DI

DIY-DRY Password System

Hacker News

DIY-DRY Password System

My system is to use a standard, common word, such as "cat." Let it begin with a capital letter followed by the service name. Last is a favorite number; for example, Catinstagram17 for Instagram and Catfacebook17 for Facebook. Even if your password is stolen from Facebook, the hacker can't use it to brute-force themselves into your Instagram account, as the password won't match. Instagram and other services usually code and "salt" user passwords with a hash system. For example, the Catinstagram17 to you is obfuscated in the checked against file at the service and it looks something like this: "9 dC90Oy26#^Y." So, along with all the other encrypted user passwords in the list, they likely won't see your pattern. For the banks and work with heavy restrictions, such as special characters, I just add that to the end. *DIY is "Do it yourself," and DRY is "Don't repeat yourself."

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Actual performance

3points
5comments
Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
81%81% 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.
TrustMRRFits verified-revenue profile · Missing: mobile apps, ios, personal
60%60% predicted probability of success on TrustMRR, 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.
Product HuntOn track for Day 1 leaderboard · Strong signals: user, code · Missing: mac, agents, macos
51%51% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
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.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
14%14% 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
3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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