Mountaineer – Webapps in Python and React

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Hey HN, I’m Pierce. Today I’m open sourcing a beta of Mountaineer, an integrated framework for building webapps in React and Python. I’ve written a good 25+ webapps over the last few years in almost every major framework under the sun. Python and React remain my favorite. They let you get started quickly and grow to scale. But the developer experience of linking these two worlds remains less than optimal: — Sharing typehints and schemas across frontend and backend code — Scattered fetch() calls to template data and modify server objects — Server side rendering / gateway support — Error handling on frontend fetches Mountaineer is an attempt to solve those problems. I didn’t want to re-invent the wheel of what Python and React are good at, so it’s relatively light on syntax. It provides one frontend hook for React apps and introduces a MVC convention on the backend for managing views. Support files are generated progressively through a local watcher, so IDE type-hints and function calls work out of the box. It’s more intuitive to explain with some code, so pop over to the Github if you’re interested in this stack and taking a look: Github: https://github.com/piercefreeman/mountaineer More context: https://freeman.vc/notes/mountaineer-v01-webapps-in-python-a... Would love to hear your thoughts!

Visit product View on Hacker NewsFeb 27, 2024

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Product HuntOn track for Day 1 leaderboard · Strong signals: apps, context, notes · Missing: mac, agents, macos

85%85% 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.

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

71%71% predicted probability of success on Indie Hackers, 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

58%58% 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: apps, way · 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: intuitive, calls · Missing: plus, platform, reviews

31%31% 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

16%16% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

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

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