AnyoneCanBuy.com
20 ad slots under a Harberger tax
What it does
Twenty slots. You set the price yours is for sale at, you pay 5% of it a week in rent, and anyone can buy it from you at that price any second — and you get paid it.
Set what your slot is for sale at. Pay 5% of that a week in rent. Anybody can buy it off you at your own number, any second — and when they do, you get paid it. Say what the business is. It writes and publishes a real website for it, live in about three minutes — then keeps working on it. Because the same number does two jobs at once, and they pull in opposite directions. Your rent is cheap — and somebody takes the slot off you tomorrow morning for exactly that little. Nobody can afford to take it from you — and the rent quietly drains your deposit until the slot forecloses on its own. The only comfortable place to stand is the number you would genuinely be happy to be paid. That is the…from anyonecanbuy.com
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Launched alongside, August 2026
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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.
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