DomainHacks – no more boring .coms

Hacker News

Hi folks, I am Till, the solodev behind the little side project domainhacks.info. The story began 7 years ago, when I taught myself coding using the good old LAMP stack. This was my very first project and was as-is on the internet for the next 7 years. Fast-forward to beginning of the year, when I got laid off during the great startup recession. I needed something to entertain me and I decided to revamp the whole site using NextJS and FaunaDB. Now, the site has 15 unique hacking methods like LEET hacks and phonetic hacks, using all 1466 available TLDs. I've also just introduced pricing tiers after the previous affiliate program only earned $60 despite over 2000 (possible) domain registrations in the previous 7 years. Try it out with keywords 'grapefruit', 'awesome', or 'outrageous' for a sneak peek of all results. However, with the free tier you'll already get 99.99% of the good results. Happy to discuss the tech stack, features, feedback and lessons learned!

Visit product View on Hacker NewsAug 10, 2023

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Actual performance

3points

2comments

Did not reach leaderboard

Launch Intel predictions

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

91%91% 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.

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

76%76% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: ide, 000, io · Missing: https docs, excited, just released

51%51% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

38%38% 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

25%25% 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

14%14% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: introduce · Missing: web3, chat, crypto

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

Incorrect prediction on native model

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