2025 holiday reading
Posted: January 9th, 2026 2:41 PM
How to Self-learn Anything - Thinknetic
⭐⭐
Not really proud of even buying this book. I think I stumbled on it from a short review that someone left on X somewhere. This book could have been easily generated by AI with a long enough context window. The overall book gave good insights into the specific jargon and techniques of goal setting, but there really wasn't anything 'novel'. Similarly, rather than focusing purely on the actual method of self-learning there was a lot of context, background, and framing (more-so than it felt needed to be written)
Deep Work - Cal Newport
⭐⭐⭐⭐⭐
Candidly, this was a reread. I enjoy the way Newport writes because the book feels like a form of spaced repetition. They frequently make call-backs to earlier material and the entirety of the book forms into something between art and helpful. The core idea is to decide the time frame of doing deep work, focus that deep work on meaningful tasks, and create a habit of removing yourself from the pull of distractions. This is the modern take on monastic seclusion with an acute focus on the knowledge worker. One idea that I walked away with was there is a way to optimize a goal for both the outcome and the journey and that is through a general outline of the process and a commitment to the outcome. Typically I would have thought of these things in the inverse.
Man's Search for Meaning - Viktor Frankl
⭐⭐⭐⭐⭐
To avoid political discussion, the summation of the first 75% of the book is that 'a bad thing is a bad thing' and atrocities seem other worldly because they surgically remove human nature while simultaneously expressing it in (arguably) it's most pure form. A former Dockhand (Michael Wagner) was the grandson of Joyce Wagner who authored a book about her firsthand account. This is more of a historical document than a link to the field of psychology and has become a book that I read yearly (A Promise Kept to Bear Witness) and came to mind while reading this first portion of Frankl's book.
The last 25% of the book is about logotherapy and how meaning plays a central role to the function of each human. This is true even in its absence where the function of meaning has exponentially greater consequences than in its presence. However, a person who has come to found their meaning is much better equipped to deal with hardship, good times, and taking part in the journey for the means of reaching some end goal (less of an outcome and more of a 'fulfillment of the meaning'). In essence, I took this book to be a western-academic approach to Buddhist teachings. A method of testing one's meaning is by reviewing how you react to and work through hardship. Your meaning may need to be modified if you interact poorly with hardship. This may mean that you need to discover a new meaning or that you may need to discover, in a new way, how you fit into the meaning.
So Good They Can't Ignore You - Cal Newport
⭐⭐⭐⭐
I am definitely biased towards enjoying Newport books. They focus more on the concepts of 'doing great work' with an argument that meaning is discovered by doing great work, so explore things and commit to do them rather than seeking for something you are passionate about. I also am biased towards this idea (hard work - over - passion). There also were some examples of treating work more like a craft than an art, which means some days will be less enjoyable than others.
Are Your Lights On? - Donald C. Gause, Gerald M. Weinberg
⭐
The first N-pages are written like a School House Rock show. If you enjoy that, buy this book. If you don't, it's not even worth touching. The premise is that before solving a problem you should think deeply and pick apart what the problem is. We can do this by identifying what the real problem is, what was the cause of the problem, who does the problem really belong to, of all the possible solutions which one resolves the problem in the best way, what are all the possible solutions etc. The most helpful part of the book was a description of how the book got its title.
Imagine a tunnel that has construction happening and at the end of the tunnel is a park. There was a large number of complaints that cars in the park would have dead batteries, because families would go to the park at different hours of the day to have picnics or play. There was a sign at the front of the tunnel that said 'Turn Your Lights On while in the tunnel due to construction'. When the cars got to the park, they would forget to turn their lights off. The possible solutions were adding a sign at the end of the tunnel that would tell a person to turn their lights off depending on the time of day. The best and simplest solution was simply to create a sign at the end of the tunnel as a reminder "Are your lights on?"
In total, not worth the read.
