Python Mastery Skill Released: Expert-Level Reference for Senior Engineers

 🚀 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

Most developers use __init__ every day. But here’s the catch: We use it so often that we stop questioning how objects are actually created. And that’s where __new__ quietly gets ignored. The truth is: 👉 __𝐧𝐞𝐰__ creates the object 👉 __𝐢𝐧𝐢𝐭__ initializes the object Python doesn’t create objects in one step. It happens in two phases: 1️⃣ Memory is allocated → __new__ 2️⃣ Object is configured → __init__ A quick example: class Example: def __new__(cls): print("Creating instance") return super().__new__(cls) def __init__(self): print("Initializing instance") obj = Example() 𝘖𝘶𝘵𝘱𝘶𝘵: Creating instance Initializing instance Here’s the part most people never think about 👇 You’ve probably never written __new__. Yet your objects still get created perfectly. Why? Because Python is already doing this behind the scenes: obj = MyClass.__new__(MyClass) MyClass.__init__(obj) And if you don’t define __new__, Python uses: object.__new__() We treat __init__ like a constructor. But technically… it isn’t. 👉 𝑻𝒉𝒆 𝒓𝒆𝒂𝒍 𝒄𝒐𝒏𝒔𝒕𝒓𝒖𝒄𝒕𝒐𝒓 𝒊𝒔 __𝒏𝒆𝒘__. ⚡ Why __new__ matters: - Controls object creation - Used in Singleton patterns - Important for immutable types (int, str, tuple) - Can even return a different object ⚡ What __init__ actually does: - Just initializes the already-created object - Cannot create or return a new instance - Always returns None 💡 Real takeaway: We rely on __init__ so much that we rarely think about what happens before it. Understanding __new__ is what shifts you from: 👉 writing Python code to 👉 understanding how Python actually works Once you see it, you can’t unsee it 🙂 #Python #PythonProgramming #SoftwareDevelopment #BackendDevelopment #Coding #Programming #LearnToCode #DeveloperMindset #TechCareers #SoftwareEngineer #CleanCode #ProgrammingConcepts

Topic 1/100 🚀 🧠 Topic 1 — Metaprogramming Ever wondered how frameworks like Django feel “magical”? That’s because they use metaprogramming. 👉 What is it? Metaprogramming means writing code that can modify or generate other code at runtime. 👉 Use Case: Frameworks dynamically create models, APIs, and admin panels without you writing everything manually. 👉 Why it’s Helpful: Reduces repetitive code Makes systems flexible Powers automation in large applications 💻 Example: def add_method(cls): def new_method(self): return "Hello from metaprogramming!" cls.new_method = new_method return cls @add_method class MyClass: pass obj = MyClass() print(obj.new_method()) 🧠 What’s happening here? We are dynamically adding a method to a class — at runtime. ⚡ Pro Tip: If you understand this, you’ll finally understand how frameworks actually work under the hood. 💬 Follow this series for more Topics #Python #BackendDevelopment #100TopicOfCode #SoftwareEngineering #LearnInPublic

This is worth reading and understanding. It is the DeepWiki documentation of Claude Code with code-level coverage. Of course, they cannot share the original code, which is in TypeScript (due to legal issues), so the author has converted it to Python and then generated this document. If you are building any autonomous agentic system, this can unlock many opportunities to learn how to control the agentic loop. Also, if you are preparing for the Claude Certified Architect – Foundations (CCA-F), it has many practical takeaways. https://lnkd.in/gKstUTyU

We’ve all been there… “Code works perfectly on local” Production: 💥 I wanted to fix that gap — not with theory, but with something practical and repeatable. So I built a production-ready Python service setup: • Docker for consistency • systemd for reliability • Nginx + Blue-Green for zero-downtime deployments The idea was simple: 👉 Your service should never go down during deployment This blog is what I wish I had when I started building real systems. If you’re moving from scripts to production systems, this will help: https://lnkd.in/gZThnvK8 Would love your thoughts 🙌 #DevOps #Python #SystemDesign #Backend #Tech

𝗨𝗡𝗟𝗘𝗔𝗦𝗛 𝗧𝗛𝗘 𝗨𝗟𝗧𝗜𝗠𝗔𝗧𝗘 𝗣𝗬𝗧𝗛𝗢𝗡 𝗣𝗢𝗪𝗘𝗥𝗛𝗢𝗨𝗦𝗘: 𝗕𝗨𝗜𝗟𝗗 𝗟𝗜𝗚𝗛𝗧𝗡𝗜𝗡𝗚 𝗙𝗔𝗦𝗧 𝗔𝗣𝗜𝗦 𝗧𝗛𝗔𝗧 𝗟𝗘𝗔𝗩𝗘 𝗘𝗩𝗘𝗥𝗬𝗧𝗛𝗜𝗡𝗚 𝗘𝗟𝗦𝗘 𝗜𝗡 𝗧𝗛𝗘 𝗗𝗨𝗦𝗧 As we move into 2026, the demand for lightweight, high-speed backend services continues to accelerate. This tutorial provides the essential foundation for engineers looking to shift from legacy frameworks to modern, asynchronous Python development. ASYNCHRONOUS REQUEST HANDLING The core advantage of FastAPI lies in its native support for asynchronous programming. By leveraging the async and await keywords, the framework allows your application to handle multiple concurrent connections without blocking the event loop. This is critical for scaling I/O-bound services in a production environment. AUTOMATIC API DOCUMENTATION One of the most significant developer experience improvements is the built-in integration with Swagger UI and ReDoc. FastAPI automatically generates interactive documentation based on your code type hints. This removes the manual overhead of maintaining external API specs, ensuring that your documentation remains perfectly synchronized with your endpoint logic. PYDANTIC DATA VALIDATION Type safety is enforced through Pydantic, which utilizes Python type annotations to validate request bodies and query parameters. This pattern ensures that incoming data strictly adheres to defined schemas before reaching your business logic, effectively preventing common runtime errors related to data structure mismatches. Conclusion: Senior Engineer takeaway FastAPI has effectively bridged the gap between rapid prototyping and production-grade performance. By focusing on standard Python type hints and asynchronous patterns, it allows teams to reduce boilerplate code while maintaining the rigorous structure required for enterprise systems. For developers aiming to stay competitive in the current hiring landscape, mastering these patterns is no longer optional. Tags: #FastAPI #Python #API #Backend #WebDevelopment 📺 Watch the full breakdown here: https://lnkd.in/dwv_5gyE

