SaecretHeaven – Zero-knowledge encrypted photo vault with IPFS backup

Hacker News

Hey HN, I built SaecretHeaven because I was frustrated that every cloud photo service can see my images. Google scans for content, Apple has master keys, and most services store unencrypted. SaecretHeaven is different: • *True zero-knowledge*: Photos are encrypted client-side with XSalsa20-Poly1305 before upload. The server only sees encrypted blobs. • *Decentralized storage*: Encrypted files go to IPFS with Pinata pinning across multiple EU nodes. No single point of failure. • *Key anchoring*: Your 12-word recovery phrase derives your encryption key via Argon2id. We never see it. • *Works offline*: Full PWA with local-first architecture. Desktop apps via Tauri. Tech: Next.js 14, Better Auth, Supabase (metadata only), IPFS/Pinata The beta opens Feb 3rd with 30 spots. Would love feedback from the HN community on the security model. Site: https://saecretheaven.com Happy to answer any questions about the architecture!

Visit product View on Hacker NewsJan 27, 2026

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

71%71% 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: model, apple, google · Missing: mac, agents, macos

63%63% 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

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

nativeThis product was originally launched on this platform.

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

37%37% 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

33%33% 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

22%22% 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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