To

Too Many Domains? Track Them Easily

Hacker News

Too Many Domains? Track Them Easily

Hey there! How many domains do you own? How do you organize them? I'm launching an MVP of a tool that will help you organize and give a general overview of insights on your domains, so you can keep track of them, and all in one place. There are still features I’d like to add, but then it wouldn’t be an MVP, right? :) Email notification should be added too, with weekly updates on them. The domain is still temporary also. As I'm still validating the concept. Let me know your thoughts; would you use something like this? And more importantly, would you pay for it? Cheers.

Visit product View on Hacker NewsFeb 21, 2025

Actual performance

1points

5comments

Did not reach leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: email · Missing: mac, agents, macos

65%65% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive

53%53% 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

43%43% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

41%41% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

29%29% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

12%12% 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

3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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