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Affinity.js – A simple view component library with store

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Affinity.js – A simple view component library with store

with affinity you can make multiple apps (components eventually) and store (ala redux) - while a store can be shared through apps. This started as a case study and as a small reusable code for creating self contained dom elements with functionality - during the time of development, ideas and inspiration came from angular, react and vuejs. suggestions and feedback is mostly welcomed. The interesting challenges were finding ways for these topics: 1. create a component lib using dom and js only, without any 3rd party . 2. producing semantic html in dom . 3. allow template micro syntax (using @click to bind an event listener) . 4. allow reusing custom elements within a component's template . 5. creating a simple store interface and auto connect it with a component to allow re render whenever the store changes . any feedback and suggestions are welcomed https://github.com/orizens/affinity

Visit productView on Hacker NewsNov 7, 2018
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33points
12comments
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Launch Intel predictions

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AppSumoStrong fit for a featured deal · Strong signals: interface · Missing: plus, platform, intuitive
56%56% predicted probability of success on AppSumo, 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.
Product HuntUnlikely to reach the leaderboard · Strong signals: apps, using, code · Missing: mac, agents, macos
43%43% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Strong signals: started · Missing: supports, reddit linkedin, podcasting
43%43% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: apps, way · Missing: mobile apps, ios, personal
40%40% 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
17%17% 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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