Meta's Rust-based Python type checker, Pyrefly, boosts speed and efficiency

I built a Python automation engine that can hit 11ms reaction times. ⚡ To put that in perspective, the average human reaction time is about 250ms. Macro Studio is responding to screen changes before a human brain could even register that something happened. If you've ever tried building a high-speed desktop bot in Python, you know the struggle. Standard libraries are great for simple tasks, but the moment you try to scale up, time.sleep() bottlenecks your logic, and managing OS threads usually ends up freezing your UI thanks to the Global Interpreter Lock (GIL). I wanted an engine that actually respected Python's capabilities while providing ultimate control, so I built it myself. I’m excited to share Macro Studio. It’s a free, open-source automation framework I developed that bridges the gap between simple macro recorders and complex software development. Instead of heavy OS threads, it uses a cooperative multitasking yield architecture and direct GDI polling. The result? High-frequency pixel retrieval that easily averages superhuman reaction times on the Human Benchmark, while keeping the UI and subsequent tasks completely responsive. Because it runs pure Python, the possibilities are infinite. You can hook in OpenCV, ping external APIs, or integrate LLMs directly into your automation loop. Check out the full showcase below demonstrating how it flawlessly navigates complex game environments (like Roblox). Building this architecture from the ground up has been an incredible month-long side project. I'm keeping it 100% free and open-source under GPLv3 for anyone who wants to build high-performance macros without the paywalls. I've dropped a link to the YouTube showcase, the GitHub repository, and the documentation in the comments below. 👇 Drop a star, build something ridiculous, and let me know what you think of the architecture! #Python #SoftwareDevelopment #OpenSource #Automation #ComputerScience

When someone new joins a Python codebase, their first PR tells you everything: single-letter variables, inconsistent imports, functions that return str | None | dict depending on the mood of the day. We have onboarding docs for this, but tooling is what actually enforces it. My default move is three things: Ruff, Pyright, and pre-commit. Ruff with format + lint, fix-on-save, and import sorting. They hit save and the file snaps into shape, which alone kills most style discussions before they ever reach a PR. Then Pyright via Pylance in standard mode. Not strict, because strict in Python is counterproductive unless the whole codebase is designed for it. Standard catches wrong argument types, None-access, and missing returns, the stuff that otherwise shows up as runtime bugs or reviewer nitpicks. Once people see type issues flagged while they're still typing, something clicks and they stop shipping avoidable bugs. That peace of mind is something I never want to go back from. Pre-commit makes all of it non-optional. The same checks run for everyone, every time, before the code even gets pushed. It turns "we should all follow the coding guides" into "you literally can't commit without following them." That's the difference between a convention and a guarantee. With this, reviews actually focus on correctness, design, and edge cases instead of formatting and obvious type errors. You're moving the stuff humans shouldn't be wasting time on to machines that do it better and faster, and you're doing it at the cheapest possible point, before it ever hits a PR. I remember when I first set this up for myself and it felt like someone turned the lights on. I want that for everyone joining the team. What's the first thing you set up when onboarding someone new? #Python #SoftwareEngineering #CodeQuality #DeveloperExperience #DevTools

Your build server is running out of space. You run df -h. 94% full. Great. So you du -sh your way through directories like it's 2005, mentally adding up numbers, until you finally find the 6 .venv folders nobody cleaned up. There's a better way — and building it yourself is half the point. In my latest article, I walk through building pydusk: a terminal disk usage analyzer in Python, inspired by ncdu. Keyboard-driven, non-blocking, with a delete confirmation flow and a clean TUI — all in a single file with two dependencies. Stack: Textual · Typer · os.scandir Things worth stealing from this project even if you never run it: → Why os.scandir beats os.walk for disk traversal → Textual's @work(thread=True) pattern for background tasks → ModalScreen[T] + dismiss() for confirmation dialogs https://lnkd.in/dmKvDSgH #Python #Textual #CLI #OpenSource #Developer

Stop writing try/finally blocks. Use context managers instead. I see this pattern everywhere in Python codebases: connection = get_db_connection() try: result = connection.execute(query) finally: connection.close() Nothing wrong with it — until you have 15 resources to manage across your project. Here's the cleaner version with contextlib: from contextlib import contextmanager @contextmanager def db_connection(): conn = get_db_connection() try: yield conn finally: conn.close() with db_connection() as conn: result = conn.execute(query) Why this is better: → Resource cleanup logic lives in ONE place → You can't forget to close — it's automatic → Stack multiple with a single `with` statement → Works with async too (`@asynccontextmanager`) My favorite use case: temporary environment changes in tests. @contextmanager def override_settings(**kwargs): old = {k: getattr(settings, k) for k in kwargs} for k, v in kwargs.items(): setattr(settings, k, v) try: yield finally: for k, v in old.items(): setattr(settings, k, v) Small abstraction. Massive reduction in bugs. What's your favorite Python pattern that most people underuse? 👇 #Python #Programming #BackendDevelopment #SoftwareEngineering #CleanCode

Machine Learning Data Visualization using Pyecharts #machinelearning #datascience #datavisualization #pyecharts Pyecharts is a Python library, serving as a class library that allows you to effortlessly generate interactive and visually compelling charts using ECharts, an open-source data visualization JavaScript library developed by Baidu. With Pyecharts, users can easily create dynamic and customizable charts for data visualization in web applications or Jupyter notebooks. The Pyecharts library uses Echarts to generate charts. Pyecharts library provides the interface between Python and Echarts. The Pyecharts work usually like we use visualization library in Python or R in our Jupyter Notebook. Pyecharts have flexible configuration options, so you can easily match the desired chart you want to make. Detailed documentation and samples to help developers get started the faster project. This Pyecharts library supports the chained calls and can easily integrate them into Flask, Django Web framework, and other mainstream. https://lnkd.in/g2VzqRJm

