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3 AI Projects That Can Actually Make Your GitHub Portfolio STAND OUT in 2026 🚀 If you’re learning AI Engineering, Machine Learning, Generative AI or LLMs and wondering “What projects should I build to get a job?” start here. 👇 In this video, I break down 3 job-ready AI projects that you can build and showcase on your GitHub portfolio. You’ll learn: ✅ What to build ✅ Why each project matters ✅ Which AI/ML skills it demonstrates ✅ How to structure the project ✅ What to add to your GitHub README
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What is RAG in AI? Retrieval-Augmented Generation (RAG) is a technique that improves the output of large language models (LLMs) by allowing them to access and reference external knowledge sources before generating a response. LLMs are powerful AI models that can generate human-like text for various tasks, but they may sometimes produce irrelevant or outdated information. RAG enhances LLMs by enabling them to retrieve relevant information from a knowledge base, such as an organization's internal
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The future of AI could be right in front of us! Think about your needs and how you currently use AI. LLMs have familiar pain points that users recognize around the world. Thinking Machines is working to solve them all at once! They just dropped “Inkling,” a 975 billion parameter MoE model. It’s not competing with frontier models on the main benchmarks. Instead, it introduces leaps in efficiency and customization. It can be fine-tuned using “Tinker,” which is the real asset behind Thinking Machin
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