This article provides a comprehensive guide to Joint Embedding Predictive Architectures (JEPA) and its applications in self-supervised learning. It details the construction of I-JEPA, exploring how visual prediction fosters understanding of complex concepts without relying on labels. Key extensions to video prediction are also discussed.

Noma Labs identified a significant vulnerability in GitHub's Agentic Workflows, called GitLost. This flaw allows unauthorized users to extract data from private repositories by creating deceptive GitHub Issues. The issue highlights the security risks associated with prompt injection attacks in AI-driven systems and emphasizes the need for stricter safeguards.

This article explores the crucial relationship between cost per token and cost per successful task in AI models. It highlights how understanding this connection influences budget decisions and routing strategies for AI tasks, ensuring optimal use of resources while minimizing unnecessary costs.

This article explores the development of a real-time AI tutor designed to teach math and reading to young children. The authors discuss the architectural decisions that prioritize immediate responses and effective pedagogy, emphasizing the importance of maintaining a child's engagement without compromising learning outcomes.

In a comparative build-off, Grok 4.5, GPT-5.5, and Claude models tackled the same app prompts, showcasing strengths and weaknesses across tasks like a 3D Rubik's Cube and a Breakout game. Grok excelled in speed and cost, but the Claude models led in complex outputs.

This article discusses the evolution of Project Glasswing, focusing on developing a flexible security architecture for enterprise codebases amid rapidly changing AI models. It emphasizes the need for model-agnostic systems to enhance vulnerability detection and management, allowing for diverse strategies to strengthen infrastructure security.

Dr. Ben Bernanke, former Federal Reserve Chair and Nobel laureate, has joined Anthropic's Long-Term Benefit Trust. The Trust aims to guide responsible AI development for humanity's long-term advantage. Bernanke's expertise will enhance the organization's understanding of AI's economic impact and promote sound governance in this evolving field.

This article explores the performance differences between a high-end NVIDIA RTX 5090 GPU and a compact AMD Ryzen AI Max+ 395 mini PC when running large language models. It highlights the significance of unified memory in capacity, while contrasting it with bandwidth limitations in generating text.

After seven years of relying on Haskell, Scarf has decided to transition to Python for new API development. This shift stems from increasing challenges with Haskell's compilation time and ecosystem, especially in an age where AI-enabled coding tools now impact software development efficiency.

The Mexican cavefish illustrates how organisms adapt to their environments, losing traits like sight over generations. Similarly, successful companies may develop "competence blindness," forgetting the skills that fueled their rise. This piece explores how internal complacency can lead to stagnation, all while masquerading as success.

Tim Roughgarden explores the limits and capabilities of computation, tracing the evolution of computer science from Turing's foundational ideas to the challenging P vs. NP question. He highlights algorithmic shortcuts and the implications of these concepts for cryptography, AI, and our understanding of computation. No prior expertise is required.

The QuadRF is a handheld phased-array radio that can detect drones and visualize WiFi signals through walls. Built on a Raspberry Pi 5, it offers advanced signal processing features, including an AR visualizer. While still in development, it's generating excitement for its potential applications and user-friendly capabilities.

Log-Insight is an innovative system designed to automate incident diagnosis in large-scale microservice environments. By streamlining the investigative process for Site Reliability Engineers, it efficiently identifies root causes while significantly reducing the amount of raw data processed. The system demonstrates high accuracy and usability, facilitating effective problem resolution.

This article explores the safety of large language models (LLMs) in safety analysis by applying a method called Constitutional Meta-STPA. It raises critical questions about the reliability of LLMs and presents principles for their effective governance, ensuring they can be audited and validated in safety-critical applications.

This study explores how optimizing the generation order of Diffusion Language Models can enhance text-to-image synthesis and multimodal understanding. By introducing a control module trained via Group Relative Policy Optimization, the authors demonstrate notable improvements in image alignment and multimodal reasoning tasks.