The "Shadow" Fix: Python Version Compatibility **Hook:** Building for the "Latest & Greatest" is easy. Building for the "Real World" is where the engineering gets messy. **Body:** While finalizing my Enterprise RAG pipeline, I hit a silent production-breaker: A `TypeError` buried deep in a third-party dependency. The culprit? The `llama-parse` library uses Python 3.10+ type union syntax (`|`), but the production environment was locked to Python 3.9. Result: Immediate crash on boot. Instead of demanding a system-wide upgrade—which isn’t always possible in locked-down enterprise environments—I implemented a **Graceful Fallback Logic**: ✅ **Dynamic Imports**: Wrapped the cloud-parser initialization in a guarded `try-except` block. ✅ **Smart Routing**: If the Python environment is incompatible, the system automatically redirects to a local, high-fidelity `PyMuPDF` parser. ✅ **System Resilience**: The app stays online, the UI remains responsive, and 99% of RAG functionality remains available without a single user noticing a failure. Real Engineering isn't just about using the best tools—it’s about writing code that doesn't break when the environment isn't perfect. #Python #SoftwareEngineering #RAG #AIEngineering #SystemDesign #Resilience

I shipped a model to a production server and it crashed within five minutes. Wrong Python version. A library I had not pinned had updated overnight. The model worked perfectly on my machine. That was the day I learned Docker is not optional for ML deployment. Here is the complete Dockerfile for a FastAPI ML model, every line explained, plus the four mistakes that will cost you hours if you skip them. The one thing that took me too long to understand: the order of COPY and RUN in a Dockerfile changes how long every single build takes. Copy requirements.txt first, run pip install, then copy your code. That single reordering takes builds from minutes to seconds on every code change. The other thing nobody mentions: always add .dockerignore before your first build. Without it, Docker sends your entire project into the image including your datasets. Swipe through for the complete setup including multi-stage builds and a mistake checklist. What was the most painful deployment problem you have hit with a containerised model? #Docker #MLOps #Python #MachineLearning

🏗️ Scaling Up: Moving from Scripts to Systems As my Python projects grow, I’m learning that writing code that works is only half the battle. Writing code that is maintainable is where the real skill lies. I’ve started refactoring my automation scripts by breaking them down into reusable functions. Here’s why this shift is a game-changer: ♻️ Reusability (DRY - Don't Repeat Yourself) Instead of copying and pasting logic, I can write a function once and call it whenever I need it. It makes the codebase smaller and much easier to update. 📖 Readability By abstracting complex logic into functions with clear names like clean_data() or export_to_excel(), my main execution flow now reads like a story rather than a wall of text. Anyone (including my future self) can understand the logic at a glance. 🧪 Testability Organizing code into functions allows me to test individual "units" of logic in isolation. If something breaks, I know exactly which function is responsible, making debugging significantly faster. The Evolution: Level 1: Write a long script that runs top-to-bottom. Level 2: Organize logic into functions for better flow. Level 3: Move functions into separate modules for a professional project structure. I’m currently at Level 2 and feeling the difference in how I approach problem-solving! 💻 #PythonProgramming #CleanCode #SoftwareDevelopment #LearningToCode #CodeRefactoring #TechCommunity

Day 29: The Anatomy of a Bug — Three Types of Errors 🐞 In programming, not all "crashes" are created equal. We categorize errors into three levels of severity, ranging from "The computer doesn't understand you" to "The computer does exactly what you said, but you said the wrong thing." 1. Syntax Errors (The "Grammar" Mistake) These happen before the code even starts running. Python’s "Parser" looks at your script and realizes it violates the rules of the language. The Cause: Missing colons :, unclosed parentheses (, or incorrect indentation. The Result: The program won't start at all. 💡 The Engineering Lens: These are the "cheapest" errors to fix. Your code editor (IDE) will usually highlight these with a red squiggly line as you type. 2. Runtime Errors (The "Panic" Mistake) The syntax is perfect, and the program starts running—but then it hits a situation it can't handle. The Cause: Dividing by zero, trying to open a file that doesn't exist, or calling a variable that hasn't been defined yet (NameError). The Result: The program "crashes" in the middle of execution. 💡 The Engineering Lens: We handle these using Exception Handling (try/except). Professional code assumes things will go wrong (like the internet cutting out) and builds "safety nets" to keep the program alive. 3. Semantic Errors (The "Logic" Mistake) These are the most dangerous and difficult to find. The program runs perfectly from start to finish. There are no crashes and no red text. But the output is wrong. The Cause: You used + when you meant -, or your loop stops one item too early. The Result: The program gives you the wrong answer (e.g., a calculator saying $2 + 2 = 22$). 💡 The Engineering Lens: The computer is doing exactly what you told it to do; the "error" is in your logic. We find these using Unit Testing and Debugging tools. If you don't test your code, you might not even know a semantic error exists until a customer reports it. #Python #SoftwareEngineering #Debugging #ProgrammingTips #LearnToCode #TechCommunity #PythonDev #CleanCode #BugHunting