Write and read children stories using on-device computing

Hacker News

I'm pretty excited about the potential of small language models. Advances in language models, coupled with increasingly powerful mobile devices, have made it possible to carry language models with billions of parameters in our pockets. While I was on paternity leave, I started building Pico Library to make storytime more engaging for both me and my child. As a new father, I've come to deeply appreciate the importance of reading to my child. Pico Library uses a language model to write stories, and it can leverage Apple's on-device Personal Voice feature to read them aloud. Everything is powered by on-device computing, meaning no internet connection is required. I'm happy to chat about the technology in Pico Library—what I'm excited about and what I found surprising. I'm also curious to hear what others would build with similar technology.

Visit product View on Hacker NewsJun 21, 2024

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, apple, new · Missing: mac, agents, macos

86%86% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Hacker NewsStrong engagement from HN community · Strong signals: excited, io · Missing: https docs, just released, exist

59%59% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Indie HackersFits the IH revenue-focused audience · Strong signals: started, para · Missing: supports, reddit linkedin, podcasting

55%55% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: personal, para · Missing: mobile apps, ios, entrepreneurs

47%47% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

45%45% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: arr · Missing: mrr, revenue, profit

13%13% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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