Here's the situation you've probably been in already: you join a new project and soon notice the page load speed is… underwhelming. You might assume it's due to your slow network, but after running a Lighthouse report, you find the score is 20 out of 100. Something is definitely wrong here! When I see something like this, I usually have a real urge to fix it. Besides, sometimes it's a nice way to get acquainted with the project as you inevitably go through a lot of code when doing it. Now, what's important is that performance optimizations are a real rabbit hole, and there's almost always something else that can be optimized. The Pareto principle applies here perfectly: a set of things can be done relatively easily, but they can have a meaningful impact.

Until recently, just getting started writing your first line of machine learning code required a hefty upfront investment in time and money. Last year, I built my own PC specifically for machine learning. I researched the parts and assembled it myself. I'm still trying to wrangle the computer's configuration and libraries and make it work with various frameworks. The good news is that getting started with machine learning today has never been easier.