RankHeist.lol
Outbid the competition. Hijack live startup traffic
What it does
RankHeist.lol turns early startup distribution into a live, real-time battleground: 🔹 Pay-to-Rank Leaderboard: Your position is strictly determined by your live bid—no black-box algorithms or review delays. 🔹 Real-Time Traffic Routing: Hold the #1 spot and capture active, direct visitors to your product immediately. 🔹 Targeted Categories: Compete on global or niche boards so you reach relevant buyers and builders. 🔹 Clean, Fast UI: Zero clutter, no ads, and instant live updates.
The real-time attention leaderboard for startups. Outbid rivals, claim the #1 spot, and hijack live traffic to your product.
New spots start at $5. Placements below the #1 threshold will claim their matching rank on the board. Browse top projects and leaders across each specialized industryfrom rankheist.lol
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WebsiteRank.lol4d ago · websiterank.lol · ▲1Pay-to-Rank Real-Time Leaderboard for Websites & Startups



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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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