IO Ninja and Python Can Jam Together. As you know, IO Ninja excels as a UI debugger for serial, network, USB, and all other forms of communication. It offers a slick, polished user interface, a beautiful and lightning-fast logging engine, a sophisticated hex packet editor with packet templates, regex-based data markup, and many other powerful features. Read more via Tibbo: https://lnkd.in/dxyzDyft

Astral is quietly doing to Python tooling what nobody else had the guts to do: rewrite everything in Rust and make the old tools feel embarrassing. Three tools. Same playbook. Absurd results. uv killed pip, poetry, pipx, and pyenv. One tool. Installs packages 10-100x faster. Manages Python versions too. If you're still using pip in 2026, I genuinely want to know why. And they ship a first-class Docker image too: docker run -it --rm astral/uv:bookworm-slim uv --version No manual installs. No version mismatches. Just works. Ruff replaced flake8, isort, and Black. Lints and formats your entire codebase in milliseconds. FastAPI, Airflow, and half the major Python projects adopted it within months of launch. It now has 100k+ GitHub stars. And now there's ty. A type checker to replace mypy and Pyright. Written in Rust. Still in beta but already: - 10-60x faster than mypy - 500x faster incremental updates than Pyright (4.7ms vs 2.38s on PyTorch) - Full language server with autocomplete, go-to-definition, auto-import One user checked a large codebase in 0.5 seconds. mypy took 18 seconds on the same codebase. The pattern Astral runs is simple: find a slow Python tool, rebuild it in Rust, ship compatibility, iterate fast on accuracy. They've done it three times now. It keeps working. For backend engineers writing Python services this isn't just a quality of life upgrade. Faster type checking in CI means shorter feedback loops. Faster linting means pre-commit hooks people actually leave enabled. That adds up. https://astral.sh #Python #SoftwareEngineering #BackendEngineering #DeveloperTools #Rust #OpenSource #PythonDev #Docker

Two days ago I wrote about how Astral was embarrassing legacy Python tooling with Rust rewrites. Today OpenAI announced they’re acquiring them. And it’s not just a talent grab. OpenAI is betting that the future of AI development runs on Python infrastructure that doesn’t waste developer time waiting for slow tooling. Think about it: if you’re building AI systems, you’re living in Python. You’re running type checks on massive codebases. You’re linting thousands of lines before every commit. You’re installing dependencies constantly. Astral made all of that 10-100x faster. Now OpenAI is acquiring the tooling that makes their core development workflow faster. But here’s what makes this interesting: Anthropic acquired Bun back in December. Two of the biggest AI labs. Two runtime/tooling acquisitions. Two completely different bets. OpenAI is betting on Python infrastructure. The language AI researchers already use. Make it faster, smoother, less painful. Anthropic bet on JavaScript/TypeScript infrastructure. The language the web runs on. The ecosystem where AI applications actually ship to users. OpenAI optimizes the research loop. Anthropic optimizes the deployment loop. Both make sense. Both signal something bigger: the tooling layer matters now. AI labs aren’t just building models anymore. They’re building the entire development stack around them. The compilers, the runtimes, the package managers, the type checkers. Whoever controls the tooling controls the developer experience. Whoever controls the developer experience shapes what gets built. This isn’t about Python vs JavaScript. It’s about two different theories of where AI development friction lives and how to eliminate it. We’ll see which bet pays off. Previous post on Astral: https://lnkd.in/gRys5FNW OpenAI announcement: https://lnkd.in/g-Sf3fSH Anthropic announcement: https://lnkd.in/gaEdxsTi #AI #Python #JavaScript #OpenAI #Anthropic #DeveloperTools #SoftwareEngineering #Rust #Bun #Astral

agentpad is live. A multi-language runtime for AI agents that actually runs your code — bash, Python, JavaScript, SQL - against a real project directory. Not a sandbox pretending to be one. Most agent runtimes fake the environment. agentpad doesn't. You get full control over what the agent can touch, what it can write, and exactly what happened when it's done. > Overlay mode to stage changes before they stick. > Read-only mode when writes aren't allowed. > Glob allowlists and timeouts so nothing runs wild. > Structured output every time - stdout, stderr, exitCode, files written. > A full session run log so nothing is a black box. > OpenAI tool helpers so you're not rewriting the function-calling loop from scratch. Just run npm install agentpad Or pip install agentpad Website/Docs: https://slaps.dev/agentpad #openSource #aiAgents #developerTools #typescript #python

When I was building agents-js and Slapify, the browser side was only half the story. The other half was always: run this in the repo - a script, a test, a SQL check, a one-off patch - and get back something structured I could log, replay, or hand to the next step. I didn't want another bespoke subprocess wrapper per project. agentpad is that layer. Bash, Python, Node, SQL against a real working tree, with timeouts, glob allowlists, and a clear result object every time. Same idea in TypeScript and Python, because agents don't care which language your stack speaks. The piece I reach for most is overlay mode - full temp copy of the directory, agent edits there, then apply or discard. It made "let the model touch files" feel survivable in production, not reckless. If you're wiring agents, CI, or internal copilots on top of real codebases - try it and tell me what breaks. Github: https://lnkd.in/gi4Ccqb2 Website: https://lnkd.in/grJXTicc #openSource #aiAgents #developerTools #typescript #python