ALT Screenshot of: Building a Runtime with QuickJS Mar 2026 A JavaScript engine (e.g. V8, JavaScriptCore) executes JavaScript code. It doesn't know about things like files, HTTP requests, or timers. On the other hand, a JavaScript runtime (e.g. Node.js, Bun) is a more complete environment where JavaScript runs. It contains a JavaScript engine, extra APIs, an event loop and task queues, and platform-specific features. That's what I'm hacking on today: a tiny runtime with console.log, process.uptime(), setTimeout and clearTimeout, fs.readFileSync and fs.readFile, as well as an event loop and worker pool for file I/O. Built on top of QuickJS. Here's an example program it can run: const startedAt = process.uptime();

ALT Screenshot of: To recap a bit, I'll step through what happens when ls $HOME | grep foo is entered. It's tokenized into ["ls", "$HOME", "|", "grep", "foo"]. Then expanded into ["ls", "/Users/andrew", "|", "grep", "foo"]. The flat token list is separated into structured pipeline commands: ["ls", "/Users/andrew"] ["grep", "foo"] The shell creates a pipe to connect the output of ls to the input of grep. The child commands start via fork, and execvp swaps them into the target programs. By the wonderful design of Unix: if grep reads faster than ls writes, it blocks waiting for more data; if ls writes faster than grep reads, the pipe buffer fills and ls temporarily blocks. This synchronization happens automatically through the pipe, while the shell simply waits for both child processes to finish. The output of grep isn't connected to a pipe as it's the last command, and any results are displayed to the user.

ALT import "context" type Semaphore struct { // some internal state } func NewSemaphore(n int64) *Semaphore func (s *Semaphore) Acquire(ctx context.Context) error func (s *Semaphore) Release()

ALT (screenshot of blog) Counting Words at SIMD Speed Aug 2025 I've written some progressively faster word counting programs. First, we'll start with Python, and then we'll drop down to C, and finally, we'll use single instruction, multiple data (SIMD) programming to go as fast as possible. The task is to count the words in an ASCII text file. For example, Hello there! contains 2 words, and my 1 GiB benchmark text file contains 65 million words. At a high level: read bytes, scan while tracking minimal state, write the count to stdout. These are the results from my Apple M1 Pro that I'll dig into: Python (byte loop): 89.6 s Python re: 13.7 s C (scalar loop): 1.205 s C ARM NEON SIMD: 249 ms C ARM NEON SIMD threads: 181 ms You can also jump straight to the source files.