Conscious Engines
Story of Attention - Part 1: Before the Transformer
From n-gram counting and RNNs to the first attention mechanisms.
guide
Read more →Research and Development
Experiments in model architecture, training, evaluation, and efficient inference
Papers
2 publishedThe Joint Effect of Quantization and Sampling Temperature on LLM Safety Alignment
How weight quantization and sampling temperature jointly affect LLM safety alignment — a factorial analysis of the interaction.
arXiv
LLM Inference at the Edge
Sustained-load benchmarks across mobile, NPU, and GPU platforms reveal how LLM inference performance really degrades under back-to-back requests.
arXiv
Featured
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
Read more ↗Conscious Engines
The Need for Speed: Why LLMs Are Slow and What Speculation Promises
Your GPU sits nearly idle during token generation — the memory-bandwidth bottleneck behind LLM latency, and how speculation exploits it.
Inference
Read more →Conscious Engines
Kinetic-4B: A 4-Billion Parameter Model That Outperforms Claude Haiku at Tool Calling
A fine-tuned 4B open-source model that beats frontier models at tool calling, with p95 latency under two seconds on a single GPU.
Agentic AI
Read more →All research
Kinetic-4B: A 4-Billion Parameter Model That Outperforms Claude Haiku at Tool Calling
Conscious Engines
A fine-tuned 4B open-source model that beats frontier models at tool calling, with p95 latency under two seconds on a single GPU.
Agentic AI