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Cheaki.com – I hate online cheatsheets, so I built a tool to share them

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Cheaki.com – I hate online cheatsheets, so I built a tool to share them

Cheatsheets could be a powerful tool for lots of people, especially a full-stack developer like me, but I hate online cheatsheets for a few reasons: 1. Most online cheatsheets are pdf files or webpages. I can't add my own stuff, nor only take the parts that are useful to me. 2. There is no easy way to search across all the cheatsheets I've collected. 3. Can't make part of a cheatsheet private, therefore, I have to delete sensitive stuff before sharing mine with others. I ended up using multiple txt files to store mine over the years which is cumbersome as well. So I built Cheaki.com. On Cheaki, if you see a useful cheatsheet somebody else wrote, you can clip the whole sheet or just some of the cards (a cheatsheet contains multiple cards), edit the content, delete some or add new cards. You could set a card private, then it will be encrypted and only visible to you. Finally all your cheatsheets' content will be on a single page, and you can search all using Ctrl+F. This is a cheatsheet I've created for NodeJS Interview questions: https://www.cheaki.com/nodejs/nodejs-interview-questions The website is at: https://www.cheaki.com

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Indie HackersFits the IH revenue-focused audience · Strong signals: created · Missing: supports, reddit linkedin, podcasting
64%64% 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.
AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive
58%58% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
56%56% 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: new, single, using · Missing: mac, agents, macos
51%51% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
43%43% 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
9%9% 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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