CEL-expr-Python: Open-Source Implementation for Dynamic Expressions and Policy Enforcement

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. 🚀💡🔗

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

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/5PPQ

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