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Hu

Hurlurl, a Randomizing Link Shorter

Hacker News

Hurlurl, a Randomizing Link Shorter

For an event at https://hellopaint.io we needed something that forwards to a random url from a list of urls (We created multiple drawing rooms and wanted people to evenly distribute across them during a twitch stream). It could also be used to forward to a random Website or a random YouTube video, but I'm thinking if I should limit it so all links have to redirect to the same domain, to limit the potential of abuse. Since I couldn't find any such service, I made one myself. (later I found this: https://randurl.com/ but it doesn't provide any statistics) It's made using Rust, Yew, daisyUI and Postgres https://github.com/lucasmerlin/hurlurl

Visit productView on Hacker NewsNov 17, 2022
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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 · Strong signals: created · Missing: supports, reddit linkedin, podcasting
71%71% 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.
TrustMRRFits verified-revenue profile · Strong signals: video · Missing: mobile apps, ios, personal
52%52% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, 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.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
39%39% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: using · Missing: mac, agents, macos
30%30% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
13%13% 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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