#python3

Learn Python. ⠀ Learn JavaScript. ⠀ Learn React. ⠀ Learn Docker. ⠀ Learn AWS. ⠀ Learn Kubernetes. ⠀ Finally, you start feeling confident. ⠀ Then someone says, “I built the whole app with a prompt.” ⠀ Welcome to being a developer. ⠀ After 16+ years in engineering, I understand why this feels exhausting. Every time you get comfortable, the industry gives you more homework. ⠀ But I’d redraw the last part of this curve. ⠀ Your engineering knowledge still matters when you have to explain why: ⠀ • The app works locally but fails in production. ⠀ • One database query takes down the API. ⠀ • A retry charges the customer twice. ⠀ • An AI-generated endpoint lets users access someone else’s data. ⠀ Knowing how to investigate those problems takes experience. Every layer you learned gives you somewhere to start. ⠀ That knowledge helps while you’re prompting, too. ⠀ You can spot a missing requirement, question a design choice, and catch a dangerous assumption before it spreads across twenty files. ⠀ I want AI to handle more of the repetitive work. I also want to understand what I’m putting into production. ⠀ If you’re feeling behind, try this: ⠀ Take one small feature you already know how to build. Ask an AI coding tool to implement it. Then review the result like a teammate’s pull request. ⠀ 1. Read the diff. ⠀ 2. Check the dependencies. ⠀ 3. Test the failure cases. ⠀ 4. Find something you would change and explain why. ⠀ You’ll learn the tool while exercising the judgment you’ve spent years developing. ⠀ And if you’re just starting, follow the generated code all the way through: from the button, to the API, to the database, and back. ⠀ You don’t have to restart your career every time the tools change. ⠀ You do have to keep learning. That part of the picture has always been accurate. ⠀ Which skill on this chart took you the longest to learn?

The future of software is code that reasons. And learning Python is your first step in this direction. In this video course, you'll learn Python fundamentals, NumPy, Pandas, SQL, Flask, LLMs, and everything else you need to dive into AI Agent development.

✅ How to Learn Python Programming in 10 days ! .#pythonprogramming #python3 #pythoncode #pythonlearning #pythondeveloper #pythonprojects

You may have heard about the Model Context Protocol and wondered how it lets AI tools connect to other tools, APIs, and systems. Well, it's all thanks to MCP servers. In this guide, @manishmshiva explains how MCP servers work and helps you build your own with Python and the FastMCP library.

Python Quiz: What is the Output? x = 10 if x == 10: print("Hello")

🚨 CRITICAL SECURITY ALERT PHP, GitLab & Node.js were already facing serious security vulnerabilities. Now, Linux, Python & Java are also in the spotlight. The potential impact across the tech stack is huge.

THE WINNER OF ANTHROPIC'S OFFICIAL HACKATHON HAS PUBLISHED HIS CLAUDE CODE SETUP AND IT'S PURE GOLD 68 subagents, 286 skills, 94 commands, MIT license. ECC turns Claude Code into a full engineering team. The plan comes before the code. The failing test comes before the fix. Every change is reviewed by a context that hasn't seen how it was written. → Planning: you give it a sentence and approve the plan before a single line exists → Review: a different reviewer per language that reads your diff from scratch → Build fixes: a fixer per toolchain, including PyTorch and CUDA → Security: full OWASP sweep and injection scanning in the agent config → Architecture: detects design errors when they're still cheap, before migration → Domain: databases, ML pipelines, E2E tests, and documentation The security part is what almost nobody sets up. An external OWASP audit costs four figures and a week. This one is ready before you eat. The skills cover testing, Python, Go, Rust, Django, Next.js, Docker, and CI/CD. That said, don't activate all 286 at once or you'll end up with a config worse than the one you had. This is how it integrates well: → Make a fork and read the README before copying anything → Start only with the planning agent and the rules pack for your main language → Copy to your .claude folder only the agents and skills you're going to use this week → Add the code reviewer and the security one afterward, as they yield the most → Test it first on a small repo, not in production → If something's extra, cut it. If you miss something, add just one more piece The rule is simple: if you don't know what an agent is for, you don't need it yet. Whoever integrates it well this weekend will spend the next quarter reviewing work instead of writing it. Save this post, you'll thank me 🔖 and follow @rakib_hossen_ai for more content..

