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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