DeepSeek Leaks MODEL1: New Flagship AI Shocks The Industry

DeepSeek may have just exposed its next flagship model on GitHub — and the signals point to something far bigger than a routine upgrade. At the same time, Zhipu AI officially released GLM-4.7-Flash, a long-context MoE model built for real coding and reasoning that people can actually deploy. Japan unveiled published research on emotion-computing AI that models how feelings form from body signals. And Nous Research dropped NousCoder-14B, a competitive-programming model trained by executing code and rewarding only correct solutions. Together, these releases show where AI is heading next: deeper architectures, longer context, real execution feedback, and models moving closer to real-world reasoning.

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🧠 What You’ll See
0:00 Intro
0:44 DeepSeek V4 found in GitHub code
1:51 New architecture hints: KV cache, sparsity, FP8
3:29 mHC & Engram research integration
4:04 Mid-February release expected
5:12 31B MoE model for coding & reasoning
6:55 Context length
7:12 Benchmark comparisons
8:51 Emotion computation from body signals
10:09 mMLDA model learns without labels
11:15 75% match with human emotions
12:01 NousCoder-14B release
12:23 Reinforcement learning with code execution
13:35 Code execution reward/punishment system

🚨 Why It Matters
AI progress is shifting from bigger chatbots to systems that reason, remember, execute, and interact with the real world. DeepSeek hints at a redesigned flagship architecture. Zhipu pushes long-context coding into practical deployment. Japan shows emotions can be modeled as computation. And NousCoder proves execution-based learning works at scale.

#AI #DeepSeek #Zhipu