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Contextual Privacy: Can something be private yet discoverable?

Hacker News

Contextual Privacy: Can something be private yet discoverable?

Hey everyone, Over the past few months, we’ve been exploring a new way to think about online privacy and social interactions. The concept we’re calling "contextual privacy" moves beyond the typical public vs. private divide. In real life, privacy is more nuanced—think of a coffee shop or a conference where conversations are private but you can still overhear interesting snippets. We’ve applied this idea online, giving users more control over when they’re discoverable. Here is the demo video: https://x.com/indexnetwork_/status/1828847341001924833 And our short article about it: https://blog.index.network/can-something-be-private-yet-disc... Curious about your thoughts.

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Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
58%58% 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, new, context · Missing: mac, agents, macos
57%57% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
49%49% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: users · Missing: plus, platform, intuitive
40%40% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: video, month, users · Missing: mobile apps, ios, personal
37%37% 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 · Strong signals: real life · Missing: web3, chat, crypto
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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