CGWire’s Post

An animation pipeline can start with something very basic, but the whole concept of deployment is a showstopper for many small studios that don't have sufficient IT resources. There are solutions to make it simpler, and this one is Open Source. Check it out.

Unfurl, Debug Mode Activation Vulnerability (Critical) How the Vulnerability Works The vulnerability exists in how the Unfurl web application processes its debug configuration. The `web_app()` function in `unfurl/app.py` reads the `debug` setting from the `unfurl.ini` configuration file via config['UNFURL_APP'].get('debug'). This value is always retrieved as a string. This string is then passed directly, without type conversion, to the Flask application's `app.run(debug=unfurl_debug)` method. In Python, a non-empty string (like…...

Unfurl, Debug Mode Activation Vulnerability (Critical) How the Vulnerability Works The vulnerability exists in how the Unfurl web application processes its debug configuration. The `web_app()` function in `unfurl/app.py` reads the `debug` setting from the `unfurl.ini` configuration file via config['UNFURL_APP'].get('debug'). This value is always retrieved as a string. This string is then passed directly, without type conversion, to the Flask application's `app.run(debug=unfurl_debug)` method. In Python, a non-empty string (like…...

Hey there! 😄 If you’ve ever used Mayapy (Maya’s embedded Python) or Hython (Houdini’s embedded Python) to run operations in scenes in batch mode, you know how frustratingly slow it can be to repeatedly initialize the interpreter. Whether you're writing unit tests, running automation, or building standalone tools that load multiple scenes, each startup adds unnecessary overhead. To solve this pain point, I built something that might help you: a TCP-based hot-reload server that keeps a persistent Mayapy or Hython process running in the background. Instead of launching the interpreter from scratch for every script execution, this service allows you to: ✨ Keep the DCC process alive — no more repeated startup time 📡 Connect over TCP and send Python snippets dynamically 🐍 Execute code in the live interpreter context, inside the currently loaded scene 🔄 Keep state between calls (e.g., imported modules, loaded scenes, global variables) 🔌 Close or reuse the connection depending on your needs Right now it supports both Mayapy and Hython, and the architecture makes it easy to extend to other embedded Python environments too. If you’re building automation tooling, test suites, or interactive Python-driven pipelines for Maya/Houdini, this might save you a lot of time. Check it out: 👉 https://lnkd.in/gGcUy7Xu Let me know what you think — feedback and contributions are welcome! 🙌 #Maya #Houdini #Python #Tooling #DCC #Development #OpenSource

n8n is a powerhouse for workflow automation, offering the perfect balance between a user-friendly UI and the flexibility to inject custom logic where it’s needed most. Self-hosting n8n gives you incredible control, but things get interesting the moment you try to bring Python into the mix. I recently spent some time navigating the complexities of adding a Python runner to a self-hosted setup. While it sounds straightforward, the reality of Docker permissions and environment pathing can lead to a fair share of troubleshooting. I’ve documented the exact roadblocks I hit and the final fix that actually works. If you’re looking to supercharge your n8n workflows with custom Python scripts without the headache, this is for you. Check out the full guide here: https://lnkd.in/gwK4bvjH #n8n #Python #SelfHosting #Automation #DevOps #LowCode #DataEngineering

Python's package management has never been its strongest point. Many people still use tools like pip for many things but there are much faster options. The newer tool "uv" from Astral is worth knowing about—it's written in Rust and handles virtual environments and dependencies noticeably faster than the traditional pip workflow. The article below shows a VS Code setup including getting the interpreter to recognize uv environments, configuring debugging, and pairing it with Ruff for linting. All the cool kids use uv and if you're not already using it yourself it's worth trying out. ライフポータル put together a good intro to using uv. This is a decent starting point for getting it working in your editor. https://lnkd.in/guTv8zmQ

Top 20 Python's Built-in Functions Written by $DiligentTECH💀⚔️ Today, we are discussing the 20 most impactful built-in functions—some classic, some brand new—that will turn your scripts into sleek, professional masterpieces. 1: The Wave of Modern Logic In the recent evolution from Python 3.10 to 3.13, the language didn't just get faster; it got smarter. We are starting with the "freshmen" of the built-in world. https://lnkd.in/dp63AuWS 1. aiter() (Added 3.10): The gateway to asynchronous iteration. Think of it as the iter() for the "fast and furious" async world. 2. anext() (Added 3.10): The partner to aiter(). It fetches the next item from an asynchronous iterator. 3. breakpoint(): Your "emergency brake." It drops you straight into the debugger without needing to import pdb manually.

New article: Building Your First Python Backend API (Flask or FastAPI) From zero to: • Running backend server • Real API endpoints • Frontend → backend communication Practical, minimal, and beginner-friendly. https://lnkd.in/d3Mvrfep #Python #BackendDevelopment #APIs #FullStackPython #WebDevelopment

One of the most common Python mistakes I still see, especially in growing developers, is misunderstanding variable scope. It leads to code that runs but behaves unpredictably. And those are the hardest bugs to debug. Once I truly understood how Python handles local, global, and nonlocal variables, my code became easier to reason about and my debugging time dropped significantly. I wrote a short, practical guide explaining variable scope the way I wish it had been explained to me, with real examples. 👉 Read it here: https://lnkd.in/djp6HJdD #Python #LearnPython #SoftwareEngineering #DeveloperGrowth

Python packaging for binary extensions (C/C++) is finally getting more developer-friendly. The core shift is moving away from ad-hoc setup.py scripts (and the brittle “run python setup.py bdist_wheel and hope” workflow) toward modern, standardized builds where the build requirements are declared up front and tools can reliably produce wheels across environments. The old approach often ran into a classic chicken-and-egg problem: builds would fail because a build dependency wasn’t installed yet, and the build step itself would also “pollute” your current environment because it wasn’t isolated. The newer packaging flow (centered around pyproject.toml and isolated builds) addresses this directly—declare build dependencies, run the build in a clean environment, and get a reproducible wheel output that’s much easier to automate in CI/CD. If you maintain Python projects with compiled components, modern packaging is not just “new syntax”—it’s a structural improvement in reliability, repeatability, and onboarding. It reduces surprise failures, makes builds more deterministic, and sets a clearer foundation for future tooling improvements. #Python #Packaging #DevTools #OpenSource #SoftwareEngineering