How To Not Be Wrong - Jesse Ellenberg
⭐⭐⭐
Ellenberg is a math savant. Perfect SAT score, early college graduate, young Phd. Reading this book felt like a giant conversation with someone with that kind of background. They introduce a concept, like gambling and how a team of mathematicians and thinkers found a way to rig the game (even though the government was already aware that it could be rigged and were fine with that), then they (Ellenberg) goes on a 75 page tirade on Bayesian Statistics, to end on a happy note of 'if you think you can achieve something, do the math first, and make sure you talk to the legal department in charge of the thing'. Each chapter was some form of:
- I think I will understand and enjoy what the author is about to talk about
- I know pieces of what they are talking about
- I don't know calculus and I would need to study a textbook and a course in order to understand the math they are talking about
- I understand the summary of the author is giving, but I really wish I knew the math they explained
It came off as kind of 'whiplash-y' and I've debated on whether or not that is my fault and I think it mostly is. I want to study maths better and have a deeper understanding of calculus and bayesian statistics. However, I think the book could have more simply explained some of the mathematics. I'll say 90/10 split on my fault vs. the authors.
The stories were really captivating and the major takeaway is: learn more math and apply it with everything you encounter. If you do read this book you can either read it like a novel or read it like a textbook. I read this book in a day, so reading as a textbook was not an option.
The Art of Statistics - David Spiegelhalter
⭐⭐⭐
Spiegelhalter writes this book as a guide that moves from start to finish. It was a good read, although there were less analogies, stories, and applications of statistics as there were explanations of. It is a hard thing to write a book about a domain without explaining the ideas of the domain and statistics has many of them. I think the 'enjoyableness' of the book comes out of someones ability to read the book as a guide vs. as a novel. I found that most of the ideas in the book (RCT, Chi Square Tests, Bayesian Inference) were well explained and fit into the core of the book well. Some might not enjoy that.
The Book of Why - Judea Pearl
⭐⭐⭐⭐
Many reviews online despise the book because of it's lack of technical depth. Personally, as someone who lacks the technical depth in Bayesian Statistics, I enjoyed the book. Pearl spent, what seemed to be, an adequate amount of time explaining the context, the framework of their idea, the history involving the idea, and the application of the idea in the world today. It felt fair to provide as much historical context as Pearl did given that the idea is without Overton's Window.
Pearl explains that causality is not a feature of intelligence, rather the source of. We understand much better the idea of any modern event (smoking causing cancer, gender disparity in college acceptance) not by reasoning through the math and probabilities of it all, but in an effort to acquire the cause of the effect we are seeing. From an outsider-to-mathematics view, the reasoning Pearl gave for mathematically finding cause seemed intuitive. However, towards the end of the book, it became less intuitive and felt more 'shoe horned' which is either (ironically) an effect of the theory being less accurate than Pearl thinks or a lack of my own ability to reason through the idea of causality that Pearl discussed.
My takeaway was when something happens that includes some kind of statistics to accompany it, there is much more effort that has to be put into investigating the thing (political polling, a chess game, news report) than what we initially assume. Similarly, we are always much better off in terms of overall understanding and comprehension of an event or a thing when we consider the counterfactual.
The Craft of Research - Gregory G. Colomb, Joseph M. Williams, and Wayne C. Booth
⭐⭐⭐
Don't try to read this book cover to cover in a day. Most of the topics are too specific to actually carrying out a research paper that they essentially fall out of the context window. I did enjoy the book and it will be more useful as a reference than as a book.
My key takeaway was that framing an idea is more about the evidence, consequences, and context of the idea than the idea itself. In other words, the framing of an idea is the negative consequences for not applying the idea, the positive consequences for applying the idea in the suggested manner, and the evidence of the ideas validity gained through some real event. This applies well beyond the idea of a research paper. In general, for knowledge workers, it seems that we are attempting to tackle and frame conceptual problems with practical consequences that require practical solutions.
How to Solve It - George Polya
⭐⭐⭐⭐⭐
The online reviews are pretty terrible. I think this is due to the reader and not the author. Most of the reviews are bad for the reason that the book is written as a 25% (the first 25%) guide and 75% (the last 75%) as a dictionary. I read this book cover to cover and actually think that the way the book reads, in the order of the pages given, is quite nice. The framework for solving anything is to study the problem, understand the problem, separate the data, the unknown, and the constraints. Interrogate the core question (which is likely a preliminary piece to solving anything is that there must be a well formulated question to solve) by building context. By building your own context of the problem you can then modify that context by planning and thinking of similarities, ways to manipulate the context, and modifying the context to move closer to a solution. The last two parts are carrying out that plan and then reviewing the entirety of the execution to think of another way to come to a solution and another way of understanding the problem in the context of the solution you have made.
