One instruction file doesn't scale. The moment your codebase has a Python service and a TypeScript frontend and a Go worker, a single CLAUDE.md becomes either too generic to be useful or too bloated to trust. Scoped context solves this the way filesystems already do — by nesting. Org-level rules wrap user-level rules wrap project-level rules wrap directory-level rules. The agent reads whichever scope it's working inside, the same way a developer picks up conventions walking into a new folder. Example: the org says "never commit .env files." The project says "use Zod for validation." The ./src/api/ directory says "return JSON, validate schema." The agent sees all three, cleanly composed. The trade-off is discoverability. When rules live in four places, it's harder to answer "what does the agent actually see right now?" Good tooling here isn't optional — it's the whole pattern. Treat context as a tree, not a file. How are you organizing rules across a multi-language codebase? #AI #AgenticAI #SoftwareArchitecture #DeveloperTools #Clausey

𝗦𝘁𝗼𝗽 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗲𝘃𝗲𝗿𝘆 𝗯𝗮𝗰𝗸𝗲𝗻𝗱 𝘁𝗵𝗲 𝘀𝗮𝗺𝗲 𝘄𝗮𝘆. FastAPI isn't just "another Python framework." It's a deliberate choice — and knowing when to reach for it matters more than knowing how to use it. 𝗣𝗶𝗰𝗸 𝗙𝗮𝘀𝘁𝗔𝗣𝗜 𝘄𝗵𝗲𝗻:  • You're building ML/AI-powered APIs and your team already lives in Python  • You need async performance without the boilerplate of Go or Java  • Auto-generated docs (Swagger/OpenAPI) aren't a nice-to-have — they're a requirement  • You want type safety that actually catches bugs before production 𝗦𝘁𝗶𝗰𝗸 𝘄𝗶𝘁𝗵 𝘁𝗿𝗮𝗱𝗶𝘁𝗶𝗼𝗻𝗮𝗹 𝗯𝗮𝗰𝗸𝗲𝗻𝗱𝘀 (𝗦𝗽𝗿𝗶𝗻𝗴, 𝗗𝗷𝗮𝗻𝗴𝗼, 𝗘𝘅𝗽𝗿𝗲𝘀𝘀, .𝗡𝗘𝗧) 𝘄𝗵𝗲𝗻:  • Your org already has deep expertise and infra around them  • You need battle-tested ORM support and a massive plugin ecosystem  • You're building monoliths where convention-over-configuration saves months 𝗧𝗵𝗲 𝗿𝗲𝗮𝗹 𝗮𝗻𝘀𝘄𝗲𝗿? 𝗜𝘁'𝘀 𝗻𝗲𝘃𝗲𝗿 𝗮𝗯𝗼𝘂𝘁 𝘁𝗵𝗲 𝗳𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸. 𝗜𝘁'𝘀 𝗮𝗯𝗼𝘂𝘁 𝘁𝗵𝗲 𝗽𝗿𝗼𝗯𝗹𝗲𝗺. FastAPI shines where speed-to-deploy, async I/O, and Python-native ML pipelines intersect. Forcing it into a legacy enterprise CRUD app is like using a scalpel to chop wood. Choose your tools like an engineer, not a fan. Thoughts? When did FastAPI click (or not click) for you? #FastAPI #Python #BackendDevelopment #SoftwareEngineering #WebDevelopment #APIDevelopment #TechCommunity #Programming #MLOps #SystemDesign

I just recently implemented JWT authentication from scratch in FastAPI —  and doing it with TDD taught me something I wouldn't have  learned otherwise. The test I wrote first: def test_login_with_wrong_password_returns_401(client):   response = client.post("/api/v1/auth/login",      json={"email": "user@test.com", "password": "wrongpassword"})   assert response.status_code == 401 Simple. But writing that BEFORE the implementation forced me to  think about the user experience of authentication failure before  thinking about the code. The thing that surprised me most: the `yield` pattern in FastAPI  dependencies. Instead of returning a database session, you yield it  — which means cleanup code runs after the route finishes, even if  it crashes. One pattern, zero connection leaks. Building Tidal — a multi-currency budgeting app — in Python + React  after 4 years of Scala. The mental model shift is real but the  engineering fundamentals are identical. What's the most useful thing TDD has taught you about your own code? #Python #FastAPI #TDD #SoftwareEngineering #OpenAI #Remote

When code runs millions of times a day, even minor enhancements lead to significant compute savings. So I built xmltodict-fast. 🦀🐍 xmltodict is a Python library many of us use without a second thought. With ~5K GitHub stars, it’s a quiet workhorse powering ETL pipelines, SOAP clients, and invoice processors. It’s a drop-in replacement that maintains the same public API, but rewrites the performance-critical sections in Rust using PyO3 and quick-xml. Importantly: if the Rust extension isn't available on a platform, it seamlessly reverts to the original Python implementation. It's completely safe for incremental adoption. local benchmarks :  🚀 parse(): 2.1 × faster on typical XML  🚀 unparse():5.9 × faster (massive for serialization-heavy workflows)  On pathologically deep XML (500+ nesting levels), the Rust version is actually slower. :( (Side note: Thanks to my kind and patient AI coding assistant for helping me building this!) If you work with XML in Python, I welcome your feedback, testing, and pull requests! 🔗 Repo & Benchmarks: https://lnkd.in/exhfBuD7 #Python #RustLang #PyO3 #OpenSource #DataEngineering #PerformanceOptimization

