Private Markdown-based note taking

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I wanted to share a note-taking app I've been working on for the past 1.5 years called Octarine - https://octarine.app/ It's designed to help you stay organized without all the bells and whistles that can sometimes make note-taking feel overwhelming. Lots of similarities with Obsidian, but here’s a quick rundown of the features - All notes are stored locally as markdown - Fast and lightweight. Weighs in at less than 10MB, and is blazing fast due to Rust usage. - Dedicated Daily Desk for taking notes in a calendar date fashion. - Wikilinks, Graph and a Powerful search - Cmd + K bar for doing almost everything in the app. - NLP date parsing for going to a date quickly or attaching a daily note to a note. - Templates, Nested Tagging, Drag and Drop attachments - Multiple Workspaces with their own distinct settings - Heavy keyboard accessible. - One click setup to backup via Git to Github/Gitlab - Opinionated design and focus on a specific scope rather than `build you own via plugins` Also just recently launched a 1-time license purchase that gives access to additional features. Licensing works on a `early access` basis where you pay less to support the app right now with less additional features, but as new features get added in, the cost will go up, but not for people that have already purchased (similar to how Steam does Early Access games) — https://octarine.app/pricing Pro Features available right now are: - 13 new themes! - Access to Ask Assistant with OpenAI to quickly help gpt write/rewrite/improve your writing. Is available on Mac and Linux at the moment, with Windows looking like an End of Year release, and mobile apps after that! Give it a go at https://octarine.app/releases Some pro features that are on the way are: - Dedicated Task management with Kanban boards, Calendar. - Exporting notes to PDF, JPG, Text formats. - A global spotlight like Quick Note capture - Automation steps. - Github PRs and Linear Integration for tasks. ---

Visit product View on Hacker NewsNov 30, 2024

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

90%90% 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

70%70% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released

55%55% 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 · Missing: plus, platform, intuitive

32%32% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

27%27% 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

15%15% 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.

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