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Fakekey – never expose real API keys in the agent environment

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Fakekey – never expose real API keys in the agent environment

With the widespread adoption of AI Agents, configuring various service API Tokens directly in environment variables has become common practice. Your api_key will be inserted into context and known by model service providers, known by the lobsters you trust, perhaps captured and read by some skill, and even more likely to be directly learned by strangers asking your lobster. There are too many leak cases, I cannot trust to bind my credit card-linked api_key directly exposed to any Agent and local environment variables, so FakeKey was created, the safest measure is to never expose the real api_key. FakeKey is a high-performance API key proxy program developed in Rust. Through intelligent proxy technology, it can automatically replace fake keys with real keys in HTTP headers and URLs without exposing real credentials, while maintaining complete HTTP API compatibility and performance.

Visit productView on Hacker NewsMar 31, 2026
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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, model · Missing: mac, macos, cursor
84%84% 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.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
51%51% 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
37%37% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Strong signals: created · Missing: supports, reddit linkedin, podcasting
35%35% 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
31%31% 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
18%18% 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
7%7% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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