Confcrypt – Encrypt Secrets in YAML/JSON Configs with FIDO2/Age/SSH

Hacker News

I built confcrypt to encrypt sensitive values in config files – hostnames, usernames, URLs stay readable. Makes reviewing configs and debugging much easier than tools that encrypt everything. Think sops, but simpler. Multiple key types as recipients: - Native age keys (X25519) - SSH keys (ed25519, RSA) – use your existing keys - FIDO2 devices (YubiKey 5, SoloKey, etc.) via hmac-secret - YubiKey OTP via HMAC challenge-response Hardware keys derive the private key on-demand with a touch – never stored on disk. How it works: - Pattern-based: only keys matching /password$/, /api_key$/, etc. (configurable) get encrypted - Values encrypted with AES-256-GCM, key wrapped per recipient - `confcrypt check` for CI – exits 1 if unencrypted secrets found

Visit product View on Hacker NewsJan 27, 2026

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

69%69% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

62%62% 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

42%42% 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

34%34% 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

33%33% 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

21%21% 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

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

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