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Lade – automatically load secrets from your preferred vault as env vars

Hacker News

Lade – automatically load secrets from your preferred vault as env vars

Hey, I have created Lade to help me manage the secrets from different vaults with - hopefully - a greater user experience than the current approaches. Based on the shell command you are submitting, Lade will fetch the required secrets from Infisical, 1Password, Vault or Doppler and inject them in the environment variables with your desired name. It will also clear them once the command is over to avoid any leak. It is especially powerful with Terraform where you need to prefix some secrets or when you need to share those secrets "dependencies" with other teammates. Currently, Lade acts like a wrapper of the CLIs, and I wait on having more feedback before switching to the "native" APIs. I am curious to hear what you think and see whether it might be useful for you. Feedback warmly welcomed on https://github.com/zifeo/lade .

Visit product View on Hacker NewsApr 20, 2023

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

81%81% 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 HackersIH features products with proven revenue · Strong signals: created · Missing: supports, reddit linkedin, podcasting

42%42% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

40%40% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

35%35% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released

30%30% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

11%11% 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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