This paper presents six design patterns designed to enhance the security of LLM agents against prompt injection attacks. By imposing constraints on agent actions, these patterns offer a balance between performance and safety, paving the way for more reliable tools in AI applications.

This article explores the growing concern about artificial intelligence, particularly large language models, becoming overly supportive and consensus-driven. It highlights the risks of fostering environments lacking constructive disagreement and emphasizes the need to cultivate curiosity and skepticism in AI to promote critical thinking and healthy debate.

This article explores the emerging field of foundation models in time-series forecasting, focusing on their potential to simplify and enhance predictive analytics across diverse datasets. It benchmarks several leading models against traditional approaches, assessing their performance on real-world observability data and addressing practical considerations for scalability and adaptability.

Andrew Ng critiques the term "vibe coding," calling it misleading and suggesting it undervalues the intellectual effort involved in AI-assisted coding. He advocates for everyone to learn at least one programming language to enhance communication with technology, ensuring better productivity across various job functions.

This article explores the potential for large language models (LLMs) to inadvertently reveal sensitive training data properties. Introducing the PropInfer benchmark, the study assesses property inference attacks across different LLMs, revealing vulnerabilities that could pose risks in sensitive applications like healthcare and finance.

BugGen is an autonomous multi-agent pipeline that leverages Large Language Models to generate and validate realistic bugs in RTL designs. This innovative tool significantly improves bug synthesis efficiency, demonstrating over five times the speed of traditional methods and enhancing verification coverage by identifying previously undetected bugs.

Large language models can unintentionally introduce racial and gender biases, especially in hiring contexts. This study reveals that typical bias-mitigation strategies fall short under realistic conditions. The authors propose an internal bias mitigation approach that significantly reduces bias while maintaining model performance, advocating for improved evaluation methods in hiring scenarios.

The Econometrics AI Agent is a specialized AI tool that automates complex econometric analysis, making advanced techniques more accessible without costly fine-tuning. Leveraging a zero-shot learning approach, it enhances productivity and reproducibility for researchers, practitioners, and students in the field of econometrics.

This collection of PyTorch tutorials caters to all skill levels, offering practical examples and explanations on various concepts and applications. From basics like tensors and neural networks to advanced topics such as reinforcement learning and Bayesian deep learning, there's something for everyone looking to enhance their PyTorch skills.

Cosmos DiffusionRenderer is an advanced video diffusion framework that enhances image and video de-lighting and re-lighting. This update significantly improves quality, leveraging NVIDIA Cosmos and a refined data pipeline. The new code and model weights are available, making high-quality rendering more accessible.