ShortcutRef.com – discover and learn shortcuts easily and quickly

Hacker News

Hi, my name is Yadong and I'm learning to build web apps. I just made a website ( https://shortcutref.com ) for shortcut references in the past week. It has very clean UI and shows only the relevant shortcuts you need with short and precise descriptions. The goal of this tool is to help users learn shortcuts of a specific application in a few minutes. Let me know if you like it and I'll add GIF and search support. Thanks!

Visit product View on Hacker NewsAug 24, 2017

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5points

2comments

Did not reach leaderboard

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

66%66% predicted probability of success on TrustMRR, 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

59%59% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

57%57% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

52%52% 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: users · Missing: plus, platform, intuitive

44%44% 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

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.

Incorrect prediction on native model

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