We’re testing a new, AI-powered Google Finance.

Beginning this week, you'll see us testing a new Google Finance, reimagined with AI at its core. Here’s what to expect:Research your finance questions with AI: Now, you …

Introducing Authorization for Apollo MCP Server: Secure AI Access to Your GraphQL APIs

Unlock microservices potential with Apollo GraphQL. Seamlessly integrate APIs, manage data, and enhance performance. Explore Apollo's innovative solutions.

Accelerate ND-Parallel: A Guide to Efficient Multi-GPU Training

We’re on a journey to advance and democratize artificial intelligence through open source and open science.

Introducing AI Sheets: a tool to work with datasets using open AI models!

We’re on a journey to advance and democratize artificial intelligence through open source and open science.

Mastra Changelog 2025-08-08

Breaking changes to Scorer API, critical fixes for message handling and parallel workflows, plus improvements to memory filtering and type safety across the board. Plus, we're announcing our first ever conference: TypeScript AI

vLLM Beijing Meetup: Advancing Large-scale LLM Deployment

Advancing Low-Bit Operators in PyTorch and ExecuTorch: Dynamic Kernel Selection, KleidiAI, and Quantized Tied Embeddings

The DIVA logistics agent, powered by Amazon Bedrock

In this post, we discuss how DTDC and ShellKode used Amazon Bedrock to build DIVA 2.0, a generative AI-powered logistics agent.

Automate enterprise workflows by integrating Salesforce Agentforce with Amazon Bedrock Agents

This post explores a practical collaboration, integrating Salesforce Agentforce with Amazon Bedrock Agents and Amazon Redshift, to automate enterprise workflows.

The AI model Perch, updated today, uses audio to help protect endangered species.

Our Perch AI model helps conservationists analyze bioacoustic data to protect endangered species like birds and coral reefs.

How AI is helping advance the science of bioacoustics to save endangered species

Our new Perch model helps conservationists analyze audio faster to protect endangered species, from Hawaiian honeycreepers to coral reefs.

The latest AI news we announced in July

Here are Google’s latest AI updates from July 2025

How Amazon Bedrock powers next-generation account planning at AWS

In this post, we share how we built Account Plan Pulse, a generative AI tool designed to streamline and enhance the account planning process, using Amazon Bedrock. Pulse reduces review time and provides actionable account plan summaries for ease of collaboration and consumption, helping AWS sales teams better serve our customers.

Vercel collaborates with OpenAI for GPT-5 launch

The GPT-5 family of models released today, are now available through AI Gateway and are in production on our own v0.dev applications. Thanks to OpenAI, Vercel has been testing these models for a few weeks in v0, Next.js, AI SDK, and Vercel Sandbox.

GPT-5 and the new era of work

GPT-5 is OpenAI’s most advanced model—transforming enterprise AI, automation, and workforce productivity in the new era of intelligent work.

Introducing GPT-5 for developers

The best model for coding and agentic tasks.

Achieving 10,000x training data reduction with high-fidelity labels

Vision Language Model Alignment in TRL ⚡️

We’re on a journey to advance and democratize artificial intelligence through open source and open science.

Pioneering AI workflows at scale: A deep dive into Asana AI Studio and Amazon Q index collaboration

Today, we’re excited to announce the integration of Asana AI Studio with Amazon Q index, bringing generative AI directly into your daily workflows. In this post, we explore how Asana AI Studio and Amazon Q index transform enterprise efficiency through intelligent workflow automation and enhanced data accessibility.

Insulin resistance prediction from wearables and routine blood biomarkers

Responsible AI for the payments industry – Part 1

This post explores the unique challenges facing the payments industry in scaling AI adoption, the regulatory considerations that shape implementation decisions, and practical approaches to applying responsible AI principles. In Part 2, we provide practical implementation strategies to operationalize responsible AI within your payment systems.

Responsible AI for the payments industry – Part 2

In Part 1 of our series, we explored the foundational concepts of responsible AI in the payments industry. In this post, we discuss the practical implementation of responsible AI frameworks.

