#Github Awesome

ChatGPT is great, but if you are serious about Software Engineering and ML, you need a specialized stack. ​Here are the 3 AI tools I actually use daily to write better Python and build faster: ​1. Cursor (The AI IDE) Stop copying and pasting from browser windows. Cursor is a fork of VS Code with an LLM built directly in. You can highlight your codebase and ask it to refactor, find bugs, or write tests instantly. ​2. Hugging Face Spaces This is the GitHub of Machine Learning. When I need to test an open-source model or deploy a quick GenAI prototype, I spin it up here. It’s the fastest way to get your ML projects live. ​3. Phind It’s a search engine optimized specifically for developers. Instead of giving you generic SEO articles, it searches documentation and gives you the exact code snippet you need to solve complex SDE problems. ​🔥 Bonus Tip: The tool doesn't matter if you don't understand the fundamentals behind the code. ​I’ve built a library of over 750+ free tutorials breaking down Python, GenAI, and Software Engineering step-by-step. ​👇 Comment "TOOLS" and I’ll DM you the link to my top 5 must-watch tutorials! . . . ​🏷️ #softwareengineering #codingtools #pythondeveloper #machinelearning #genai artificialintelligence developers techstack

GitHub repo to start Machine Learning for absolute beginners 👨‍💻👾 Comment “Repo” to get all links! . . . . . . . [machine learning, code, github, learn, study,trend] #fyp #trend #ml

Comment “ML” and I’ll send you the links. You don’t need overpriced AI courses to break into machine learning. Some of the best ML and LLM resources are free, open-source, and built by top engineers and researchers. 📌 5 High-Impact GitHub Repos to Master Machine Learning & AI: 1️⃣ ML-For-Beginners A structured, beginner-friendly roadmap covering machine learning fundamentals like regression, classification, clustering, and model evaluation. Perfect if you want a guided introduction to ML without jumping between random tutorials. 2️⃣ bitsandbytes Learn 8-bit and 4-bit quantization for large language models. Essential if you want to run or fine-tune LLMs efficiently on limited GPU memory using techniques like QLoRA and low-bit training. 3️⃣ LLMs-from-scratch Build large language models step by step to truly understand transformers, attention mechanisms, tokenization, and training loops. Ideal for developers who want to understand how GPT-style models actually work under the hood. 4️⃣ LangChain A framework for building AI agents, RAG pipelines, and LLM-powered applications. Connect models to tools, vector databases, APIs, and real-time data sources to create production-ready AI systems. 5️⃣ LoRA (Low-Rank Adaptation) A powerful method for parameter-efficient fine-tuning. Train massive models by updating only a small fraction of weights — reducing cost while keeping performance strong. These repos cover core AI skills like deep learning, transformers, quantization, LLM fine-tuning, retrieval-augmented generation (RAG), AI agents, and efficient model deployment. Whether you're preparing for an ML engineer role, building AI startups, experimenting with open-source LLMs, or leveling up your deep learning knowledge, these resources will dramatically accelerate your progress. Save this, share it, and start building real AI skills the smart way.

It’s 2026 and if you’re not learning and leveraging AI, you’re gonna get left behind! comment “Learn AI” to get links to these GitHub Repo to learn more about AI and AI systems #coding #softwareengineer

Comment “ROADMAP” to get links! 🚀 Want to learn Machine Learning in a way that actually sticks? This mini roadmap helps you go from zero to building real projects without feeling lost. 🎓 30 Days Python Start here if Python still feels shaky. You will build daily momentum, practice the fundamentals, and get comfortable writing code fast. This makes every ML course easier because you stop struggling with syntax and focus on concepts. 📘 Stanford ML Now build real intuition. This course helps you understand the core ideas behind machine learning like models, training, optimization and evaluation. You will learn the why behind the math and start thinking like an ML engineer instead of just copying code. 💻 Andrew Ng ML Time to get practical. This specialization walks you through the most important ML algorithms and how to apply them. You will learn how to train models, debug issues, improve performance, and turn what you learn into portfolio projects. 💡 With these ML resources you will: Build a strong Python foundation for ML Understand the core ML concepts and math clearly Create portfolio ready ML projects with confidence If you are serious about AI, data science, or ML engineering, this roadmap will save you weeks of confusion. 📌 Save this post so you do not lose the roadmap. 💬 Comment “ROADMAP” and I will send you all the links. 👉 Follow for more content on AI, machine learning and data science.

