Learn Rust by Doing with Rust Learning Bytes

Markdown is quietly becoming the primary programming language of our time. It’s not about syntax or compilers anymore. It’s about what actually gets used by both humans and AI agents. I'm seeing developers spending more time writing (and debugging) Markdown than traditional code. Skills, context files, prompts, specs, and documentation are often the things devs actually change to ship features and improve AI behavior. It feels like the next programming language isn’t another fancy syntax. It’s the one we already know how to write. #Markdown #AI #Developer #FutureOfCoding

I almost skipped practicing today. Tired. Distracted. Told myself I'd "catch up tomorrow." But I opened my laptop anyway. And that one decision reminded me why consistency beats motivation every single time. Day 11 of my Python journey — and today I went back to Classes. Not because it was new. Because it wasn't. Here's what nobody tells you about learning to code 👇 Revisiting something you've "already learned" hits completely differently the second time. The first time you meet Classes, you're just trying to survive the syntax. The second time? You start seeing the design. Today I built a NeedForSpeedDefaultCar class — private attributes, instance methods, a speed booster, and a customCar() method that let me swap the BMW M3 for an Audi A4 in a single line. The code worked. But more importantly — I understood why it worked. That's a different feeling entirely. 🔑 The real lesson from Day 11: Repetition isn't going backwards. It's the only way to go deeper. Every senior developer I've spoken to says the same thing: they re-read, re-build, and re-practice the fundamentals more than beginners do — not less. So if you've been learning something and feel like you're "just reviewing" — You're not stuck. You're solidifying. Keep going. 🚗💨 What's something you went back to and understood better the second time around? I'd love to hear it 👇 #Python #CodingJourney #100DaysOfCode #LearnToCode #PythonDeveloper #OOP #GrowthMindset #Programming

🚀 AI CHEAT CODE #026 🚀 Most devs use GitHub Copilot wrong. Here's the trick that 10x'd my output 👇 Stop accepting single-line suggestions. Use Copilot Chat with THIS prompt pattern: Step 1: Highlight your entire function Step 2: Open Copilot Chat (Ctrl+I) Step 3: Type: "Refactor this for readability, add error handling, and write unit tests" You just got 3 tasks done in 10 seconds. 🤯 Step 4: Ask: "What edge cases am I missing?" Step 5: Paste those edge cases directly into your test file ⚡ Pro Tip: Add your tech stack to the prompt: "Refactor for Python 3.11 with FastAPI best practices" — Copilot tailors everything perfectly. Drop a 🔥 if this saved you time today! Save this post for your next code review session. #AI #GitHubCopilot #Coding #DevProductivity #SoftwareEngineering #Python #CloudComputing #DevOps

After several days of building, debugging, and refining, I successfully structured and shipped a reproducible Machine Learning project setup using UV + Git 🥰 I simply wanted to build a clean and scalable foundation for ML workflows following my recent class on Git and GitHub Workflow. What I implemented: • Initialized a structured Python ML project using UV • Managed dependencies with uv.lock for reproducibility • Set up a clean virtual environment workflow • Organized project structure for real-world ML development • Integrated Git for proper version control • Successfully pushed the complete workflow to GitHub This is not just setup, it is a production-style ML foundation that ensures consistency, reproducibility, and scalability across environments. What I learned: • How modern Python tooling (UV) simplifies dependency management • Why reproducible environments matter in ML engineering • The importance of clean project architecture before model building • How Git integrates into real ML workflows This is the foundation I will continue building on as I move into full machine learning projects. Mentorship for Acceleration 🔗 GitHub Repository: https://lnkd.in/eqRmyY5p #MachineLearning #Python #DataScience #Git #MLEngineering #UV #BuildInPublic

"Pattern matching is where Rust started feeling elegant to me." Day 9 of Thinking like a Rust Programmer After learning Option and Result, one question comes up: “How do I actually use them cleanly?” In many languages, we write: → if-else chains → null checks → nested conditions It gets messy quickly. Rust gives you something better: → Pattern Matching (match) Instead of guessing what might happen, you explicitly handle every case. Example with Option: match value { Some(v) => println!("Value: {}", v), None => println!("No value found"), } Example with Result: match result { Ok(data) => println!("Success: {}", data), Err(e) => println!("Error: {}", e), } What’s powerful here? → You MUST handle all cases → No silent failures → No unexpected crashes At first, it feels like more code. But look closely: It’s not more code. It’s clear thinking written as code. Pattern matching teaches you: → Don’t assume outcomes → Handle every possibility → Make logic explicit And once you get used to it… It feels cleaner than if-else chains. Rust doesn’t hide complexity. It helps you manage it properly. Tomorrow → Traits (this is how Rust defines behavior) #RustLang #LearnRust #SystemsProgramming

