The slightly broken output of opencode deepseek v4.1 flash used via Codex-router has been fixed, and it…
The slightly broken output of opencode deepseek v4.1 flash used via Codex-router has been fixed, and it is now fully upward compatible with gpt-5.6 luna, which is exciting. This speed is addictive. 200-300 tokens/sec.
Andrej karpathy could have charged $2,000 for this course.
He put it on YouTube. The full training stack. Tokenization. Neural network internals. Hallucinations. Tool use. Reinforcement learning. RLHF. DeepSeek. AlphaGo. 3 hours of the most comprehensive LLM education that
284B-class DeepSeek-V4.
Two 24GB 3090s. 18.26 tokens/s decode. The routed experts live in system RAM. The GPUs keep attention. That is the author’s own `llama-sweep-bench` on a hybrid `--cpu-moe` box — not an H100 rack, not a Discord screenshot. 🆕 ik_llama.cpp
DeepSeek-V4.1-Flash's performance metrics suggest superior reasoning and efficiency
But the "psyop" narrative ignores the technical rigor of open-source contributions. The real panic should be about the potential for misuse, not the model itself.
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