AI Red Team Engineering notes, research references, and field notes on AI systems, responsible AI, governance, and compliance.
Global search

Search AI Red Team

Search across the complete knowledge base.

Enter at least two characters to begin.
↑↓ select Enter open Browse the full research archive
AI Engineer · August 18, 2026

Generative Video at the Speed of Light — Keegan McCallum, uRun

video AI EngineeringModel EvaluationPrompt Engineering
Generative Video at the Speed of Light — Keegan McCallum, uRun video thumbnail
Why it matters

Ten dollars now buys roughly three hours of continuously generated video, and fifty buys fifteen. Keegan McCallum sets that against the room's own habits, since plenty of hands went up for burning that much on coding tokens inside a single hour.

My takeaway: Generative Video at the Speed of Light — Keegan McCallum, uRun is an AI-engineering signal. The practical read is to connect the implementation pattern to reliability, data boundaries, observability, and the controls needed when AI leaves prototypes.
Keep exploring

More curated notes connected through AI Engineering and Model Evaluation.

OpenAI News · framework

OpenAI’s Frontier Governance Framework

OpenAI's 22-page Frontier Governance Framework maps its frontier-model processes to California's Transparency in Frontier AI Act and the EU AI Act's general-purpose AI code. It documents lifecycle risk assessment, cyber-offense and other risk tiers, mitigation and residual-risk decisions, critical-incident handling, security risk management, model reporting, external review, responsibility allocation, and change control.

OpenAI News · framework

Cybersecurity in the Intelligence Age

OpenAI proposes a five-pillar strategy for AI-enabled cyber defense: tiered access for trusted defenders, faster government-industry coordination, stronger protection of frontier models and infrastructure, risk-scaled deployment monitoring, and broader defensive support for individuals and small organizations.

OECD.AI Wonk · guide

A five-step roadmap to closing the AI evaluation gap

The roadmap addresses evaluation results that overstate real-world performance or fail to transfer across deployment contexts. Its five steps balance standardized and local tests, evaluate throughout the lifecycle, build qualified assurance and communication capacity, tailor tests to each value-chain actor and technology, and use a coordinated, trusted process for updating methods.