Uv is fantastic, but its package management UX is a mess

Original Article ↗ Hacker News Discussion ↗

Overall sentiment

  • Many see uv as a major improvement for Python packaging: fast, reliable resolution, good env management, “best thing that’s happened to Python in a long time.”
  • Others think the article’s “UX is a mess” framing is too strong / click‑driven, but still agree there are real usability issues.
  • A minority say they don’t feel much benefit over existing tools (pip, poetry, conda) for their workflows and are wary of fanboyism.

CLI & UX criticisms

  • Command layout feels designed for correctness and machines, not humans: key actions split across uv, uv lock, and uv pip, with surprising defaults.
  • Examples:
    • uv lock --upgrade upgrades everything, including majors, which users find risky; there’s no obvious “safe, semver‑bounded update” path.
    • Two different “outdated” views (uv pip list --outdated vs uv tree --outdated) with inconsistent UX.
    • Useful commands (e.g., uv pip show) feel “hidden” and miss information compared to pip.
  • Some users work around this with command runners, aliases, or by editing pyproject.toml directly.

Version bounds, semver, and upgrades

  • Big debate over upper bounds:
    • Pro‑bounds (especially for apps): want protection from breaking majors; see boundless updates as unsafe.
    • Anti‑bounds (especially for libraries): upper bounds frequently cause unsatisfiable trees; Python packages often don’t follow semver anyway.
  • Uv deliberately avoids upper bounds by default; flags and config exist to change this, but discoverability is poor.
  • Many prefer exact pins via lockfiles and use bots (Dependabot/Renovate) plus uv tree --outdated rather than relying on version ranges.

Dependency resolution & performance

  • Uv’s resolver is widely praised: resolves trees pip/poetry struggle with, much faster, benefits from caching and parallel downloads.
  • Some debate how much speed comes from Rust vs newer packaging standards vs dropping legacy features; even a uv maintainer disputes the “it’s just dropped features” narrative.

Workflows & ecosystem fit

  • Some only use uv for env creation, then stick with pip/requirements.txt.
  • Others lean on uv tool/uv run but report friction around auto‑upgrading shared scripts.
  • Python’s inability to load multiple versions of a package in one process underlies many constraints; import‑hook hacks are discussed but seen as brittle.

Scientific & ML packaging

  • Uv doesn’t fix deep issues around heavy compiled deps (PyTorch, CUDA, long‑tail scientific packages).
  • Scientific maintainers note progress (e.g., moving away from Fortran, more C wheels), but acknowledge this area remains painful.