Froging AI
Create stunning AI videos from a prompt or image
What it does
Froging AI turns text prompts and images into polished videos using leading models including Veo, Kling, Seedance, Wan, and more. Create social ads, product teasers, explainers, and cinematic clips in one simple workspace—no editing skills required.
Create AI videos and images from text or existing visuals with leading models in one studio. Generate, edit, and animate with Froging AI. Start free.
Create videos and images from text or existing visuals with Kling, Veo, Seedance, Nano Banana, GPT Image, and more—all in one creative studio. Click any video to see its model, settings, and complete prompt. Click any image to see its model, settings, and complete prompt. Start with words or an existing visual. Every workflow uses the same account, credit balance, generation history, and model workspace. Use one workspace instead of learning a different interface and managing a separate balance for every model. Pick the model that matches the result—not the subscription you happen to have. Choose the motion, audio, duration, and resolution profile that fits the shot. Move between fast…from froging.ai
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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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