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_hyperscript – an async-transparent scripting langauge for the web

Hacker News

_hyperscript – an async-transparent scripting langauge for the web

Hi Folks, We have been working on an experimental front end programming langauge, hypescript, as a companion project for htmx. https://hyperscript.org/ Its syntax is based loosely on HyperTalk, the scripting language for HyperCard. It has some feature that the HN crowd might find interesting: Async-transparency - as much as possible, the runtime tries to hide the distinction between synchronous and asynchronous code https://hyperscript.org/docs/#promises https://hyperscript.org/docs/#async Event driven - events are treated as first class citizens, including event-based control flow https://hyperscript.org/docs/#event-control-flow CSS Literals - you can refer to elements in the DOM directly with syntax like #foo, .bar and <button/> on click add .highlight to <button:not([disabled])/> Extendable language - the parser is pretty open and flexible, allowing you to add new "commands" (statements) to the language https://hyperscript.org/docs/#extending Integrated Debugger - the language has a (very alpha) integrated debugger https://hyperscript.org/docs/#debugging Inline Web Workers - the ability to define a web worker directly in your page https://hyperscript.org/docs/#workers There's a fair bit of other stuff too, SSE and Websocket support, a slew of useful commands for working with the DOM, etc. This is very early, 0.0.6, so please be gentle, but it is being used in a few places in production. I used it to make the / key jump to the search box on htmx.org, for example.

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Product HuntOn track for Day 1 leaderboard · Strong signals: new, code, open · Missing: mac, agents, macos
81%81% 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: including · Missing: supports, reddit linkedin, podcasting
61%61% 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, including · Missing: https docs, excited, just released
54%54% predicted probability of success on Hacker News, based on ML models trained on real launch data.
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42%42% 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
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
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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.

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