waitforit.lol
One ad. One falling price. First buyer takes the page.
What it does
One ad. One owner. One falling price. Wait and it gets cheaper. The problem? Everybody else is waiting too. Buy cheap and you're cheap to replace. Pay more and the next buyer has a much taller price to wait down. Meanwhile, everyone waiting to steal the page is staring at your ad. Someone blinks. They buy. You're gone. Then it starts again.
The price is visibly frozen at €1.00. No secret decay, no invisible race.
The whole front page, one brand, until somebody pays to replace you. The only question is whether you are faster than everyone else reading this. Buy now. The next buyer faces €100.00, then gravity starts pulling the price down all over again. One brand owns the giant ad. Their name gets the whole front window. The price to kick them out falls every second. No bids. No backroom deal. Wait too long and somebody else pays first. Your bargain becomes their screenshot. They take the ad. The next price starts higher. Then it falls again. There. You now understand the whole company. Some pitch decks take 84 slides and still achieve less. Nobody is ‘featured’ and nobody ‘partnered with us’. They…from waitforit.lol
Does the same job
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Take First Spot12d ago · takefirstspot.lol · ▲3Take first spot by just paying $1 extra. Forever backlink.


More growth this month
the category →
AstraPixels▲267A pixel-art solar system at its real current positions.
Growth · 29d ago · astrapixels.com

Launched alongside, August 2026
the whole month →- TL
Life & fun · 10d ago · louisabraham.github.io


- SA
Hello HN! I found that picking out plausible but diverse skin tones for my digital art and game development projects was kind of difficult, and I got curious about if there was a way to define a color space that made it easy. I've built a color picker and procedural generation algorithm based on the space as well as a bunch of other fun js features and demos throughout the page that use the equations. If you find it interesting, I have lots of explanations of how I built it and what properties the space has. The methodology might be a bit shaky, but hopefully the result is as helpful for…
Life & fun · Aug 2026 · toneyalexander.github.io


I trained a 125M-parameter transformer to autocomplete piano performances in real time (~108 notes/sec on an iPhone 15). The idea is basically GitHub Copilot or Tabnine, except instead of prompting it with code, you prompt it by playing a few notes on a MIDI piano. The model then continues what you played, entirely on-device. The app is free if anyone wants to try it. Happy to answer questions about the model, training, Core ML, or the many things that didn't work.
AI · 16d ago · simedw.com