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从结果来看,开源大大降低了开发者的使用门槛,这也是Kimi能够快速接住这一波OpenClaw开发流量的主要原因,关键时候,Kimi能在市场上冲得更快。

The code runs as a standard Linux process. Seccomp acts as a strict allowlist filter, reducing the set of permitted system calls. However, any allowed syscall still executes directly against the shared host kernel. Once a syscall is permitted, the kernel code processing that request is the exact same code used by the host and every other container. The failure mode here is that a vulnerability in an allowed syscall lets the code compromise the host kernel, bypassing the namespace boundaries.

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It’s Not AI Psychosis If It Works#Before I wrote my blog post about how I use LLMs, I wrote a tongue-in-cheek blog post titled Can LLMs write better code if you keep asking them to “write better code”? which is exactly as the name suggests. It was an experiment to determine how LLMs interpret the ambiguous command “write better code”: in this case, it was to prioritize making the code more convoluted with more helpful features, but if instead given commands to optimize the code, it did make the code faster successfully albeit at the cost of significant readability. In software engineering, one of the greatest sins is premature optimization, where you sacrifice code readability and thus maintainability to chase performance gains that slow down development time and may not be worth it. Buuuuuuut with agentic coding, we implicitly accept that our interpretation of the code is fuzzy: could agents iteratively applying optimizations for the sole purpose of minimizing benchmark runtime — and therefore faster code in typical use cases if said benchmarks are representative — now actually be a good idea? People complain about how AI-generated code is slow, but if AI can now reliably generate fast code, that changes the debate.

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