Cruftbox: My Box of Cruft

This post is for people on Bluesky, if you’re not on Bluesky, keep scrolling.

I built an app that sends you a daily DM with final scores and today’s game schedule for teams you follow.

Looks like this:

MLB, NBA, WNBA, NFL, NHL, MLS, NWSL, Premier League, and tournaments.

Silence on days your teams don’t play.

No login, no app to install. Text only.

DM @sportscrawl.app on Bluesky to start.

We have had a PurpleAir PA-II on our back porch for years. It works fine. The problem is that checking it requires going online to look at a map to find ours.

What I wanted was a small device that is green when the air is fine and turns other colors when it’s worse. Here in LA between smog, wildfires, structure fires, and fireworks, the air quality is all over the place.

No specific numbers needed, just a color that gives us a good sense of what’s in the air.

So I built one. I called it Ashlight.

The details

The device is about as simple as it gets, a 16-LED WS2812B ring driven by an ESP32-C3 SuperMini, running ESPHome. It polls the PurpleAir over the home network every few minutes, computes the AQI, and shows the result as a color.

Nothing leaves the house. No cloud service, no API key. The PurpleAir has a local JSON endpoint that’s simple to query.

If the internet goes down, the light keeps working, good for a device whose job is to tell you about a wildfire.

Building it seemed pretty straightforward: Get parts, write a little code, test it, make an enclosure.

The parts are crazy cheap. The ESP32 was ~$4 and the LED ring was ~$5. A capacitor and resistor for the minimum of circuit board protection.

My soldering is an abomination, mainly because I didn’t want to build it on a breadboard. The resistor and capacitor are just kind of floating in the air.

I leaned on Claude for the coding. After flashing the ESP32 with ESPHome, Home Assistant was able to see it easily.

We made the color a gradient, shifting into the next color as the AQI increased instead of six distinct steps: green -> yellow -> orange -> red -> purple -> maroon. I set it to shift to lowest brightness at night to avoid lighting up the room.

I did learn that the PurpleAir sensor is also running a tiny processor and doesn’t always respond to the HTTP request for JSON. Evidently, when it’s uploading every two minutes to the PurpleAir map, it ignores everything else. The code is set to be OK with this and retry as needed.

After getting it up and running, I found that the ESP32-C3 SuperMini’s onboard wifi antenna is bad. Not marginally bad, bad bad.

The signal strength was so bad the device was unusable when in the dining room.

I was pretty frustrated, but found Peter Neufeld’s blog on adding a small antenna.

The fix is a sweet hack: solder a 31 mm piece of wire to the existing antenna endpoints. 31 mm is a quarter wavelength of the 2.4 GHz wifi signal.

I tinned a wire, bent it into shape, and soldered it onto the ESP32.

Amazingly, it worked with the Ashlight residing in the dining room.

If I were doing this over, I’d get an ESP32 with the U.FL connector for an external antenna and a small flexible patch antenna. Lesson learned.

For the enclosure, I couldn’t find something online that fit the bill, so I built something in Tinkercad.

I wanted to use a translucent PLA, but the closest I had on hand was some glow-in-the-dark PLA. A little more finicky to print with, but it ended up working out. It’s a super simple enclosure with a hole for the USB-C cable to bring in power. Here are the STLs I designed.

The lid is just a friction fit, but holds together nicely.

Ashlight is in the dining room, glowing a pale yellow-green. In the morning, Michele and I read the paper and drink coffee there, so it tells us what to expect at a glance.

Here’s a short video about the process.

Code is on GitHub at github.com/cruftbox/ashlight.

The README has the full wiring, AQI math, and considerably more detail than anyone needs but that Claude feels is essential.

tl;dr: https://github.com/cruftbox/llm-friendly-preview

I’ve gotten into the habit of asking AI assistants like Claude, ChatGPT, and Gemini to review my draft posts before I hit publish. A second pair of eyes catches typos, confusing phrasing, and the occasional bad take. The problem was getting them to actually see the draft.

WordPress’s Public Post Preview plugin, my previous go-to, generated preview URLs that a human could click in their own browser, but AI assistants would balk at the link token style. The work around was creating a PDF of the preview and handing that off. That was clunky, took time, and I am lazy.

So I built my own plugin: LLM Friendly Preview. The idea is simple, generate a URL that looks and behaves like a completely normal public page (real theme templates, real styling, no ?preview=1), but is gated by a long, random, single-use token instead of a login session.

From the post editor, I click Generate LLM Review Link, and it hands me something like:

That link works for 3 days and then expires automatically. I can regenerate it or revoke it outright from the same panel, and it’s auto-revoked the moment the post actually gets published, so there’s no lingering way to view a draft that isn’t a draft anymore.

Under the hood it’s all standard WordPress: a rewrite rule, a token stored in protected post meta, time comparison for validation, rate-limiting by IP, and cache-busting headers so no caching plugin accidentally serves up unpublished content to a stranger.

Which LLMs actually work

Here’s where it got interesting. Once the plugin was live, I gave the same link to every the popular models to see who could actually fetch it.

Working: Claude, Gemini, Meta AI, Mistral, Kimi, DeepSeek, Qwen, Grok, HuggingChat, and Ernie Bot all fetch and read the page without issue.

Not working: ChatGPT, Perplexity, and Microsoft Copilot all fail to open the link.

ChatGPT’s failure is the one I actually tracked down: its web-retrieval system rejects newly generated URLs containing private tokens before it even makes an HTTP request. That’s not a bug in WordPress, the plugin, or the response headers. I confirmed the exact same URL returns a clean 200 with the correct content when fetched directly, even spoofing ChatGPT’s own user-agent string. It’s a deliberate caution built into ChatGPT’s URL-safety layer, and it’s exactly the kind of link this plugin is designed to produce: anonymous, temporary, and impossible to guess. Perplexity and Copilot weren’t diagnosed as thoroughly, but the failure pattern looks similar.

For everything else, it works exactly like I wanted: paste a link -> get a review.

All the code is here: https://github.com/cruftbox/llm-friendly-preview