Apple offers three ML frameworks: MLX, Core ML, and Create ML. A practical comparison of when to use each for training, deployment, and on-device inference on Apple platforms.
A technical comparison of ONNX Runtime and Core ML for running ML models on Apple Silicon — performance, operator support, conversion workflow, and when to choose each.
How Apple's on-device and server foundation models power Apple Intelligence, how the Foundation Models framework works, and practical Swift examples.
Explore MLX Swift architecture, unified memory, GPU-backed array operations, model workflows, and practical constraints for machine learning on Apple platforms.
A practical guide to running ONNX models on Apple Silicon — from Python prototyping to shipping inside a macOS app with ONNX Runtime's CoreML Execution Provider.
How I ported the Chatterbox TTS model to run fully on-device using ONNX Runtime Swift bindings — including KV cache management, memory-safe autorelease scoping, and audio resampling without a single third-party DSP library.
Here's how I built a background analyzer that uses an LLM to generate titles and tags for every draft — fully automatic, non-blocking, with retries and concurrency control.
Elevate your creative production with the 10 essential AI tools dominating 2026, from deep-research collaborators to cinematic video engines. No more manual bottlenecks.
The future of AI is not just about generating text or images. It's about building models that can see, hear, and understand our world in its full, rich, and multifaceted complexity.
These models serve as a base infrastructure for many applications. After pretraining, foundation models are not used directly but adapted to downstream tasks.