A simple inbox for all your bookmarks

Hacker News

Hey everyone, I want to share a side project I've been working on for managing bookmarks easily and effectively. I needed a way to manage my links without getting overwhelmed. There are many bookmarking sites, but they often lack good user experience. I wanted a tool where you can treat your links like an inbox—a centralised place to store, search, and share them with friends, colleagues, and clients. I know there are big players in the market, But I find their workflows too complicated. I just wanted a way to save interesting stuff and refer back to it without slowing down browser. Here’s what my project aims to solve: Reducing the number of open browser tabs to avoid losing links. An easy-to-use interface for storing, searching, and sharing links. Easily transfer and download your browser links easily. It took 30 days to build this as a non-tech person, and I hope you find it valuable. I’m looking feedback to improve the experience even more so please go easy on me. Any feedback will help me on.

Visit product View on Hacker NewsMay 27, 2024

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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: user, inbox, open · 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 · Missing: supports, reddit linkedin, podcasting

75%75% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

46%46% 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, ide · Missing: https docs, excited, just released

41%41% 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: way · Missing: mobile apps, ios, personal

38%38% 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

8%8% 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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