Process multi-page documents with human review using Amazon Bedrock Data Automation and Amazon SageMaker AI

In this post, we show how to process multi-page documents with a human review loop using Amazon Bedrock Data Automation and Amazon SageMaker AI.

PyTorch 2.8 Release Blog

Introducing Open SWE: An Open-Source Asynchronous Coding Agent

The use of AI in software engineering has evolved over the past two years. It started as autocomplete, then went to a copilot in an IDE, and in the fast few months has evolved to be a long running, more end-to-end agent that run asynchronously in the cloud. We believe

Highly accurate genome polishing with DeepPolisher: Enhancing the foundation of genomic research

New Gemini app tools to help students learn, understand and study even better

Try these new tools to learn, study and understand complex topics even better.

Introducing Vercel MCP: Connect Vercel to your AI tools

Vercel now has an official hosted MCP server (aka Vercel MCP), which you can use to connect your favorite AI tools, such as Claude or VS Code, directly to Vercel.

Introducing AI Elements: build AI interfaces faster

Focus on your AI’s intelligence, not the UI scaffolding. AI Elements is now available as a new Vercel product to help frontend engineers build AI-driven interfaces in a fraction of the time.

Build an AI assistant using Amazon Q Business with Amazon S3 clickable URLs

In this post, we demonstrate how to build an AI assistant using Amazon Q Business that responds to user requests based on your enterprise documents stored in an S3 bucket, and how the users can use the reference URLs in the AI assistant responses to view or download the referred documents, and verify the AI responses to practice responsible AI.

Meet your new AI coding teammate: Gemini CLI GitHub Actions

Today, we’re introducing Gemini CLI GitHub Actions. It’s a no-cost, powerful AI coding teammate for your repository. It acts both as an autonomous agent for critical rou…

Introducing Scorers in Mastra

We're excited to announce the release of scorers in Mastra, a new way to evaluate and rank the quality of your agent's responses.

Providing ChatGPT to the Entire U.S. Federal Workforce

GPT OSS models from OpenAI are now available on SageMaker JumpStart

Today, we are excited to announce the availability of Open AI’s new open weight GPT OSS models, gpt-oss-120b and gpt-oss-20b, from OpenAI in Amazon SageMaker JumpStart. With this launch, you can now deploy OpenAI’s newest reasoning models to build, experiment, and responsibly scale your generative AI ideas on AWS. In this post, we demonstrate how to get started with these models on SageMaker JumpStart.

Discover insights from Microsoft Exchange with the Microsoft Exchange connector for Amazon Q Business

Amazon Q Business is a fully managed, generative AI-powered assistant that helps enterprises unlock the value of their data and knowledge. With Amazon Q Business, you can quickly find answers to questions, generate summaries and content, and complete tasks by using the information and expertise stored across your company’s various data sources and enterprise systems. […]

Newsroom

Anthropic is an AI safety and research company that's working to build reliable, interpretable, and steerable AI systems.

Genie 3: A new frontier for world models

Today we are announcing Genie 3, a general purpose world model that can generate an unprecedented diversity of interactive environments. Given a text prompt, Genie 3 can generate dynamic worlds...

gpt-oss-20b and gpt-oss-120b are now supported in Vercel AI Gateway

You can now access gpt-oss by OpenAI, an open-weight reasoning model designed to push the open model frontier, using Vercel's AI Gateway with no other provider accounts required.

Claude 4.1 Opus is now supported in Vercel AI Gateway

You can now access Claude Opus 4.1, a new model released by Anthropic today, using Vercel's AI Gateway with no other provider accounts required.

Welcome GPT OSS, the new open-source model family from OpenAI!

We’re on a journey to advance and democratize artificial intelligence through open source and open science.

Introducing gpt-oss

gpt-oss-120b and gpt-oss-20b push the frontier of open-weight reasoning models

gpt-oss-120b & gpt-oss-20b Model Card

We introduce gpt-oss-120b and gpt-oss-20b, two open-weight reasoning models available under the Apache 2.0 license and our gpt-oss usage policy.

