As a lifelong self-learner, I use all sorts of methods to learn new things, and AI is what I’m currently into. Although I’ve been in the game since 2022, my background wasn’t focused on AI. So, like everyone else, I had to do some “AI For Dummies” level study in order to get more involved. Below is a list of learning materials that I find very helpful for myself to get started with and might also be helpful for someone else in the same situation.
Quick Start
1. Large Language Models Explained Briefly
-
Type: Video
-
Note: Created by the popular channel 3Blue1Brown, this video uses intuitive visuals and simple language to explain how large language models (LLMs) work. Perfect for beginners who want a quick, accessible understanding of LLMs without diving into technical details.
2. Large Language Models: A Survey
-
Type: Research paper
-
Note: This survey provides a high-level summary of LLM developments, making it ideal for curious learners interested in understanding the evolution and variety of these models without getting overwhelmed by book.
-
Type: Research paper
-
Note: Provides an in-depth examination of LLM architecture, training strategies, and multimodal applications. Essential for researchers and advanced practitioners seeking detailed insights into the state-of-the-art in LLMs.
-
Type: Comprehensive report
-
Note: This annual report from Stanford offers insights into the latest AI trends, including research, ethics, and global adoption. A must-read for policymakers, educators, and tech enthusiasts seeking a broad overview of AI’s impact.
- Type: Web article
- Note: Gartner’s accessible yet detailed coverage is perfect for business professionals and anyone interested in how generative AI can be applied across industries.
From Basic to Advanced
1. Hands-On Large Language Models
-
Type: Book
-
Note: This practical guide teaches you how to build, fine-tune, and deploy LLMs. Ideal for developers, data scientists, and AI practitioners ready to get their hands dirty. Also available on shadow libraries.
2. A Survey of Large Language Models
-
Type: Bookish paper
-
Note: Dubbed more of a book than a paper, this resource dives deep into the technical architecture and methodologies of LLMs. Best suited for researchers and advanced learners.
3. The 2025 AI Engineer Reading List
- Type: Reading list
- Note: By the popular podcast show Latent Space. A goldmine for aspiring AI engineers, this list highlights the most influential papers, books, and resources to stay ahead in AI development for this year.
- Type: Reading list
- Note: Offers a roadmap to mastering generative AI, from fundamentals to advanced topics. Great for learners who value organized study paths.
- Type: Digital magazine
- Note: Authored by AI expert Sebastian Raschka, this magazine covers technical and philosophical discussions about AI. Ideal for anyone interested in both the practical and theoretical aspects of the field.
6. Awesome List of Cybersecurity and AI
- Type: Reading list
- Note: This curated list focuses on the intersection of AI and cybersecurity, making it invaluable for security professionals and researchers.
Blogs
- Note: Simon’s posts trending news and technical details with accessible narratives, catering to open-source developers and AI enthusiasts.
2. AI, Software, Tech, and People by X
- Note: Xavier Amatriain’s blog offers insightful commentary on AI’s impact on society, making it a thoughtful read for non-technical audiences.
3. The Kaitchup – AI on a Budget
- Note: Benjamin Marie’s posts The Weekly Kaitchup news and running open LLMs locally, ideal for small businesses, startups, and hobbyists.
Podcasts
- Note: Delving into AI innovation with leading experts, uncovers untapped potential and provides practical insights for enthusiasts and newcomers alike.
2. The Gradient
- Note: Provides diverse perspectives on AI from researchers, builders, and users across academia, engineering, art, and entrepreneurship, making it ideal for a broad audience seeking multifaceted insights.
3. Practical AI
- Note: By Changelog Media, makes artificial intelligence practical, productive, and accessible to everyone.
- Note: Sam Charrington’s discussions with top industry experts on cutting-edge machine learning and AI technologies, providing valuable knowledge for researchers, engineers, and tech leaders.
- Note: By AI / Security Researcher and Entrepreneur, Daniel Miessler. Unsupervised Learning is a website, newsletter, and podcast about how to survive and thrive as humans in a post-AI world.
- Note: A weekly podcast where host Nathan Labenz interviews AI innovators, diving into the transformative impact AI will have in the near future.
- Note: Top 10 US Tech podcast. Exploring AI UX, Agents, Devtools, Infra, Open Source Models for AI Engineers.
Afterwords
This list may get updated after time. But keep practicing while learning theory is the best way to go—using AI via self-hosting local LLMs, like my Homebrew AI Lab. It was done in 2023 and there are better tools nowadays, such as Ollama, jan and llamafile, which provide user friendly, efficiency and privacy. I found harbor as a great testbed for experimenting with different LLM stack. So there might be new post on that in the near future.