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yazin

@yazins • 8,024 subscribers

messing with AI / founder @amalinvest (YC, W22)

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Introducing: OpenGranola 🔥 I built an open source meeting copilot for macOS. It transcribes both sides of your call on-device, searches your own notes in real time, and hands you talking points right when the conversation needs them. No audio leaves your Mac. Point it at a folder of markdown files, pick any LLM through OpenRouter (Claude, GPT-4o, Gemini, Llama), and it just works. It's invisible to screen share too — nobody knows you have it. The whole thing is open source. Link below

Introducing: OpenGranola 🔥 I built an open source meeting copilot for macOS. It transcribes both sides of your call on-device, searches your own notes in real time, and hands you talking points right when the conversation needs them. No audio leaves your Mac. Point it at a folder of markdown files, pick any LLM through OpenRouter (Claude, GPT-4o, Gemini, Llama), and it just works. It's invisible to screen share too — nobody knows you have it. The whole thing is open source. Link below

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BREAKTHROUGH 85% accuracy at just 115MB (!) i almost can't believe it 📹 demo video below for context: i spent days on offline Quran recognition. Whisper, Moonshine, wav2vec2 fine-tunes, layer pruning, CTC rescoring, beam search tricks. nothing cracked 81% accuracy. i then plugged in NVIDIA's pretrained FastConformer and immediately got 90.7%. also, it's 10x faster 🐇 (0.33s vs 3.24s) and 10x smaller 👌 (115MB vs 1.2GB). i tried fine-tuning it on Quranic audio, figuring I could push it even higher. Ran four different configs. every single one came back worse than the stock pretrained model. no idea what's going on there yet. code/repo in next tweet
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BREAKTHROUGH 85% accuracy at just 115MB (!) i almost can't believe it 📹 demo video below for context: i spent days on offline Quran recognition. Whisper, Moonshine, wav2vec2 fine-tunes, layer pruning, CTC rescoring, beam search tricks. nothing cracked 81% accuracy. i then plugged in NVIDIA's pretrained FastConformer and immediately got 90.7%. also, it's 10x faster 🐇 (0.33s vs 3.24s) and 10x smaller 👌 (115MB vs 1.2GB). i tried fine-tuning it on Quranic audio, figuring I could push it even higher. Ran four different configs. every single one came back worse than the stock pretrained model. no idea what's going on there yet. code/repo in next tweet

yazin

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