When I experimented with Elixir a few years ago, I fell in love with the pipe operator |>. Since then, I’ve always dreamt of a similar tool in PHP.

Last year, we finally got it! Unfortunately, the standard array & string manipulation functions in PHP aren’t exactly known for their API consistency, which makes the pipe operator awkward to use.

I ported Laravel’s excellent collection & string helpers to a pipe operator-optimized API. Piper is an attempt to wrap the standard library to make it compatible with the pipe operator.

use function Spatie\Piper\Arr\{filter, join, map};
use function Spatie\Piper\Str\{prefix, suffix};

[1, 2, 3, 4, 5, 6]
    |> filter(fn (int $i) => $i % 2 === 0)
    |> map(fn (int $i) => pow($i, 2))
    |> join(', ', ', and ')
    |> prefix('The winning numbers are ')
    |> suffix('.');

// "The winning numbers are 4, 16, and 36."

I wrote a technical introduction to the package on the Spatie blog. Documentation can be referenced here, and the repository is hosted on GitHub.

The sad part (maybe?)

I’ve never seen a coding agent return code using a pipe operator unless I’ve explicitly asked it to. That makes sense: the models we use are trained on years of historical data, and the pipe operator is a fairly new feature.

I might never use this library. I might never use the pipe operator at all. Agentic coding is pushing syntax to a standstill. New programming APIs that bring benefits to execution will be pushed forward. But the whole fight for making code look good seems to have been extinguished as AI is slowly moving us to focus on outcomes more than line-by-line code cleanliness. That’s not necessarily a bad thing, but part of our craft might get lost along the way.

Ironically, I convinced myself to actually write this because I have AI at my disposal. Manually porting 234 functions and a full test suite is not something I was looking forward to. Instead, the time spent building and refining this package is measurable in hours.

Either way, this is a fun experiment to conduct. And I look forward to tinkering further and giving it a shot in some real projects.

This blog has gone through a few technical iterations. Most recently: from a Laravel app with Sheets, to a static site with Hugo, to a CMS-driven site with Statamic. And now, back to Hugo!

I love Hugo. It’s purposefully built with the right abstractions, and incredibly fast. The kind of fast that makes you wonder why other software can be that fast. Fast software, the best software. But the templating language… it might be an acquired taste, but I never acquired it. Updating my theme or adding a custom page was always a chore. I got tired of it. Coincidentally, I wanted to experiment with more dynamic workflows on here (posting to social media, more control over scheduling…) so I migrated to Statamic (a lovely CMS!)

Fast forward three years and a blogging hiatus, and I started to crave the simplicity of the good old static site. Having a dynamic system was fun, but it requires maintenance and having it made me realize I didn’t need it. Add to that we’ve commoditized robots building things for us, and switching back to Hugo was the right move. A single prompt and 15 minutes later, my entire blog was translated from Statamic to Hugo. The best part: I don’t need to care about the templates anymore. Sure, I’ll dive in occasionally for some surgical styling tweaks. But generally, changes are handled with a quick prompt.

GitHub pings my webserver to auto-deploy on push. Scheduling is solved by running the deploy script every hour. As a static site, everything’s fast by default and there are almost no points of failure. Life is simple. All that remains is to write!

I have a blank canvas problem. I’m way better at staying in flow when I have something to work through, so these days, the first thing I do is ramble.

I open Codex or Spiral, turn on Monologue (my preferred text-to-speech tool), and dump everything I can think of on the topic. Before Monologue, I never considered typing speed to be a bottleneck. My fingers move faster than the speed I shape ideas. But when I want to vomit a bunch of unstructured thoughts onto a canvas, they suddenly feel slow and error-prone.

Instead, I can start a task by talking through it to generate a transcript. Then I ask an agent to structure my ramblings (the other day, the agent responded with “Your brain dump is rich."—probably the nicest compliment I’ve ever gotten from a robot!). This gives me a great starting point to really dig into the problem. Before I know it, I will have rewritten all that rambling into coherent a string of thought.

I still can’t shake the dystopian feeling of talking to my computer. But text-to-speech has made some tasks feel so natural, I almost can’t imagine working without it anymore.

I wrote a piece on the Spatie blog about the place packages/library have in the ecosystem when first-party code is cheaper than ever with AI.

Scrolling to the end:

The question before every composer require is no longer “is there a package for this?” Almost certainly there is. The new question is: “do I want to own this problem?”