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Phraisely.com – look up words by their description

Hacker News

Phraisely.com – look up words by their description

I did not get much feedback last time I shared [ https://news.ycombinator.com/item?id=33286893 ] I built phraisely as an AI assistant to look up words by their descriptions. Since my last posts, I've added: - free trial with *no signup* required - improved quality of the model and results. - improved usability. Example usage: if you query for "being home at night watching Netflix on the couch", results can contain "relaxing" and "unwinding" but also "binge-watching" since I mentioned Netflix. Bonus points when I get some slang in the results (e.g. couch-potatoing). Remember: (i) the more detailed your query is, the better and (ii) phraisely is creative so try to run a query multiple times. I'm planning to keep working improving the model and the results. Comments and suggestions are welcome. Thank you.

Visit product View on Hacker NewsOct 27, 2022

Actual performance

5points

2comments

Did not reach leaderboard

Launch Intel predictions

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

69%69% 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.

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: model, new · Missing: mac, agents, macos

49%49% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

34%34% 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

28%28% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

13%13% 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

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

Correct prediction on native model

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