TalkBuddy
Practice speaking real conversations a few minutes a day.
What it does
Learn a language by actually speaking it. TalkBuddy is your AI speaking partner for real conversations. -> PREPARE: Learn key phrases, hear them, say them aloud before you start. -> SPEAK: Have real out-loud conversations with an AI buddy — no judgment, no pressure. -> LEARN: After each session, TalkBuddy pulls out useful phrases from what you actually said. -> BUILD: Save phrases with pronunciation, organized by topic, into your own library. Start practice now!
TalkBuddy is your AI speaking partner. Practice real conversations out loud, learn the words you need, and build confidence a few minutes at a time.
TalkBuddy is your AI speaking partner. Learn the words, lock them in with quick games, then practice real conversations out loud — building confidence a few minutes at a time. You don't need to memorize hundreds of words before you can have a conversation. Every lesson walks you from new words, through quick games, into a real conversation with your AI buddy — then helps you remember what you actually said. Every lesson opens with the words you'll actually use. Hear each phrase in a natural voice, slow it down, and say it out loud — so it already feels familiar by the time you speak. Right after you learn them, lock the words in with a quick match game. Tap a word, then its meaning — fast,…from talkbuddyapp.com
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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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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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