A highly opinionated, fully functional Obsidian vault

Hacker News

A few months ago I noticed that I was quickly approaching my 10GB sync limit for my daily driver vault. I considered deprecating some of the heavier files and images, but I was worried how it would affect the integrity of my vault. Instead, I took the opportunity to think to myself -- what would the perfect vault look like? I began to write down some of the key philosophies and strategies I use in my driver vault which led to indispensable plugins, which led to more indispensable philosophies and on and on it went. I've chronicled these results into a fully working vault template that includes templates, dataviews, macros, scripts, and powerful but simple and intuitive structural elements. This vault is truly a condensation of all of my knowledge pertaining to Obsidian (the README is very long), so please do give it a go! I promise you'll like what you see!

Visit product View on Hacker NewsDec 18, 2022

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

80%80% 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.

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

69%69% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

53%53% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: month · Missing: mobile apps, ios, personal

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

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

46%46% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

17%17% 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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