📰 Missed a few days of AI news? Here's what happened this week in Data Points, our twice-weekly newsletter. 👇 🔐 PHP2Shell: GPT-5.6 Sol helped find a memory bug in software running up to 70% of sites. Patch is out. Update now. ⚡ MiniMax M3.1 Flash: 428B total / ~23B active, 1M context, 0.15/0.60 per million tokens. Weights due Oct 23. 🔌 Meta's Muse Wonder: 4/20 per million tokens, OpenAI compatible endpoint. Trying it = new base URL + model name. No full safety report yet. 🐍 Python 3.15 is out, 3.10 is end of life. Lazy imports (PEP 810), unpacking in comprehensions (PEP 798), sampling profiler in the stdlib. 🏛️ GPT-6.1 Sol achieved 97.9% byte for byte output parity migrating COBOL at higher effort. GPT-5.6 Sol only scored 5.4% at low effort. Expert review still required. 📬 Subscribe to Data Points: #DeepLearningAI #AIEngineering #SoftwareEngineering

Best YouTube Playlists to learn Programming: C C++ Python Java C# SQL Go PHP Swift Kotlin Dart Ruby Rust TypeScript R React Next.js Node.js HTML/CSS Machine Learning Deep Learning DSA

Python Quiz: What is the Output? A) 5 B) 7 C) 8 D) 4

Learn Coding by playing games 1. Kubernetes 2. DevOps 3. Linux 4. Git 5. Python 6. CSS & HTML 7. Cybersecurity 8. Mobile Coding (like Duolingo) 9. For Complete Beginners 10. 25+ Programming Languages

If you’re serious about PYTHON, This is all you need!!

You've never seen 🐍Python like this, trust me :) I've built an interactive 3D explorer of Python's parser & compiler pipeline. If you ever wanted to look under the hood and understand how Python actually works this is your chance. Thread🧵

Steal these free YouTube courses and actually learn to code. Bookmark this. No fluff. 1). C ↳ 2). C++ ↳ 3). Python ↳ 4). Java ↳ 5). C# ↳ 6). SQL ↳ 7). Go ↳ 8). PHP ↳ 9). Swift ↳ 10). Kotlin ↳ 11). Dart ↳ 12). Ruby ↳ 13). Rust ↳ 14). TypeScript ↳ 15.) R ↳ 16). React ↳ 17). Next.js ↳ 18). Node.js ↳ 19). HTML/CSS ↳ 20). Machine Learning ↳ 21). Deep Learning ↳ 22). DSA ↳ Pick one. Finish it. Build something. Repeat. Which one are you starting with?

Algorithms — Free PDF (472 Pages!) Want to strengthen your problem-solving skills and become a better programmer? This 472-page Algorithms book is a great resource for anyone interested in understanding how algorithms work and how to solve computational problems efficiently. What you can learn: ✅ Algorithm design and problem-solving ✅ Time and space complexity ✅ Efficient approaches to computational problems ✅ A stronger foundation for Data Structures & Algorithms (DSA) ✅ Skills useful for coding interviews and competitive programming Perfect for Python developers, computer science students, software engineers, and aspiring programmers! Get the free PDF here:

Climbing the 17-step Python roadmap but hitting a scalability wall in production? 🧠 Most junior builders sprint straight to step 11 (FastAPI/Django) while completely ignoring step 02 and 08 reference count mechanics under CPython under the hood. Which step did you fail the most in senior interviews? 👇

