ModelKit, Model Framework for Objective-C (iOS/Cocoa)

Hacker News

Hey HN, wanted to share an open source project I'm working on: https://github.com/jawngee/modelkit It's a model/entity framework that allows you to write your model layer quickly and easily, managing persistence (local and network) for you as well as querying facilities. It also can be tied into a BaaS like Parse (comes with a working implementation based on their REST api). You could think of it as CoreData-lite or Github's Mantle on steroids. It started out as Parse API wrapper because I was running into issues with their iOS SDK (I love Parse btw and rave about it to anyone that will listen - their iOS SDK not so much). It's nearly finished and I'm incorporating it into a commercial product so I'd thought I'd share with fellow iOS HN'ers. Enjoy!

Visit product View on Hacker NewsNov 7, 2012

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

93%93% 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: started, ios · Missing: supports, reddit linkedin, podcasting

72%72% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: ios · Missing: mobile apps, personal, entrepreneurs

55%55% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: open source, io · Missing: https docs, excited, just released

42%42% 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

27%27% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

14%14% 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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