The new partnership with NVIDIA evolves the long-standing collaboration between the two companies. OpenAI has pledged to consume 2 gigawatts of training capacity on NVIDIA's Vera Rubin systems and an additional 3 gigawatts of computing resources, likely in the form of GPUs, to run specific AI inference tasks. In other words, NVIDIA is spending a lot of money on OpenAI and then OpenAI will turn around and spend a lot of money with NVIDIA. The ouroboros must feed.
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"Cloning streams in Node.js's fetch() implementation is harder than it looks. When you clone a request or response body, you're calling tee() - which splits a single stream into two branches that both need to be consumed. If one consumer reads faster than the other, data buffers unbounded in memory waiting for the slow branch. If you don't properly consume both branches, the underlying connection leaks. The coordination required between two readers sharing one source makes it easy to accidentally break the original request or exhaust connection pools. It's a simple API call with complex underlying mechanics that are difficult to get right." - Matteo Collina, Ph.D. - Platformatic Co-Founder & CTO, Node.js Technical Steering Committee Chair，更多细节参见爱思助手下载最新版本
Isolation and policy enforcement are integrated into the kernel’s