Smort.io – Edit, annotate and share articles easily

Hacker News

Hey HN, I created Smort as I wanted to highlight an article and share it with a friend but couldn't find an easy way of doing it. Smort lets you easily edit, annotate and share an article. To read any article, just prepend smort.io/ before a URL in a browser. (I recently found out that even 12ft.io/ uses the same technique). Smort is free to use and shareable links are valid for 7 days from creation. Permanent links will be supported soon. I'm primarily a backend/ML engineer so had to learn all things frontend to build Smort. I started out developing in SvelteKit and loved it but later migrated to React & Nextjs due to better third party library support. I have lots of ideas on how to take Smort forward and would love for you all to use it. Please join our Discord to share your curated articles and feedback! This is a walkthrough article detailing everything Smort can do at the moment - https://smort.io/demo-walkthrough . Smort's name was inspired by the memes [1] [1] https://www.google.com/search?q=smort+meme&tbm=isch

Visit product View on Hacker NewsMar 14, 2022

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86points

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

68%68% 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, started · Missing: supports, reddit linkedin, podcasting

62%62% predicted probability of success on Indie Hackers, 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

50%50% 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 · Strong signals: soon · Missing: plus, platform, intuitive

41%41% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: google, way · Missing: mobile apps, ios, personal

34%34% 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

10%10% 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.

Incorrect prediction on native model

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