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Competitor Finder – Paste your domain, get your top competitors

Hacker News

Competitor Finder – Paste your domain, get your top competitors

I built a simple tool to help founders figure out who their actual competitors are! You know... the ones your potential customers already know and compare you to. Just paste your domain, and we generate a focused list of 10 competitors with names, sites, and a quick positioning note for each. Why I built it: I run a competitor monitoring tool ( https://champsignal.com ), and I realized that before people can monitor competitors… they first need to find them. This is harder than you think for people that have not been around for years aha (It's free and doesn't require signup) Would love feedback, especially if you don't think it's giving you the right competitors. Happy to improve the product.

Visit product View on Hacker NewsJul 27, 2025

Actual performance

5points

16comments

Did not reach leaderboard

Launch Intel predictions

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

75%75% 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.

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

47%47% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

44%44% 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 · Missing: mac, agents, macos

42%42% 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

31%31% 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

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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