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View Amazon search trends and optimize listings
Hacker NewsView Amazon search trends and optimize listings
This is a very small weekend proof-of-concept that combines OpenAI's embedding model with 9 months worth of Amazon search trend data (available via their reporting API). I also added a small GPT4 wrapper to optimize listings based on the search data. Right now it is available for free. Would love feedback from Hacker News!
Share cardActual performance
3points
Did not reach leaderboard
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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting77%77% 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, openai · Missing: mac, agents, macos68%68% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: hacker news · Missing: https docs, excited, just released50%50% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive38%38% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: month · Missing: mobile apps, ios, personal37%37% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto15%15% predicted probability of success on BetaList, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue11%11% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
Correct prediction on native model
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