AI in Technology

Discover RHEO’s web version—an instant, private, browser-based fluid simulation that offers calm, creativity, and ambient experiences without downloads.

A new framework reveals how to reduce AI memory costs by building, renting, or quantizing models, with quantization offering significant savings without quality loss.

Apple Silicon offers a unique memory architecture that enables larger AI models locally, with lower power and cost, despite slower bandwidth.

A new deep-work app for Apple Vision Pro creates distraction-free environments, making focus easier by removing interruptions physically. Details are emerging.

Discover RHEO’s web version, offering instant, private, browser-based fluid simulations that promote calm and creativity without downloads or sign-up.

Exploring how AI developers can reduce memory expenses through building, renting, or quantizing models, with a focus on the emerging role of quantization techniques.

Analyzing the expenses and hardware considerations for running large language models locally in 2026, including VRAM needs and hardware options.

Memory prices are expected to remain high through 2028–2029, with relief delayed beyond 2027 due to industry capacity constraints and demand trends.

An analysis of the four levels of agentic loops in AI, what each enables, and their implications for automation and control.

Apple Silicon chips offer a unique, cost-effective solution for large AI models through unified memory, despite lower bandwidth than GPUs.