Algorithms from computer science are a source of knowledge that can be used to improve our lives. Problems like optimal stopping, exploration vs. exploitation tradeoffs, or how to sort things, are ubiquitous not just in computer science but also in real life. To me, the most memorable part is the 37% rule, which states that after having explored 37% of the time, I should leap into the best of the things found so far, and pursue only that.
The number of 37% changes to 61% when there are second chances involved and the second chance of something has a success rate of roughly 50%. The general idea is: Look, then leap. If there are multiple chances, generally keep looking for longer.
This means, concretely, that most of a career should be spent finding the right thing to do, and only then start exploiting it. Say after 50% or more of the time spent looking.
Scheduling is the other big topic, where I can derive value from the book for personal matters. Scheduling is about determining which things have to come first, and which things are parallelizable. Effectively, prioritize what is most important and has to be done, and then frontload those things, giving them the biggest priority and exclusive focus and resources.
Sometimes wasting more resources on deciding is not worth it. Especially in cases where there is a limited amount of data and a high amount of uncertainty, gut feeling can often be better. Especially with reversable decisions, where mistakes are ok to be made. Related here is Thinking Fast and Slow by Daniel Kahnemann.
Algorithms, even though they are structured sets of steps, can still involve randomness. Every "once in a while" it is good to mix up patterns to find better solutions. We can get stuck in local optima, where the only way to get unstuck is to think out of the box. The same is true for algorithms. Sometimes, a bit of random back and forth, and unexpected action, can help an algorithm come to a conclusion.
If you can't explain it simply, you don't understand it well enough.
In a way, algorithms are a way to explain things simply. They are a very reduced set of steps designed to get to a certain outcome. If we design the appropriate algorithms and implement them as behaviors in our lives, we can live much better lives.
Overall, this book is a good introduction to concepts in Computer Science, as well as filled with solid advice on living life. The combination makes it a unique blend, worthy of reading and even re-reading.
If changing strategies doesn't help, you can try to change the game.