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Envbee – Manage environment variables without sharing .env files

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Envbee – Manage environment variables without sharing .env files

We built envbee ( https://envbee.dev ) to simplify how teams manage environment variables across environments like dev, staging, and prod. We were tired of sharing .env files over chat, copy-pasting secrets, and accidentally committing them to Git. We wanted a better way to keep configuration in sync and secure. envbee is a SaaS that combines: - A webapp to manage configuration across multiple environments - A RESTful API with SDKs to make reading your config a simple and frictionless experience Instead of syncing full .env files across machines, your app pulls the latest config at runtime using our SDK. The only thing your app needs locally is an API key and secret. We currently provide SDKs for Python and Node.js. For other languages, you can use our REST API directly — full docs are available. We're actively working on new features and SDKs for additional languages. https://envbee.dev Happy to answer any technical questions or hear suggestions. Thanks!

Visit productView on Hacker NewsMay 22, 2025
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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, new, using · Missing: agents, macos, agent
83%83% 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
75%75% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
42%42% predicted probability of success on TrustMRR, 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
38%38% 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 · Missing: plus, platform, intuitive
32%32% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: saas, active · Missing: arr, mrr, revenue
14%14% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency
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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