From 9b8625e2a4faed54e33653ff3d5b572554e3a6e9 Mon Sep 17 00:00:00 2001 From: Francesco Cozzuto Date: Sun, 17 Aug 2025 19:23:03 +0200 Subject: [PATCH] Remove old code --- old/login.wl | 147 --------------------- old/new.wl | 121 ----------------- old/notfound.wl | 114 ---------------- old/signup.wl | 21 --- old/thread.wl | 340 ------------------------------------------------ 5 files changed, 743 deletions(-) delete mode 100644 old/login.wl delete mode 100644 old/new.wl delete mode 100644 old/notfound.wl delete mode 100644 old/signup.wl delete mode 100644 old/thread.wl diff --git a/old/login.wl b/old/login.wl deleted file mode 100644 index cdfcf51..0000000 --- a/old/login.wl +++ /dev/null @@ -1,147 +0,0 @@ - - - cozis news - login - - - - -
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- - - - -let posts = [ - { title: "Show HN: Kitten TTS - 25MB CPU-Only, Open-Source TTS Model", date: "3 hours ago", num_comments: 127, link: "https://github.com/KittenML/KittenTTS" }, - { title: "Open models by OpenAI", date: "15 hours ago", num_comments: 651, link: "https://openai.com/open-models/" }, - { title: "Anthropic rejects the main developer of the library they use", date: "40 minutes ago", num_comments: 437, link: "https://grell.dev/blog/ai_rejection" }, -] - -let style = - - -let content = -
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- submitted 3 hours ago by kittenlover42 | 127 comments -
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- Hey HN! I've been working on Kitten TTS for the past 6 months - a tiny text-to-speech model that runs entirely on CPU. At just 25MB, it's perfect for embedded applications and privacy-focused projects where you don't want to send text to external services. -

- The model is trained on a diverse dataset and supports multiple languages. Performance is surprisingly good for the size - would love to hear your thoughts! -
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- techgeek99 2 hours ago -
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- This is incredible! I've been looking for something exactly like this for my IoT project. The fact that it's only 25MB and runs on CPU is a game changer. How does the voice quality compare to larger models? -
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- kittenlover42 2 hours ago -
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- Thanks! The voice quality is obviously not as good as larger models like Tacotron2 or the commercial APIs, but it's surprisingly decent for most use cases. I'd say it's about 75% of the quality at 1% of the size. Perfect for things like reading notifications aloud or simple voice interfaces. -
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- embedded_dev 1 hour ago -
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- That sounds perfect for embedded applications! What's the inference speed like on something like a Raspberry Pi? -
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- ml_researcher 2 hours ago -
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- Very impressive work! I'm curious about the architecture - are you using knowledge distillation from a larger model, or did you train this from scratch? The 25MB constraint must have required some clever optimizations. -
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- kittenlover42 1 hour ago -
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- Great question! It's a combination approach. I started with a modified Tacotron architecture but with much smaller hidden dimensions, then used knowledge distillation from WaveNet to get the vocoder down to size. Also heavily quantized the weights and used some pruning techniques. Happy to share more technical details if you're interested! -
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- privacy_advocate 1 hour ago -
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- Love that this runs locally! So tired of having to send my text to Google/Amazon/etc just to get speech synthesis. This is exactly what we need more of in the open source community. -
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