Uv is the best thing to happen to the Python ecosystem in a decade

Role of uv vs existing tools

• Many see uv as the “npm/cargo/bundler” Python never had: one fast, unified tool instead of pip + venv + pyenv + pipx + poetry/pipenv.
• Others argue the same concepts existed (poetry, pip-tools, pipenv, conda) and uv is mainly a better implementation with superior ergonomics and performance.
• Some prefer minimalism (plain python -m venv + pip) and feel uv mostly repackages workflows they never found painful.

Perceived benefits

• Speed is repeatedly called out: dependency resolution, installation, and reuse from cache feel 10–100x faster than pip/conda/poetry.
• “Batteries-included” workflow: uv init / add / sync / run handles Python version, venv, locking, and execution without manual activation.
• Inline script metadata (PEP 723) + uv run makes single-file scripts self-contained and shareable without explicit setup.
• Good fit for beginners, non-engineers, and “I don’t want to think about environments” users; reduces the biannual “debug Python env day.”
• For some, uv finally makes Python pleasant again compared to ecosystems with strong tooling (Node, Rust, Ruby).

Security & installation debates

• Strong pushback on curl | sh / iwr | iex install instructions: seen as unsafe, unauditable, and bad practice in 2025.
• Counter-arguments: installing unsigned .deb/.rpm is not inherently safer; trust in source matters either way; scripts can be downloaded and inspected.
• Similar concern about scripts that auto-install dependencies at runtime: convenient but expands the attack surface unless constrained to trusted indexes/mirrors.

Limits, pain points, and skepticism

• Some report uv failing where plain venv+pip worked, and note it’s still young with rough edges.
• Complaints: “does too many things,” confusion around new env vars, perceived friction with Docker, lack of global/shell auto-activation, project-centric mindset vs “sandbox” global envs.
• A few hit specific bugs (e.g., resolving local wheels, exotic dependency constraints) and still keep poetry or pip+venv.

Conda, CUDA, and non-Python deps

• Consensus: uv is excellent for pure-Python; conda (or pixi, which uses uv under the hood) still wins for complex native stacks (CUDA, MPI, C/C++ toolchains, cross-OS binary compatibility).
• Some hope uv (or pixi+uv) will eventually reduce reliance on conda, especially in ML/scientific environments, but that’s not solved yet.

Ecosystem, governance, and fragmentation

• Debate over a VC-backed company steering core tooling: some see risk of future “Broadcom moment,” others point to MIT licensing and forking as safety valves.
• Harsh criticism of PyPA’s historic decisions and the long-standing packaging “garbage fire”; uv (and Ruff) are seen as proof that fast Rust-based tools can reset expectations.
• Fragmentation (pip, poetry, conda, uv, pixi, etc.) is still viewed as a barrier for newcomers, even if uv is emerging as a de facto standard for many.