Category: Scripting

Robin Sloan’s post about “home-cooked” code has been making the rounds, and for good reason. It’s about highly personalized programs; when you build something purely for yourself or a small audience, it’s more like home cooking than industrial production. I really love the analogy, and it’s inspired me to write about one of my own “home-cooked” tools: wat.

The Problem

I spend a lot of my day in a terminal, and command line interfaces have notoriously poor discoverability. It’s easy to remember the handful of commands+flags you use on a regular basis; it’s much harder to remember the ones you only use a couple times a year. So, like many people, I used to spend a lot of time digging through man pages when 1) I know what I want to accomplish 2) I can’t remember the exact CLI syntax to make it happen.

Unfortunately, man pages usually aren’t designed for this. The average man page is more like a lengthy novel than Cliff’s Notes; it’s thorough+exhaustive documentation when I just want a quick reference.

There are a few excellent projects that provide succinct documentation for *nix commands: tldr and cht.sh are my favourites. But they don’t provide good facilities for adding personalized documentation of my own; that’s where wat comes in.

What Wat Does

wat started out as a simple wrapper around tldr and cht.sh. The two provide similar content, but they do so in different ways; tldr is installed locally, cht.sh is usually accessed through a dead-simple web interface: curl cht.sh/command. The first iteration of wat was just a simple fish function that would call tldr if it’s installed, and otherwise fall back on cht.sh.

These days, wat is written in Python and it’s a tiny bit more sophisticated: it also displays custom Markdown notes which are synced across all my machines using Git.

Say I want to get some information about a video file, but I can’t remember the exact ffprobe syntax. I just type wat ffprobe in my terminal:

This returns a handy tldr/cht.sh summary and it renders the contents of ~/notes/ffprobe.md if it exists. In this case, it looks like I got annoyed enough by ffprobe to write down a note for the -hide_banner flag.

I’ve been maintaining command line notes for a long time, in various tools that just happened to be at hand: OneNote, Notes.app, Markdown in a repo… but wat lets me organize that information better, and view it inline with excellent community-provided documentation.

Bret Victor – Inventing on Principle

The hunt for shorter edit/compile/debug cycles

Bret Victor’s talk Inventing on Principle (video, transcript) changed the way I think about computing in 2019. Inventing on Principle is partly about Bret’s guiding principle:

Creators need an immediate connection to what they create. And what I mean by that is when you’re making something, if you make a change, or you make a decision, you need to see the effect of that immediately.”

The Edit-Compile-Run Cycle

Although Bret doesn’t use the term, programmers are deeply familiar with his principle. We’ve all worked with toolchains that introduce significant delay before you can “see” the results of a change, and we know they’re painful. Everyone wants a short edit-compile-run cycle.

But until IoP, I’d assumed that slow cycles wouldn’t materially change the output – you’d eventually get to the same place. This was wrong. I also didn’t appreciate the very small time scales involved; a 5 second delay used to seem trivial to me, but it’s still meaningfully different from a response time measured in milliseconds.

Through some very impressive custom tools, Bret shows how immediate feedback enables exploration, which then gives birth to ideas which would otherwise never see the light of day. This was an epiphany for me. Since IoP I’ve constantly been looking for better ways to code, and re-evaluating my existing processes for shorter feedback cycles. The results:

Rust

My typical Rust development workflow goes something like this:

  1. Write a small function that does roughly what I want
  2. Write a small unit test inline to exercise the function (even if it’s a private function)
  3. Iterate using cargo test until the function is correct
  4. Later, “productionize” the tests if necessary

Rust’s native support for inline unit tests helps a lot here, and the excellent type system catches a lot of issues before I even run cargo test. On the other hand, Rust’s compiler is notoriously slow and that extends to IDE tooling that depends on the Rust Language Server. I’m looking forward to Cranelift for faster debug builds.

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