AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
A laboratory professional supervising a large glass reactor vessel connected to an extensive network of pipes, valves, sensors, and control equipment in a research environment.
Reproducible research practices are context engineering for AI coding agents. I argue that agents lower the cost of maintaining tests, commit histories, repository structure, instructions, and decision records while making their benefits immediate. Researchers remain responsible for verifying these artifacts and the sc...
This seminar explores how to make AI-assisted academic research verifiable, meaning inspectable, reproducible, and defensible by turning vague prompting into bounded, auditable workflows. It offers practical methods for building audit trails, testing workflow reliability, and documenting AI use in ways that support pub...
The most important findings about frontier artificial intelligence (AI) are also the hardest to verify. Much of the information needed to understand its capabilities and risks-including results from evaluations of prerelease models and containment experiments-remains largely inaccessible outside the labs that produce i...
This seminar explores how agentic AI can be integrated into academic research as a disciplined, verifiable, and human-led method rather than a generic productivity tool. It provides practical frameworks for responsible use, workflow verification, and multi-agent research design across literature work, analysis, writing...
This workshop explores how AI can be used responsibly across the research lifecycle, from literature discovery and question development to computational analysis, writing, and publication and gives a practical framework for deciding what AI can assist with and what must remain human-led.
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