Tiny filesystem honeypot for macOS with zero dependencies in Go

Hacker News

When malware, a rogue script, npm module, or an attacker with shell access starts scanning your home directory for credentials, the canary trips and fires an alert. Developer's computers are increasingly being targeted with attacks, for example the LiteLLM attacks demonstrate the recent risks and complex supply chain attacks. I made this tool for macOS systems that helps detect when a package accesses something it shouldn't. It's a tiny go binary (less than 2k LOC) with no dependencies that will mount a webdav filesystem (no root) or NFS (root required) with fake secrets and send you a notification when anything accesses it. Very stupid simple. I've always really liked the canary/honeypot approach and this at least may give some folks a chance to detect (similar to like LittleSnitch) when something strange is going on! When to use which mode? Use WebDAV for low-friction canaries you can spin up anywhere. Use NFS for canaries that need to survive an attacker who has your user shell and is looking around.

Visit product View on Hacker NewsMar 27, 2026

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Actual performance

2points

Did not reach leaderboard

Launch Intel predictions

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

75%75% 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: filesystem, io · 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.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

46%46% 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

36%36% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

28%28% 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

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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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