JavaScript debug inspector made with ES6 and web components

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I want to share something I made to help me automatically generate UIs for arbitrary data models: https://github.com/arodic/io Hopefully someone else finds it useful as much as I do. I'll be building my next couple of projects with it and I'll try to maintain it. The UI library can be used as an alternative to React or Vue, but it can also be used alongside. If you want to use just the object inspector alone, simply create <io-object> element and set it's value to an object data you want to interact with. There are some more advanced configuration options but I haven't documented them yet. It has a very simple data flow model which makes data-binding work almost automatically. Its DOM is completely virtualized. The code is MIT licensed, free and open source. To get what I'm talking about check out io-object and io-inspector elements in the demo: https://akirodic.com/io/#page=2 They are automatically generated from the object data. Notice that all values in the demo update automatically. For example, if you modify an option label in menuoptions object, the menu labels will update automatically. Let me know if you have any questions or comments.

Visit product View on Hacker NewsMar 25, 2019

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

91%91% 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

71%71% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

61%61% 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

43%43% 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

33%33% 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

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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