Rachael Hinkle’s work with machine learning intersects political science, legal training and computational methods.

Leverage AI as a personalised "code coach" to bridge the gap between manual testing and automation by translating plain English into executable scripts and providing line-by-line logic explanations.

This hands-on PoC shows how I got an open-source model running locally in Visual Studio Code, where the setup worked, where it broke down, and what to watch out for if you want to apply a local model ...

Publishers are exploring automation through agentic AI such as sales agents who actively make decisions in the auction.

It makes it much easier than typing environment variables everytime.

Recursive self improvement is the key to solving intelligence. Human intelligence is built on our ability to characterize the ...

A defining challenge facing agentic AI may not be model capability, but rather its containment and governance.

Integrating AI into chip workflows is pushing companies to overhaul their data management strategies, shifting from passive storage to active, structured, and machine-readable systems. As training and ...

Decoupling application logic from hardware lets engineers test firmware on host machines instead of waiting for dev boards.

Chinese researchers have published a new AI-driven system designed to interpret scramjet combustion simulations at speeds that could compress years of computational work into weeks. The tool, called ...

For as long as scientists have been trying to understand the behavior of the electrically charged fourth state of matter known as plasma, there have ...

Model selection, infrastructure sizing, vertical fine-tuning and MCP server integration. All explained without the fluff. Why Run AI on Your Own Infrastructure? Let’s be honest: over the past two ...