Horse Whisprer
Local Whisper dictation for Mac
What it does
Horse Whisprer is a menu bar app that runs Whisper locally on your Mac, then inserts the transcript into whatever app you are already using. Choose a model, trigger dictation from the keyboard, and speak through drafts, prompts, support replies, notes, and technical text without cloud transcription minutes or a subscription. Built for the moments where built-in dictation is too short, too rigid, or too much cleanup.
Does the same job
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TongueType for macOSMay 2026 · tonguetype.app · ▲109Local dictation for macOS without the subscription
- OFOpenWhisper – free, local, and private voice-to-text macOS appFeb 2026 · github.com · ▲37
I wanted a voice-to-text app but didn't trust any of the proprietary ones with my privacy. So I decided to see if I could vibe code it with 0 macOS app & Swift experience. It uses a local binary of whisper.cpp (a fast implementation of OpenAI's Whisper voice-to-text model in C++). Github: https://github.com/richardwu/openwhisper I also decided to take this as an opportunity to compare 3 agentic coding harnesses: Cursor w/ Opus 4.6: - Best one-shot UI by far - Didn't get permissioning correct - Had issues making the "Cancel recording" hotkey being turned on all the…
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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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Hey HN, Henry here from Cactus. We open-sourced Needle, a 26M parameter function-calling (tool use) model. It runs at 6000 tok/s prefill and 1200 tok/s decode on consumer devices. We were always frustrated by the little effort made towards building agentic models that run on budget phones, so we conducted investigations that led to an observation: agentic experiences are built upon tool calling, and massive models are overkill for it. Tool calling is fundamentally retrieval-and-assembly (match query to tool name, extract argument values, emit JSON), not reasoning. Cross-attention…
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