Git packfiles use delta compression, storing only the diff when a 10MB file changes by one line, while the objects table stores each version in full. A file modified 100 times takes about 1GB in Postgres versus maybe 50MB in a packfile. Postgres does TOAST and compress large values, but that’s compressing individual objects in isolation, not delta-compressing across versions the way packfiles do, so the storage overhead is real. A delta-compression layer that periodically repacks objects within Postgres, or offloads large blobs to S3 the way LFS does, is a natural next step. For most repositories it still won’t matter since the median repo is small and disk is cheap, and GitHub’s Spokes system made a similar trade-off years ago, storing three full uncompressed copies of every repository across data centres because redundancy and operational simplicity beat storage efficiency even at hundreds of exabytes.
icon-to-image#As someone who primarily works in Python, what first caught my attention about Rust is the PyO3 crate: a crate that allows accessing Rust code through Python with all the speed and memory benefits that entails while the Python end-user is none-the-wiser. My first exposure to pyo3 was the fast tokenizers in Hugging Face tokenizers, but many popular Python libraries now also use this pattern for speed, including orjson, pydantic, and my favorite polars. If agentic LLMs could now write both performant Rust code and leverage the pyo3 bridge, that would be extremely useful for myself.
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Cosmic rays hit computer RAM all the time. If your RAM is not ECC protected, it will likely flip a random bit. A single bit in billions of bits. Does it matter? Yes. Yes it does.
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