Open Weights and AI for All

AI’s next frontier isn’t just about capability—it’s about who gets to use it. Our mission to put AI in the hands of as many people as possible is what drives us. Today’s release of our most capable open-weights models is a major step forward that makes advanced AI more open, flexible, and accessible worldwide.

Estimating worst case frontier risks of open weight LLMs

In this paper, we study the worst-case frontier risks of releasing gpt-oss. We introduce malicious fine-tuning (MFT), where we attempt to elicit maximum capabilities by fine-tuning gpt-oss to be as capable as possible in two domains: biology and cybersecurity.

How we’re using AI to help track and predict cyclones

We’re partnering with the National Hurricane Center, supporting their forecasts and warnings this cyclone season.

AI judging AI: Scaling unstructured text analysis with Amazon Nova

In this post, we highlight how you can deploy multiple generative AI models in Amazon Bedrock to instruct an LLM model to create thematic summaries of text responses. We then show how to use multiple LLM models as a jury to review these LLM-generated summaries and assign a rating to judge the content alignment between the summary title and summary description.

Building an AI-driven course content generation system using Amazon Bedrock

In this post, we explore each component in detail, along with the technical implementation of the two core modules: course outline generation and course content generation.

How Handmade.com modernizes product image and description handling with Amazon Bedrock and Amazon OpenSearch Service

In this post, we explore how Handmade.com, a leading hand-crafts marketplace, modernized their product description handling by implementing an AI-driven pipeline using Amazon Bedrock and Amazon OpenSearch Service. The solution combines Anthropic's Claude 3.7 Sonnet LLM for generating descriptions, Amazon Titan Text Embeddings V2 for vector embedding, and semantic search capabilities to automate and enhance product descriptions across their catalog of over 60,000 items.

Cost tracking multi-tenant model inference on Amazon Bedrock

In this post, we demonstrate how to track and analyze multi-tenant model inference costs on Amazon Bedrock using the Converse API's requestMetadata parameter. The solution includes an ETL pipeline using AWS Glue and Amazon QuickSight dashboards to visualize usage patterns, token consumption, and cost allocation across different tenants and departments.

Rethinking how we measure AI intelligence

Kaggle Game Arena is a new platform where AI models compete head-to-head in complex strategic games.

Rethinking how we measure AI intelligence

Kaggle Game Arena is a new platform where AI models compete head-to-head in complex strategic games.

v0: vibe coding, securely

Vibe coding makes it possible for anyone to ship a viral app. But every line of AI-generated code is a potential vulnerability. Security cannot be an afterthought, it must be the foundation. Turn ideas into secure apps with v0.

What we’re optimizing ChatGPT for

Introducing Amazon Bedrock AgentCore Browser Tool

In this post, we introduce the newly announced Amazon Bedrock AgentCore Browser Tool. We explore why organizations need cloud-based browser automation and the limitations it addresses for FMs that require real-time data access. We talk about key use cases and the core capabilities of the AgentCore Browser Tool. We walk through how to get started with the tool.

Introducing the Amazon Bedrock AgentCore Code Interpreter

In this post, we introduce the Amazon Bedrock AgentCore Code Interpreter, a fully managed service that enables AI agents to securely execute code in isolated sandbox environments. We discuss how the AgentCore Code Interpreter helps solve challenges around security, scalability, and infrastructure management when deploying AI agents that need computational capabilities.

Observing and evaluating AI agentic workflows with Strands Agents SDK and Arize AX

In this post, we present how the Arize AX service can trace and evaluate AI agent tasks initiated through Strands Agents, helping validate the correctness and trustworthiness of agentic workflows.

Building AIOps with Amazon Q Developer CLI and MCP Server

In this post, we discuss how to implement a low-code no-code AIOps solution that helps organizations monitor, identify, and troubleshoot operational events while maintaining their security posture. We show how these technologies work together to automate repetitive tasks, streamline incident response, and enhance operational efficiency across your organization.

