Browsing: Tools

Firefox is developing a new feature to display direct results in the address bar, aiming to provide faster access to information and websites by bypassing traditional search engine results pages. This initiative prioritizes user convenience and incorporates a robust, privacy-preserving architecture using Oblivious HTTP, ensuring no single entity can link user queries to their identity. The feature will roll out gradually, starting in the United States, and may include highly relevant sponsored results.

Zoomer is an automated, comprehensive platform designed for debugging and optimizing AI training and inference workloads across large-scale infrastructure. It delivers deep performance insights that contribute to energy savings, accelerated workflows, and enhanced efficiency in AI systems. The platform has significantly reduced training times and improved query-per-second (QPS) rates, establishing itself as a crucial tool for AI performance optimization.

The current trajectory of artificial intelligence risks creating a world where intelligence is a rented service, controlled by a few large platforms. This article explores Mozilla’s vision for an open-source AI ecosystem, drawing parallels from its history with the open web, and outlines key areas of focus to ensure AI empowers users and fosters innovation rather than centralizing control.

AWS introduced three updated Well-Architected Lenses at re:Invent 2025: the Responsible AI Lens, the Machine Learning (ML) Lens, and the Generative AI Lens. These lenses offer comprehensive guidance for designing, building, and operating AI workloads, covering ethical considerations, foundational ML practices, and specialized approaches for generative AI and large language models.

GitHub’s engineering teams employ a structured approach to identify, diagnose, and resolve issues that impact the platform’s stability and performance. This article explores the methodologies and cultural practices that enable engineers to maintain a robust and reliable service.

Generative AI agents operating in production environments necessitate resilience strategies that extend beyond conventional software patterns. These agents make autonomous decisions, consume significant computational resources, and interact with external systems in unpredictable ways. Such characteristics introduce failure modes that traditional resilience methods may not adequately address. This article introduces a framework for analyzing AI agent resilience risks, applicable across most AI development and deployment architectures, and examines practical strategies to help prevent, detect, and mitigate common resilience challenges encountered when deploying and scaling AI agents.

Cloudflare’s internal security division faces the challenge of consistently securing hundreds of production accounts globally. Manual configuration is prone to errors, leading to the adoption of “shift left” principles and Infrastructure as Code (IaC). This approach integrates security checks early in the development lifecycle, treating configurations as code to ensure consistency, scalability, observability, and proactive governance, thereby minimizing human error and preventing incidents.

A new capability, delegated alert dismissal, allows organizations to mandate a review process before Dependabot alerts are closed. This feature, available to GitHub Code Security customers, enhances security risk management, aids in meeting compliance requirements, and provides similar governance controls as other code security features.