Building My Own HTTP Server with CodeCrafters Challenge

Built a web-based network scanning tool using Python and Flask. It performs multi-threaded port scanning and shows results in a browser dashboard with live progress, scan history, and export options. I also added a simple risk analysis layer that gives a short explanation for each open port, along with basic safety controls to restrict unintended scans. Features: - Multi-threaded TCP scanning - Browser UI with real-time results - Scan history stored in SQLite - Export reports (TXT, JSON) - Safe mode for controlled usage GitHub: https://lnkd.in/gKDBtUs3

Someone finally built a production-grade Agent OS in pure Rust. OpenFang isn't a Python wrapper or a generic multi-agent orchestrator. Here are the specs: → 137,728 lines of code. → 14 crates. → 1,767+ passing tests. → Zero clippy warnings. Because it's Rust, the performance metrics are insane. Cold starts happen in under 200ms. It uses just 40 MB of idle memory. It includes a SQLite memory backend with vector embeddings and canonical sessions. For me, this is what battle-tested infrastructure actually looks like. GitHub Repo: https://lnkd.in/d9p5-shX

Version 1.0 Of DorkSINT is live!! DorkSINT is an open-source Python CLI that speeds up Google dorking by turning your search objective into structured, paste-ready queries. It supports interactive prompts and non-interactive flags, includes reusable query templates, and runs natively in both PowerShell and CMD. Built for legal, authorized security research ONLY. https://lnkd.in/gYg2yhGA

We ran PRFlow on 10 real pull requests across three open-source repos. Python. TypeScript. Go. Ruby. JavaScript. Results: PRFlow: 4.3 out of 5 average rating. Other AI reviewers: 2.5 out of 5. PRFlow won all 10. 7.7 issues found per PR vs 2.3. The biggest gap was not the score. It was what other tools missed entirely. XSS vulnerabilities, SQL injection risks, race conditions. PRFlow caught all of them. Why? Because it reads the whole codebase, not just the diff. Launching May 4. Try here: https://lnkd.in/dnP_K4a7

Built my first MCP server this weekend just to understand how they work. The idea was simple to start: connect Claude to my Gmail, Google Calendar, and a local task DB, then ask it "what do I need to know?" It came back with a full Sunday rundown , unread emails triaged by priority, tomorrow's stand-up, pending tasks. One question, actual context from my real data. The interesting part wasn't the output. It was realizing how MCP works under the hood, you're just exposing Python functions as tools over stdio. Claude decides when to call them and how to combine the results. Repo if you're curious: https://lnkd.in/dRNNVgxS

Fully realise the peanut gallery will say who cares, no one uses it anyway, but I have started on doing a major bit of house cleaning of my Apache/mod_wsgi module, ripping out lots old old Python 2 and Apache httpd 1.3/2.0/2.2 code and other old cruft. I am looking at jumping from minimum Python version of 3.8 all the way up to 3.12. This is because 3.12 has major changes to module setup for C extensions which would lessen brain tax when working on code. Good idea or bad idea?

𝐃𝐚𝐲 𝟔/𝟑𝟎: 𝐇𝐨𝐰 𝐝𝐨 𝐏𝐲𝐭𝐡𝐨𝐧 𝐝𝐞𝐯𝐬 𝐡𝐚𝐧𝐝𝐥𝐞 𝐭𝐡𝐢𝐬? 🐍 Coming from C++, I’m used to the compiler being my safety net. If I try to add a string to an int there, the code won’t even run. ➡️ But yesterday I realized Python is... a different world! def add(a, b): return a + b add(10, "5")💣 Boom! Runtime Error In a tiny script, this is a 2-second fix.. But in a massive codebase with thousands of functions? This feels like a 𝐡𝐢𝐝𝐝𝐞𝐧 𝐫𝐢𝐬𝐤 𝐰𝐚𝐢𝐭𝐢𝐧𝐠 𝐭𝐨 𝐡𝐚𝐩𝐩𝐞𝐧. So, the real question for the experts here: How do you guys stop these "sneaky" errors before they hit production? Is it: A) Just remember everything B) Test everything thoroughly (Unit tests for every single edge case) C) Use Python type hints (a: int, b: int) (👀but do they actually stop the crash?) D) Something else(MyPy? Pylint?) I’ve been digging into this 𝐜𝐥𝐚𝐬𝐡 𝐛𝐞𝐭𝐰𝐞𝐞𝐧 𝐟𝐥𝐞𝐱𝐢𝐛𝐢𝐥𝐢𝐭𝐲 𝐚𝐧𝐝 𝐬𝐚𝐟𝐞𝐭𝐲 𝐭𝐨𝐝𝐚𝐲. I’m hunting for a way to make Python feel just as safe as C++. I'll share what I find tomorrow! Drop your choice below 👇 What’s the standard industry workflow for catching these? #Python #Cpp #LearningInPublic #30DaysOfCode #SoftwareEngineering #Programming

Built a multithreaded file cipher engine in C++17 and Python that handles thousands of files without choking; here's how. I just finished one of the most challenging projects I've built so far. A few weeks ago, I asked myself, what happens if you try to encrypt 10,000 files at once? So I actually tried it. My system nearly crashed. Turns out spawning a new thread for every file is a terrible idea at scale. So I went back to the drawing board and built the Parallel Cipher Engine in C++17 and Python. Honestly had to learn a lot of new concepts along the way: → Thread pools that adapt to your actual CPU cores → Reading files in 64KB chunks instead of byte by byte (genuine game changer) → Building a lightweight stream cipher from scratch → Created UI for better interaction → Managing threads safely without race conditions It's not perfect, and there's a lot I'd do differently now. But seeing it process thousands of files without breaking a sweat, that feeling makes it all worth it. If you're a beginner like me and haven't explored multithreading yet, just start. You'll break things, you'll get frustrated, but you'll learn so much faster than any course. Would love feedback from anyone who's been down this road. Github: https://lnkd.in/dJXCWuPF #CPP #Multithreading #BuildInPublic #SoftwareEngineering #LearningInPublic #Programming

💡 Ever wondered how file uploads actually work behind the scenes? Not just “click upload” — but what really happens under the hood? 🚀 As part of my daily Python learning, I built a chunk-based file transfer system using sockets — similar to how real-world systems handle large file uploads and streaming. 👉 Instead of sending the whole file at once, my system: Breaks files into 4096-byte chunks Sends metadata (filename, size) Transfers each chunk with its length Reconstructs the file safely on the server side This approach improves: ✅ Reliability ✅ Memory efficiency ✅ Scalability for large files 📄 I’ve attached the complete client-server implementation in the document. 🎯 What this project demonstrates: Strong understanding of Python networking (TCP sockets) Handling streamed/partial data (recv loops) Designing a custom communication protocol Writing robust backend logic 📈 What I’m working on next: Multi-client support (threading / async) Progress tracking 📊 Retry mechanism for failed transfers Secure transfer (encryption) 🔐 #Python #BackendDevelopment #Networking #Sockets #Developers #LearningInPublic

🎯 Tech Learning Journey - Day 03: Web APIs & Requests - Talk to the Internet! Web APIs are how your Python code talks to websites and online services. You send a request, just like asking for information, and the API sends back data that you can use in your application. import requests # Get data from a public API response = requests.get\('https://lnkd.in/gUpgfHqa) data = response.json\(\) print\(f"User: \{data\['login'\]\}"\) print\(f"Repos: \{data\['public\_repos'\]\}"\) Where I use this: Fetching weather updates, getting user data from databases, and integrating third-party services into my apps. #Python #Coding #Programming #WebAPI