Containerize legacy Spring Boot application using Amazon Q Developer CLI and MCP server

In this post, you’ll learn how you can use Amazon Q Developer command line interface (CLI) with Model Context Protocol (MCP) servers integration to modernize a legacy Java Spring Boot application running on premises and then migrate it to Amazon Web Services (AWS) by deploying it on Amazon Elastic Kubernetes Service (Amazon EKS).

Try Deep Think in the Gemini app

Deep Think utilizes extended, parallel thinking and novel reinforcement learning techniques for significantly improved problem-solving.

MLE-STAR: A state-of-the-art machine learning engineering agents

Figma uses AI to transform digital design

A conversation with David Kossnick, Head of AI Products at Figma.

Introducing AWS Batch Support for Amazon SageMaker Training jobs

AWS Batch now seamlessly integrates with Amazon SageMaker Training jobs. In this post, we discuss the benefits of managing and prioritizing ML training jobs to use hardware efficiently for your business. We also walk you through how to get started using this new capability and share suggested best practices, including the use of SageMaker training plans.

Structured outputs with Amazon Nova: A guide for builders

We launched constrained decoding to provide reliability when using tools for structured outputs. Now, tools can be used with Amazon Nova foundation models (FMs) to extract data based on complex schemas, reducing tool use errors by over 95%. In this post, we explore how you can use Amazon Nova FMs for structured output use cases.

AI agents unifying structured and unstructured data: Transforming support analytics and beyond with Amazon Q Plugins

Learn how to enhance Amazon Q with custom plugins to combine semantic search capabilities with precise analytics for AWS Support data. This solution enables more accurate answers to analytical questions by integrating structured data querying with RAG architecture, allowing teams to transform raw support cases and health events into actionable insights. Discover how this enhanced architecture delivers exact numerical analysis while maintaining natural language interactions for improved operational decision-making.

Amazon Strands Agents SDK: A technical deep dive into agent architectures and observability

In this post, we first introduce the Strands Agents SDK and its core features. Then we explore how it integrates with AWS environments for secure, scalable deployments, and how it provides rich observability for production use. Finally, we discuss practical use cases, and present a step-by-step example to illustrate Strands in action.

Build dynamic web research agents with the Strands Agents SDK and Tavily

In this post, we introduce how to combine Strands Agents with Tavily’s purpose-built web intelligence API, to create powerful research agents that excel at complex information gathering tasks while maintaining the security and compliance standards required for enterprise deployment.

AI SDK 5

Introducing type-safe chat, agentic loop control, new specification, tool enhancements, speech generation, and more.

Build an AI Shopping Assistant with Gradio MCP Servers

We’re on a journey to advance and democratize artificial intelligence through open source and open science.

Introducing Stargate Norway

We’re launching Stargate Norway—OpenAI’s first AI data center initiative in Europe under our OpenAI for Countries program. Stargate is OpenAI’s overarching infrastructure platform and is a critical part of our long-term vision to deliver the benefits of AI to everyone.

Automate the creation of handout notes using Amazon Bedrock Data Automation

In this post, we show how you can build an automated, serverless solution to transform webinar recordings into comprehensive handouts using Amazon Bedrock Data Automation for video analysis. We walk you through the implementation of Amazon Bedrock Data Automation to transcribe and detect slide changes, as well as the use of Amazon Bedrock foundation models (FMs) for transcription refinement, combined with custom AWS Lambda functions orchestrated by AWS Step Functions.

Streamline GitHub workflows with generative AI using Amazon Bedrock and MCP

This blog post explores how to create powerful agentic applications using the Amazon Bedrock FMs, LangGraph, and the Model Context Protocol (MCP), with a practical scenario of handling a GitHub workflow of issue analysis, code fixes, and pull request generation.

Deep Agents

Using an LLM to call tools in a loop is the simplest form of an agent. This architecture, however, can yield agents that are “shallow” and fail to plan and act over longer, more complex tasks. Applications like “Deep Research”, “Manus”, and “Claude Code” have gotten around this limitation by

AlphaEarth Foundations helps map our planet in unprecedented detail

New AI model integrates petabytes of Earth observation data to generate a unified data representation that revolutionizes global mapping and monitoring

Google Earth AI: Our state-of-the-art geospatial AI models

Google Earth AI is our collection of geospatial models and datasets to help tackle the planet's most critical needs.

