Tag: faang

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”

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 …

Read more “Fine-tune NVIDIA Nemotron 3 models with Amazon SageMaker AI serverless model customization”

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?”

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”

Enterprises deploying Claude Code and Claude Desktop across development teams need centralized control over access, cost, and policy. At scale, this is hard to manage: each developer needs an individual credential, settings must be distributed manually, and spend is difficult to track or cap. Without a centralized control point, governance is left to whatever tooling …

Read more “Introducing Claude apps gateway for AWS”