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Cheatsheets for Sublime 2 and 3 (MacOS X) and Anki Flashcards

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Cheatsheets for Sublime 2 and 3 (MacOS X) and Anki Flashcards

I'm obsessed with mastering editors and this past year I've been using sublime text everyday at work. During that time, I was slowly creating cheat sheets + flash cards for myself that helped me become truly fluent with the editor. Here they are :D Note: these are specific for MacOSX (if you want to help me convert them to other platforms, let me know). Abridged version (shortcuts for the most essential commands that will make you a competent user): https://github.com/sublime-text-university/cheatsheet/blob/m... Expanded version (shortcuts for most of the commands that will make you a very advanced user): https://github.com/sublime-text-university/cheatsheet/blob/m... Anki Flash Cards Deck (a deck for the most popular [spaced repetition system]( https://en.wikipedia.org/wiki/Spaced_repetition) ): https://ankiweb.net/shared/info/1240035032 The Anki deck includes all of the shortcuts in the expanded sheet. Enjoy.

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
74%74% 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
70%70% 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, macos, user · Missing: agents, agent, cursor
55%55% 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
46%46% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform · Missing: plus, intuitive, reviews
36%36% 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
19%19% 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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