Three lessons for creating a sustainable AI advantage

By experimenting early, measuring rigorously, and building an architecture that evolves with each model, Intercom created a scalable AI platform that ships new capabilities in days, not quarters.

Mistral-Small-3.2-24B-Instruct-2506 is now available on Amazon Bedrock Marketplace and Amazon SageMaker JumpStart

Today, we’re excited to announce that Mistral-Small-3.2-24B-Instruct-2506—a 24-billion-parameter large language model (LLM) from Mistral AI that’s optimized for enhanced instruction following and reduced repetition errors—is available for customers through Amazon SageMaker JumpStart and Amazon Bedrock Marketplace. Amazon Bedrock Marketplace is a capability in Amazon Bedrock that developers can use to discover, test, and use over […]

Simulating large systems with Regression Language Models

Introducing Align Evals: Streamlining LLM Application Evaluation

Align Evals is a new feature in LangSmith that helps you calibrate your evaluators to better match human preferences.

Generate suspicious transaction report drafts for financial compliance using generative AI

A suspicious transaction report (STR) or suspicious activity report (SAR) is a type of report that a financial organization must submit to a financial regulator if they have reasonable grounds to suspect any financial transaction that has occurred or was attempted during their activities. In this post, we explore a solution that uses FMs available in Amazon Bedrock to create a draft STR.

Fine-tune and deploy Meta Llama 3.2 Vision for generative AI-powered web automation using AWS DLCs, Amazon EKS, and Amazon Bedrock

In this post, we present a complete solution for fine-tuning and deploying the Llama-3.2-11B-Vision-Instruct model for web automation tasks. We demonstrate how to build a secure, scalable, and efficient infrastructure using AWS Deep Learning Containers (DLCs) on Amazon Elastic Kubernetes Service (Amazon EKS).

The inside story of building NotebookLM

Hear how Googlers developed and tested NotebookLM, your virtual research assistant — straight from the source.

New ways to learn and explore with AI Mode in Search

AI Mode in Search has several new features to help learners, educators and anyone who’s curious about the world.

Discover the potential of agentic AI in higher education

Discover how Azure AI Foundry in education helps institutions build scalable AI solutions to drive innovation and digital transformation.

Scaling generative AI in the cloud: Enterprise use cases for driving secure innovation

In our technical guide, “Accelerating Generative AI Innovation with Cloud Migration” we outline how IT and digital transformation leaders can tap into the power and flexibility of Azure to unlock the full potential of generative AI. Learn more.

How Nippon India Mutual Fund improved the accuracy of AI assistant responses using advanced RAG methods on Amazon Bedrock

In this post, we examine a solution adopted by Nippon Life India Asset Management Limited that improves the accuracy of the response over a regular (naive) RAG approach by rewriting the user queries and aggregating and reranking the responses. The proposed solution uses enhanced RAG methods such as reranking to improve the overall accuracy

Z.ai's GLM-4.5 and GLM-4.5 Air are now supported in Vercel AI Gateway

You can now access GLM-4.5 and GLM-4.5 Air, new flagship models from Z.ai designed to unify frontier reasoning, coding, and agentic capabilities, using Vercel's AI Gateway with no other provider accounts required.

Introducing study mode in ChatGPT

A new way to learn in ChatGPT that offers step by step guidance instead of quick answers.

Introducing Trackio: A Lightweight Experiment Tracking Library from Hugging Face

We’re on a journey to advance and democratize artificial intelligence through open source and open science.

PyTorch on Kubernetes: Kubeflow Trainer Joins the PyTorch Ecosystem

Build a drug discovery research assistant using Strands Agents and Amazon Bedrock

In this post, we demonstrate how to create a powerful research assistant for drug discovery using Strands Agents and Amazon Bedrock. This AI assistant can search multiple scientific databases simultaneously using the Model Context Protocol (MCP), synthesize its findings, and generate comprehensive reports on drug targets, disease mechanisms, and therapeutic areas.

