Introducing CEL-expr-python: Secure Python Implementation of Common Expression Language

Are you seeking a more powerful, secure, and efficient way to manage dynamic expressions and policies within your Python projects? We are thrilled to announce the open-source release of CEL-expr-python, a native Python implementation of the Common Expression Language (CEL). CEL is renowned for its simplicity, speed, safety, and portability, making it an ideal solution for applications requiring decision-making, data validation, or rule enforcement. This new implementation, maintained by the CEL team, provides a robust Python API, built upon the stable C++ core, ensuring seamless integration and immediate access to the latest features and optimizations. CEL-expr-python is designed to empower Python developers working with dynamic expressions, policy enforcement, and data validation. If your work involves evaluating expressions loaded from external sources, enforcing clear and secure policies, or validating data against predefined rules, then this tool is precisely what you need. By leveraging CEL-expr-python, you can harness the proven benefits of CEL, including guaranteed side-effect-free and terminating expressions for enhanced safety, efficient evaluation speeds, and language-agnostic portability. This allows you to seamlessly integrate this potent technology into your existing Python stack. We invite you to explore the capabilities of CEL-expr-python and contribute to its growing ecosystem. Discover how it can streamline your development process and enhance the robustness of your applications. We are eager to hear about your experiences and feedback, so please share your thoughts via the GitHub issue queue. Explore the repository and delve into the accompanying codelab for a comprehensive understanding of how to get started and unlock the full potential of CEL within your Python projects. 🚀💡🔗 https://google.smh.re/5QvN

OpenAI is acquiring open source Python tool-maker Astral OpenAI announced Thursday that it has entered into an agreement to acquire Astral, the company behind popular open source Python development tools such as uv, Ruff, and ty, and integrate the company into its Codex team. The deal, whose financial terms were not publicly disclosed, will help OpenAI “accelerate our work on Codex and expand what AI can do across the software development lifecycle,” the company said in an announcement post....

OpenAI will acquire Astral, pending regulatory close. It will fold Astral's open-source Python tools — uv, Ruff, and ty — into Codex. Teams will integrate the tools. Codex will plan changes, modify codebases, run linters and formatters, and verify results across Python workflows. System shift: This injects production-grade Python tooling into an AI assistant. It marks a move from code generation to more AI-driven execution of full development toolchains. Codex won't just spit snippets. It will run the build. https://lnkd.in/deCpG2BF --- Want more? Join us 👉 https://faun.dev/join

Recently I published an open-source utility I built back in 2024: skeletonpy. TL;DR: If you do AI-assisted Python coding and want a simple way to include your project's code summary without bloating the context, with pretty much zero setup or installation, give it a try. Simply run this in your project's source root: uvx skeletonpy src And include the generated `summary.py.txt` file in your favorite LLM's context. The Backstory I built this because I had no luck with RAG. Granted, back then it was just naive RAG, and there were very few good reranking models. Maybe RAG is simply not a good fit for code, or maybe it was just me. Either way, I always ended up with a context half-full of irrelevant garbage. Furthermore filling up the prompt often leads to standard context rot or the "lost in the middle" phenomenon, where models just start ignoring or confusing data. Today top-tier models got better but some performance degradation is still there. I use skeletonpy occasionally when coding Python projects even today, especially when working with coding assistants like Cline and I want to have full control and insight into the generated code (no vibes :-) ). It gives the AI a focused, accurate map of the repo with class-level resolution to quickly find what it needs. How it works Skeletonpy is deliberately "simple and stupid": it does exactly one thing. It parses your source code offline using AST (no LLMs, no vector DBs, no complex local indexers) to generate a highly compressed skeleton of your repository. It squashes a few pages into a few lines while still providing references back to the original code so the LLM agent can "dig deeper" in the right location. At the same time, the output summary perfectly resembles Python. I've tested it against various LLMs over the last two years and they all had no problems navigating and understanding this structural pseudo-code. https://lnkd.in/dW7QYBkF

