SmartOnCall
The invisible AI copilot for live calls. Zero data stored.
What it does
Your audio and transcripts never touch our servers. That's not a marketing claim - it's the architecture. SmartOnCall streams audio directly from your device to the speech-to-text provider, and transcripts go straight to Google Gemini. No middleman. No storage. It listens to live calls, detects questions in real-time, and displays AI answers in a floating window by your webcam. Add your resume or playbook for context-aware response. Native Windows app. Pay-per-use, no subscription. 60 mins free
Does the same job
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- AAAnyCall – Answer any call with AI2023 · anycall.ai · ▲18
Hey HN, Chris here. I have been playfully hacking on a few ideas that leverage some of the latest AI tools (both open-source and proprietary). One in particular seems somewhat useful and is ready for a few more eyeballs. The idea is to make building very capable AI phone agents as easy as creating an online form. We think the ease of production will lead to some interesting enterprise use cases. I'd love your feedback (order a Pizza from Vito in the demo to see how it performs) and in particular your ideas about potential uses. Thanks!
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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…
AI · 27d ago · cactuscompute.com
Launched alongside, June 2026
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Today, I’m proud to announce Homebrew 6.0.0. The most significant changes since 5.1.0 are a new tap trust security mechanism, the new faster, smaller, default internal Homebrew JSON API, sandboxing on Linux, better defaults informed by our user survey, many brew bundle improvements, improved performance and initial support for macOS 27 (Golden Gate). Happy to discuss any questions here!
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