149 karma · joined January 10, 2026
I was setting a up a new computer the other day and got confused that the first three links for a search were ads, then I remembered it was google and they hide the things you’re looking for.
Site priority, lenses and the slop filter are all icing on the cake.
Not refuting anything here, just adding some context from over here. Whichever way you slice it, it’s always been an awful name.
It’s an interesting project, certainly not for everyone, but it does serve a legitimate and interesting niche.
“Others might find this interesting! Let’s get the docs in shape and release it as a proper FOSS project!” the quite naive me said to himself.
Writing good docs, and I’m not calling my docs good, just an attempt at them, is such a hard thing to do. I learned so much and it took SO long to make sure I had covered all the features, that I added as much sassdoc as I could for API docs, tone etc.
I’ve gained so much respect for the people who maintain open source docs and it has reminded me to contribute more than I do currently.
The TLDR of Crayon is it leverages some familiar utility patterns for prototyping but draws the line at anything not derived from a shared scale.
Think of it as the most common parts of tailwind list of classes but _almost_ no single property classes, no arbitrary values and no inline breakpoint, state or dark mode stuff. I promise it does make some sense
Scoped CSS exists in most frameworks and CSS and Sass provide more power and better ergonomics for larger projects. Crayon says to lean into those tools and provides other tools and mixins to keep those values centralised and some additional things you can’t do with tailwind or plain CSS with some pretty powerful composition mixins. Oh and if you’re an LLM-interested person, they take to Crayon really quickly. There’s nothing groundbreaking here, Crayon is intentionally teeny-tiny.
This is not me trying to make a “tailwind but better” or “I hate utility classes” it’s a hybrid approach and as I said earlier, definitely not for everyone. Hope some of you find this interesting!
...and yes, I know you don't mean that. I just disagree.
Not to diminish your enjoyment, you’re completely entitled to it, I just think this is a bit of a false equivalence
Meanwhile the guy who leaned in a year ago and gave up reading the output is beginning to see work grind to a halt and throwing more agents at it is increasingly not working.
You can see these tropes all over social media near constantly.
“But it’s different this time” - several people, several times over the last couple of years.
This is not at all a dig at you, I’m very sorry if it reads that way. My point is these things only get truly better in anecdotes. The ways in which they fail is yet to change. Just yesterday I had gpt 5.3 generate completely awful code for the Cinema 4D Python API. Also an anecdote. But for all of the people saying they are truly intelligent and truly reason, they still make obvious mistakes, write around problems, fail entirely at architectural decisions, fail at random, generate FAR too much code.
And no amount of harnesses, methodologies, loops make much of a difference. If you listen to people on the internet they say it’s all working. You listen to people on the job and they mostly say it’s creating tech debt and a review bottleneck. Also burnout, so much burnout.
I think LLMs are mediocre. I think it’s fine they’re mediocre. You can work with low expectations. But the hype cycles are so tiresome.
LLMs really do still just reassemble things in their training data. There’s just a lot of it now, people anthropomorphise and struggle visualising large things. Some people say it’s truly reasoning but hit a topic that is under represented in the data of any LLM and it’ll transport you very quickly back a couple of years and ruin the illusion quickly.
Similarly I've run into several "you just gotta know" type problems where LLMs still just fail. My favourite was a quirk in how async relationships work inside emberjs. Three different models gave a variation of the same incorrect answer. When I searched myself, I initially came up with nothing and eventually found what I think might be the only example of the same issue on the internet, a single stack overflow question wth two responses. The first is what Claude, gemini and chatGPT said, the second response was the OP saying it was wrong. I asked in the Ember discord and a core team member responded instantly with the answer.
There's a video I love on Youtube, where a guy uses ML to assemble a blank jigsaw. It performs amazingly, the jigsaw being blank is of no consequence and if it did have an image, it'd perform worse. That's all LLMs do. Just because the jigsaw pieces are smaller, they're still just getting assembled in whatever way fits, there's no mechanic for interpreting the image on the front.
"If one teenager works hard and saves for a few years to buy themselves a car and another has theirs bought for them on a whim, who would appreciate their car more?"
Odds are it's the former, not always. But mostly.
Worst of all I've seen good engineers lean in and begin thinking uncritically and magically.
But overall yes, time spent thinking is the thing that matters.
It's less "there should be more friction" and more "LLMs remove too much". Instant gratification is great if you're a consumer, awful if you're a creator.
See, table saws are dangerous. Famously so. One of, if not the most dangerous tools available to the general public. They spin quickly with lots of torque and pull things in faster than you can react. Pressure can also send loose pieces of wood backwards at high speed. Fast enough to pass through a person sometimes. It's like being hit with an arrow.
Tablesaw accidents can remove fingers and hands instantly, puncture organs.
They can be used safely but they're circumstantial, the worst thing you can do with a table saw is experiment. Once you realise there's a 12-inch razor sharp blade spinning at 3000rpm with up to 5HP you begin to respect how dangerous it could be and want to warn others.
It's intentionally hyperbolic. But you see what I'm saying here?
But for sure a systems language is going to be far faster on paper and Rails is far from perfect and does have some performance foot guns you need to avoid. And yeah, architecture is everything.