FAQ from MagicaLCore
What is MagicaLCore?
MagicaLCore is a groundbreaking iPad-native application that lets anyone — regardless of technical background — design, train, and deploy production-grade image classification models using only personal photos and intuitive visual tools. It eliminates traditional ML barriers by unifying data curation, neural network training, evaluation, and export into one seamless, privacy-first experience optimized for Apple Silicon.
How to use MagicaLCore?
Start by curating labeled image sets using your iPad’s camera or photo library. Tap to create categories, drag-and-drop images, then press “Train.” MagicaLCore handles preprocessing, architecture selection, and optimization behind the scenes — delivering a validated model in minutes. Test instantly via live camera preview, review confidence scores and misclassifications, and export your model with one tap for use in Xcode, Swift Playgrounds, or third-party automation tools.
What devices are compatible with MagicaLCore?
MagicaLCore runs on iPadOS 18.0 or later and requires an iPad powered by Apple Silicon (M1, M2, M3, or newer) or the A16 Bionic chip (iPad mini 6th gen and later). These chips provide the necessary Neural Engine performance and memory bandwidth for efficient on-device training.
Is coding necessary to use MagicaLCore?
Not at all. MagicaLCore replaces code with context-aware gestures, visual labeling interfaces, and guided workflows — making AI development accessible to students, artists, scientists, and professionals who want to harness machine learning without learning to program.
Can I test my machine learning models in real-time with MagicaLCore?
Absolutely. Its ultra-low-latency inference engine delivers frame-by-frame classification directly through the iPad camera — turning your device into an interactive AI sensor. You’ll see bounding overlays, class labels, and confidence percentages overlaid in real time, with instant feedback as lighting, angle, or subject changes.