Chrome Extension to Test Multiple Endpoints

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A problem I have at work is that when there is a bug in an environment we usually have to check a handful of endpoints to rule out any particular problems. This process is tedious and way to manual for 2020! We can use postman, but our testing are usually endpoints that are easier to test with the browser. So in that vein I created this Chrome Extension that allows you to upload a JSON or Yaml file with all of the endpoints you want to test and produces a report with the response body and other particulars of the calls. I built this with Stenciljs and Semantic Ui. github: https://github.com/grantlouisherman/rest-diagnostic-stencil Chrome Webstore: https://chrome.google.com/webstore/detail/rest-diagnostic-tool/bhjmilcdbpbhpnokhkhmphfgbikhiojd I would love feedback of other ideas on how to improve. If you hate it, please be kind :)

Visit product View on Hacker NewsFeb 24, 2020

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

67%67% 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 · Strong signals: created · Missing: supports, reddit linkedin, podcasting

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

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

50%50% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: google, way · Missing: mobile apps, ios, personal

37%37% 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

30%30% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

16%16% 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.

Correct prediction on native model

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