Oreniq
AI receptionist that answers every business call
What it does
Oreniq is an AI receptionist for businesses that answers calls 24/7, qualifies leads, answers questions, books appointments, and transfers urgent calls to your team.
Oreniq provides AI voice receptionists that answer calls, qualify leads, book appointments and support customers 24/7.
An AI receptionist that answers your business calls 24/7 - qualifying leads, booking appointments, and handing urgent calls to a person. Good evening, Northgate Dental. This call is answered by an AI assistant and is recorded - how can I help? Every unanswered call is a customer who calls the next business instead. Here's what happens on every call Oreniq takes. A customer dials your existing business number. Nothing about how you advertise your line changes. A customer dials your existing business number. Nothing about how you advertise your line changes. Oreniq picks up on the first ring with your greeting, your business name, and your tone of voice. It works out what the caller actually…from oreniq.in
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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…
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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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