Trend-Analytic
AI YouTube Intelligence, Knowledge Engine & Video Studio
What it does
Trend-Analytic is a multimodal AI intelligence platform for YouTube. We analyze video transcripts, comments, and narrative pacing to uncover Brand Safety, manipulation index, and psychological drivers. Features include head-to-head creator battles, B2B media packs with predictive CPM/CPE, an automated knowledge factory converting transcripts into illustrated books, and a programmatic video studio producing multi-speaker vertical videos with studio-grade audio.
Advanced YouTube Influencer Analytics, AI scoring, and creator battles. Discover top trends and creators.
Our intelligent algorithm matches data from hundreds of channels so you can see the real difference in engagement, audience quality, and growth rates. "In-depth YouTube creator battle between @pbdpodcast and @politrickswatch. Analyzing reach, engagement, and marketing potential." "In-depth comparison of YouTube channels KLT Geography and Vladimir Surdin's Podcast. Analyzing audience, engagement, and advertising potential." "Comparative analysis of "Lazerson Podcast" and "Vladimir Surdin's Unearthly Podcast": reach, audience engagement, and advertising potential." "In-depth analysis of KLT Geography and Podkast Lazerson channels, comparing reach, engagement, and advertising…from trend-analytic.com
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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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Hey HN, Henry from Cactus here! We previously released Cactus Needle, a 14MB agentic LLM for tool call, device use, and structured extraction for phones, wearables, smart homes, small robots and microcontrollers. We got really great feedback here, and have now incorporated the suggestions to release Needle 2. The whole model is a single 14MB binary that runs a full session in 28MB of RAM; 45m parameters at 2bit compression. Needle hits 500 tokens/sec decode speed on a Raspberry Pi 5, sits between 400-1,500 tokens/sec on VR devices like Meta Quest 3S and Apple Vision Pro, and ranges…
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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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