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Still Scared AI Is Coming For Your Job?
Here's 20 Repos To Actually Do Something About It1. Prompt-Engineering-GuideExplains how to actually get useful, reliable output from AI models instead of guessing and hoping.
2. AI-For-BeginnersCovers the broader AI landscape, not just LLMs, so you're not caught off guard by the next big shift either.
3. generative-ai-for-beginnersA calm, lesson-by-lesson intro to building with generative AI, no ML background assumed.
4. anthropic-cookbookReal code examples for building things with Claude, straight from the people who make it.
5. openai-cookbookThe same idea for OpenAI's models, practical patterns instead of theory.
6. ML-For-BeginnersTwelve weeks of classic machine learning basics, useful groundwork before jumping into AI specifically.
7. llama_indexHandles connecting AI models to your own data and documents, a skill that comes up constantly in real jobs now.
8. ollamaLets you run AI models on your own machine, good for understanding them without relying on an API key.
9. llama.cppShows what's actually happening when a model runs, right down to the hardware level.
10. transformersThe library behind most modern AI models, worth exploring even just to see how they're built.
11. awesome-ai-agentsA running list of AI agent projects and frameworks, handy for keeping track of a space that moves fast.
12. nanoGPTA small, readable implementation of GPT, built by Andrej Karpathy so you can see the whole thing without getting lost.
13. AutoGPTOne of the earliest autonomous AI agent projects, still useful for understanding how agents plan and act.
14. gpt-engineerAn AI agent that writes entire codebases from a prompt, worth trying just to see what it's actually capable of.
15. OpenHandsAn open source AI coding agent, a good look at where AI-assisted development is heading.
16. awesome-machine-learningA broad, well-organized list of ML resources and libraries across every language, a solid reference to keep bookmarked.
17. langchainThe most widely used framework for wiring AI models into actual applications, worth knowing even if you end up not using it.
18. Awesome-LLMA curated list of papers, models, and tools, good for going deeper once the basics feel comfortable.
19. applied-mlReal-world write-ups from companies on how they actually use ML and AI in production, not just in demos.
20. llm-courseA structured path from "what is an LLM" to actually fine-tuning and deploying one yourself.
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