Hyperdiv – Reactive, immediate-mode web UI framework for Python

Hacker News

Hello HN, I'm releasing Hyperdiv ( https://hyperdiv.io ), a framework for rapidly developing reactive browser UIs in Python, with immediate-mode syntax and using Shoelace ( https://shoelace.style ) as its built-in component system. This short coding video will give you a good idea of what it is: https://www.youtube.com/watch?v=4XJKfxaqvGE I wrote a brief article about the motivation and approach: https://hyperdiv.io/intro.html Hyperdiv doesn't aim to compete with serious full-stack frameworks. The core aim was to make it easy and fast to prototype apps and build UI-based tools. I was originally motivated by internal tools at work -- feeling the need to quickly put together UI-based tools to share with both technical and non-technical coworkers, without having to stand up and maintain a full internal stack. This is my first major open source release. I really appreciate your feedback and support. - Marius

Visit product View on Hacker NewsFeb 20, 2024

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

78%78% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

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

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

57%57% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive

53%53% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: apps, video · Missing: mobile apps, ios, personal

34%34% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: active · 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 · 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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