Unlock Claude Code with Python: DeepWiki Documentation

 🚀 Excited to announce Claw Code Agent — our open-source Python reimplementation of the Claude Code agent architecture!  💡 Inspired by the reverse-engineering work shared here:  🔗 https://lnkd.in/dzrrj6da  We took that foundation and built a fully working Python agent from it.  🐍 Pure Python. No Rust. No TypeScript. Just Python.  🔧 Full Agent Capabilities:   ✅ Agentic coding loop with tool calling   ✅ File read/write/edit, grep, glob, shell   ✅ Slash commands & context engine   ✅ Session persistence & resume   ✅ Tiered permission system  ⚡ Works with any OpenAI-compatible API:   🟢 vLLM   🟢 Ollama   🟢 LiteLLM Proxy  🐉 run a full coding agent locally, for free.  📬 We're actively developing this — if you have feature requests, ideas, or want to contribute, we'd love to hear from you. Open an issue or submit a PR!  👉 https://lnkd.in/dmuAAYah  ⭐ Star the repo if you find it useful!  #OpenSource #AI #CodingAgent #Python #LLM #vLLM #Qwen #MachineLearning #DevTools #AIAgents #ClaudeCode

Chaofan Shou posted the discovery at 4:23 AM. Within hours, Python rewrites were already on GitHub That's the part I keep coming back to. Not the leak itself — the turnaround A developer took 512,000 lines of TypeScript and rewrote the core in Python before most of the world was awake. Mirrors spread faster than takedowns. When DMCA requests hit, the code had already moved to decentralized platforms. More forks kept appearing. A Rust rewrite is underway It wasn't clean. The same day saw a separate npm supply-chain attack hit the ecosystem. The community moved so fast it outpaced its own security instincts But here's what I actually noticed: nobody was waiting for permission. No one filed a request. No one asked Anthropic what was okay to build. They just... built. At 4 AM. In hours. In three languages We talk a lot about what AI is doing to developers. We talk less about what developers do the moment a tool they depend on becomes open Turns out the answer is: immediately, in parallel, with forks What does that say about the relationship between builders and the platforms they build on?

I spent too much time reconciling logs and traces until I understood how OpenTelemetry logging actually works. 🔑 The key insight: OTel doesn't try to be your logging library. It's a bridge. Your existing logger (Log4j, Python logging, winston) keeps working exactly as it does today. But behind the scenes, an appender automatically enriches every log record with trace context — the TraceId and SpanId from the active span. ✨ That's it. That's the whole idea. And it changes everything. ⚡ Suddenly, debugging is faster. You see logs in context of their span. You see which logs caused a trace anomaly. Your backend (Jaeger, Tempo, Elastic, whatever) can now correlate logs to traces without you writing SQL joins or doing manual detective work. 📖 Just published a 16-minute technical guide walking through log formats, the unified LogRecord schema, the Logs API and SDK, processors, and exporters. Available on LearnObservability — link in comments. #OpenTelemetry #Observability #DevOps #DistributedTracing #SRE #Logging

From the leaked Claude source code, people are porting the architecture and logic to a different language (python) to avoid legal issues. One example is this one: https://lnkd.in/eVpnWN-n I remember using claude code to reverse engineering it itself, the intention? to enable a custom llm proxy, and it worked! The llm proxy didn't follow the regular authentication mechanism (required extra headers, custom url pattern) plus a custom change in the response. It was the opencode before opencode 😅🤣

How to Build a Secure Local-First Agent Runtime with OpenClaw Gateway, Skills, and Controlled Tool Execution In this tutorial, we build and operate a fully local, schema-valid OpenClaw runtime. We configure the OpenClaw gateway with strict loopback binding, set up authenticated model access through environment variables, and define a secure execution environment using the built-in exec tool. We then create a structured custom skill that the OpenClaw agent can discover and invoke deterministically. Instead of manually running Python scripts, we allow OpenClaw to orchestrate model reasoning, skill selection, and controlled tool execution through its agent runtime....

