BeenuLabs
Reply to every app review, in every language, in minutes.
What it does
BeenuLabs reads every review on the App Store and Google Play and drafts a reply in your brand voice — in the language the reviewer used, with an English translation so you can approve one you cannot read. Nothing is published without you unless you switch auto-publish on yourself, and even then only for reviews that clear rules you set. Negative reviews are emailed to you the moment they land. Free for 10 replies a month, no card.
An AI review assistant for the App Store and Google Play. StoreReply reads every review and drafts a reply in your brand voice and the reviewer’s language.
StoreReply drafts one for every review the moment it lands — in your voice, in their language. Approve it, or let it publish itself. Real output from the live product — read, drafted, translated and posted in about a second. Every other claim on this page is ours. This one you can check in about a second, on an app you already know. Free, no sign-up, about a second. No link handy? Try Todoist or Evernote . The problem isn't getting feedback. It's keeping up with it. What the system does, not what we wish it did. The cadence is the one in our own cron schedule and every language was tested by drafting a reply in it and reading the answer back. No invented statistics anywhere on this page. No…from beenulabs.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.
AI · 17d ago · simedw.com
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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…
AI · 27d ago · cactuscompute.com


Launched alongside, August 2026
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Life & fun · 11d ago · louisabraham.github.io



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