Pr

Privacy Focused Voice Journal

Hacker News

Privacy Focused Voice Journal

Greetings, I've been meditating for a while; the aim is to have an empty mind for some time. One technique to get rid of an unwanted thought is to allow it to expand to all possible conclusions. This is surprisingly effective. Some thoughts are worth keeping though, so I made a place to put them. https://turquoisehexagon.co.uk/anima https://apple.co/4wERObR AI features are all implemented using on device FoundationModels, CloudKit for backups. Thanks for your consideration, would appreciate any feedback! Cheers, Rahil

Visit product View on Hacker NewsMay 20, 2026

Actual performance

2points

1comments

Did not reach leaderboard

Launch Intel predictions

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

66%66% 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

54%54% 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

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

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

42%42% 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 · 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

15%15% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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