Amazon Nova Act SDK (preview): Path to production for browser automation agents

In this post, we’ll walk through what makes Nova Act SDK unique, how it works, and how teams across industries are already using it to automate browser-based workflows at scale.

Optimizing enterprise AI assistants: How Crypto.com uses LLM reasoning and feedback for enhanced efficiency

In this post, we explore how Crypto.com used user and system feedback to continuously improve and optimize our instruction prompts. This feedback-driven approach has enabled us to create more effective prompts that adapt to various subsystems while maintaining high performance across different use cases.

Build modern serverless solutions following best practices using Amazon Q Developer CLI and MCP

This post explores how the AWS Serverless MCP server accelerates development throughout the serverless lifecycle, from making architectural decisions with tools like get_iac_guidance and get_lambda_guidance, to streamlining development with get_serverless_templates, sam_init, to deployment with SAM integration, webapp_deployment_help, and configure_domain. We show how this conversational AI approach transforms the entire process, from architecture design through operations, dramatically accelerating AWS serverless projects while adhering to architectural principles.

SensorLM: Learning the language of wearable sensors

Driving the grid of the future: How Microsoft and our partners are reenvisioning energy with AI

Learn how we're embracing digital tools, AI-powered forecasting, and collaborative workflows to help build the grid of the future.

Why agent infrastructure matters

Learn why agent infrastructure is essential to handling stateful, long-running tasks — and how LangGraph Platform provides the runtime support needed to build and scale reliable agents.

Build an intelligent eDiscovery solution using Amazon Bedrock Agents

In this post, we demonstrate how to build an intelligent eDiscovery solution using Amazon Bedrock Agents for real-time document analysis. We show how to deploy specialized agents for document classification, contract analysis, email review, and legal document processing, all working together through a multi-agent architecture. We walk through the implementation details, deployment steps, and best practices to create an extensible foundation that organizations can adapt to their specific eDiscovery requirements.

How PerformLine uses prompt engineering on Amazon Bedrock to detect compliance violations

PerformLine operates within the marketing compliance industry, a specialized subset of the broader compliance software market, which includes various compliance solutions like anti-money laundering (AML), know your customer (KYC), and others. In this post, PerformLine and AWS explore how PerformLine used Amazon Bedrock to accelerate compliance processes, generate actionable insights, and provide contextual data—delivering the speed and accuracy essential for large-scale oversight.

Qwen3-Coder is now supported in Vercel AI Gateway

You can now access Kimi K2 from Moonshot AI using Vercel's AI Gateway, with no Moonshot AI account required.

Model Context Protocol (MCP) explained: An FAQ

Model Context Protocol (MCP) is a new spec that helps standardize the way large language models (LLMs) access data and systems, extending what they can do beyond their training data.

Say hello to `hf`: a faster, friendlier Hugging Face CLI ✨

We’re on a journey to advance and democratize artificial intelligence through open source and open science.

Introducing the Mastra Streaming Protocol

We're introducing a new streaming protocol that provides real-time visibility into agent and workflow execution, with comprehensive cost tracking and unified messaging interfaces.

How Index Built an AI-First Data Analytics Platform with Mastra

Index is building a data analyst agent that lets users query their data in natural language.

Boost cold-start recommendations with vLLM on AWS Trainium

In this post, we demonstrate how to use vLLM for scalable inference and use AWS Deep Learning Containers (DLC) to streamline model packaging and deployment. We’ll generate interest expansions through structured prompts, encode them into embeddings, retrieve candidates with FAISS, apply validation to keep results grounded, and frame the cold-start challenge as a scientific experiment—benchmarking LLM and encoder pairings, iterating rapidly on recommendation metrics, and showing clear ROI for each configuration

Benchmarking Amazon Nova: A comprehensive analysis through MT-Bench and Arena-Hard-Auto

The repositories for MT-Bench and Arena-Hard were originally developed using OpenAI’s GPT API, primarily employing GPT-4 as the judge. Our team has expanded its functionality by integrating it with the Amazon Bedrock API to enable using Anthropic’s Claude Sonnet on Amazon as judge. In this post, we use both MT-Bench and Arena-Hard to benchmark Amazon Nova models by comparing them to other leading LLMs available through Amazon Bedrock.

