A birds-of-a-feather session on agentic engineering in San Francisco on October 14th, where builders and experimenters can share their work, experiments, and unfinished projects with coding agents.

This paper presents Governance-as-Code (GaC), a framework for translating the EU AI Act's technical requirements into executable compliance pipelines for generative AI systems. GaC addresses seven technical gaps in the Act's existing requirements, including data governance and human oversight. It provides a machine-checkable acceptance criteria framework with six compliance modules and a Rego policy code implementation. The authors validate GaC on two enterprise deployments, demonstrating a 75% reduction in audit labor compared to manual expert audits.

This survey consolidates and analyzes recent developments in using Electroencephalography (EEG) signals to generate images, text, and audio with generative AI. The study finds that EEG-to-image models use encoder-decoder architectures, EEG-to-text approaches use transformer-based language models, and EEG-to-audio methods map EEG signals to mel-spectrograms. The survey highlights the challenges in the field, including small and heterogeneous datasets, limited cross-subject generalization, and the absence of standardized benchmarks. It provides a foundational reference for advancing EEG-based generative AI and offers open-source datasets and baseline implementations for systematic benchmarking.

Thomas Ptacek discusses the importance of using LLMs as copyeditors, rather than relying on them to generate content directly. He suggests adopting the rule of not using a single word suggested by an LLM to maintain creative control and discipline.

Researchers propose a framework to measure human contribution in AI-assisted content generation using information theory. This framework calculates the proportional information contribution of humans in content generation, effectively discriminating between varying degrees of human contribution across multiple creative domains.

A 25-year analysis of English Wikipedia editing patterns shows declining participation in coordination spaces, with a shrinking core of editors performing governance work. The study also investigated the impact of LLMs on these trends, finding little evidence of a fundamental change.