Wingman – A client-agnostic agent harness (written in Go)

Hacker News

Hey HN! This is a project I've been working on for a while, it's still rough around the edges but I wanted to share it and hopefully get some feedback. Wingman is a client-agnostic agent harness (written in Go). Client-agnostic means Wingman isn’t coupled to any specific problem (like a coding agent or answer engine). It’s a portable agentic runtime that lets you build arbitrary clients on top of it. So you can build a client to: - Write code - Perform automatic triage - Classify emails - Review a PR and any other LLM adjacaent problem. There are surely features that exist that aren't well documented, a bunch of small bugs/race conditions, and all kinds of things I am hoping to add in the near future so any feedback is appreciated.

Visit product View on Hacker NewsJul 29, 2026

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

91%91% 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, io · Missing: https docs, excited, just released

60%60% 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

43%43% 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

33%33% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

33%33% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

20%20% 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.

Incorrect prediction on native model

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