Web Guide: An experimental AI-organized search results page

We’re launching Web Guide, a Search Labs experiment that uses AI to intelligently organize the search results page, making it easier to find information and web pages.

Beyond the Hype: 10 Best AI Agents That Truly Work

This guide explores five powerful types of AI agents gaining traction in 2025—and how platforms like n8n make it easier than ever to build or integrate them. Ready to find out which AI agents are worth your attention? Let’s dive in.

Synthetic and federated: Privacy-preserving domain adaptation with LLMs for mobile applications

PyTorch Conference 2025 Schedule Announcement

Resolving digital threats 100x faster with OpenAI

Customize Amazon Nova in Amazon SageMaker AI using Direct Preference Optimization

At the AWS Summit in New York City, we introduced a comprehensive suite of model customization capabilities for Amazon Nova foundation models. Available as ready-to-use recipes on Amazon SageMaker AI, you can use them to adapt Nova Micro, Nova Lite, and Nova Pro across the model training lifecycle, including pre-training, supervised fine-tuning, and alignment. In this post, we present a streamlined approach to customize Nova Micro in SageMaker training jobs.

Listen to a conversation about the newest AI capabilities in Search.

What does it take to enable billions of people to truly ask anything in Search?In the latest episode of the Google AI: Release Notes podcast, host Logan Kilpatrick sits …

Multi-tenant RAG implementation with Amazon Bedrock and Amazon OpenSearch Service for SaaS using JWT

In this post, we introduce a solution that uses OpenSearch Service as a vector data store in multi-tenant RAG, achieving data isolation and routing using JWT and FGAC. This solution uses a combination of JWT and FGAC to implement strict tenant data access isolation and routing, necessitating the use of OpenSearch Service.

Enhance generative AI solutions using Amazon Q index with Model Context Protocol – Part 1

In this post, we explore best practices and integration patterns for combining Amazon Q index and MCP, enabling enterprises to build secure, scalable, and actionable AI search-and-retrieval architectures.

Aeneas transforms how historians connect the past

Writing was everywhere in the Roman world — etched onto everything from imperial monuments to everyday objects. From political graffiti, love poems and epitaphs to business transactions, birthday...

Build your own AI app builder with the v0 Platform API

Learn how to build, extend, and automate AI-generated apps like BI tools and website builders with v0 Platform API

Startups can apply now for the Google for Startups Gemini Founders Forum.

Applications are now open for the first-ever Google for Startups Gemini Founders Forum, a two-day, in-person summit hosted by Google DeepMind and Google Cloud.Held Novem…

TimeScope: How Long Can Your Video Large Multimodal Model Go?

We’re on a journey to advance and democratize artificial intelligence through open source and open science.

Fast LoRA inference for Flux with Diffusers and PEFT

We’re on a journey to advance and democratize artificial intelligence through open source and open science.

Introducing Mastra Templates

Announcing Mastra Templates: pre-built, production-ready, customizable agent and workflow projects. Plus: join our MASTRA.BUILD hackathon to create and showcase your own templates.

Announcing OpenAI DevDay 2025

We’re hosting our third annual OpenAI DevDay on October 6, 2025 at Fort Mason in San Francisco.

Model ML is helping financial firms rebuild with AI from the ground up

A conversation with Chaz Englander, CEO & Co-founder of Model ML.

Beyond accelerators: Lessons from building foundation models on AWS with Japan’s GENIAC program

In 2024, the Ministry of Economy, Trade and Industry (METI) launched the Generative AI Accelerator Challenge (GENIAC)—a Japanese national program to boost generative AI by providing companies with funding, mentorship, and massive compute resources for foundation model (FM) development. AWS was selected as the cloud provider for GENIAC’s second cycle (cycle 2). It provided infrastructure and technical guidance for 12 participating organizations.

