An Android app that scans your Obsidian notes to set reminders

Hacker News

I'm an on and off Obsidian user who recently came back to Obsidian again. As flexible as the app is, one thing it can't do is set notifications on mobile devices. The feature seemed to be in demand on forums, reddit, etc but it's not on the Obsidian roadmap. So I figured I'd create a free app to set notifications from your Obsidian notes! It's called Notifian and it's a "companion app" for the Obsidian Android app. It scans your Obsidian vault folder and identifies reminder properties in your notes to set: - An initial reminder date/time - (optional) A recurring schedule for reminders - (optional) A "stop on" date/time, to stop scheduling reminders It runs these scans in the background on a recurring interval and, as a bonus, it also has support for the Obsidian "tasks" plugin's task format! The app just got out of closed testing and onto the play store, so I'd love to get more feedback on it now that it's released!

Visit product View on Hacker NewsJan 15, 2025

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

79%79% 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.

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

52%52% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

49%49% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

34%34% predicted probability of success on AppSumo, 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

26%26% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Acquire.comPre-revenue stage for this audience · Strong signals: recurring · Missing: arr, mrr, revenue

21%21% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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