SwiftUI Reorderable – Pure SwiftUI Drag and Drop Components

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Hey there! I made a Swift Package containing a UI component I built for an upcoming app I'm working on. It's my first time writing something serious using SwiftUI, and I've been annoyed with many of its limitations, especially when it comes to drag and drop. The native `onDrag` or `draggable` doesn't allow you to customize the gesture it uses [0], has a really opinionated and finicky preview, and sometimes fires `onDrag` more than once in a seemingly non-deterministic fashion [1]. A common answer to SwiftUI frustrations seems to be to just write a UIKit component instead. Given that a month ago, I knew neither SwiftUI nor UIKit and decided to go with the former, I didn't really want to start learning a second framework while barely mastering the first one. So, I got fed up and decided to write my own component instead. I was a little shocked by how few high-quality SwiftUI open-source components there are. Coming from a mainly web-development background, I expected to find components I could use, and if not, at least look at their code to figure out how it's done. It's possible components like these already exist, and I haven't found them. If that's the case, oh well. In any case, this was a good learning experience and I hope this component can help some of y'all! [0] https://stackoverflow.com/questions/65311379/how-to-customiz... [1] https://stackoverflow.com/questions/74087137/swiftui-ondrag-...

Visit product View on Hacker NewsSep 26, 2024

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

75%75% 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

68%68% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

49%49% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

40%40% 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

37%37% 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

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.

Correct prediction on native model

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