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Build a serverless AI Gateway architecture with AWS AppSync Events

AWS AppSync Events can help you create more secure, scalable Websocket APIs. In addition to broadcasting real-time events to millions of Websocket subscribers, it supports a crucial user experience requirement of your AI Gateway: low-latency propagation of events from your chosen generative AI models to individual users. In this post, we discuss how to use …

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BigQuery AI supports Gemini 3.0, simplified embedding generation and new similarity function

The digital landscape is flooded with unstructured data — images, videos, audio, and documents — that often remain untapped. To help you unlock this data’s potential with minimal friction, we have integrated Gemini and other Vertex AI models directly into BigQuery, simplifying how you work with generative AI and embedding models using BigQuery SQL.New launches …

Managing and Securing VS Code Extensions at Scale

Editor’s Note: In this blog post, Palantir’s Information Security (InfoSec) team shares their approach to implementing a comprehensive VS Code extension management program, demonstrating practical solutions to a frequently overlooked attack vector. Introduction Integrated development environments (IDEs) serve as the primary interface for authoring code and managing infrastructure, sitting at the heart of every software company. …

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Build AI agents with Amazon Bedrock AgentCore using AWS CloudFormation

Agentic-AI has become essential for deploying production-ready AI applications, yet many developers struggle with the complexity of manually configuring agent infrastructure across multiple environments. Infrastructure as code (IaC) facilitates consistent, secure, and scalable infrastructure that autonomous AI systems require. It minimizes manual configuration errors through automated resource management and declarative templates, reducing deployment time from …

Monitoring Google ADK agentic applications with Datadog LLM Observability

Google’s Agent Development Kit (ADK) gives you the building blocks to create powerful agentic systems. These multi-step agents can plan, loop, collaborate, and call tools dynamically to solve problems on their own. However, this flexibility also makes them unpredictable, leading to potential issues like incomplete outputs, unexpected costs, and security risks. To help you manage …

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Securing Agents in Production (Agentic Runtime, #1)

Editor’s Note: This is the first in a series exploring Palantir AIP’s Agentic Runtime — the integrated toolchain for building, deploying, and managing agents in mission-critical settings. Since Day 1, Palantir’s customers have demanded rigorous security and governance capabilities that stretch far beyond conventional role-driven policies on buckets of data. This includes a security architecture that can …

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How PDI built an enterprise-grade RAG system for AI applications with AWS

PDI Technologies is a global leader in the convenience retail and petroleum wholesale industries. They help businesses around the globe increase efficiency and profitability by securely connecting their data and operations. With 40 years of experience, PDI Technologies assists customers in all aspects of their business, from understanding consumer behavior to simplifying technology ecosystems across …

Scaling WideEP Mixture-of-Experts inference with Google Cloud A4X (GB200) and NVIDIA Dynamo

As organizations transition from standard LLMs to massive Mixture-of-Experts (MoE) architectures like DeepSeek-R1, the primary constraint has shifted from raw compute density to communication latency and memory bandwidth. Today, we’re releasing two new validated recipes designed to help customers overcome the infrastructure bottlenecks of the agentic AI era. These new recipes provide clear steps to …

DiffuCoder: Understanding and Improving Masked Diffusion Models for Code Generation

Diffusion large language models (dLLMs) are compelling alternatives to autoregressive (AR) models because their denoising models operate over the entire sequence. The global planning and iterative refinement features of dLLMs are particularly useful for code generation. However, current training and inference mechanisms for dLLMs in coding are still under-explored. To demystify the decoding behavior of …