Python: The Versatile Language Powering the Tech Landscape Python's rise to prominence in the tech world has been nothing short of meteoric. As a general-purpose, high-level programming language, Python has established itself as a go-to choice for a wide range of applications, from web development and data analysis to machine learning and automation. One of Python's key strengths lies in its simplicity and readability. With a focus on clean, expressive syntax, Python allows developers to write code that is both efficient and easy to understand. This makes it an excellent choice for beginners and experienced programmers alike, as it reduces the time and effort required to get up and running with new projects. Another factor contributing to Python's popularity is its extensive ecosystem of libraries and frameworks. From NumPy and Pandas for data manipulation to Django and Flask for web development, the Python community has created a wealth of tools and resources that simplify and accelerate the development process. This level of support and flexibility has made Python a favorite among developers in various industries. Beyond its technical merits, Python's versatility is what truly sets it apart. The language can be used for a diverse array of tasks, from building robust web applications to powering complex scientific computations. This versatility has made Python an indispensable tool in the arsenal of tech leaders and senior engineers, who often rely on its capabilities to tackle complex challenges and drive innovation. As the tech landscape continues to evolve, the demand for skilled Python developers is only expected to grow. Companies across industries are actively seeking professionals who can leverage Python's strengths to solve real-world problems, automate processes, and unlock new insights from data. If you're a tech leader or senior engineer looking to stay ahead of the curve, it's time to consider mastering Python. Whether you're new to the language or looking to deepen your expertise, investing in your Python skills can open up a world of opportunities and position you as a valuable asset in the ever-evolving tech ecosystem. #Python #TechLeaders #SeniorEngineers #ProgrammingLanguages #DataAnalysis #WebDevelopment #MachineLearning #Automation

I've just released version v0.10.0 of the Makrell language family. Makrell was originally for the Python platform only, but has expanded into a family of programming languages and tools for metaprogramming, code generation, and language-oriented programming on multiple platforms. I still consider it alpha, so expect errors and missing bits and pieces, but there's a lot of ground covered now: • the first release of the whole family as a coherent public system, with a specs-first approach and explicit parity work between the Python, TypeScript, and .NET tracks • the first version of Makrell#, the .NET/CLR implementation of the Makrell language • the first version of MakrellTS, the TypeScript implementation of the Makrell language • a browser playground for MakrellTS • MRDT, a typed tabular data format in the Makrell family • a new version of the VS Code extension, covering all three language tracks plus the data formats • a more consolidated docs and release story The stuff is at https://makrell.dev . There's functional programming, macros and other metaprogramming features, multi-host language design, languages embedded in other languages, structural notations, extensible compiler pipelines, extensible pattern matching that includes regular expressions for data objects, and a lot of other fun language design features in there. For an in-depth introduction, go straight to the article at https://lnkd.in/dYitkTb5 For a MakrellTS playground, go to https://lnkd.in/dQq6hj7E An AI usage declaration: Done by me: All language design, MakrellPy, the MakrellPy bits in VS Code extension and the MakrellPy LSP, sample code, basic documentation. Done by coding agents: Porting, packaging, a lot of documentation, testing and refinements, the MakrellTS playground and more boring things. Earlier this year I had to retire after 30 years as a software developer. Due to Parkinson's disease I suffer from fatigue and fine motor control issues that make it hard to do a lot of coding, or regular work at all. Luckily, my congnitive abilities are still good, though. This ironically coincided with the rise of AI coding assistants, which means I can still produce a lot of code while concentrating on design and high-level directions. The Makrell project had been dormant for two years, but now I was suddenly able to make a lot of progress again by using coding agents to do the actual coding work under my direction. I think it's great. I can concentrate on the interesting bits and not spend my limited energy on the more mechanical coding work. Which really isn't that interesting, I should say. Now the question is if anyone is going to use or care about this. Probably not. And I believe the future of coding is agents compiling directly from specs to machine code and other low level targets, and that few will care about our beatiful programming languages. Maybe I'll just submit this somewhere as a piece of conceptual art.

