Infisical – open-source secrets manager for developers

Hacker News

Two months ago, we left our jobs at AWS and Figma to continue building Infisical. It is an open-source end-to-end encrypted tool that helps you manage developer secrets across your team, devices, and infrastructure. During the previous Show HN, we got a lot of useful feedback which we’ve been iterating on A LOT! In the past month, we’ve been pretty much working 24/7, and we added: - Integrations for Vercel, Netlify, GitHub Actions, Render, and Fly.io - Public API - User activity logs - Point-in-time recovery and secret versioning - Custom environments - Kubernetes operator ( https://infisical.com/docs/integrations/platforms/kubernetes ) And made lots of other performance improvements both on the frontend and backend. Our repo is published under the MIT license so any developer can use Infisical. The goal is to not charge individual developers. We make money by charging a license fee for some enterprise features as well as providing a hosted version and support. In the coming weeks, we plan to add features like key rotation, alerts, and secret groups - as well as continue adding more integrations. Give it a try ( https://github.com/Infisical/infisical )! We’d love to hear what you think! Main website: https://infisical.com/

Visit product View on Hacker NewsJan 24, 2023

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

85%85% 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 · Missing: supports, reddit linkedin, podcasting

80%80% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

77%77% 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: month · Missing: mobile apps, ios, personal

45%45% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: platform, host · Missing: plus, intuitive, reviews

24%24% 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

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: make money · Missing: web3, chat, crypto

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

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