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I Built Hashtini – Turn Hashtags into Shareable Landing Pages

Hacker News

I Built Hashtini – Turn Hashtags into Shareable Landing Pages

Hey HN, I built Hashtini in my spare time to solve a problem my son, a YouTube creator, faced: sharing his channel offline with friends and schoolmates. He found it difficult to get people to visit his content when he couldn’t just drop a link in a chat. That got me thinking—what if there was a way to create a simple, memorable word-of-mouth tool for online and offline sharing? Hashtags are easy to remember, so I built Hashtini as a way to turn hashtags into shareable landing pages, notes, and links. Instead of complicated URLs, creators can now use something like #MyBrand to direct people to their content from anywhere. I iterated through multiple rounds of user interviews, refining the tool based on feedback from creators, educators, solopreneurs, and small businesses. To help users spread the word, I also set up a referral program that gives you $5 for each paid subscription you refer. I’m sharing Hashtini here to get your feedback on how I can improve user acquisition and make this more valuable for the community. Would love to hear your thoughts! Try it out: https://hashtini.com

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Actual performance

1points
Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
85%85% 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: user, notes · Missing: mac, agents, macos
78%78% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Strong signals: users · Missing: plus, platform, intuitive
54%54% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: lua, 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.
TrustMRRLess likely to generate early MRR · Strong signals: users, way · 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 · Strong signals: subscription · Missing: arr, mrr, revenue
19%19% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: chat, paid · Missing: web3, crypto, cryptocurrency
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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