Massimo A.’s Post

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. 🚀💡🔗

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. 🚀💡🔗

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

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/5QDV

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

What is the use of self in Python? If you are working with Python, there is no escaping from the word “self”. It is used in method definitions and in variable initialization. The self method is explicitly used every time we define a method. The self is used to represent the instance of the class. With this keyword, you can access the attributes and methods of the class in python. It binds the attributes with the given arguments. The reason why we use self is that Python does not use the ‘@’ syntax to refer to instance attributes self is used in different places and often thought to be a keyword. But unlike in C++, self is not a keyword in Python. self is a parameter in function and the user can use a different parameter name in place of it. Although it is advisable to use self because it increases the readability of code. In Python, self is the keyword referring to the current instance of a class. Creating an object from a class is actually constructing a unique object that possesses its attributes and methods. The self inside the class helps link those attributes and methods to a particular created object. Self in Constructors and Methods self is a special keyword in Python that refers to the instance of the class. self must be the first parameter of both constructor methods (__init__()) and any instance methods of a class. For a clearer explanation, see this: When creating an object, the constructor, commonly known as the __init__() method, is used to initialize it. Python automatically gives the object itself as the first argument whenever you create an object. For this reason, in the __init__() function and other instance methods, self must be the first parameter. If you don’t include self, Python will raise an error because it doesn’t know where to put the object reference. Is Self a Convention? In Python, instance methods such as __init__ need to know which particular object they are working on. To be able to do this, a method has a parameter called self, which refers to the current object or instance of the class. You could technically call it anything you want; however, everyone uses self because it clearly shows that the method belongs to an object of the class. Using self also helps with consistency; hence, others-and, in fact, you too-will be less likely to misunderstand your code. Why is self explicitly defined everytime? In Python, self is used every time you define it because it helps the method know which object you are actually dealing with. When you call a method on an instance of a class, Python passes that very same instance as the first argument, but you need to define self to catch that. By explicitly including self, you are telling Python: “This method belongs to this particular object.” What Happens Internally when we use Self? When you use self in Python, it’s a way for instance methods—like __init__ or other methods in a class—to refer to the actual object that called the method. #Python #Data_analaysis

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

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?