1time – Zero-knowledge secret sharing with E2E encryption

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I started this 8 years ago to learn Go. The idea was simple: a secret sharing tool where you don't have to trust the server at all. Secrets are encrypted in your browser (AES-256-GCM) before anything leaves your device. The key lives in the URL fragment (#), which browsers never send to the server. Key derivation uses HKDF-SHA256 with separate keys for encryption and auth. No tracking, no cookies, no accounts. Open source (MIT). Runs on a $10 VPS — Go stdlib, Redis, static Next.js. CLI included: printf 'db_password' | 1time send Self-hosting: 4-line Docker Compose (amd64 + arm64). Would love feedback on the crypto approach and UX. Live: https://1time.io | Source: https://github.com/shingrus/1time

Visit product View on Hacker NewsMar 25, 2026

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

70%70% 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.

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

66%66% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

57%57% 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

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: para · Missing: mobile apps, ios, personal

36%36% 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

16%16% 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.

Incorrect prediction on native model

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