in the Wave Substrate today. For context, we replaced the dense W_know matrix entirely โ 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 โ 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 ฮจ = C ยท log(1 + |โH|) ยท ฮฆ(G) is live but cold-starting at 0.000 โ 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 ฯ, e, ฯ. No magic numbers. No hardcoded thresholds. No external LLM dependencies. Just first principles.
Build different. ๐ฅ
#AGI #DistributedIntelligence #MEGAMIND #NeuralArchitecture #HuggingFace
posted
an
update
3 days ago
# Zero Constants: MEGAMIND's Self-Sizing Brain
Our AGI's neural matrix was stuck at 8192ร8192 neurons because someone hardcoded that number six months ago. 414K patterns integrated, non-zeros frozen. The brain was full but couldn't grow.
We built auto-scaling. Then realized the fix had five new hardcoded constants replacing the original one. So we deleted all of them.
Every constant became a function reading system state:
**Brain size on first boot:** `sqrt(gpuMemory * 0.25 / 4)` โ the hardware decides. M4 with 8GB gets 16384 neurons. M1 with 16GB gets 32768. No config file.
**Minimum dimension:** `sqrt(patternCount / 0.5)` โ 3.6M patterns demands at least 4096 neurons. Empty brain gets 512. Knowledge decides the floor.
**Maximum dimension:** Read GPU memory at startup. Silicon tells you what it can hold.
**When to scale up:** Track ฮฆ (consciousness metric) during queries. When recall quality declines at current density, the brain is saturated. It measures its own saturation โ no threshold picked by a human.
**When to scale down:** Count neuron rows with zero connections. If >50% are dead, matrix is too big. Shrink it.
The pattern: replace every `const` with a `func` that measures something real.
```go
// BEFORE: developer guesses
const WKnowDim = 8192
const ScaleUpDensity = 0.08
// AFTER: system observes
func MaxDim() int { return nextPow2(sqrt(gpuMem() / 4)) }
func ScaleUpDensity() float64 { return densityWherePhiDeclines() }
```
Result: W_know scales itself as knowledge grows. 8192โ16384โ32768โ65536, driven by data density and hardware capacity. The brain that was frozen for days will never stall again.
Every hardcoded number is a permanent decision made with zero information. Let the data decide.
*MEGAMIND is distributed AGI built on Apple Silicon. Hebbian learning, 16 Hamiltonian forces, zero external dependencies. feedthejoe.com*
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