Cu

Custodian – A Modern Web Directory

Hacker News

Custodian – A Modern Web Directory

Hi all, I developed custodian because I wanted a tool to let me organize and search my bookmark collections. That, and I wanted a reason to try out Elixir and Phoenix LiveView, as I'd heard good things about it (and can confirm it's quite fun to develop in). Many of my links come from HN (I often learn about interesting things I didn't even know existed from reading the comments), but I know that once a submission inevitably leaves the home page most people will probably not see them again. Maybe this project can help with that. The other reason for creating this is that I find it's getting more and more difficult to find good content using search engines. I hope manual curation might help in some cases, if enough people are interested in contributing. I'm curious what you think, so please feel free to try it out and let me know. Thank you!

Visit product View on Hacker NewsFeb 6, 2023

Actual performance

1points

Did not reach leaderboard

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

64%64% 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 NewsMay not resonate with HN audience · Strong signals: exist, io · Missing: https docs, excited, just released

49%49% 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 HuntUnlikely to reach the leaderboard · Strong signals: using · Missing: mac, agents, macos

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

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

35%35% 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

29%29% 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

11%11% 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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