Hi, I'm Christian

Interesting read.

Great book. Highly recommended if you're working with AI.

A modern guide to building AI services. This is a good read if you're a complete beginner that hasn't developed in the AI space. If you have, it's very likely you'll know what's presented in this book.

A solid reference work with useful insights, though the detailed explanations and repeated examples can feel verbose at times. The repetition, while potentially frustrating on a linear read-through, actually serves the book well as a reference tool—you can easily jump back in and find comprehensive explanations of concepts without needing to hunt through the entire text. Great for dipping in and out of as needed.

Solid coverage of LLM system concepts—feature pipelines, RAG optimization, MLOps. There's some super useful stuff here. The writing tends to over-explain trivial details while skipping over the interesting architectural decisions. You get a lot of what and how, but not much why or discussion of alternatives. The code examples are a bit rough around the edges—inconsistent patterns, small bugs, implementations that feel more educational than practical. It's good for understanding the concepts and how pieces fit together, but you'll need to rethink things for real projects. Worth reading if you want a conceptual overview of building LLM systems, just don't expect production-ready solutions.

The 80/20 principle is a great mental model to have in your arsenal.

I read this book early in my life, but I still remember the focusing question. The core theme of asking questions, and thinking hard about which questions to ask, has been useful.

Adler changed how I think about reading. He argues convincingly against speed-reading fads and surface-level skimming—something I once got caught up in—and instead lays out clear techniques for actively engaging with books. His advice on systematically breaking down a book’s arguments, spotting key ideas, and practical reading techniques is especially valuable.

After hearing so much about this book in AI circles, it didn't quite match the hype. Bostrom's philosophical background shows as he lists every possible superintelligence scenario, building complex logical structures that often feel like they support predetermined conclusions about inevitable doom. While the constant catastrophe focus gets exhausting and feels disconnected from actual AI development, the book deserves credit for legitimizing AI safety research and raising important questions. Chapters 14-15 on practical governance and preparation stand out with genuinely useful insights.

I enjoyed this book. It’s a great, informative deep dive into JS. I don’t always agree with the opinions that are presented, but it’s super refreshing to hear them, as they are often nuanced and reasonable—and they help me question my own beliefs. It’s a fantastic resource for developers looking to truly understand JavaScript’s inner workings.

Hamming's book is a masterclass in intellectual ambition and the pursuit of excellence. It's not just about science or engineering—it's a philosophy for living a meaningful life through deliberate effort and focus on important problems.

A brilliant exploration of timeless human patterns. Housel masterfully shows how understanding what never changes is more valuable than predicting specific events.

Dawkins presents a compelling case for evolution by natural selection, dismantling arguments for design while explaining how complexity can emerge without a designer. His analogies are brilliant, particularly the "blind watchmaker" itself and the biomorphs program. The book thoroughly addresses common misconceptions about evolution and offers clear explanations of complex evolutionary concepts. A foundational text for understanding how the incredible diversity and complexity of life arose through natural processes.

Filled with practical advice and frameworks for making better decisions.

A fascinating look at how Jim Simons and Renaissance Technologies revolutionized investing through mathematics and data science. Highly recommended for anyone interested in quantitative finance, systematic trading, or how unconventional approaches can lead to extraordinary success.

I learned a ton from this book. There were some great explanations for most of the concepts. I feel like the book succeeded at doing what the authors set out to do. The book was somewhat scattered in focus. There was a great depth on some topics, while others had multiple explanations and surface-level explorations at various parts of the book. But they make up for this by covering a lot of ground. I simply skipped what felt redundant or uninteresting to me. In some regards it felt somewhat dated (3rd edition, release 2020), but I've noticed that the Github repository for the 4th edition is actively being worked on. I hope that, in the new edition, they switch to TypeScript and properly annotate the code examples.

It's an incredibly interesting book - and overall great read. It's hard to put down, but rewards careful reading, so I took my time with it.

This book is a good, well-organized guide to building large language models (LLMs). It walks you through everything, from transformer basics to a working GPT-like model. The explanations are clear, and the code examples are helpful. You'll learn the steps, from pretraining to fine-tuning for instruction and classification tasks. The book covers important concepts like tokenization, embeddings, and the self-attention mechanism. It’s good at explaining each part's purpose and how it fits into the whole. The step-by-step implementation of each component is a plus. However, the book could go deeper. It doesn't always explain why things are the way they are, or the mathematical intuition. It focuses on the how, but I'd liked to see more on the mathematical underpinnings and reasoning behind some choices. I get why this could be considered beyond the scope of the book.

This is a fantastic book. It's great at what it tried to do: give a hands-on introduction to ML. That is, you get a good breadth on the ML field. However, part of the way through, I started missing depth. It doesn't shy away from introducing technical concepts using the mathematical underpinnings, but does not go into some of the (rather interesting) details. I really appreciated that the author included so many links to relevant papers. Some sections give a fascinating history lesson on the relevant field. As a final note, the associated GitHub repository is fantastic. What a great resource to include. Has helped me many times (e.g., due to outdated dependencies-Géron keeps it updated!).