AI
AI Analyzes Your Guitar Playing from Video – Built with Gemini 2.5
Hacker NewsAI Analyzes Your Guitar Playing from Video – Built with Gemini 2.5
Built an AI Guitar Teacher over the weekend! Code here: https://github.com/tekaratzas/GuitarGPT Uses Gemini 2.5 Flash to analyze video of you playing, Highlights segments then pumps out a personalized practice routine. Been using it to get unstuck on some more difficult passages. Pretty insane how good it is given this is just the start.
Share cardActual performance
3points
Did not reach leaderboard
Launch Intel predictions
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Product HuntOn track for Day 1 leaderboard · Strong signals: gemini, using, code · Missing: mac, agents, macos66%66% 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.
AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive55%55% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Strong signals: gemini · Missing: supports, reddit linkedin, podcasting50%50% 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, video · Missing: mobile apps, ios, entrepreneurs40%40% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide · Missing: https docs, excited, just released38%38% 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, revenue20%20% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto2%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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