Ne

New Python GUI Framework (better than others?)

Hacker News

New Python GUI Framework (better than others?)

Hey HN, I built [*WinUp*]( https://github.com/mebaadwaheed/winup ) — a modern GUI framework for Python that works like React or Flutter, but with native Qt widgets under the hood. ### Why I made this: Python has great libraries for data, backend, scripting… but most GUI libraries are either: - Verbose (Tkinter, PyQt) - Old-school imperative - Slow or bloated (Electron) WinUp gives you: - Declarative UI with `@component` functions - Hot reload for real-time editing - CSS-like styling system - State binding with `State()` like React - Native rendering via PySide6 ### Example: ```python import winup from winup import ui def App(): # 1. Create a state object with an initial value. counter = winup.state.create("counter", 0) # 2. Create the UI widgets. label = ui.Label() # 3. Bind the state to the label's 'text' property. # The lambda function will re-run whenever the counter changes. counter.bind_to(label, 'text', lambda c: f"Counter Value: {c}") def increment(): # 4. Use the state object's methods to update the value. counter.set(counter.get() + 1) return ui.Column(children=[ label, ui.Button("Increment", on_click=increment) ]) if __name__ == "__main__": winup.run(main_component_path="file_name:App", title="New State Demo") ```

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Actual performance

2points
Did not reach leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: new · Missing: mac, agents, macos
67%67% 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.
TrustMRRFits verified-revenue profile · Strong signals: widgets · Missing: mobile apps, ios, personal
56%56% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
55%55% 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 HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
53%53% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
35%35% 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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