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Looking for feedback on a Webpack/etc plugin

Hacker News

Looking for feedback on a Webpack/etc plugin

I say "/etc", because it kind of covers a bunch of different topics. https://github.com/ryanmcgrath/react-iconpack react-iconpack is essentially a suite of plugins: - A Babel plugin to track <Icon> usage across your builds - A Webpack or Browserify plugin to inject SVG source as a module - A React Component that gets injected to handle displaying icons from the aforementioned module The build on npm is the old one, which the docs are still more or less current with. The code in the repository linked above is the updated version to work with Webpack; while it does work, it falters when caching is enabled on the upstream stuff (i.e, Webpack/Babel/etc). It's probably because I've just been staring at it for too long but I'd be interested in anyone's thoughts on integrating with said cache setups - the crux of the issue is that the Babel plugin won't run on already-built and cached modules (which makes sense, but is annoying in this particular case). The rationale for the project is that I don't want to have to specifically import an icon at the top of each file just to display it - I'd rather just write the <Icon ... /> JSX and have it auto-pulled in. It rides on the mental ease of JSX all being inline (not interested in the for-or-against JSX debate). Apologies if the formatting is off on this, I can never remember how it works on HN.

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Product HuntOn track for Day 1 leaderboard · Strong signals: code · Missing: mac, agents, macos
85%85% 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 · Missing: supports, reddit linkedin, podcasting
79%79% 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
42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
29%29% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
23%23% 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
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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