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Scoop.it!
NinevehGL is a 3D engine made with pure Objective-C, working with OpenGL ES 2.0 Programmable Pipeline for iOS devices (iPad, iPhone and iPod). 
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Scoop.it!
The Briefs toolkit was created for rapidly building & iterating app prototypes for the iPhone. These prototypes run directly on the phone, like actual apps, but require much less time and code to produce. Less code means faster cycles and cheaper development.
The purpose of Briefs is to create better apps that have a tested interaction design before they are built. Unlike traditional mockups, briefs are used just like an actual app not just seen. And because they use the same frameworks that regular apps are built upon, briefs can potentially take advantage of animation, device rotation and gestures. You can even design the experience around app interruption due to an incoming phone call. Thinking about these interactions will ensure a more thought out experience for your users.
Scoop.it!
GMGridView - A performant Grid-View for iOS (iPhone/iPad) that allows sorting of views with gestures (the user can move the items with his finger to sort them) and pinching/rotating/panning gestures allow the user to play with the view and toggle
✔ Sources: https://github.com/gmoledina/GMGridView#readme Via Vincent Demay
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Scoop.it!
Recently, Apple introduced several new developer stuff including Xcode 4, ARC, LLVM Compiler 3.0 and iOS 5.
Scoop.it!
The GPUImage framework is a BSD-licensed iOS library that lets you apply GPU-accelerated filters and other effects to images, live camera video, and movies. In comparison to Core Image (part of iOS 5.0), GPUImage allows you to write your own custom filters, supports deployment to iOS 4.0, and has a simpler interface. However, it currently lacks some of the more advanced features of Core Image, such as facial detection.
For massively parallel operations like processing images or live video frames, GPUs have some significant performance advantages over CPUs. On an iPhone 4, a simple image filter can be up to 24X faster to perform on the GPU than an equivalent CPU-based filter.
However, running custom filters on the GPU requires a lot of code to set up and maintain an OpenGL ES 2.0 rendering target for these filters. I created a sample project to do this:
http://www.sunsetlakesoftware.com/2010/10/22/gpu-accelerated-video-processing-mac-and-ios
and found that there was a lot of boilerplate code I had to write in its creation. Therefore, I put together this framework that encapsulates a lot of the common tasks you'll encounter when processing images and video and made it so that you don't need to care about the OpenGL ES 2.0 underpinnings.
✔ Sources: https://github.com/BradLarson/GPUImage
Via Vincent Demay
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