Deputies, an open source background agent

Hacker News

Inspired by Ramp's inspect, Stripe's Minions, and open source versions like open-inspect & open-swe, I decided to build my version of a background agent! It is built on https://flueframework.com/ with deployability as a core feature (aka it can deploy on a server in your closet or to an EKS cluster in AWS). These types of systems are most valuable when you can deploy them into your existing infrastructure where they can safely access resources you wouldn't want to expose otherwise. Check it out and let me know what you think!

Visit product View on Hacker NewsMay 22, 2026

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Actual performance

3points

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Launch Intel predictions

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

89%89% 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: exist, lua, open source · Missing: https docs, excited, just released

61%61% 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

43%43% 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

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

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

25%25% 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

17%17% 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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