Gochan – A library of channel architectures for Go, inspired by Rust

Hacker News

Hi All, I felt like I was re-inventing the wheel by bolting similar channel architectures onto different Go structs repeatedly so I decided to extract some common types into into one library so that they would be easier to re-use: - oneshot - spsc - spmc - mpsc - mpmc - broadcast - watch The types are inspired by Rust channels so if you're coming from Rust they should feel familiar. So far I'm really enjoying using them but it'd be great to get some external feedback if you have time! https://github.com/amorey/gochan Andres

Visit product View on Hacker NewsMay 27, 2026

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

52%52% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Product HuntUnlikely to reach the leaderboard · Strong signals: using · Missing: mac, agents, macos

46%46% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

40%40% 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

38%38% 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

38%38% 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

10%10% 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.

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