Anthropic Leaks Claude Source Code

Paying per token for internal automation? There's a better way. I built a proxy that wraps Claude Code CLI as a standard Anthropic API — same format, same SDKs, zero per-token billing. The trick: Claude Code CLI runs under a flat-rate Claude Max subscription. We exposed it as POST /v1/messages on our internal server. Now every machine on the network calls it like the real API. One line change in Python: base_url = "https://your-server:port" # that's it Bonus: Claude CLI can read and fix actual files on the server — something the real API cannot do. Stack: Flask + Podman + config.json. Fully self-hosted. #ClaudeCode #DevOps #Anthropic #AI #CloudNative #PlatformEngineering

"Official" VTTs have spent millions to deliver a loading spinner. 🐌🛑 I’m claiming a 0.94s p99 latency on a live AI rules engine. Corporate devs say that’s impossible for a solo architect. I say your stack is just bloated. Prove me wrong. Go to the site, hit the "Real-Time Rules Lawyer," and time it. If you see a loading bar for more than a second, post the screenshot and I’ll delete my GitHub. If you can't, tell your boss to open the checkbook. 🖕🐉 [Take the Timer: https://lnkd.in/gAFdGR4a] #SystemDesign #LatencyWar #Python #VibeCoding

I searched my Claude Code session history and found something I didn't expect. Here's what I found: https://lnkd.in/dFcHWNGS 185 sessions in 13 days. 1,501 commands. 94+ commits shipped. Only 3 references to an IDE — and two were about window management. I'm an EM who codes daily. Java, Python, Kubernetes, Helm,  infrastructure-as-code across multiple repos. The kind of stack  that should demand a heavyweight IDE. Instead, I've been shipping from a terminal. And the work has  never moved faster. I wrote about why the IDE's core value proposition — "we'll help  you find and edit text in files" — feels increasingly like a card  catalog in the age of Google. The shift from file navigation to intent-driven development  already happened. Most of us just haven't checked our own  session history yet. #SoftwareEngineering #AI #DeveloperTools #EngineeringManagement

I just wrapped up Anthropic's course on the Model Context Protocol (MCP). If you’ve ever built integrations between AI models and external services, you know it usually means writing a lot of custom boilerplate code and manually handling JSON schemas. The most valuable takeaway from this course was seeing how MCP standardizes that entire process. Instead of building one-off connections, MCP shifts the integration burden to a consistent architecture based on three clean primitives: Tools (controlled by the model), Resources (controlled by the app), and Prompts (controlled by the user). Getting hands-on with the Python SDK to build an MCP server—and replacing manual schema writing with simple decorators—showed exactly how much this protocol reduces the friction of connecting AI to real-world data and APIs. A really practical look at how AI infrastructure is maturing and becoming easier to scale. #ArtificialIntelligence #ModelContextProtocol #Anthropic #Python #SoftwareEngineering

I built a tool that lets you ask questions about your codebase in plain English. 🧠 Like literally just type — "where is the FAISS vector store initialized?" — and it finds the exact file, function, and code for you. No more ctrl+F. No more digging through 20 files manually. It's called CodeMind. Getting started is super simple too — just paste your GitHub repo link and it'll clone it automatically, or upload a ZIP file if you prefer. That's it, you're ready to start asking questions. Here's how it works under the hood: → Loads your entire codebase → Breaks it into chunks and converts them into embeddings → Stores everything in a FAISS vector store → When you ask something, it pulls the most relevant code and sends it to Groq LLM for a proper answer Built with Python · LangChain · FAISS · Groq · Streamlit 🔗 Try it: https://lnkd.in/gYV8UfC8 🐙 GitHub: https://lnkd.in/gk3F5kZf Still a lot to improve but happy with how v1 turned out. Would love honest feedback from anyone into AI or dev tooling! 🙌 #RAG #LangChain #GenerativeAI #Python #OpenSource #BuildInPublic #AIEngineering

Handling complexity in long running Python services often feels like juggling fragile glue code, retry loops, watchdogs, and scattered flags. Di Lu’s article, “A supervisor tree library for building predictable and resilient programs,” offers a compelling approach with Runsmith, a Python library inspired by Erlang/OTP supervisor trees that models each unit as a typed worker with an explicit lifecycle. You can read the full breakdown here: https://lnkd.in/dgxjFnpx. What stands out is the shift from brittle process level restarts to fine grained fault isolation and health monitoring that catches stalls and constraint violations, not just crashes. This aligns with challenges I’ve faced building multi component platforms where uptime matters and failure domains must be confined. One caveat is that adopting such a framework requires upfront discipline in designing worker lifecycles and state machines, which can add complexity early on. However, this investment pays dividends when shipping real products that demand maintainability and predictable fault recovery. How have others balanced this upfront design effort against the operational resilience gains in production? #python #softwarearchitecture #systemdesign #reliabilityengineering #productdevelopment #founders #engineering #faulttolerance #opensource #devtools #resilience #longrunningservices

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

I've been stress-testing the new Codex desktop app for three weeks and honestly? It's the first AI coding tool that doesn't make me want to throw my laptop out the window. The context awareness is scary good - it actually remembers what I was working on yesterday and picks up conversations mid-thread. But here's what nobody's talking about: the real magic isn't the code generation, it's how it handles debugging existing codebases. I fed it a gnarly legacy Python script with zero documentation and it mapped out the entire data flow in minutes. Sure, GitHub Copilot writes decent boilerplate, but Codex actually understands architecture. Still has weird hallucinations with newer frameworks though. #OpenAICodex #DeveloperTools #AIcoding

I've been stress-testing the new Codex desktop app for three weeks and honestly? It's the first AI coding tool that doesn't make me want to throw my laptop out the window. The context awareness is scary good - it actually remembers what I was working on yesterday and picks up conversations mid-thread. But here's what nobody's talking about: the real magic isn't the code generation, it's how it handles debugging existing codebases. I fed it a gnarly legacy Python script with zero documentation and it mapped out the entire data flow in minutes. Sure, GitHub Copilot writes decent boilerplate, but Codex actually understands architecture. Still has weird hallucinations with newer frameworks though. #OpenAICodex #DeveloperTools #AIcoding

A supply chain attack hit a Python package with ~3 million daily downloads. Malicious code executed automatically on every Python process startup for roughly 40 minutes, enough time to harvest credentials and install a persistent backdoor. That package was LiteLLM, one of the most widely used AI gateway libraries in production environments. And the attack didn't even come through LiteLLM's own code; it came through a compromised GitHub Action in their CI/CD pipeline. The deeper lesson here isn't specific to LiteLLM. It's about how engineering teams think (or don't think) about AI gateways as infrastructure. A proxy that sees your LLM API keys, your prompts, and sits in the request path between your applications and your model providers isn't a dev tool. It's critical infrastructure. We wrote a breakdown of what happened, what the migration path looks like, and what questions to ask of any AI gateway you're evaluating. Link in comments.