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yano

yano2mch
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AI & ML interests

LLM's for Roleplay and Story generation. And of course good image generation too.

Recent Activity

new activity about 5 hours ago
google/gemma-4-12B-it:Fairly Useless
repliedto kingkw1's post about 23 hours ago
I built Read-Along AI for the Hugging Face Build Small Hackathon. It is an offline-capable reading practice app for early readers: one short sentence at a time, tap-to-hear word help, record a read-aloud attempt, then get gentle feedback. The goal is Backyard AI in the literal sense: a tool for real home reading practice, where feedback needs to be patient, developmentally fair, and private. A child’s voice should not need to leave the app just to practice “The dog ran fast.” What makes it small-model native: - Exact clean readings pass immediately. - Close or ambiguous child-speech transcripts get a second look from a fine-tuned MiniCPM phonetic evaluator. - Meaning-changing mistakes still fail closed, e.g. “blue hat” should not pass for “red hat.” - Off the Grid Mode runs local ASR plus the MiniCPM GGUF evaluator through llama.cpp. - Turbo Mode uses Modal endpoints for lower-latency ASR/TTS/evaluation. - The UI is custom Gradio with a child-facing reading canvas, clickable words, progress feedback, and celebration on success. Targeted tracks and badges: Backyard AI, Off-Brand, Off the Grid, Llama Champion, Well-Tuned, Tiny Titan, Sharing is Caring, Field Notes. Space: https://huggingface.co/spaces/build-small-hackathon/read-along-ai Demo video: https://youtu.be/4bpbwhipLU4 Repo: https://github.com/kingkw1/read-along-ai Built with Codex as the lead development partner.
repliedto kingkw1's post about 23 hours ago
I built Read-Along AI for the Hugging Face Build Small Hackathon. It is an offline-capable reading practice app for early readers: one short sentence at a time, tap-to-hear word help, record a read-aloud attempt, then get gentle feedback. The goal is Backyard AI in the literal sense: a tool for real home reading practice, where feedback needs to be patient, developmentally fair, and private. A child’s voice should not need to leave the app just to practice “The dog ran fast.” What makes it small-model native: - Exact clean readings pass immediately. - Close or ambiguous child-speech transcripts get a second look from a fine-tuned MiniCPM phonetic evaluator. - Meaning-changing mistakes still fail closed, e.g. “blue hat” should not pass for “red hat.” - Off the Grid Mode runs local ASR plus the MiniCPM GGUF evaluator through llama.cpp. - Turbo Mode uses Modal endpoints for lower-latency ASR/TTS/evaluation. - The UI is custom Gradio with a child-facing reading canvas, clickable words, progress feedback, and celebration on success. Targeted tracks and badges: Backyard AI, Off-Brand, Off the Grid, Llama Champion, Well-Tuned, Tiny Titan, Sharing is Caring, Field Notes. Space: https://huggingface.co/spaces/build-small-hackathon/read-along-ai Demo video: https://youtu.be/4bpbwhipLU4 Repo: https://github.com/kingkw1/read-along-ai Built with Codex as the lead development partner.
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