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Fake-xmlservice – Local XmlService implementation for GAS testing

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Fake-xmlservice – Local XmlService implementation for GAS testing

I often write Google Apps Script (GAS) projects that rely on XmlService, but I found it hard to test them locally. So I created @yoshi389111/fake-xmlservice , an npm package that implements XmlService in Node.js. - Works with Jest (e.g., put it in `setupFilesAfterEnv`) - Allows local unit tests for GAS code using XmlService - Supports parsing and serializing XML (partially complete, not 100%) - API is designed to be a drop-in replacement for GAS's XmlService Repo: < https://github.com/yoshi389111/fake-xmlservice > Package: < https://www.npmjs.com/package/@yoshi389111/fake-xmlservice > It's not fully complete yet, but it already works for my use cases. I'd love feedback on: - What features you'd expect in a testing library for GAS XmlService - Any edge cases you ran into with XmlService in your projects Thanks!

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Indie HackersFits the IH revenue-focused audience · Strong signals: supports, created · Missing: reddit linkedin, podcasting, latex
70%70% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: google, apps, using · Missing: mac, agents, macos
65%65% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
53%53% 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
39%39% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: apps, google · Missing: mobile apps, ios, personal
36%36% 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
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.

Incorrect prediction on native model

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