7 Days of Advanced Python — Learning Beyond Basics 𝗗𝗮𝘆 𝟮 — 𝗪𝗿𝗶𝘁𝗶𝗻𝗴 𝗰𝗹𝗲𝗮𝗻𝗲𝗿 𝗰𝗼𝗱𝗲 𝗮𝗻𝗱 𝗱𝗲𝗯𝘂𝗴𝗴𝗶𝗻𝗴 𝘀𝗺𝗮𝗿𝘁𝗲𝗿 Yesterday I focused on improving how I manage Python projects. Today, I noticed something else. Even when the setup is clean, the actual coding process can still get messy — especially when debugging or maintaining code. I used to rely on: print statements for debugging basic linting (or sometimes none) and manual effort to keep code clean It worked… but not efficiently. So today I explored three tools that completely changed how I approach writing Python code: 𝗥𝘂𝗳𝗳, 𝗟𝗼𝗴𝘂𝗿𝘂, 𝗮𝗻𝗱 𝗜𝗰𝗲𝗖𝗿𝗲𝗮𝗺. --- 𝗥𝘂𝗳𝗳 — 𝗙𝗮𝘀𝘁 𝗮𝗻𝗱 𝘀𝘁𝗿𝗶𝗰𝘁 𝗰𝗼𝗱𝗲 𝗾𝘂𝗮𝗹𝗶𝘁𝘆 Earlier, I either ignored linting or used slower tools that I didn’t run consistently. Ruff feels different. It’s extremely fast and catches issues instantly — unused imports, formatting problems, and code style inconsistencies. Compared to traditional linters: • Much faster execution • Combines linting + formatting • Helps maintain consistency without extra effort If you want to explore it: https://lnkd.in/d2DkJKn6 --- 𝗟𝗼𝗴𝘂𝗿𝘂 — 𝗟𝗼𝗴𝗴𝗶𝗻𝗴 𝘄𝗶𝘁𝗵𝗼𝘂𝘁 𝘁𝗵𝗲 𝗯𝗼𝗶𝗹𝗲𝗿𝗽𝗹𝗮𝘁𝗲 Before this, I used Python’s built-in logging module. It’s powerful, but setting it up always felt a bit heavy for small projects. Loguru simplifies everything. With just a few lines, you get: • Clean and readable logs • Better formatting • Easy configuration Compared to traditional logging: • Less setup • More readable output • Faster to integrate into projects Documentation: https://lnkd.in/d-C4FKWv --- 𝗜𝗰𝗲𝗖𝗿𝗲𝗮𝗺 — 𝗗𝗲𝗯𝘂𝗴𝗴𝗶𝗻𝗴 𝘁𝗵𝗮𝘁 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗵𝗲𝗹𝗽𝘀 I used to debug mostly with print statements. But the problem is: You only see values — not context. IceCream improves this in a very simple way. Instead of writing multiple prints, you get: • Variable names + values together • Cleaner debugging output • Faster understanding of what’s happening Compared to print debugging: • More informative • Less repetitive • Easier to trace issues Explore here: https://lnkd.in/dBTU5t84 --- What changed for me today: I stopped thinking of debugging and code quality as “extra effort”. With the right tools, they become part of the natural workflow. And that changes everything. Because now, instead of fixing messy code later, I can write better code from the start. Curious — what do you usually rely on for debugging and code quality in Python? #Python #AdvancedPython #CleanCode #Debugging #DevTools #LearningInPublic

OpenAI acquires Astral to accelerate development of next-generation Python developer tools Major news in the AI and developer tools space as OpenAI announces its acquisition of Astral, a move that will significantly accelerate the growth of their Codex platform. This strategic acquisition positions OpenAI to power the next generation of Python developer tools, building on their already impressive foundation with GitHub Copilot and other AI-powered coding solutions. Astral brings valuable expertise and technology that will enhance OpenAI's ability to serve Python developers worldwide. The integration is expected to boost Codex capabilities, making AI-assisted Python development more powerful and accessible than ever before. This could revolutionize how developers write, debug, and optimize Python code across industries. For the Python community, this means potentially faster development cycles, smarter code suggestions, and more sophisticated AI tools tailored specifically for Python workflows. The acquisition underscores the growing importance of AI in software development and OpenAI's commitment to advancing developer productivity. What are your thoughts on this acquisition? How do you see AI changing the future of Python development?

Python developers have been duct-taping together PyPDF2, Tesseract, Pillow, and three other libraries to process documents. There's a better way. Nutrient Python SDK brings production-grade document processing to Python in a single, Pythonic API — conversion, OCR in 100+ languages, template-based generation, redaction, digital signatures, and async support for Django, Flask, and FastAPI. Built to handle multi-GB documents with disk streaming, no cobbled-together dependencies required. https://twp.ai/9PbA5x

If you've ever struggled with document processing in Python, this new SDK is designed to replace that mess of different libraries with one clean API.