#performance

Embedding pipelines do not feed whole documents straight into the model. They first use the embedding model's tokenizer to measure and split text into token-bounded, overlapping chunks, then embed each chunk and store the vectors. Tokenizer must match the model. Tools like marcelroed/gigatoken accelerate this preparation at corpus scale but do not create embeddings themselves.

[karpathy/autoresearch] is a concept/framework that allows one to leverage LLM to run an optimization loops based on a criteria. davebcn87/pi-autoresearch takes this further has built a generic optimization long run loop method on top of pi agent. gemini

Hono based on web standards is a great option for js runtimes esp. if you plan to deploy your code to cloudflare workers, bun or deno. You get faster startup time and cross-platform compatibility.

how uv got so fast. UV performance is because of design decisions while rust contributes to micro optimizations.