Us
Use the new Gemini model to generate customized ABC storybooks
Hacker NewsUse the new Gemini model to generate customized ABC storybooks
I built this website with v0 to test Gemini's new image capabilities, while 4o is still rate-limited/not available on the API I wrote up some more information on it here: https://x.com/max_leiter/status/1906492622551884187 and here's the chat that built it: https://v0.dev/chat/ai-story-book-creator-zw7TrmkN2Eb
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Launch Intel predictions
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Indie HackersFits the IH revenue-focused audience · Strong signals: gemini · Missing: supports, reddit linkedin, podcasting84%84% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: model, new, gemini · Missing: mac, agents, macos75%75% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive53%53% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal39%39% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released33%33% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue18%18% 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, cryptocurrency2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.
Correct prediction on native model
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