实测LLM提示缓存：相同提示从0%到91%命中率，揭示不同提供商缓存策略的巨大差异。
We run an AI companion bot. Every chat turn, the model sees the same ~5K-token prefix — character persona, content-tier rules, formatting guardrails, …
从Go和Rust的实战对比中，揭示Rust在特定高并发场景下反而成为扩展瓶颈的反直觉真相。
The Problem We Were Actually Solving Our treasure-hunt engine, running on Go 1.21 and a 3-layer micro-service stack, was supposed to scale to 50,000 c…
用少量GPU忠实模拟千卡级LLM训练环境，降低开发调试成本与复杂度。
arXiv:2605.15617v1 Announce Type: cross Abstract: Large language model (LLM) training today runs on clusters spanning thousands of GPUs. While this sc…