Cryptid Zero Trust Authentication and Authorization, Open Source Oberon

Hacker News

Oberon makes it possible for a service provider to issue capability tokens to clients in such a way that the service provider never sees the value of the capability token. This prevents the service provider from being able to impersonate the client. Oberon relies upon zero-knowledge proof presentation of the capability token so that the token is never transmitted norrevealed. Instead of the client sending the token to the service—as is done with API tokens today—the client sends a zero-knowledge proof proving that they have a valid capability token issued by the service provider; this is called proof-of-knowledge. GitHub: https://github.com/cryptidtech/oberoncryptid Get cryptid: https://www.cryptid.tech]

Visit product View on Hacker NewsJun 3, 2022

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

50%50% predicted probability of success on Hacker News, based on ML models trained on real launch data.

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Product HuntUnlikely to reach the leaderboard · Strong signals: open · Missing: mac, agents, macos

50%50% 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

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

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

46%46% 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

21%21% 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

17%17% 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

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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