Never Miss a Lead | Krisper
AI receptionist that answers every call & text, 24/7
What it does
Krisper is an AI phone and text receptionist built for small to medium service businesses. It answers every call and text instantly, day or night, so you never miss a lead to a missed call again. Krisper books appointments, answers common questions, and routes urgent messages — all automatically, freeing you up to focus on the work instead of the phone. Start your free trial and never lose another customer to voicemail.
Stop losing money from missed calls & late replies. Krisper’s AI answers every call & text instantly, 24/7. Book more business—start your free trial now!
Every missed call is a lost customer. Krisper answers instantly, 24/7/365, so you never miss a lead again. REVENUE LEAK AUDIT The Cost of Silence Every missed call and delayed response isn't just an inconvenience—it's a direct hit to your bottom line. See the real numbers. Potential bookings lost to voicemails that never get left and inquiries abandoned tofrom krisper.ai
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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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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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