Rem: Remember Everything (open source)

Hacker News

An open source approach to locally record everything you view on your Apple Silicon computer. Note: Relies on Apple Silicon, and configured to only produce Apple Silicon builds. I think the idea of recording everything you see has the potential to change how we interact with our computers, and believe it should be open source. Also, from a privacy / security perspective, this is like... pretty scary stuff, and I want the code open so we know for certain that nothing is leaving your laptop. Even logging to Sentry has the potential to leak private info.

Visit product View on Hacker NewsDec 27, 2023

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Product HuntOn track for Day 1 leaderboard · Strong signals: apple, computer, code · Missing: mac, agents, macos

93%93% 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.

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

76%76% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

53%53% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

33%33% 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

25%25% 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

4%4% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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