HookLab: Analyze and improve the first 3 seconds of short-form videos
HookLab is a free tool that analyzes the first 3 seconds of short-form videos (TikTok, Reels, Shorts, X videos) to score their hook effectiveness. It uses LLMs to classify hook types and generate rewritten alternatives that stop the scroll, helping creators improve viewer retention.
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Viral Hook Maker: app to create vertical promo videos with hook clips
Viral Hook Maker is a mobile app that helps small businesses and creators quickly produce vertical promo videos (Reels/Shorts) by selecting a language-based hook video, adding their own footage, and making simple edits. It uses an LLM to generate or curate hook scripts, solving the problem of losing time finding an engaging hook and stitching it to footage in a full editor.
AI virality score predictor for short videos
A tool that scores short videos for viral potential before posting, using a model that analyzes faces, motion, and sound. It helps creators optimize content but has blind spots: it cannot evaluate curiosity hooks, captions, or storytelling elements that drive sharing.
PrismClip: Search for moments in long videos
PrismClip is a tool that lets users search for specific moments in long videos, giving more editorial control than automated clipping tools like OpusClip. It uses LLMs to understand natural language queries and find relevant scenes. Built for creators who want to extract specific clips without dealing with complex editing software.
Orty: AI tool that scores Instagram/YouTube creators as potential video editing clients
Orty is an AI tool for freelance video editors that scores Instagram and YouTube creators on a 1-10 scale based on how well they fit the editor's style and rates. Users drop in a screenshot of a creator's profile, and optionally a screen recording of their videos, to get a fit score instead of guessing who to cold-email.
YouTube watch history analyzer for parents
A tool that analyzes a child's YouTube watch history and generates a 'brainrot risk report' for parents. It uses LLMs to identify patterns like repeat channels, addictive loops, late-night spikes, and content quality, helping parents understand what content shapes their child's feed without relying on screen time alone.