Share-links, save, manage and share interesting links

Hacker News

Hi HN! I feel that the state of this small project of mine is advanced enough to be shared here. I created this thing because I was growing frustrated of how Shaarli ( https://github.com/shaarli/Shaarli ) worked (but I don't remember why now, though). Share-links is fairly simple; you add links, maybe tags and a description, and it store them on a small django website, that you can share to your friends too (I'm missing the "links" page on personal websites that made me discover a lot of cool websites). However, it allow you some more features (favicons before links, autofetch lang & title, basic comment system, multiple users, basic search, highlight posts...) that may interest you. I'm using an instance since 1 and half year, and it's been great to store the links I find interesting on the web! (I don't took the time to add tags & comments to my links, but I use the search feature a lot). I hope some of you will start using this project and suggest new features :) (sorry for my bad english, I'm writing this in a hurry before leaving the computer) (don't worry if my own instance is down, it's selfhosted and I have a very bad upstream)

Visit product View on Hacker NewsJun 10, 2023

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Product HuntOn track for Day 1 leaderboard · Strong signals: user, computer, new · Missing: mac, agents, macos

78%78% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

61%61% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoStrong fit for a featured deal · Strong signals: host, 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: io · Missing: https docs, excited, just released

45%45% 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: personal, users · Missing: mobile apps, ios, entrepreneurs

42%42% 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 · 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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