My password

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My password

Thought I'd share this because I find it useful: I don't want to use the same password on all websites but I can't remember a different password for each new service I register. I therefore use an algorithm in my head to construct the password. Say I want to create an account for yahoo.com. I always use the same user name. For the password, I apply my "algorithm" to the service URL. Eg. my algorithm could be "reverse the first five letters of the URL, make the now last char upper case and append '1337!'". So in the Yahoo example, my password would be 'oohaY1337!'. For gmail.com, it would be 'liamG1337!', etc. This way, I get unique passwords for each site, that are at the same time easy to remember. [It goes without saying that I am using a different algoritm than the above.]

Visit product View on Hacker NewsFeb 8, 2014

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Did not reach leaderboard

Launch Intel predictions

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

67%67% predicted probability of success on Product Hunt, 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

66%66% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Missing: https docs, excited, just released

48%48% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal

44%44% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

35%35% 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

12%12% 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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