TwiseReply
AI review replies grounded in your business knowledge
What it does
TwiseReply helps businesses create personalized responses to customer reviews using their own business knowledge, policies, and information. Unlike generic AI reply generators, TwiseReply uses approved business information to create relevant and professional responses, helping businesses save time while avoiding generic or inaccurate replies. Built for hotels, restaurants, and small businesses.
Twisereply helps hospitality businesses put their own knowledge — policies, services, FAQs — to work in every customer reply, so review responses and guest messages are more accurate and faster to send, not generic AI text.
Your business already has valuable knowledge — policies, services, FAQs, guest preferences. Twisereply brings that knowledge into every reply to Google reviews and customer messages, so responses are more accurate and go out faster, instead of starting from a blank page or a generic template. ★★★★☆ "We stayed for a wedding weekend. The room was spotless and the front-desk team were incredibly kind — but breakfast ran out of hot food by 9am on Saturday, and we had nowhere to store our garment bags before the ceremony." "Thank you for celebrating your wedding weekend with us, and for the kind words about our housekeeping and front-desk team — we'll pass that along. We're sorry the Saturday…from twisereply.com
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Life & fun · 10d ago · louisabraham.github.io
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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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