Part one of of this article is about AI programming at a high level and where it might take us. This second part is about the practical lessons I learned in using it. Read this piece, take all of my advice as gospel, and by the end of the article, you’ll be an expert too.¹

If you didn’t catch part one, I used AI to rewrite in Go a program that I wrote by hand in Java more than ten years ago. The project comprises about 15,000 lines of Go, plus another 5,000 lines of Python, JavaScript, and shell scripts. It took two weeks and change to write using AI, a small fraction of the time it took to hand-write the original.

The program does a simple thing very fast: it reads the X (formerly Twitter) firehose and extracts the newly emerging subjects. It does this on up to about 8,000 Tweets/second when reading from RabbitMQ, and at about 50,000 Tweets/second reading directly from disk files.

This program makes a good test project for using AI because of its diversity. It’s a high-performance program with lots of concurrency, lots of disk reads and writes, text parsing, tokenizing, some intense algorithmic processing, some esoteric graphing libraries, and a simple Web front end.