Font Awesome Icon Picker Using AI

Hacker News

I am a big fan and user of Font Awesome icons. Although their search function is pretty neat, sometimes I find it difficult to search for icons related to a particular concept or topic. As a solution, I have created an app where you can enter any concept or topic and receive the corresponding Font Awesome icon code. Here is an example for the word "prediction": When I searched for "prediction" using Font Awesome's search, no icons were returned: https://fontawesome.com/search?q=prediction&o=r However, the app suggested that I should use the "chart-line" icon, which was accurate: https://cookup.ai/o/prediction-font-awesome-icon-picker-usin... Please try it out and let me know your feedback.

Visit product View on Hacker NewsFeb 17, 2023

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5points

1comments

Did not reach leaderboard

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

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

nativeThis product was originally launched on this platform.

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

46%46% 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

42%42% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: user, using, code · Missing: mac, agents, macos

38%38% 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

35%35% 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

11%11% 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

7%7% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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