Conscious Engines
Building a tasks app that puts all iOS 27 on-device AI features to work
How we used an ensemble of on-device models to build consumer AI that works offline, costs nothing to run, and never leaves the phone
apple silicon
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Experiments in model architecture, training, evaluation, and efficient inference
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Building a tasks app that puts all iOS 27 on-device AI features to workexternal ↗
Conscious Engines
How we used an ensemble of on-device models to build consumer AI that works offline, costs nothing to run, and never leaves the phone
apple silicon
Running Bonsai 27B as an agent on my phone
Conscious Engines
Running a 27B model (1-bit) on my phone and letting it perform agentic tasks
apple silicon
How we trained a ternary mixture-of- experts model from scratch and deployed it on the Apple Watch Neural Engine
Conscious Engines
A 203M-parameter language model, trained for 14.76B tokens and deployed as one fixed specialist.
apple silicon
Squeezing a 26B diffusion LLM onto a Mac
Conscious Engines
Optimizing DiffusionGemma (26B-A4B-IT-4Bit) on an Apple M5 Pro, what worked, what didn't.
apple silicon
Does the 27B Bonsai actually deliver 27B?
Conscious Engines
A ternary-quantized 27B model that scores like an 8B on tool-calling - but deletes active records when nobody double-checks the honest answer.
apple silicon
Apple Foundation Model 3, what even is it?
Conscious Engines
A walkthrough of Apple Foundation Model 3's on-device architecture — the routing changes that let a 20B-parameter model run on a phone.
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Speculative Decoding in MLX using DFlash
Sabesh B
An empirical evaluation of speculative decoding on Apple Silicon, across a 300-run parameter sweep
apple siliconInference