Browse past weeks of engineering reads.
How to deploy high-intelligence AI models with agentic capabilities to consumer hardware and mobile devices without requiring cloud infrastructure.
Enterprise systems need to react to events in real-time rather than relying on slow batch jobs or inefficient polling microservices that create dangerous delays in detecting critical issues like fraud or supply chain disruptions.
Organizations need to securely build, deploy, and govern autonomous AI agents at enterprise scale as the industry transitions from experimental LLMs to production agentic AI systems.
Deploying and managing AI agents at scale in production requires infrastructure for state management, security governance, and complex workflow orchestration that goes beyond demo implementations.
BASF needed to manage and optimize thousands of interdependent supply chain decisions across 180 global production sites where weather and regulatory changes can cause cascading disruptions in a two-year production pipeline.
Building safe, reliable, and autonomous agents that can act independently across multiple enterprise systems while maintaining security, governance, and reliability guardrails.
Migrating business-critical load balancer configurations from on-premises hardware solutions to Google Cloud while preserving existing traffic manipulation logic.
How to help developers transition from understanding AI concepts to building and maintaining production agentic systems in cloud environments.
Organizations need to secure their AI systems and infrastructure against emerging AI-era threats while maintaining the ability to leverage AI's potential at scale.
Enabling seamless connectivity, governance, and security across multi-agent AI systems and core applications distributed globally at planet scale.