Memoram – A user-owned memory layer to personalize any AI tool

Hacker News

Hey HN, We've developed Memoram, a platform that allows users to securely store and manage their personal data—like preferences, conversations, and other relevant information—across various AI tools. This ensures a consistent and personalized experience, regardless of the AI application in use. Key Features: Secure Data Storage: Users can store their data with end-to-end encryption, ensuring privacy and security. Cross-Platform Integration: Memoram integrates with multiple AI tools, allowing seamless access to user data across platforms. User-Controlled Sharing: Through unique MemoryKeys, users have granular control over which AI tools can access specific data. Developer Platform: We've launched a Developer Platform that provides APIs for developers to integrate Memoram into their AI applications, enhancing personalization without building user data management from scratch. We're eager to hear feedback from the community, especially from developers working on AI applications. How do you currently handle user personalization? Would a centralized, user-controlled memory system like Memoram be beneficial in your projects? Looking forward to your thoughts and discussions.

Visit product View on Hacker NewsMay 23, 2025

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

87%87% 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.

Product HuntOn track for Day 1 leaderboard · Strong signals: user, apis · Missing: mac, agents, macos

85%85% predicted probability of success on Product Hunt, 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

46%46% 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 · Strong signals: platform, users · Missing: plus, intuitive, reviews

38%38% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

31%31% 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

9%9% 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

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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