The OpenAI all-hands came just after President Trump announced that the federal government will stop working with Anthropic, in a dramatic escalation of the government’s clash with the company over its AI models.
None of this is wrong. These guarantees matter in the browser where streams cross security boundaries, where cancellation semantics need to be airtight, where you do not control both ends of a pipe. But on the server, when you are piping React Server Components through three transforms at 1KB chunks, the cost adds up.
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After OpenAI released GPT-5.3-Codex (high) which performed substantially better and faster at these types of tasks than GPT-5.2-Codex, I asked Codex to write a UMAP implementation from scratch in Rust, which at a glance seemed to work and gave reasonable results. I also instructed it to create benchmarks that test a wide variety of representative input matrix sizes. Rust has a popular benchmarking crate in criterion, which outputs the benchmark results in an easy-to-read format, which, most importantly, agents can easily parse.
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Players can also rearrange and shuffle the board to make spotting connections easier. Additionally, each group is color-coded with yellow being the easiest, followed by green, blue, and purple. Like Wordle, you can share the results with your friends on social media.