UploadPack
Evidence-backed YouTube ideas, titles and upload plans
What it does
UploadPack helps creators decide what to publish next using channel data, audience signals, competitor research and saved context. Instead of generating generic ideas, it identifies relevant opportunities, explains the evidence behind them and turns each one into complete publishing packs with titles, thumbnail directions, hooks, descriptions and promotional assets.
Free YouTube title and thumbnail generator backed by real research — see if your next video idea is worth making before you film it.
UploadPack is YouTube publishing intelligence software that helps creators, channel managers and agencies decide which YouTube idea deserves production time before they film. Bring an idea, research the opportunity, compare four packaging routes and build one complete upload Pack. The expensive mistake is often made before filming: choosing an idea without enough research. UploadPack moves the thinking to before the work so you can see which opportunity deserves scripting, filming and editing. Search, Curiosity, Authority and Commercial routes give one idea four distinct jobs. Each route includes a title, thumbnail direction and rationale, so you choose the viewer promise before building…from uploadpack.io
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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.
AI · 17d ago · simedw.com