Outbound, a minimal reverse tunnel in Go

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Hi HN, I built Outbound, a minimal self-hosted HTTP tunneling tool in Go. An agent running behind a firewall opens a long-lived bidirectional gRPC stream to an edge server. The edge accepts inbound HTTP requests and forwards them over the stream to the agent, which proxies them to local services and streams the response back. It is intentionally small: single-tenant pre-shared secret auth for agent registration, keepalives, and TLS termination handled by a reverse proxy (Caddy/nginx) in front of the edge. I would love any feedback on the design, security assumptions, ergonomics, and what you would change or add if you were using this.

Visit product View on Hacker NewsFeb 28, 2026

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1points

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

76%76% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

74%74% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: nginx, io · Missing: https docs, excited, just released

57%57% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

44%44% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

38%38% 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

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

5%5% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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