All of that being said, this is essentially the written version of a great coding interview. Despite all of that wonderful information, my key takeaway is that this year I want to study the region of Austria-Hungary. So many great minds came out of that area (and the one not so good mind). Jon von Neumann, Abraham Wald, George Polya, Ludwig von Mises, F.A Hayek, and many more. There is something about that region of the world and it being the best spawn point for the 19th century and one of the most unfortunate for the 20th century (at least partially) that make it so interesting.
The Art of Doing Science and Engineering - Richard Hamming
⭐⭐⭐⭐⭐
This is a book that had a lot of calculus and mathematics that I did not understand and that I want to. The entirety of math that underpins the modern computer is abstracted so far away that it really does seem like AI will take over the world. Until we learn how much cognitive work had to be thrown at the transistor and hamming codes. Hamming writes chronologically, moving through the history of the modern computer and often touches on the raw math that went in to bringing the invention to life.
Two other things that I am going to be studying more deeply this year is math (specifically the math of digital signal processing) and the Bell Labs phenomena. Bell Labs had a grammy, an emmy, turing awards, nobel prizes, and more. So much good came out of a private company focusing on a single domain and attacking it viciously.
The chronological idea of the book is nice because it does build on itself and explains some of the insider details to how science and engineering happens. The 'Learning to Learn' part of the book is reinforced with Hamming's argument that great work ought to be the pursuit of everyone, there is room for being kind, and mathematics is how we reason through a difficult problem to solve. A side note that Hamming mentions often is that there are two auxiliary actions we all must take to accomplish great work:
- Be prepared for the great work by being equipped and ready to catch the great work when it is in flight
- Spend time away from the raw action of carrying out deep work. It is when we ponder that some greatness happens and avoiding time to ponder reduces your chances that greatness finds you. Like depression, greatness often has difficulty hitting a moving target.
80,000 Hours - Benjamin Todd
⭐⭐
The low rating is less a reflection of the content and more of the idea of the book itself. The ideas of the book were mostly alright, although I do have some contention with the idea of charitable giving. My paradigm of charitable giving in the world is that most organizations misappropriate funds and there seems to be disparity in the likelihood your funds go to a real positive impact in the lives of many and the likelihood your funds go to a western well-off worker.
Todd and the effective altruism movement within 80k Hours focuses on the idea that important work is available, we can objectively rate work, and currently AI is the most important work that anyone can work on. As an aside, there seems to be this bubble of people that believe that AI work will go away and there is 'too much' hype. This seems similar to the Dot-COM bubble and boom of the 90's with one gigantic difference and one key insight that seems to never be talked about in the comparison of these two events.
- There was a lot of hype around the Dot Com bubble, but it was warranted. Similarly, yes the bubble burst, but to say it 'went away' and it was 'truly a bubble' is not accurate. Everyone interacts with the internet, everything is a website now. The economy is propped up by tech giants (or i guess a better wording is 'the NASDAQ is attempting to prop up') that make money from a kind of website or internet tech.
- Websites and the internet have taken over the lives of every individual and society as a whole. To say that AI is truly a bubble and once it bursts it will all go away, is simply not true. You can't just remove the tech of AI. Sure, there is a lot of hype around AI, but to say that once the bubble bursts things will go back to how they were is not only preposterous but it is negligent at best and criminal at worst.
The core of the book is around finding meaningful work, making a plan to do meaningful work, and then picking a career that allows you to save money for your livelihood and gives you the opportunity to have a real impact in the world. It definitely isn't for everyone. One comment I saw on Reddit that seemed like a good critique was the odd focus on the 80k Hours movement towards research. It does seem to obscure or avoid 'direct impact through making something or doing something that actually solves X', which is either an articulation that breakthroughs (the leg work of them) happen in research or is signal about the movement as a whole.
lesson
study Bayesian statistics at all costs, learn to enjoy math and apply it in life. without solid foundational reasoning, life becomes less enjoyable.
the knowledge you need to become the type of person you want to be is behind the cover of some book. go find it.