The Waymo World Model enhances autonomous driving by simulating complex scenarios in hyper-realistic environments. Leveraging advanced AI, it prepares the Waymo Driver for rare events and varied conditions, ensuring safety and reliability as it expands across diverse urban settings.

Claude Opus 4.6 enhances coding and multitasking abilities, offering improved planning, debugging, and context handling with a new 1M token window. It excels in complex tasks and is praised for its reasoning, autonomy, and safety. Available now across multiple platforms, it proves to be a significant upgrade from previous models.

Caltech researchers have developed a new DNA synthesis technology called Sidewinder, enabling the rapid assembly of long DNA sequences. This innovation addresses a significant bottleneck in biotechnology, paving the way for advances in agriculture, personalized medicine, and other fields by allowing the construction of entire genes and potentially genomes.

Claude Opus 4.6 has made significant strides in enhancing AI's role in cybersecurity, particularly in identifying high-severity vulnerabilities within open source software. By analyzing code like a human expert, it has uncovered over 500 previously undetected issues, demonstrating the potential for AI to bolster security efforts.

NVIDIA's NVFP4 introduces a new low-precision AI format that greatly enhances training and inference performance while maintaining accuracy. This article explores its key benefits, including substantial throughput gains on the Blackwell architecture, proven accuracy in industry benchmarks, and increasing support from various development ecosystems.

This article explores the concept of "Company as Code," proposing a digital representation of organizational structures and policies. It highlights the disconnect between advanced digital operations and outdated documentation practices, suggesting that a programmatic approach could streamline compliance, policy changes, and organizational design for modern businesses.

Neural networks often struggle with hierarchical data, like clinical trial results varying among hospitals. This article explores hypernetworks, which adaptively generate parameters based on dataset characteristics. We will illustrate how they improve model performance compared to standard neural nets and discuss the potential of Bayesian models in this context.

Telecom companies confront growing supply chain security threats as open-source components increasingly underpin their operations. By adopting a 'trust nothing' approach, standardizing security measures, and using domain-specific tools, they aim to protect networks and maintain resilience in an evolving landscape of vulnerabilities and risks.

At Jane Street, the innovative "expect tests" pattern transforms the testing process to feel more like interactive programming. By simplifying assertions and facilitating rapid feedback, it enhances test-writing, making it efficient and intuitive, while also serving as documentation and regression prevention for developers.

This article critiques GitHub Actions as an inefficient continuous integration (CI) tool, comparing it unfavorably to alternatives like Buildkite. It highlights frustrations over its cumbersome interface, unreliable log viewer, and complex YAML configurations, illustrating how these issues can sap engineers' productivity and morale.

Software development sees two primary approaches: evolutionary growth and upfront design. Each has its pros and cons, impacting complexity management and team dynamics. While evolutionary methods feel more flexible and can be engaging, upfront systems often lead to smoother development and better long-term outcomes. Understanding dependencies is key to successful implementation.

A new bill in New York mandates news organizations to label AI-generated content and ensures human oversight before publication. The NY FAIR News Act aims to promote transparency and protect journalism from potential misinformation and labor impacts due to AI adoption, with strong support from industry unions.

This study examines 100 fabricated citations from papers accepted at the 2025 NeurIPS conference, revealing a significant failure in peer review. It introduces a taxonomy of hallucinations, highlighting the need for automated citation verification to combat inaccuracies in scientific literature effectively.

This article explores the relationship between Stochastic Gradient Descent (SGD) noise and the curvature of loss landscapes in deep learning. It challenges previous assumptions about noise covariance and presents a more general framework for understanding this connection, supported by experimental validation across various architectures and datasets.

This article introduces a novel approach to time series analysis using a multimodal pretraining paradigm. It presents the new MM-TS dataset and the HORAI model, which enhances understanding by integrating various modalities. The model demonstrates impressive performance in forecasting and anomaly detection tasks after training on this expansive dataset.