Gov.uk Vue, a Vue component library based on Gov.uk

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Hi everyone, TLDR: I've just released an alpha version of GOV.UK Vue, an easy to use, accessible component library for Vue 3, and I'd love to hear any feedback you have. This post links to the GitHub repo but there's full documentation on https://govukvue.org I'm in the UK and our government is very bad at a lot of things, but it's very good at building digital services - there have been discussions praising how easy to use GOV.UK is in the past: https://news.ycombinator.com/item?id=18252300 The frontend framework they've built, the GOV.UK Design System, is great, and I use it all the time in my day job. But no-one had built a Vue library for it yet, so I decided to do it. My aim is for it to be the most accessible Vue component library available - the Design System components already have brilliant accessibility, so all I have to do is not mess it up when porting it to Vue, and I think I've done OK (but let me know if you find any bugs) At the moment my focus has been on implementing the components in the Design System, so GOV.UK Vue doesn't include things like modals or autocomplete, but I'm planning to build a companion library which includes those kinds of components. Let me know if you'd be interested in that. Please check it out and let me know what you think!

Visit product View on Hacker NewsAug 24, 2023

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

70%70% 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 HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

60%60% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

48%48% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: new · Missing: mac, agents, macos

42%42% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

31%31% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

10%10% 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.

Incorrect prediction on native model

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