tl;dr:"My favourites of these that I use already are entr, ripgrep, git-delta, httpie, plocate, and jq." Julia breaks this list into replacements for standard tools, new inventions, and less-new tools.
tl;dr:"Here are a few examples of small personal programming projects I’ve done. I’m not going to talk about “learning projects” where my goal was to learn something specific because I’ve already written a billion blog posts about that. These are more about just doing something fun with no specific learning goal."
tl;dr:(1) SSL certificates, with Let’s Encrypt (2) Concurrency, with async/await (in several languages) (3) Centering in CSS, with flexbox / grid. (4) Building fast programs with Go, and many more.
tl;dr:"I built a site where you can do experiments with DNS called Mess With DNS. It has examples of experiments you can try, and you’ve very encouraged to come up with your own experiments. In this post I’ll talk about why I made this, how it works, and give you probably more details than you want to know about how I built it (design, testing, security, writing an authoritative nameserver, live streaming updates, etc)."
tl;dr:(1) Ask yes / no questions to check your understanding quickly. (2) State your current understanding. (3) Be willing to interrupt. (4) Don’t accept responses that don’t answer your question. (5) Take a minute to think, especially if you're surprised by an answer and need time for a new question.
tl;dr:"I’ll start out by talking about a “backwards” approach to learning that I think a lot of you will recognize (do projects first without fully understanding what you’re doing, fill in missing knowledge after), and then talk about I try to teach in a way that works better with that mode of learning."
tl;dr:Julia guides us through what a quadratic time function looks like, why it's slow, how to convert a quadratic algorithm into a linear one using a hashmap.
tl;dr:Start with real code that you wrote and then "remove irrelevant details to make it into a self-contained example, instead of coming up with examples out of thin air." Julia runs through two types of examples - (1) realistic examples that sell the concept and (2) surprising examples that change someone's mental model.
tl;dr:Julia outlines 5 reasons why bugs might feel impossible to solve, each of which is explained in detail: (1) The bug is hard to reproduce. (2) You don’t understand the overall system well. (3) It’s hard to get data about the bug. (4) One of your assumptions is wrong. (5) The bug is really complex.