Pynecone – Web Apps in Pure Python

Hacker News

Hello, we just launched the alpha release of Pynecone - a way to build full-stack web apps in pure Python. The framework is easy to get started with even without previous web dev experience and is completely open source / free to use. We made Pynecone for Python devs who want to make web apps, but don’t want the overhead of having to learn or use Javascript. We wanted more flexibility than existing Python frameworks like Streamlit/Dash that don't allow the user to make real, customizable web apps. With Pynecone, you can make anything from a small data science/python project to a full-scale, multi page web app. We have over 60+ built-in components and are adding more. We are actively trying to grow this project so no matter you skill level we welcome contributions! Open up an issue if you find missing features/bugs or contribute to existing issue.

Visit product View on Hacker NewsDec 9, 2022

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

92%92% 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

81%81% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: exist, open source, existing · Missing: https docs, excited, just released

73%73% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRFits verified-revenue profile · Strong signals: apps, way · Missing: mobile apps, ios, personal

55%55% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

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

Acquire.comPre-revenue stage for this audience · Strong signals: active · Missing: arr, mrr, revenue

22%22% 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

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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