RevenuePack
AI receptionist for the trades — every missed call answered
What it does
RevenuePack answers the calls service businesses physically can't — roofers on roofs, plumbers under sinks. An AI receptionist on your own line captures who called, what they need, and how urgent it is, then hands you the callback record. $99/mo, 250 AI minutes, written guarantee. Built solo in Indiana.
RevenuePack answers the missed and after-hours calls your team cannot, captures the customer’s needs, and gives you the callback record. $99 a month with 250 connected minutes. Built in Indiana, works nationwide.
RevenuePack answers the phone when you cannot, chats with visitors on your website, texts back missed calls, books appointments, and puts every lead in one inbox with follow-up built in. One flat price, the whole Pack. The whole Pack. 250 receptionist minutes included. Month to month. Hear it before you buy: call the live AI at (765) 200-9397 . Card details come next, entered right here and processed by Stripe. Nothing answers a live customer until you approve the rules. Give the live AI a difficult service call before you trust it with one of yours. Enter your business, email, and website. Stripe handles the card. Nothing answers a live customer until every rule is approved. One carrier…from revenuepack.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…
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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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