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Notice how the highlighted region shrinks at each step. The algorithm never examines points outside the narrowing window. In a balanced tree with nnn points, this takes about log⁡4(n)\log_4(n)log4​(n) steps. For a million points, that's roughly 10 steps instead of a million comparisons.

The Web streams spec requires promise creation at numerous points — often in hot paths and often invisible to users. Each read() call doesn't just return a promise; internally, the implementation creates additional promises for queue management, pull() coordination, and backpressure signaling.

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However, due to modern LLM postraining paradigms, it’s entirely possible that newer LLMs are specifically RLHF-trained to write better code in Rust despite its relative scarcity. I ran more experiments with Opus 4.5 and using LLMs in Rust on some fun pet projects, and my results were far better than I expected. Here are four such projects:

The BBC spoke with spectators who walked alongside the monks for the last leg of their trek, from Capitol Hill to the Lincoln Memorial.

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