Screenies
AI App Store screenshot generator for iOS apps
What it does
Most App Store screenshot tools are template editors. You still pick frames, write headlines, and design each slide. Screenies generates the full set with AI. Upload your real app screens, describe what the app does, and get one cohesive, on-brand set sized for the 6.9-inch iPhone display App Store Connect requires. Built for iOS founders who do not want to open Figma or hire a designer for every release. Pricing is $29/month or credit packs from $9. Credits never expire.
Generate polished App Store screenshot sets for your iOS app with AI. Upload your screenshots, describe your app, and export a ready-to-upload set in minutes.
Upload your screenshots, describe your iOS app, and get a polished, on-brand set of App Store screenshots that convert into downloads. Screenies turns your app screenshots and brand into a complete visual set, with every screenshot generated to work as part of the same story. Upload your app screenshots and tell Screenies what makes your app worth downloading. Every screenshot is restyled into one cohesive story built around your features and brand. Download the complete App Store set, sized and ready to upload. Claude, freelance designers, then Screenies. Here's what stuck. Subscribe for 15 credits a month, or top up with a one-time pack whenever a release needs more. Everything you need…from screenies.app
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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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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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