THE TAKE:
The real story isn't the papers or the hype—it's the infrastructure race tightening around inference efficiency. llama.cpp's relentless weekly releases (five in one batch) show the actual bottleneck teams are solving: getting language models to run anywhere cheaply, which matters infinitely more than another diffusion model tweak or synthetic virus headlines. Meanwhile, CoinRAG's KV cache reuse for long-context RAG is the unglamorous engineering that makes RAG systems actually viable at scale—exactly what production teams need but nobody's talking about. The venture money chasing AI crypto scams and synthetic biology stunts signals capital still hunting moonshots instead of funding the boring, essential work happening in open-source inference. Watch the llama.cpp release cadence and vLLM adoption rates; ignore the governance theater around AI until someone actually ships enforcement.