XSWR – The simplest React data (re)fetching library ever made

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Hey there, I made a new library for React data fetching. XSWR is very inspired from SWR (which stands for "Stale While Revalidate"). In fact, it's a side project I made to fill in the gaps of SWR, that ended up production-ready. XSWR uses two new approaches compared to other data fetching libraries like swr or react-query: - Encapsulating key, fetcher and resource type in a single abstraction called "handle". - Composing features with very simple hooks instead of having bloated configuration patterns and unexpected behaviours. By using these two approaches, XSWR aims to help you highly centralize and reuse things. Docs: https://xswr.hazae41.me/ Comparison with swr and react-query: https://xswr.hazae41.me/faq/comparison Thanks for you feedbacks!

Visit product View on Hacker NewsAug 26, 2022

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

73%73% 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

60%60% 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

52%52% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive

51%51% 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

50%50% 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

13%13% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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