Probability Theory: The Logic of Science by ET Jaynes was the book that made statistics and probability theory finally make sense to me. Before reading this book, I was already familiar with Bayesian probability, having finished (among other books) The Signal and the Noise by Nate Silver, but Jaynes took everything to a whole new level. Probability is not an objective measure of likelihood—it is a subjective measure of incomplete information. This seems like a small shift in perspective, but it is much more profound than that, and you have to read the book to understand it.
The Goal: A Process of Ongoing Improvement by Eliyahu M Goldratt opened my eyes to process optimization. It is nominally about process optimization in a manufacturing plant, but so many of the lessons can be applied to other domains, such as software development. (And also about life in general, how to think about what matters and what doesn't.) It is a bit dated now; for example The Principles of Product Development Flow offers a much more systematic treatment of the topic. But I think The Goal remains the best intro to process and systems thinking.
Qubes has an excellent security model and should a top choice (if not _the_ top choice) for security-minded and technologically sophisticated users.
I used Qubes for a year or two, and then realized that my main use case was to isolated the browser, which to me was the greatest threat vector compared to everything else I use. Then I thought, if I just wanted a system with the browser isolated from my main Linux environment, wasn't that exactly what ChromeOS provided?
So I switched to ChromeOS and have stayed on it ever since.
There is something wrong with some of those numbers.
For example, take 7-Zip Compression 22.01. The CPU Power Consumption Monitor chart states:
AmpereOne: Average 278.72W
EPYC: Average 311.64W
But the fine print under that same chart states:
AmpereOne: 6968J per run
EPYC: 14439J per run
By the Joules per run numbers, AmpereOne is far more power efficient than EPYC, requiring only less than half of the energy to complete a run.
In that case, how could the average power of EPYC to be only 11.8% higher than that of AmpereOne? For this benchmark EPYC is 14.2% faster than AmpereOne, and if the average power numbers are correct, the EPYC should have slightly lower Joules per run than AmpereOne.
That is not the only anomaly. For example, the CPU Power Consumption Monitor chart for John the Ripper 2023.03.14 also does not make sense.
Real estate properties in Florida are increasingly at risk due to climate change.
Established insurers ran their numbers and noped out of Florida quickly.
New low-quality insurers came in and filled the vacuum.
Established rating agencies looked at the new low-quality insurers and came to the conclusion that they are trash.
New low-quality rating agencies came in and declared those new low-quality insurers to be a-ok.
Lenders, fearful of being left holding the bag (from climate losses that are likely to bring down the low-quality insurers), sold mortgages in Florida to Freddie Mac and Fannie May (i.e., the GSEs, which are financial institutions that purchase mortgages en masse and implicitly backed by the US Government).
Freddie Mac and Fannie May bought those mortgages because the properties are insured by (low-quality) insurers declared a-ok by (low-quality) rating agencies.
As is usually the case, the government will be left holding the bag.
I am not the person you asked, but I have been on a similar path to improve my statistical literacy. For context, I am fairly good at math generally (used to be a math major in college decades ago; didn't do particularly well though I did graduate) but always managed to get myself extremely confused when it comes to statistics.
In terms of books: there are a few good ones aimed for the general public, such as The Signal and The Noise. How to Measure Anything: Finding the Value of Intangibles in Business is a good book of applying statistical thinking in a practical setting, though it wouldn't help you wrap your brain around things like the Monty Hall problem.
The one book that really made things click for me was this:
Probability Theory: The Logic of Science by E. T. Jaynes
This book is a bit more math-heavy, but I think anyone with a working background in a science or engineering field (including software engineering) should be able to get the important fundamental idea out of the book.
You don't need to completely comprehend all the details in math (I surely didn't); it is enough to have a high-level understanding of how the formulas are structured at the high level. But you do need enough math (for example, an intuitive understanding of logarithm) for the book to be useful.
A meta-comment: whether an advice is good or bad should not be assessed based on a specific outcome.
You can follow a good advice and end up with a bad outcome. Similarly, you can follow a bad advice and end up with a good outcome. This is more obvious in poker (Annie Duke wrote a few books on the topic), but I think the same principle also applies to life in general.
Sure, "I went against this sensible advice and look at what I accomplished!!" makes for good storytelling. But that does not help us decide which advice to follow now and in the future.
The Goal: A Process of Ongoing Improvement by Eliyahu M Goldratt opened my eyes to process optimization. It is nominally about process optimization in a manufacturing plant, but so many of the lessons can be applied to other domains, such as software development. (And also about life in general, how to think about what matters and what doesn't.) It is a bit dated now; for example The Principles of Product Development Flow offers a much more systematic treatment of the topic. But I think The Goal remains the best intro to process and systems thinking.