I've been building TerpNav's backend without leaning on AI, and it's been significantly harder. That was the point. I migrated from Flask to Django and built the backend using Django REST Framework. What I have right now: clean URL routing, API endpoints serving data, API keys saved in .env files and a project structure I understand end to end because I built it line by line. What I don't have yet: a real database, authentication, rate limiting, HTTPS config, or environment-based secrets management. The data currently lives in flat JSON files. That's the honest state of the project. But here's what I've mapped out next and actually understand well enough to implement: PostgreSQL with Django models (replacing the JSON files), Token authentication via DRF, Rate limiting with django-ratelimit, Secrets managed through environment variables, deployed behind HTTPS The hardest part isn't the code. It's slowing down enough to understand what I'm actually building. Every concept that AI used to abstract away is something I now have to research, break, and fix myself. That's the trade-off I made. It's worth it. Code is available on my gitHub #Django #Python #FullStackDevelopment #TerpNav #BuildInPublic

🚀 FastAPI: Build Powerful APIs with Less Code If you're building APIs in Python and not using FastAPI yet… you're missing out. Here’s why FastAPI is gaining massive popularity 👇 ⚡ Blazing Fast Performance Built on ASGI and Starlette, FastAPI delivers performance close to Node.js and Go. 🧠 Automatic Data Validation Thanks to Pydantic, you get type validation using Python type hints — clean and powerful. 📄 Auto-Generated Docs Swagger UI & ReDoc are generated automatically. No extra effort needed. 🔐 Easy Authentication & Security Supports OAuth2, JWT, and other modern security standards out of the box. 🔧 Developer-Friendly Less code, more productivity. You write less boilerplate and focus on logic. 💡 Example: from fastapi import FastAPI app = FastAPI() @app.get("/") def home(): return {"message": "Hello FastAPI 🚀"} 🔥 Whether you're building microservices, AI APIs, or backend systems — FastAPI is a game changer. Start learning today and level up your backend skills 💪 #FastAPI #Python #BackendDevelopment #WebDevelopment #APIs #Programming #SoftwareEngineering #100DaysOfCode #DeveloperLife #Coding

FastAPI — Why It’s Becoming a Favorite for Backend Development FastAPI is a modern Python framework used to build APIs quickly and efficiently. But it’s not just another framework. It’s designed for speed, simplicity, and performance. What is FastAPI? FastAPI is a web framework for building APIs using Python, based on modern standards like type hints. It helps developers write less code and build faster, reliable APIs. Key Features: High performance (comparable to Node.js and Go) Automatic data validation using Python types Auto-generated API documentation (Swagger UI) Built-in async support Easy integration with databases and tools Example Use Cases: Building REST APIs Backend for web and mobile applications AI/ML model APIs Microservices architecture Why developers prefer FastAPI: Clean and readable code Faster development time Strong typing reduces bugs Suitable for scalable systems Final Insight: FastAPI is not just about building APIs faster. It’s about building APIs the right way. Follow Saif Modan #Python #FastAPI #Backend #API #Tech #AI

<<AIO - Agent-Infra consolidates these requirements into a single containerized environment. The sandbox includes: Computer Interaction: A Chromium browser controllable via the Chrome DevTools Protocol (CDP), with documented support for Playwright. Code Execution: Pre-configured runtimes for Python and Node.js. Standard Tooling: A bash terminal and a file system accessible across modules. Development Interfaces: Integrated VSCode Server and Jupyter Notebook instances for monitoring and debugging.

Anthropic Leaks Its Own Source Code Anthropic ships Claude Code as an npm package. Someone runs `ls` on the source map. Entire codebase just sitting there. Unobfuscated. Plugins, skills, tools, hooks, commands — everything. Internal architecture of the most hyped AI coding agent, fully readable. Anthropic says nothing. Meanwhile, they're selling Enterprise contracts. The source map was in the registry the whole time. Nobody checked. Security through obscurity lasted about 3 months. Full code is here. https://lnkd.in/efajfgQ4

🚀 Built & Deployed a FastAPI REST API Excited to share that I’ve been working on building high-performance REST APIs using FastAPI! 🔹 Designed scalable API endpoints 🔹 Implemented CRUD operations 🔹 Integrated request validation using Pydantic 🔹 Ensured high performance with async support 🔹 Tested endpoints using Postman FastAPI makes backend development faster, cleaner, and more efficient compared to traditional frameworks. Currently exploring deployment strategies and integrating APIs with AI/LLM-based applications 🤖 #FastAPI #RESTAPI #BackendDevelopment #Python #APIDevelopment #AI #MachineLearning