Excel’s regex functions bring pattern recognition and text parsing to the spreadsheet. This article explores how Regex Pattern Matching works under the hood – from formal grammars and finite state machines to practical formulas for cleaning, extracting, and restructuring messy data.
You’re staring at ChatGPT. You’ve done this a hundred times before: typed a question, copied a response, pasted it into some half-built project or document. Maybe it helped. Maybe it wasn’t quite what you were looking for. But here’s the thing no one tells you: that chat box you’re using? It’s not the product. It’s the demo.
This is exciting! We’re nearly ready to start simulating a whole game of Chess. There’s just a few more components we need.
To begin, let’s first consider what information we need to encode to fully describe a move.
In standard chess move notation, we are given only the type of piece, and it’s new location. E.g. Ne2 which indicates that a knight should be moved to the square e2.
If you haven’t already heard, Strava provide a powerful API that allows users to access their data using code. This gives us the ability to create interesting visualisations, analyse progress, and create custom trackers and planners, all with just a few lines of code.
This is part 2 in a series of articles documenting my process of building a chess engine. I hope to share insights from my previous experience, and explain some of the concepts and techniques I’ll be using. If you haven’t already read part 1, I’d strongly recommend taking a look before reading part 2. You can check it out at the link above.
Time to pick up where we left of…
This is not my first rodeo.
I’ve built a chess engine before, but the results were… let’s say… underwhelming.
Don’t get me wrong, the program worked; it was able to play a full game of chess, and for that, I give it credit. But let’s face it, it was terrible at chess.
