Gated Encrypted Key Value Store

Hacker News

NIST FF3-1 Compliance: Octomim adheres to the NIST FF3-1 (Format-Preserving Encryption) standard. This compliance ensures that our encryption methods meet industry-recognized standards for data security and integrity. Secured Secret Database: Octomim employs a secure secret database to store your sensitive information. Rest assured, your data is protected with robust encryption techniques, ensuring only authorized access. Random Temporary Token Generation: As an added layer of security, Octomim generates random temporary tokens. These tokens provide secure access to your data for a limited time, adding an extra level of protection against unauthorized access. Encrypted In-Memory Key Value Store: With our latest feature, Octomim offers an encrypted in-memory key-value store. This feature allows you to securely store and access key-value pairs in real-time, providing efficient and protected data retrieval. Steganographic Token: Hide your tokens in images secured with your key. Gatekeeper: Authenticate with multiple users. Create a gate with authorized users and set threshold of required number of users to open the gate. Gated Key Value Store: KV access is controlled via a gate. KV is attached to a gate that is administered by the Gatekeeper. If gate is open KV can be accessed, otherwise access is denied. Stateless. Decrypted data are not cached and they are securely deleted. Implemented in C for a blazing-fast performance.

Visit product View on Hacker NewsOct 28, 2023

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Actual performance

1points

Did not reach leaderboard

Launch Intel predictions

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

93%93% 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.

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

61%61% 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 HuntUnlikely to reach the leaderboard · Strong signals: user, open · Missing: mac, agents, macos

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

AppSumoMay struggle as an AppSumo deal · Strong signals: efficient, users · Missing: plus, platform, intuitive

46%46% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

45%45% 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

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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