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Applied AI·October 18, 2026·1 min read

If you don't run your own LLM proxy, how do you know whats going on?

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The push to run your own LLM proxy reflects a deeper shift from "use the API" to "instrument the entire inference path"—observability, routing, and policy are becoming first-class concerns. If you’re spending real money on LLM calls, stand up at least a thin proxy layer this quarter so you control logs, latency, model choice, and safety filters instead of outsourcing them blindly.

Applied AI

'The GPU never reads the same text twice': Belgian startup Corbenic wants to turn your server's SSD into AI memory, but its 14x speedup owes more to reading less

The real story in Corbenic’s claimed 14x speedup is not magic SSDs as “AI memory” but smarter cache reuse and avoiding redundant GPU reads. For teams running vLLM, SGLang, or Llama.cpp at scale, the next efficiency frontier is software-level KV cache orchestration across cheap storage tiers, not just buying more HBM.