I hid 5 bugs and 1 slow function in a small Python shop library and asked MiniMax M3.1 Flash to find and fix them. One prompt, no hints. They're easy to miss: the project's own 6 tests all pass, and each bug only shows on a specific input. DURATION later, my hidden tests passed 6 of 6, and it found a 7th I never planted. → Pages dropping their last item → Median wrong on even lists → Tags leaking between items → End date excluded → Tax truncated, 399 instead of 400 → Duplicate check from 23.8s to 0.01s It also changed behavior I didn't ask for, so I'd read the diff before merging. Unlimited M3.1 Flash for MiniMax Code subscribers until Oct 25. 50% off the first M Plan month until Oct 31. #ad

AI can write code in seconds. So why are companies still looking for great engineers? Because writing code was never the whole job. The real skill is knowing what to build, how to build it, and how to make it work in production. Here’s the roadmap to becoming an AI engineer in 2026 🧵 1. Master the Fundamentals Before jumping into AI, build a strong foundation. → Python, JavaScript, or C++ → Data Structures & Algorithms → OOP and problem-solving → Databases and APIs → Computer science fundamentals AI can generate code, but understanding it is what makes you a better engineer. 2. Learn Real-World Software Engineering Knowing how to code isn't enough. You need to know how to ship. → Frontend and backend development → Git and collaboration → Cloud computing → CI/CD and DevOps → System design and scalability → Security and performance This is where you stop writing code and start engineering systems. 3. Learn to Build With AI This is where the game changes. → AI coding assistants → LLM APIs and prompt engineering → Function calling and structured outputs → RAG and vector databases → AI agents and automation → Model evaluation and observability Don't just learn how to use AI tools. Learn how to build products with them. 4. Learn Production AI Engineering A demo that works once isn't a production-ready product. You need to understand: → Hallucinations and failure handling → AI security and guardrails → Testing and evaluation → Latency and cost optimization → Monitoring and reliability → Deploying AI systems at scale This is what separates AI experimenters from AI engineers. 5. Build Projects That Solve Real Problems Tutorials teach you concepts. Projects teach you engineering. Build an AI agent. Connect it to real tools. Add authentication, error handling, logging, and deployment. Then test whether it actually solves the problem. That's how you turn knowledge into experience. The biggest mistake you can make right now? Learning to prompt AI without learning how software works. AI is making coding faster, but engineering still requires judgment. The developers who thrive won't be the ones who write the most code. They'll be the ones who know what to build, understand what AI generates, and can turn it into reliable products. Learn the fundamentals. Master the tools. Build real things. That's the path to becoming an AI engineer. Save this roadmap for your learning journey.

holy f*ck, Karpathy put an entire AI degree on GitHub for free 7 repos, 306,741 stars, and in the right order they take you from backprop to agents that run research overnight each repo is the next layer of the one before 1. micrograd -> how a neural net actually learns backprop in about 100 lines of Python ↳ 2. nn-zero-to-hero -> the video course his lecture notebooks, from one neuron to GPT ↳ 3. minbpe -> how text turns into tokens the tokenizer every LLM starts with, in clean minimal code ↳ 4. nanoGPT -> your own GPT the simplest repo to train and fine-tune one ↳ 5. llm.c -> the same training with nothing hidden raw C and CUDA, no PyTorch underneath ↳ 6. nanochat -> a full chat model end to end GPT-2 level training cost ~$43,000 in 2019 here it's about $48 ↳ 7. autoresearch -> agents that do the research an agent edits the training code, trains for 5 minutes, keeps what improved and repeats all night ↳ train one GPT from scratch and you become the person who gets handed the AI project, along with the raise that comes with it the classroom half of the same degree, what Stanford actually teaches about AI, is below ↓

Which Programming Language Is the Easiest to Learn? If you're a beginner, choosing your first programming language can feel confusing. Here’s a quick comparison of 11 popular programming languages using the classic “Hello, World!” program: My recommendation: Start with Python if you're new to programming. Its readable syntax lets you focus on problem-solving before dealing with too many language-specific details. Remember, the easiest language depends on your goals and background! Which programming language did you learn first? Save this guide and share it with a beginner! Follow @CLCODING for more coding tips and programming tutorials.