🚀 Learning Rust with Opencode I'm excited to share my journey of learning Rust using Opencode AI assistant! Here's what I built: What I Learned ✅ Variables, data types, functions   ✅ Ownership & Borrowing - Rust's core memory safety model   ✅ Structs, Enums, Option & Result   ✅ Traits & Generics   ✅ Collections (Vec, HashMap)   ✅ Closures & Iterators   ✅ Testing with #test   ✅ Modules & error handling Project Built A CLI Todo application with: - Add/Delete/List tasks - Mark complete/incomplete - JSON persistence using serde - Full test coverage How Opencode Helped - Step-by-step explanations with code examples - Interactive Q&A to test understanding - Helped debug issues in real-time - Guided through project structure The best part? Learning by doing. I wrote code, got feedback, and built something functional while understanding why Rust works the way it does. If you're starting your Rust journey, I'd highly recommend: 1. The Rust Book (official docs) 2. Build small projects early 3. Don't skip ownership & borrowing concepts Special thanks to Opencode for making this learning journey smooth and interactive! #RustLang #Learning #Programming #Opencode

#VSCode + #GitHub + #AI, i am not sure if there is a better way to code nowadays. From static to complex python code. Let’s be honest. If you’re still writing code in 2026 using a basic text editor, you are missing out valuable features. The integration of VS Code with GitHub and the power of AI (#Copilot, #Claude, #Cursor) isn’t just a cool setup. It lets you do things you wouldn't know you can. And with hundreds of extensions available, you can practically develop almost anything from scratch. ✅️ Make commits, pushes, and PRs without ever leaving your environment. Everything happens "in-house." ✅️ With AI you save you 80% of the "boring" work. Boilerplate code? Unit tests? Documentation? You name it. ✅️ With GitHub Codespaces, your environment is everywhere, ready and pre-configured. What you gain? Easy. ➡️ You write code 2x, 3x faster ➡️ Fewer Bugs with AI suggesting code while you write ➡️And the most important, your Mental Health. Instead of wrestling with syntax, you wrestle with logic. #Artificial_Intelligence #Hey_Eye_Facts

Turned recently exposed Claude Code artifacts into a practical #cookbook for building production-grade coding agents. It does NOT include "Claude Code" Code itself. It gathers key patterns and architectures into clear documentation for learning with practical Python examples. It also includes an Agents.md file, so you can plug the repo into your own agent and get guided help in building a production-ready coding agent. https://lnkd.in/gHJgfXFd #CodingAgents #AI #playbook

Most people pick the wrong AI tool for coding — not because of features, but because of context window limits. We just published our deep-dive: Cursor vs GitHub Copilot 2026 Cursor handles entire codebases (200K+ token context). Copilot lives inside your IDE and requires zero setup. The difference in day-to-day workflow is bigger than most reviews admit. Full breakdown on pricing, autocomplete quality, and which one wins for solo devs vs enterprise teams: https://lnkd.in/d9UkyczX #AITools #Coding #DeveloperTools #Cursor #GitHubCopilot

This hands-on session by Augustine Correa showcased how GitHub Copilot is evolving from a code assistant into a full AI development partner. Using the Agent Lab Python repository on GitHub Codespaces, participants explored Agent Mode, smart approvals, and Plan Mode for structured execution. The demo highlighted how Copilot can understand projects, generate instructions, redesign UI (Dakshina Kannada-themed), and even run parallel workflows using Cloud Agents. The session wrapped up with custom agents, where attendees built a Social Bingo experience—showing how AI can power real-world, domain-specific workflows. #GitHubCopilot #AI #DeveloperTools #AgentMode #AIDev #Coding #AIWorkflows #TechWorkshop #GenAI #HackersMang