For years, we’ve built with a clear priority: putting the practical needs of the enterprise first. Long before generative AI dominated the headlines, we were focused on building the global infrastructure, security frameworks, and data platforms that power the world’s largest organizations. We’ve always believed that technology is only as good as its reliability, security, …
By Parth Jain, Rakesh Sukumar, Yingwu Zhao, Renzo Sanchez-Silva & Nathan FisherA deep dive into the engineering challenges of building a real-time service dependency map at Netflix scale: from streaming architectures and distributed aggregation pipelines to time-travel queries and the methodology that made it work. Introduction In our first post, we introduced the problem: engineers at …
Read more “Building Service Topology at Scale: Architecture, Challenges, and Lessons Learned”
Build with the smartest family of models from OpenAI yet, on Amazon Bedrock’s next-generation inference engine. Organizations scaling autonomous agents and AI-powered products need frontier intelligence that performs reliably across hundreds of steps, from coding agents shipping production code to cyber security research probing novel attack surfaces to genomics workflows analyzing entire gene sequences end-to-end. …
Read more “OpenAI GPT-5.6 Sol, Terra, and Luna are now generally available on Amazon Bedrock”
How should your security team manage shadow AI? Workloads deployed by developers without formal registration can often evade traditional security scanners, because organizations are reluctant to slow down development and compromise stability by demanding privileged Daemonsets, kernel-level access, and manual pod-spec edits. To break this deadlock, today we are open-sourcing k8s-aibom. This lightweight, unprivileged Kubernetes …
Read more “Securing the AI supply chain on GKE: Introducing k8s-aibom for automated AI BOMs”
This paper was accepted at the AI4TCI (Workshop on AI for Secure and Trustworthy Critical Infrastructure Systems) Workshop at the International Conference on Availability, Reliability and Security (ARES) 2026. Autonomous negotiation agents are increasingly deployed in high-stakes settings such as insurance and procurement. While cryptographic techniques protect explicitly disclosed constraint values, they fail to address …
Model customization transforms general-purpose AI models into specialized enterprise assets. By fine-tuning foundation models (FMs) on domain-specific data, businesses teach AI their unique workflows, terminology, and deep domain specialization, along with strict adherence to brand voice and fewer hallucinations. For enterprises, this is more than an optimization. It’s the creation of proprietary intellectual property. A …
Editor’s note: Some of the most interesting questions in AI are being asked by information theoreticians, around how to provide context to an emerging class of AI agents. A few weeks ago, we waded into those waters with a blog about the Open Knowledge Format, a specification that formalizes the LLM-wiki pattern into a portable, …
Read more “Frontier and Center: Who evaluates the evaluations?”
Multimodal large language models (MLLMs) have recently shown strong performance in visual understanding, yet they often lack temporal awareness, particularly in egocentric settings where reasoning depends on the correct ordering and evolution of events. This deficiency stems in part from training objectives that fail to explicitly reward temporal reasoning and instead rely on frame-level spatial …
Read more “Incentivizing Temporal-Awareness in Egocentric Video Understanding Models”
When Model Context Protocol (MCP) tools underperform, the cause is rarely the protocol itself but the tool design. Many teams start by exposing an existing API as-is and trusting the agent to figure out the rest. It is a natural way to extend APIs to agentic systems and generative AI coding tools. For straightforward use …
Read more “MCP tool design: Practical approaches and tradeoffs”
Many of the most challenging and valuable problems in the world are related to optimization. Now, AI is now making these problems tractable. If you’ve ever tried to design a microchip, plan a delivery network, or optimize a training architecture for a large AI model, you know how hard it is to find the most …
Read more “Solve harder problems with AlphaEvolve, now available to everyone on Google Cloud”