Three AI labs—DeepSeek, Moonshot, and MiniMax—have conducted large-scale distillation attacks on Claude, aiming to extract its advanced capabilities for their own models. This raises significant security concerns as such illicitly obtained models may lack safeguards, threatening both industry and national security. Coordinated action is needed to combat this issue.

Dictionary compression is gaining traction for its potential to significantly reduce web data traffic. Initial tests show substantial size reductions for applications like YouTube and Google search results. By using known dictionaries, it allows for efficient compression of repetitive data, making loading faster and more bandwidth-friendly.

Recent studies reveal that leading AI models can produce exact copies of popular novels, challenging claims that these systems do not retain copyrighted material. Experts emphasize the implications for ongoing copyright lawsuits, as evidence suggests AI memorization of training data is more significant than previously acknowledged.

As AI models grow, challenges arise with traditional BF16 precision training. This article explores low-precision alternatives like FP8-CS, MXFP8, and NVFP4. Results indicate that these methods improve training throughput, conserve memory, and maintain model accuracy, providing viable options for efficient large-scale model training.

Cognizant, Tata Consultancy Services, and Infosys dominate H1B visa filings, yet their average salaries lag significantly behind tech giants. This article examines how these outsourcing firms exploit the H1B system, affecting wages and job opportunities for both immigrant and American workers, highlighting the need for reform.

Maine's 2002 laptop initiative aimed to enhance student learning but has faced criticism as recent studies show Gen Z's cognitive capabilities declining. Experts argue that excessive screen time and distractions from technology have undermined educational outcomes, creating challenges for the generation as they confront complex societal issues.

In 2025, a web crawler successfully indexed over a billion web pages in 25.5 hours at a cost of $462. This article explores the technological advances and design choices that enabled this achievement, while also emphasizing responsible crawling practices and the challenges posed by the modern web's dynamic content.

Pinterest's increasing reliance on AI is leaving many users frustrated. Artists report issues with content moderation, automated tagging of their work as "AI modified," and a flood of AI-generated art cluttering feeds. Users express concerns about account bans and a shift in the platform's community-driven spirit.

a recent scandal, Elsevier has retracted 12 economics papers linked to Brian M. Lucey, a prominent figure in finance academia. This incident highlights serious questions about editorial integrity and ethics within academic publishing, as Lucey was found to have approved his own papers, revealing a troubling citation scheme.

Creating TV apps from mobile designs often leads to unexpected challenges. This piece explores the critical differences in user interaction, design, and performance, highlighting the importance of clear navigation and understanding hardware limitations to deliver a better viewing experience on television.

This article introduces QRTape, an innovative data storage system that utilizes QR codes printed on paper instead of traditional magnetic tape. Employing modern computer vision and audio compression techniques, this cost-effective setup promises reliable data storage with a simplified design, showcasing a blend of hardware and software creativity.

Ladybird is transitioning from C++ to Rust for enhanced memory safety, starting with its JavaScript engine, LibJS. The porting process has yielded identical output to the original code, ensuring compatibility. This change aims to bolster stability while continuing to support existing C++ systems. Future work will prioritize structured porting.

Leap+Verify is a novel framework designed to speed up neural network training by predicting future model weights and validating them before use. By adapting to different training regimes, it offers improved accuracy and acceptance rates while addressing challenges faced by current prediction methods in large models.

Hexagon-MLIR is an open-source compilation stack designed for Qualcomm's Hexagon Neural Processing Unit (NPU). It streamlines the deployment of Triton kernels and PyTorch models, enhancing AI workload performance through optimized data handling. Current efforts focus on expanding features and capabilities for developers.

This article presents a new evaluation framework for assessing the risks of large language models in mental health care. By simulating therapy sessions, the study highlights critical safety gaps and potential harms in utilizing AI for mental health support, emphasizing the need for thorough evaluation before widespread deployment.