Notes on random ideals, deep work, measurement , inspiring lessons during ML/LLM, diffusion project journeys. Almost like deep algorithmic work and system design work which i sure would forget in a few months. Will want to write other casual things too, soon.
elsewhere — github , portfolio , linkedin
around here lately
manhwa, chung myung, frieren , kim dokja , dota 2, crimson desert, skyrim, *genshin
streaming notes: async def, threadpools, and bridging a synchronous pipeline into SSE
a question answering service sat on a blank spinner for a minute or more per query, and the fix went through four changes that were all really about one seam between synchronous work and an async framework
reranker evaluation notes: cross-encoder vs BM25 on a financial-filing RAG pipeline
a cross-encoder and BM25 both went into a real financial-filing RAG system, and the project's own investigation found the reranker made results worse, for reasons that turned out to be about pruning, not the model
embedding model notes: comparing providers and catching non-determinism without a baseline
five times I measured something that refused to hold still, and the only thing that saved me was measuring something else beside it
container cost notes: splitting cold start, import, and cache costs out of one measurement
three times a single reading hid the cost that actually mattered, and each time the fix was to measure again from somewhere else
multi-turn chat notes: why a stateless API has to resend the whole conversation every turn
multi-turn feels native to build because it is list.append. every genuinely hard part of it lives somewhere else.
polars learning notes: lazy scanning, streaming collect, and a 39GB read that crashed
thirteen things that actually stuck from three practice notebooks, and the eager read that undid all of them at the one file where it mattered
