This article shares insights from experiments conducted at two companies, highlighting how redesigning the software development lifecycle (SDLC) with AI as a structured participant can significantly enhance productivity. By establishing clear roles, documented rules, and defined quality gates, teams can achieve a sustainable improvement in development velocity.

Bonsai 27B is a groundbreaking AI model that brings 27 billion parameters to mobile devices for the first time. Optimized for efficiency, it enables advanced reasoning and multimodal tasks on phones without compromising performance, allowing for local execution and enhanced user privacy. Both high and low-memory variants are now available.

This article explores the rise of AI voice fraud, detailing a shocking case where a grandmother was scammed after receiving a convincing fake call from her daughter. As fraudsters harness advanced technology, AI-enabled scams are increasingly targeting older adults, prompting urgent calls for better defenses to protect vulnerable populations.

NVIDIA's CUDA 13.3 introduces native support for carryless multiplication with the clmad instruction, enhancing GPU performance for cryptographic tasks. This breakthrough significantly accelerates workloads like GHASH and sum-check protocols, optimizing essential operations in authenticated encryption and zero-knowledge proofs on Ampere and newer GPUs.

Engineers are advancing CAD design with a new system that teaches vision-language models to effectively convert 2D designs into accurate CAD programs. This innovative approach reduces computational costs and enhances prototyping, helping engineers optimize designs and improve efficiency in the engineering process.

This article explores integrating legacy Fortran simulation code with modern machine learning frameworks like JAX and PyTorch using Enzyme and LLVM. By applying automatic differentiation at the LLVM level, existing simulations can output accurate gradients without complete rewrites, providing a bridge between historic and contemporary computing methods.

A serious security vulnerability in the Cursor IDE allows malicious code to run without user consent when opening a project containing a harmful git.exe file. Despite multiple reports to Cursor over seven months, there has been no resolution or acknowledgment from the company, leaving users at risk.

High Bandwidth Flash (HBF) is an emerging memory technology that enhances NAND flash speed by utilizing advanced stacking techniques. Designed to meet the demands of AI inference workloads, HBF could ease capacity issues faced by traditional high-bandwidth memory, potentially improving energy efficiency and reducing costs for data centers.

The article explores the role of small publication servers in the Resource Public Key Infrastructure (RPKI), a system designed to secure Internet routing against prefix hijacks. It delves into reasons for their operation, their contributions, and the implications for the overall security of Internet traffic management.

Telegram Serverless allows developers to create and manage Telegram bots without the hassle of traditional server infrastructure. By using simple JavaScript modules, users can deploy code effortlessly, enabling their bots to respond to messages and store data efficiently, all while eliminating the need for persistent hosting.

This article examines the values expressed by Claude.ai across different models and languages. By organizing these values into key axes, the study sheds light on how responses vary in areas like warmth versus rigor, aiding our understanding of contextual influences on Claude's interactions.

Domain-Specific Languages (DSLs) enhance the reliability of Large Language Models (LLMs) in code generation by providing clear boundaries for their output. This article explores how DSLs, like Tickloom for distributed systems, serve as effective tools for defining domain concepts and improving collaboration with LLMs in iterative design processes.

This article explores the concept of Mechanistic World Models, focusing on how they can enhance AI's role in scientific discovery. It emphasizes the importance of understanding underlying mechanisms rather than just predictive accuracy, proposing a framework that encourages the emergence of explanatory knowledge in AI systems.

This article presents Modified Spectral-Informed Neural Networks (SINNs), which enhance accuracy in high-dimensional partial differential equations by integrating spectral methods with physics-informed neural networks. Numerical experiments show that Modified SINNs outperform traditional spectral methods and standard PINNs, offering a more effective solution to these complex mathematical problems.

This article explores the theoretical grounding of Joint-Embedding Predictive Architectures by relating them to Active Inference principles. It assesses different anti-collapse regularisers and their effects on training objectives, revealing insights into information bottlenecks and future-state coverage signals within these models.