How uv got so fast

Python bytecode and startup vs install time

• uv skips .pyc compilation on install; Python compiles on first import instead.
• People note this shifts a one-time cost from install to first run: good for interactive/dev use, bad for environments where images are started many times (Docker, serverless).
• Several comments recommend enabling UV_COMPILE_BYTECODE or equivalent flags when baking containers to avoid cold-start penalties; for large projects the first-import compilation can be hundreds of milliseconds.
• Historical justification for install-time .pyc: system-wide installs where the runtime user can’t write bytecode files.

Standards and ecosystem preconditions

• uv’s design leans heavily on modern packaging PEPs (517/518/621/658, wheels, manylinux).
• Moving away from setup.py to pyproject.toml and static metadata removed the need to execute arbitrary code to discover dependencies, especially build-time ones.
• Adoption has been slow and uneven; some major projects still have incomplete or problematic pyproject.toml setups.

Rust vs architecture

• Many argue uv’s speed mostly comes from design: metadata-only resolution, aggressive wheel-first strategy, HTTP range requests for wheel metadata, global cache, parallel downloads, skipping old formats (.egg), stricter parsing, and not supporting legacy config paths.
• Rust still matters for: single self-contained binary (no Python bootstrap), cheap real threads, zero-copy deserialization via rkyv, and much lower interpreter-startup overhead.
• Debate over “Rust-specific” claims: zero-copy is a systems-language technique generally; what’s hard is doing it in Python without copies and lifetime bugs.

pip, legacy, and greenfield constraints

• pip is seen as hard to modernize: large, old codebase, HTTP-style cache, huge import tree (hundreds of modules, including heavy dependencies like Rich), and deep backward-compat commitments.
• Some pip feature requests (e.g., robust cross-platform resolution) reportedly clash with its current architecture.
• Several comments frame uv’s success as what you get when you start fresh on modern standards, not as a pure “Rust rewrite.”

Version constraints and Python 4

• uv is described as ignoring upper requires-python bounds like <4.0, on the theory they’re defensive guesses, not known incompatibilities, and they massively increase resolver backtracking.
• Some worry this is risky given real breakage between minor 3.x releases; others argue that honoring speculative upper bounds propagates needless constraints through the dependency tree.

Where speed matters

• Biggest wins reported in CI, Docker builds, and large monoliths: installs dropping from minutes to seconds, substantial pipeline time savings.
• Speed also changes workflows: people are more willing to spin up fresh envs or use uv run/uvx ad hoc.

Tooling and security notes

• uv is contrasted with pipenv, poetry, conda, etc.; many see uv as finally making Python “pleasant” for everyday scripting and cross-platform utilities.
• Some caution that uvx’s convenience increases exposure to typo-squatting; it’s no worse than pip install but now happens more often.

Reactions to the article

• Multiple commenters like the technical content but strongly dislike the perceived LLM-edited style: repetitive “it’s X, not Y” constructions, marketing tone, and lack of clear weighting of which optimizations matter most.
• There’s broader worry about AI-shaped prose becoming the default in technical writing, and calls for clearer disclosure when LLMs are used.