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Taggy – Generate right texts based on the content of your photo

Hacker News

Taggy – Generate right texts based on the content of your photo

What can you do with Taggy? Find the right phrases and keywords for your posts. Increase the visibility of your content on social networks. - Generate custom captions and quotes based on the content of your photo. You can add or remove hashtags and keywords to generate new texts. - Easily copy the text with the hashtags and use them in your publication. - Edit and download your optimized photo adapted for social networks. How does it work? We use AI to detect and classify the keywords of your photo. Then, we translate them into trending texts, topics and hashtags.

Visit product View on Hacker NewsMay 8, 2023

Actual performance

1points

Did not reach leaderboard

Launch Intel predictions

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Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released

50%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.

Product HuntUnlikely to reach the leaderboard · Strong signals: new · Missing: mac, agents, macos

48%48% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

44%44% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

41%41% 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, personal

33%33% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

22%22% 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, crypto

16%16% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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