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Gedi, an Evented Model Interface for JavaScript

Hacker News

Gedi, an Evented Model Interface for JavaScript

Gedi is an interface to your model that lets you bind events to track changes in the model. Gedi will also track the dirty state of your model. Combined with gel (https://github.com/gaffa-tape/gel) gedi is extremely powerful, allowing expression base callbacks that will evaluate when any reference to the model is changed. Some examples of usage are in the tests: http://github.korynunn.com/gedi/test.html

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8points
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Made the leaderboard

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Product HuntOn track for Day 1 leaderboard · Strong signals: model · Missing: mac, agents, macos
74%74% 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.
Hacker NewsMay not resonate with HN audience · Strong signals: lua, io · Missing: https docs, excited, just released
50%50% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
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.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
38%38% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: interface · Missing: plus, platform, intuitive
28%28% predicted probability of success on AppSumo, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
15%15% predicted probability of success on BetaList, 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.

Incorrect prediction on native model

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