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heyhey – Building a Linktree-like app taught me more than college

Hacker News

heyhey – Building a Linktree-like app taught me more than college

Hello Hacker News! HEYHEY is a service that tries to bridge the siloed platforms of the modern-day internet and be where people come to search for others. So far, I've spent about 2 months developing it, and as a college student, it honestly helped me learn more about computer science than any undergrad course we have. Also, after getting the inspiration from HN, I wanted to prove to myself that, in 2022, anyone could build a full-fledged web service. Thanks to its modern-yet-traditional infrastructure, HEYHEY is costing me about 0 dollars a month to run (so far!). I really wanted to keep the service simple but functional. So naturally, I tried to stay away from a JS-based front-end. Instead, HEYHEY uses simple HTML pages with modern CSS and forms to function (you can disable JS!). Last week, I silently turned on registers, and if you register today, you will have a 10% chance to get Pro for life — it enables custom usernames (heyhey.to/[username]) and rich themes. I am not trying to take over the "Linktree space" nor trying to build a unicorn startup. After making HEYHEY presentable, I just wanted others to have access to it as well. I hope to keep developing HEYHEY so that it can further integrate with other social platforms while staying lightweight and functional. Would love to hear your feedback! If you registered and want to delete your account, just shoot an email to: friends@heyhey.to

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Actual performance

11points
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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
96%96% 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.
Hacker NewsStrong engagement from HN community · Strong signals: hacker news, io · Missing: https docs, excited, just released
68%68% 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 HuntOn track for Day 1 leaderboard · Strong signals: user, computer, new · Missing: mac, agents, macos
62%62% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: month, way · Missing: mobile apps, ios, personal
45%45% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform · Missing: plus, intuitive, reviews
26%26% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
18%18% 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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