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Hibiki HTML – New frontend framework – no scaffolding, no Webpack

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Hibiki HTML – New frontend framework – no scaffolding, no Webpack

Source https://github.com/dashborg/hibiki | Interactive Tutorial https://playground.hibikihtml.com/tutorial/ I love JavaScript, but for many projects -- especially internal tools and prototypes -- setting up a full frontend JavaScript stack (npm, webpack, babel, create-react-app, redux) and all of their configuration files, folders, and scaffolding is overkill. Hibiki HTML incrementally plugs into any backend, using any template language (even static HTML files) with a single script include. It includes a built-in frontend data model, Vue.js-like rendering, built-in AJAX integration, and a full component/library system. It is also fully scriptable from your backend AJAX handlers. Anything that Hibiki HTML can do on the frontend can be done with a remote handler by returning specially formatted JSON actions . This allows you to write frontend logic (that would normally be JavaScript code) in your backend handlers. Background -- Hibiki HTML is a standalone, open-source, more powerful version of the frontend language that I had built for my internal tools startup Dashborg over the past year. It is a reaction against the extreme amount of scaffolding and configuration required to set up a new frontend project, especially when you're a backend/devops/data engineer who isn't a JavaScript expert. As more Hibiki libraries are written, the advantages will hopefully become even more clear. I'd love to get all of your feedback, questions, and comments. Would love a star on Github if you like the idea. Also, feel free to email me, and/or join the Slack workspace I set up (contact info on Github or the tutorial).

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91%91% predicted probability of success on Product Hunt, 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
82%82% 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
59%59% predicted probability of success on Hacker News, based on ML models trained on real launch data.
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AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
37%37% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
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14%14% 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
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