ProntoGUI – desktop GUIs in Go with a Material look and feel

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Hi HN, Andy here. I really enjoy programming in Go and have used it for many projects unrelated to microservices. However, direct support for building GUIs in Go isn't a core language feature, and the usual options fall short for what I need: Fyne and the native-binding libraries work, but don't look modern IMO. Electron/Wails-style apps solve the look problem but mean maintaining a second project in a second language, plus a JS framework's learning curve. ProntoGUI is my attempt to close that gap: you write the whole app in Go, and it renders through Flutter under the hood inside a native desktop app — you never touch Dart directly. Getting started is simple: install the free app on Windows or macOS, import the Go library, build your GUI with plain Go structs, and respond to events. All communication between your Go code and the app happens over a streamed gRPC connection, and all state management stays in your Go program — the app itself is stateless. The app is free for personal or business use — no email gate, just download and go. If you want priority support, there's a paid add-on for that. Library repo: https://github.com/prontogui/golib App download: https://www.prontogui.com/download Docs and support: https://www.prontogui.com/support Happy to dig into the architecture or the "why not Wails" question in the comments.

Visit product View on Hacker NewsJul 28, 2026

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

75%75% 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: mac, macos, apps · Missing: agents, agent, cursor

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

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

43%43% 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 · Strong signals: personal, apps · Missing: mobile apps, ios, entrepreneurs

43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

40%40% 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

15%15% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: paid · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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