AWS enhances Amazon Bedrock AgentCore with increased compute power and memory efficiency to help enterprises scale AI agents quickly and affordably.
PuppyGraph 1.0 launches with an AI-assisted schema builder and MCP support to help enterprises integrate AI agents with complex graph data systems.
Master the art of generative AI interaction through strategic prompting, memory management, and custom communication styles for better workflows.
A mathematical resolution of the Aaronson-Ambainis conjecture establishes that quantum computers require structured data to achieve exponential speedups.
Meta has acquired Conductor to integrate its multi-agent AI coding orchestration technology into future software development tools and platforms.
MariaDB introduces four new tools to Enterprise Platform 2027 including AI Retrieval and Semantic Cache to streamline database agent development and reduce costs.
Microsoft researchers developed an AI system that reached 83% diagnostic accuracy in complex medical cases during a controlled simulation study.
GitHub transforms Copilot into a multi-agent development platform, enabling teams to manage complex workflows and coordinate AI agents across repositories.
Oracle and Google are partnering to bring Tensor Processing Units to Oracle Cloud Infrastructure to provide enterprise alternatives to standard GPUs.
Oracle is launching a Business Semantic Layer within its Autonomous AI Lakehouse to provide AI agents with a standardized understanding of enterprise data.
Vanguard warns that while new AI winners may emerge beyond large technology firms, many value funds already hold significant positions in companies like Amazon and Microsoft.
AI startup Anthropic allocates five percent of its equity to a public benefit trust to mitigate social impacts and share future profits with the community.
Technology companies are transforming into infrastructure providers to meet the massive power and networking demands of modern artificial intelligence.
Google Cloud launches Agent Compute Engine to reduce infrastructure costs for AI agents by suspending inactive tasks and preserving execution states.
Vertiv and Nvidia introduce a new strategy to significantly cut the design and deployment time for AI data center power and cooling infrastructure.
Extreme Networks announces a new technology enabling enterprises to run AI applications locally by leveraging existing network equipment's idle processing power.
Model routing automatically selects the most appropriate AI model for a task, balancing cost and performance. This approach optimizes resource use and improves efficiency.
New Cisco Splunk research shows AI agents generate up to five times more telemetry data than standard apps, leading to massive unexpected billing spikes.
Pioneering researcher Richard Socher explains how AI automation in science will surpass chatbots to drive the next era of global breakthroughs.
Anthropic and Harvard researchers utilize Claude AI to design more efficient quantum circuits with fewer qubits and operations for complex calculations.
Review critical trade-offs between speed, cost, and accuracy when moving agentic AI systems from initial prototypes to full production environments.
Researchers at MIT develop artificial neural networks that organize their own learning machinery without developer intervention to mimic brain functions.
Cisco and Nvidia introduce an Ultra-Dense Small Form Factor Pluggable (USFP) optical system, designed to facilitate the rapid construction of high-capacity Ethernet AI fabrics.
Control engineers can improve humanoid robot agility by decoupling high-level motion planning from low-level tracking to maintain professional athletic styles.
Discover how AI coding agents are transforming software engineering by automating complex tasks like debugging, testing, and system iteration.
Oracle explores trucking natural gas to power its massive New Mexico AI campus as pipeline delays threaten project timelines and investor confidence.
Experts at TechCrunch Disrupt analyze how autonomous coding agents and generative AI tools are transforming software engineering and development workflows.
Modern data teams are shifting from reactive monitoring to AI-driven proactive prevention to protect revenue and ensure data integrity.
AI tools increase code output but create bottlenecks in testing and deployment that require teams to redesign their entire delivery pipelines.
Explore the evolution of network automation, from deterministic tasks to advanced AI-driven probabilistic reasoning, enhancing network resilience and efficiency.
Tech companies are launching AI data centers into space to exploit constant solar energy and alleviate the growing strain on terrestrial power grids.
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