Comment “ML BASICS” to get a free guide to Master your Coding Foundations and Machine Learning! I studied ML for 11 hours every day for 3 months 😳 Here’s what i learned: 1️⃣ Master The Basics – Spend more time than you think on core programming concepts. 2️⃣ Bad Basics= bad projects Struggling to land a job? It might be your projects. 3️⃣ Strong Foundation = strong results – You can’t build great projects if you don’t know what you’re doing. Want to build the skills to create standout projects and land your dream job? I’ve got a free guide to help you master the coding basics and learn ML that worked for me. Comment “ML BASIC” down below, and I’ll send it your way! ⬇️ #coding #softwareengineering #techjobs #techcareers #machinelearning

Top 5 GitHub Repos Every AI Engineer Needs 🚀 Stop copy-pasting messy Jupyter notebooks and start deploying like a Senior AI Engineer. 🚀 These 5 open-source GitHub repositories are the absolute industry standard for putting Machine Learning and LLMs into production. 1️⃣ vLLM 2️⃣ BentoML 3️⃣ Ray 4️⃣ MLflow 5️⃣ FastAPI Save this video for your next deployment so you don't lose the stack! 📌 Did I miss any? Drop your favorite production repo in the comments 👇 #machinelearning #mlops #ai #python #coding #softwareengineering #github #artificialintelligence #tech

STOP USING GITHUB LIKE IT’S 2010! 🛑💻 If you aren’t using Model Context Protocol (MCP) yet, you are wasting hours of your life on ChatGPT. In 2026, GitHub is more than just a place to store code—it’s an AI-integrated ecosystem. I’m Anay, Software Engineer at Dell, and I’ve compiled the ultimate roadmap to master Git + the secret hacks top devs use. … Inside the “Git Master” Document: ✅ Best Animated Resources (No more boring docs!) ✅ How to use .dev and .mcp.io shortcuts ✅ 20+ hidden GitHub features for 2026 … Want the full list and the MCP guide for FREE? 1️⃣ Follow @[YourUsername] (to level up your dev game) 2️⃣ Comment “GIT” below. 3️⃣ Check your DMs for the magic link! 📩 … [github hacks 2026, model context protocol, mcp.io, git branching, learn git with animation, software engineer dell, vscode in browser, chatgpt for developers, github dev trick, samay raina github] ….. #github #mcp #vibecoding #softwareengineering #git

Comment “ML BASICS” to get a free guide to Master your Coding Foundations and Machine Learning! I studied ML for 11 hours every day for 3 months 😳 Here’s what i learned: 1️⃣ Master The Basics – Spend more time than you think on core programming concepts. 2️⃣ Bad Basics= bad projects Struggling to land a job? It might be your projects. 3️⃣ Strong Foundation = strong results – You can’t build great projects if you don’t know what you’re doing. Want to build the skills to create standout projects and land your dream job? I’ve got a free guide to help you master the coding basics and learn ML that worked for me. Comment “ML BASIC” down below, and I’ll send it your way! ⬇️ #coding #softwareengineering #techjobs #techcareers #machinelearning

Machine Learning isn’t just for researchers anymore—it’s for BUILDERS. 🤖🛠️ In 2026, the gap between a "Coder" and an "Engineer" is the ability to build intelligent systems. From recommendation engines to predictive analytics, ML is everywhere. 📈 Day 04/90 of The 1% Developer Club: My focus is shifting from simple CRUD apps to ML-integrated workflows. 🚀 The 3-Step ML Strategy: Focus on Data Preprocessing (80% of the work!). Learn Model Deployment (Not just training). Build for Scale. Comment "ML" if you want my curated roadmap for Machine Learning! 📩 Follow @ThePratyushGupta for Day 05. Viral Hashtags (Categorized for SEO) #MachineLearning #The1PercentDeveloper #ThePratyushGupta #AIEngineer #DataScience CodingLife SoftwareDeveloper WinterArc 90DayCommitStreak TechCommunity2026 PythonCoding

Comment “ML” and I’ll send you the links👇 Machine learning doesn’t have to feel overwhelming. With the right guidance, complex topics like models, training, and prediction start making real sense 🧠 📌 Check out these beginner-friendly ML videos: 1️⃣ Learn Machine Learning Like a Genius – by InfiniteCodes 2️⃣ All ML Concepts Explained in 22 Minutes – by InfiniteCodes 3️⃣ ML for Everybody (Full Course) – by FreeCodeCamp If terms like neural networks, supervised learning, or algorithms have ever confused you, these tutorials simplify everything into clear, practical explanations you can actually follow. Instead of getting stuck in heavy math or abstract theory, you’ll build strong intuition around how machine learning works — from foundational concepts to real-world AI applications. Whether you're interested in artificial intelligence, data science, Python projects, or future-proof tech skills, this is a powerful place to begin. ⭐ Save this so you don’t lose it, share it with someone learning AI, and start making machine learning finally click.

Hehhe hope you guys like these recs #machinelearning #coding #ai