A note on forkingA practical detail that matters is the process that creates child sandboxes must itself be fork-safe. If you are running an async runtime, forking from a multithreaded process is inherently unsafe because child processes inherit locked mutexes and can corrupt state. The solution is a fork server pattern where you fork a single-threaded launcher process before starting the async runtime, then have the async runtime communicate with the launcher over a Unix socket. The launcher creates children, entirely avoiding the multithreaded fork problem.
I was really surprised that I could beat off-the-shelf providers by a full multiple. From extensive experience working with both Vapi and Elevenlabs agent SDKs on a real production use case, I found that my initial prototype is able to reliably achieve a 2x latency improvement, which is a huge deal when it comes to serving natural-sounding and pleasant voice agent interactions.
。体育直播是该领域的重要参考
Американскому сенатору стало «страшнее, чем когда либо» после брифинга по Ирану02:37
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Москалькова заявила о новых условиях Киева для возвращения россиян с территории Украины14:51。体育直播是该领域的重要参考
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.