OpenAI Codex Rewrites Leaked Claude Code

https://lnkd.in/enAnqgKh This is how you name software Also a great solution for agents - giving them full access to Python can be a recipe for disaster but fully removing python can limit their ability as they often need it to do complex parsing or other tasks.

Released on March 16, 2014, Python 3.4 arrived with zero new syntax features. None. If you were hoping for a new operator or a shiny keyword, you’d have been disappointed. What you got instead was something more durable: a standard library that finally felt like it was built for the modern web. https://lnkd.in/ddygQziM

The Python ecosystem's insistence on solving multiple problems when distributing functions has led to unnecessary complexity. The dominant frameworks have fused orchestration into the execution layer, imposing constraints on function shape, argument serialization, control flow, and error handling. Wool takes a different approach by allowing execution to be distributed without the need for DAG definitions, checkpointing, or retry logic, focusing on simplicity and transparency. Wool provides distributed coroutines and async generators that enable transparent execution on remote worker processes while maintaining the same semantics as local execution. https://lnkd.in/eJ97fuAp --- More tech like this—join us 👉 https://faun.dev/join

Outlines a Python monorepo setup that pairs uv workspaces with Dagger and BuildKit caching. Builds container stages programmatically. Keeps things cache-friendly and predictable. Parses pyproject.toml and extracts the workspace graph. Copies required local packages into intermediate stages. Installs them in editable mode so caches survive and rebuilds stay fast. uv and Dagger turn CI from ad-hoc scripts into workspace-driven, cache-first monorepo builds. The result: per-package containerization that scales and caches sensibly. https://lnkd.in/e5GYVy2d --- Love it? Get our newsletter 👉 https://faun.dev/join

Your Django app went from 200MB to 8GB RAM usage in three weeks. Memory leaks don't crash dramatically—they creep up slowly until your servers start swapping and alerts start screaming. This guide shows you how to profile Python applications in production using memory_profiler and tracemalloc without causing downtime or performance impact. Learn to catch circular references, global variable accumulation, and resource leaks before they kill your application. #Python #DevOps #PerformanceOptimization #Django Learn More: https://lnkd.in/eWe2bRhT

PySpector v0.1.8 is out🚀 Me and the PySpector Core Team worked really hard to deploy this version, so here's what changed: - A new vulnerability leading to arbitrary code execution via plugin bypass was patched (and its #GHSA was published) - Docs were updated and improved🫡 - We fixed a bug preventing the generation of html reports, as well as 2 other bugs preventing the --wizard and -- supply-chain flag from working properly - We expanded error messages during #AST file parsing and added a new #CLI flag to enable Python SyntaxWarnings during code scanning - And last we (finally) expanded support for Python up to the latest #Python3.14 (while before v.0.1.8, Python support stopped at #Python3.12) Thanks to all the #contributors and the awesome SecurityCert community who made this possible🫶 Repo: https://lnkd.in/d7CppftJ

Learn how to build a Model Context Protocol server in Python using FastMCP, expose custom tools to Claude, and connect it to Claude for Desktop via STDIO transport. Not with a plugin or a third-party integration - just a Python file and about 15 minutes. That is what MCP (Model Context Protocol) actually is: you write a function, decorate it with @mcp.tool(), and Claude can call it directly from chat. The thing that genuinely surprised me when building this: you never write a JSON schema for the tool. FastMCP reads your type hints and docstring at startup and generates the schema automatically. That docstring is literally what Claude reads when it decides whether and how to call your function - so you write it like API documentation, not a code comment. That distinction matters more than it sounds. The full walkthrough is on my blog - 7 steps, free National Weather Service API, no API key needed. You finish with two working tools (get_alerts and get_forecast) registered in Claude for Desktop and callable from chat with real live data. https://lnkd.in/ee-EpVht

I ran `kill -9` on a Python worker processing three tasks. They vanished — no error, no retry, no record. This is the default behavior of most task frameworks: a worker dies mid-execution, and the work disappears. So I built automatic crash recovery into pynenc, an open-source distributed task orchestration framework for Python. Here's what it does: • Every runner emits periodic heartbeats • When heartbeats stop, the recovery service detects the dead runner • Orphaned tasks are automatically re-queued • A healthy runner picks them up and finishes the job No external monitoring. No manual re-queueing scripts. No lost work. I wrote up the full scenario — including a runnable demo you can try locally with zero dependencies (no Docker, no Redis): https://lnkd.in/ehWVK-3p The demo takes about 90 seconds and shows recovery happening end-to-end. How does your team handle crashed workers today? #python #distributedsystems #opensource #backend #reliability

🚀 Day 66 – Project Work | Important Python Concepts Today I focused on strengthening core Python concepts that are crucial for building scalable projects. 💻🐍 Sometimes we jump into frameworks and tools, but strong fundamentals make everything easier. 🔹 Key Python concepts I worked on: ✔️ Functions & modular coding ✔️ Classes & Object-Oriented Programming (OOP) ✔️ Exception handling (try-except) ✔️ File handling (loading models & data) ✔️ Working with JSON data (API requests/responses) 🔹 How it helped my project: 👉 Made my FastAPI code cleaner & structured 👉 Improved error handling in API 👉 Better data flow between model and backend 👉 Easier debugging and maintenance 🔹 Challenges: ⚡ Writing clean and reusable code ⚡ Handling unexpected errors properly ⚡ Structuring project files efficiently 🔹 What I learned: 💡 Strong basics = strong projects 💡 Clean code saves time later 💡 Python concepts are the backbone of ML + Backend 📌 Next Step: Refactor my project using these concepts and move closer to deployment 🚀 #Day66 #Python #ProjectWork #FastAPI #MachineLearning #Coding #LearningJourney