Full-stack _hyperscript Node.js TODO web app (with Htmx)

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Hey HN! I think _hyperscript is an insanely cool language, and while designed for simple front-end DOM interactions, I thought it would be a cool back-end scripting language as well. So, I slapped together a TODO app with a _hyperscript bootstrapping script based on the original repository's node-hyperscript example. My repo isn't clean, just something I worked out over a weekend to prove the concept, also an excuse to play with htmx. :) Please read the README, and let me know how, if anyone's interested, I can work in some of my tweaks to the original repository. I'm kind of a noob with open source stuff.

Visit product View on Hacker NewsNov 29, 2022

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

55%55% 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, io · Missing: https docs, excited, just released

54%54% 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 HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

38%38% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

37%37% 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

27%27% 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

20%20% 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

4%4% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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