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Security Advice for Regular People

Hacker News

Security Advice for Regular People

I had the idea to create a dedicated web page containing basic, easy-to-understand digital hygiene advice. So if a friend or relative asks you for advice on securing their digital lives, you can simply give them a link and hopefully they'll share it with their friends too. Hosting is on GitHub pages; just a single, semantic, accessible HTML file. I just made this today, so contributions are welcome, especially right now. I am not a security researcher, so if anyone finds an issue with what I've written in this first draft, or thinks some other point deserves mentioning, please tell me either here or in the form of a GitHub ticket or pull-request. Project goals and contribution guidelines are available in the readme. I'm also thinking about getting a custom domain name for it, but I'm not sure what has the right balance of sounding trustworthy and being available/affordable. So let me know if you have any ideas on that front as well! https://github.com/brundonsmith/basic-digital-security

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
78%78% 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.
Product HuntOn track for Day 1 leaderboard · Strong signals: single · Missing: mac, agents, macos
63%63% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
55%55% 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: host · Missing: plus, platform, intuitive
45%45% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
31%31% 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
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
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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