Bingo
AI-matched collaborations, backed by real content fit
What it does
Bingo is an AI-native marketplace connecting brands with top-tier content creators and UGC specialists. Moving beyond vanity metrics, our platform uses semantic analysis to match creators with the brand’s unique aesthetic, tone, and production quality. Whether you need high-reach influencer distribution or specialized UGC production, we streamline the entire deal lifecycle from intelligent discovery and direct outreach to secure, escrow-protected payments. Perfect for modern campaigns.
Bingo matches brands with creators by content, not follower count. AI matching, contract vault and instant payouts.
Bingo reads a creator's actual work and a brand's brief, then explains exactly why they match. No cold DMs, no follower thresholds, no guesswork. From discovery and onboarding to collaboration and final performance analysis. Creators are ranked on content signal, audience fit and conversion — never vanity metrics. Negotiation, contracting, creation, review and payout stages tracked in a single pipeline. Milestone-based escrow releases funds the moment a brand approves the cut. Campaign performance, sales and clicks, and top creator rankings in one dashboard. Every campaign-creator pair runs through the same scoring core: category, creator type, content relevance, budget, compensation and…from bingo-virid-ten.vercel.app
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Life & fun · 10d ago · louisabraham.github.io
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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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