Ellipticc Drive – open-source cloud drive with E2E and PQ encryption

Hacker News

Hey HN, I’m Ilias, 19, from Paris. I built Ellipticc Drive, an open-source cloud drive with true end-to-end encryption and post-quantum security, designed to be Dropbox-like in UX but with zero access to your data, even by the host. What’s unique: Free 10GB for every user, forever. Open-source frontend (audit or self-host if you want) Tech stack: Frontend: Next.js Crypto: WebCrypto (hashing) + Noble (core primitives) Encryption: XChaCha20-Poly1305 (file chunks) Key wrapping: Kyber (ML-KEM768) Signing: Ed25519 + Dilithium2 (ML-DSA65) Key derivation: Argon2id → Master Key → encrypts all keypairs & CEKs Try it live: https://ellipticc.com Frontend source: https://github.com/ellipticc/drive-frontend Would love feedback from devs and security folks — particularly on encryption flow, architecture, or UX. I’ll be around to answer every technical question in the comments!

Visit product View on Hacker NewsOct 30, 2025

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

71%71% 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.

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

54%54% 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: host · Missing: plus, platform, intuitive

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

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

45%45% 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

34%34% 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

15%15% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: crypto · Missing: web3, chat, cryptocurrency

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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