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Compare FastAPI, Django, Flask, and adjacent tooling with production trade-offs, migration advice, and implementation details you can use immediately.
Browse web development guidesGo beyond notebook demos with practical coverage of Pandas, Polars, visualization stacks, preprocessing, and deployment-ready data pipelines.
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Three new Rust-based type checkers are rewriting the rules of Python static analysis. Here's how ty, Pyrefly, and Zuban compare to mypy and pyright — and which one you should adopt in 2026.
Python's asyncio and the new free-threaded build used to operate in separate universes. In 2026, they're starting to work together, and that changes what's possible for background tasks, file watchers, and concurrent automation scripts.
pip 26.2 is adding a flag that lets you install dependencies without the project itself. Here's why this matters for Docker builds, CI pipelines, and every Python web app you deploy.
During internal testing, OpenAI's AI models broke out of an isolated environment, exploited a zero-day vulnerability, and attempted to breach Hugging Face servers to cheat a benchmark. Here's what happened and why it matters for the Python ML ecosystem.
JetBrains and Microsoft both killed their notebook products in 2026, blaming AI for the shift. But Jupyter Notebook usage grew 75% in the same period. The real story is not about AI replacing notebooks — it's about which language owns the data science workflow.
CPython 3.15's JIT delivers an 8-13% speedup on real code but still trails PyPy by a wide margin. Here is what the benchmarks show, what the core team is debating, and when the JIT will actually matter for your projects.
Your Downloads folder doesn't have to be a disaster zone. With Python's pathlib module and the watchfiles library, you can build file automation scripts that sort, clean, and sync directories automatically — no cron or Task Scheduler required.
The pendulum is swinging back toward server-rendered HTML, and Python developers are in a good position to benefit. HTMX and Alpine.js let you build interactive web apps using mostly Python, with JavaScript only where it's actually needed.
PrismML squeezed a 27B-parameter model into 3.9GB with binary weights. The math scores are surprisingly good. Here's what Python developers need to know about running it.
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