Developers are flocking to luongnv89/claude-howto, a visual guide to Claude Code that's making fast-moving AI workflows easier to steer and reuse in real projects. This project is more than just a tutorial – it's a practical solution to the complexity of LLM and agent workflows. By providing a clear learning path and example-driven templates, Claude How To is helping teams overcome the common pitfalls of mastering Claude Code. At its core, Claude How To is a collection of 10 tutorial modules covering every Claude Code feature, from slash commands to custom agent teams. This comprehensive approach is a breath of fresh air in a landscape where most resources leave developers scratching their heads. By focusing on the practical application of Claude Code, this project is changing the way developers work with LLM and agent workflows. Key benefits of Claude How To include: - A clear learning path that helps developers master Claude Code features - Example-driven templates that bring immediate value to real projects - A comprehensive approach that covers every aspect of Claude Code - Built with Python, making it accessible to a wide range of developers The traction makes sense: a repository sitting at #3 with around 27,548 new stars is usually solving a problem people can feel immediately. With its recent commits and active development, it's clear that Claude How To is here to stay. Repo: https://lnkd.in/gV8nN-6t #GitHub #OpenSource #GitHubTrending #LinkedInForDevelopers #Python #ClaudeHowto #ClaudeCode #Guide

🚨 𝟱𝟬𝟬,𝟬𝟬𝟬+ 𝗹𝗶𝗻𝗲𝘀 𝗼𝗳 𝗔𝗜 𝗰𝗼𝗱𝗲... 𝗹𝗲𝗮𝗸𝗲𝗱 𝗯𝘆 𝗺𝗶𝘀𝘁𝗮𝗸𝗲. 𝗔𝗻𝘁𝗵𝗿𝗼𝗽𝗶𝗰 𝗮𝗰𝗰𝗶𝗱𝗲𝗻𝘁𝗮𝗹𝗹𝘆 𝗲𝘅𝗽𝗼𝘀𝗲𝗱 𝗶𝘁𝘀 Claude 𝗖𝗼𝗱𝗲 𝘀𝗼𝘂𝗿𝗰𝗲 𝘃𝗶𝗮 𝗮 `.𝗺𝗮𝗽` 𝗳𝗶𝗹𝗲 𝗶𝗻 𝗮𝗻 𝗻𝗽𝗺 𝗿𝗲𝗹𝗲𝗮𝘀𝗲 — 𝗿𝗲𝘃𝗲𝗮𝗹𝗶𝗻𝗴 𝘁𝗵𝗲 𝗳𝘂𝗹𝗹 𝗶𝗻𝘁𝗲𝗿𝗻𝗮𝗹 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲. (Axios) Within hours, the internet did what it does best: → mirrored it on GitHub → analyzed it → rebuilt it One repo stood out: 👉 https://lnkd.in/gEJzYmXx But the real twist? Developers moved beyond copying. They created clean-room reimplementations in Rust & Python (Claw Code) — replicating the architecture without using the original code. (Claw Code) https://lnkd.in/guiUu3Ch This is classic software history repeating itself. 💡 Lesson: It’s not always hacks that break systems — sometimes it’s a single config mistake. And sometimes, that mistake teaches the whole industry how your system works. #AI #DevOps #Security #OpenSource #SoftwareEngineering

API Documentation in Django REST Framework — Simplified with drf‑spectacular Building APIs is easy. Maintaining them at scale? That’s where things get tricky. As teams grow and endpoints multiply, keeping a clear API contract becomes essential. That’s why I explored drf‑spectacular, a powerful tool that turns your DRF code into a clean, OpenAPI‑compliant schema — ready for Swagger and Redoc. In my latest Medium article, I break down: How to set up drf‑spectacular in minutes Why schema generation matters for scaling and collaboration Integrating JWT authentication for secure testing Hiding internal endpoints and documenting complex responses Best practices for production‑ready API docs Think of it as reverse‑engineering your API into documentation. 👉 Read the full article here: https://lnkd.in/dbuTaNym #Django #DRF #API #Documentation #OpenAPI #Swagger #Redoc #Python #BackendDevelopment

One misconfiguration in a GitHub Actions workflow. Fast forward to Friday evening and we’re tracking six throwaway GitHub accounts, 500+ malicious PRs, and an attacker who spent three weeks iterating on payloads before anyone noticed. Always a pleasure digging into these with Rami McCarthy, Benjamin Read and Scott Piper 🔍

Python APIs don't have to be messy. FastAPI + Pydantic changed how I think about backend development. Here's what makes this stack production-ready from day one: ✅ Type-safe request & response models via Pydantic ✅ Automatic input validation — no manual checks needed ✅ Auto-generated OpenAPI / Swagger docs, always in sync ✅ Blazing-fast serialization with Pydantic v2 (Rust core) ✅ Async support out of the box for high-concurrency workloads ✅ Clean dependency injection system for services, DB sessions, auth The real superpower? Your schema IS your documentation IS your validation IS your serializer. One source of truth. This reduces the gap between what your API contract promises and what it actually delivers — which is exactly what you want in production. Whether you're building REST APIs, GenAI tool backends, or internal services, FastAPI + Pydantic gives you the developer experience of modern TypeScript frameworks — but in Python. Have you used FastAPI in production? What's your experience been? #FastAPI #Pydantic #Python #APIDesign #BackendEngineering #GenAI