DeepSeek Just CRUSHED Big Tech Again: MHC – Better Way To Do AI

DeepSeek just challenged a ten-year-old assumption in AI design. Instead of scaling models by piling on more layers, parameters, or data, they introduced a new way to scale how information flows inside a model. In this video, we break down DeepSeek’s Manifold-Constrained Hyper-Connections (mHC), why earlier attempts failed, and how this approach delivers real reasoning gains without blowing up training cost or hardware.

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🧠 What You’ll See
•⁠ ⁠Why residual connections became the backbone of modern AI models
•⁠ ⁠How Hyper-Connections tried to widen information flow — and why they failed
•⁠ ⁠What Manifold-Constrained Hyper-Connections (mHC) actually change
•⁠ ⁠How DeepSeek stabilizes multi-stream architectures using mathematical constraints
•⁠ ⁠Real benchmark gains in reasoning, math, and general knowledge tasks
•⁠ ⁠How DeepSeek scaled internal capacity by four times with only ~6–7% training overhead
•⁠ ⁠Why this opens a new scaling path beyond “bigger models, more data”

🚨 Why It Matters
AI progress is slowing along traditional scaling paths. Compute is expensive, advanced chips are scarce, and simply making models bigger delivers diminishing returns. DeepSeek’s mHC introduces a different dimension of scaling — widening internal information flow while preserving stability.

#ai #deepseek

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