AI coding assistants can streamline development, but some pose serious risks. The MaliciousCorgi campaign reveals two popular VS Code extensions that not only assist but also secretly harvest sensitive data and files, exposing developers to significant security threats. Ensure your workspace stays safe with tools designed to verify extension behavior.

Game Arena is evolving to advance AI benchmarking by introducing new challenges in poker and Werewolf alongside chess. These games not only evaluate strategic reasoning and social dynamics but also foster agent safety research, providing crucial insights into AI behavior in real-world scenarios.

The article explores the largest numbers that can be represented and computed within 64 bits, comparing traditional data types like uint64_t with more complex constructs such as Turing machines and lambda calculus. It examines concepts like the Busy Beaver function and how they surpass commonly known large numbers, emphasizing the boundaries of programming languages.

Scaling a system from zero to over 10 million users involves understanding distinct stages of growth and their associated challenges. This article outlines seven essential stages, starting from a single server to a more complex architecture, emphasizing the importance of simplicity and incremental scaling for successful development.

OpenClaw, a popular open-source AI personal assistant, has been found to contain critical security vulnerabilities that could allow for remote code execution. This article dissects the logic flaws identified in its codebase and explains how these can be exploited, posing a serious risk to users' digital lives.

At Anthropic, AI-generated code has become the norm, with engineers like Boris Cherny relying entirely on their AI tools, Claude Code and Opus 4.5, for coding tasks. This shift raises questions about the future of software engineering, particularly for entry-level roles, as AI tools change the way software is developed.

This article delves into the architecture and scheduling of Nano-vLLM, a streamlined inference engine for large language models. It explains how prompts are processed, requests are queued, and GPU resources are managed, providing insights into efficient system design while setting the stage for a deeper analysis in Part 2.

msgvault is a new local storage and retrieval system for organizing and querying email and messaging data with impressive speed. It securely archives communications, including attachments, aimed at empowering users to easily access their information without relying on external platforms. Future features promise broader integration across messaging services.

Counterfeit Samsung 990 Pro SSDs are becoming increasingly sophisticated as NAND shortages create an environment ripe for scams. A recent case reveals a fake drive that mimicked the real model’s details but performed slower than a USB 2.0 drive. Caution is advised when shopping for SSDs to avoid falling victim to these clever counterfeits.

This article explores Hybrid-EP, an innovative communication solution designed for hyperscale mixture-of-experts (MoE) models in LLM training. It addresses the challenges of communication efficiency, load balancing, and framework adaptability, detailing Hybrid-EP's implementation in NVIDIA's Megatron framework and its benefits for real-world training scenarios.

MIT researchers have developed an AI model called DiffSyn that provides scientists with optimized synthesis routes for complex materials like zeolites. By analyzing over 23,000 recipes, it streamlines the material creation process, potentially accelerating discoveries in various applications. This innovation addresses a significant challenge in materials science.

Sparse files offer a clever caching solution for managing columnar data formats in analytics, reducing wasted disk space and enhancing efficiency. By tracking only the necessary logical blocks, this approach minimizes S3 requests and file system overhead, ultimately improving system performance in data-heavy operations.

This study explores outlier dynamics in large language model pretraining using NVFP4, highlighting how architectural components contribute to outlier sensitivity. The authors propose the Hot-Channel Patch mechanism to reduce quantization loss, resulting in improved efficiency and accuracy in training models compared to traditional methods.

This article explores how entropy reduction can enhance the tool-use behavior of large language model agents. By implementing two distinct reward strategies, the study demonstrates significant improvements in efficiency and performance, paving the way for more adaptive and effective applications in real-world scenarios.

AICD Bench introduces a comprehensive benchmark for detecting AI-generated code, featuring 2 million examples across 77 models and 9 programming languages. It presents three realistic detection tasks, highlighting the performance challenges faced by current machine learning detectors, especially in complex scenarios. Data and code are publicly available.