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.

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 It highlights the disconnect between advanced digital operations and outdated documentation practices, suggesting that a programmatic approach could streamline compliance, policy changes, and organizational design for modern businesses.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.

Kimi K2.5 is the latest multimodal language model designed for tasks like chat, coding, and reasoning. Built on the Megatron-LM framework, it features 1 trillion parameters and advanced visual processing capabilities. Customization and deployment options make it accessible for various applications, supporting both research and enterprise needs.

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.

Mac Mini sales are surging as users turn to OpenClaw, an open-source framework enabling AI agents to automate tasks. This trend highlights a missed opportunity for Apple to create a powerful AI layer that could have capitalized on its hardware and trust with users, reshaping the tech landscape.

increased significantly, the ability to access cloud services has simplified deployment and scalability for developers. This article explores Wirth's Law, the evolution of software efficiency, and how cloud computing has transformed the landscape, balancing complexity with the need for accessibility and user experience.

This article explores the advantages of owning a data center instead of relying on cloud services. It discusses cost savings, control over computing resources, and the engineering benefits of managing your infrastructure. The author shares insights from their own experience building and operating a data center, providing practical guidance for others considering the venture.

Allegro explores mobile UI performance with Lynx, an open-source framework that combines server-driven UI features with native rendering. By reimplementing a mobile screen and conducting A/B tests against their existing WebView, the team evaluates Lynx’s potential to enhance cross-platform development and user experience.

OpenClaw's powerful agent skills may seem innovative, but they also create serious security risks. As malware exploits these skills, they become vectors for infostealing attacks. Understanding the dangers and the implications for corporate security is crucial for anyone experimenting with agent technology.

Elixir stands out as an exceptional programming language for artificial intelligence, as highlighted by a Tencent study showing its top performance in problem-solving across various models. This article explores Elixir's unique features, such as immutability, clear documentation, and stability, which contribute to its high success rates in AI applications.

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.