I heard that! We used a program called Mavis Beacon with rubber sleeves that we would put over the keyboard to learn. I think it was actually pretty good. Some of my friends nowadays who didn't learn to type in school are trying to use similar programs now that they're grown up and spend a lot of time typing. 😂
Kevin King
kingkw1
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repliedto their post about 4 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 their post about 6 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. updated a Space 1 day ago
build-small-hackathon/read-along-aiOrganizations
replied to their post about 4 hours ago
replied to their post about 6 hours ago
Typing tutor in reverse—I love that! 😂 You are spot on with the homeschooling angle. I built it precisely to solve the reading practice friction with my own gaggle of young kids at home. Really appreciate the kind words!
posted an update 2 days ago
Post
2678
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:
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.
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:
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.
- 5 replies