LSM-2: Learning from incomplete wearable sensor data

Gemini 2.5 Flash-Lite is now ready for scaled production use

Explore Gemini 2.5 Flash-Lite, Google's stable and generally available model offering incredible speed, cost-efficiency, high quality, and 2.5 family features.

Streamline deep learning environments with Amazon Q Developer and MCP

In this post, we explore how to use Amazon Q Developer and Model Context Protocol (MCP) servers to streamline DLC workflows to automate creation, execution, and customization of DLC containers.

Visualize with Copilot now allows you to save your chart

Now you can save AI-generated visualizations, and all your charts get a modern new look. See how this update makes data insights easier to keep, share, and act on.

Pioneering an AI clinical copilot with Penda Health

Devin's MCP Marketplace

You can now connect your favorite MCP servers to Devin.

Stargate advances with 4.5 GW partnership with Oracle

New data center capacity will power jobs, growth, and AI that benefits more people.

OpenAI’s new economic analysis

Build an AI-powered automated summarization system with Amazon Bedrock and Amazon Transcribe using Terraform

This post introduces a serverless meeting summarization system that harnesses the advanced capabilities of Amazon Bedrock and Amazon Transcribe to transform audio recordings into concise, structured, and actionable summaries. By automating this process, organizations can reclaim countless hours while making sure key insights, action items, and decisions are systematically captured and made accessible to stakeholders.

Kyruus builds a generative AI provider matching solution on AWS

In this post, we demonstrate how Kyruus Health uses AWS services to build Guide. We show how Amazon Bedrock, a fully managed service that provides access to foundation models (FMs) from leading AI companies and Amazon through a single API, and Amazon OpenSearch Service, a managed search and analytics service, work together to understand everyday language about health concerns and connect members with the right providers.

Use generative AI in Amazon Bedrock for enhanced recommendation generation in equipment maintenance

In the manufacturing world, valuable insights from service reports often remain underutilized in document storage systems. This post explores how Amazon Web Services (AWS) customers can build a solution that automates the digitisation and extraction of crucial information from many reports using generative AI.

Advanced version of Gemini with Deep Think officially achieves gold-medal standard at the International Mathematical Olympiad

Our advanced model officially achieved a gold-medal level performance on problems from the International Mathematical Olympiad (IMO), the world’s most prestigious competition for young...

Exploring the context of online images with Backstory

New experimental AI tool helps people explore the context and origin of images seen online.

OpenAI-compatible API endpoints now supported in AI Gateway

OpenAI-compatible API endpoints now supported in AI Gateway giving you access to 100s of models with no code rewrites required

OpenAI and UK Government announce strategic partnership to deliver AI-driven growth

OpenAI partners with the UK Government to boost AI adoption, drive economic growth, and enhance public services for a thriving AI ecosystem in the UK.

AI as the greatest source of empowerment for all

I’ve always considered myself a pragmatic technologist—someone who loves technology not for its own sake, but for the direct impact it can have on people’s lives. That’s what makes this job so exciting, since I believe AI will unlock more opportunities for more people than any other technology in history. If we get this right, AI can give everyone more power than ever.

Build real-time travel recommendations using AI agents on Amazon Bedrock

In this post, we show how to build a generative AI solution using Amazon Bedrock that creates bespoke holiday packages by combining customer profiles and preferences with real-time pricing data. We demonstrate how to use Amazon Bedrock Knowledge Bases for travel information, Amazon Bedrock Agents for real-time flight details, and Amazon OpenSearch Serverless for efficient package search and retrieval.

Deploy a full stack voice AI agent with Amazon Nova Sonic

In this post, we show how to create an AI-powered call center agent for a fictional company called AnyTelco. The agent, named Telly, can handle customer inquiries about plans and services while accessing real-time customer data using custom tools implemented with the Model Context Protocol (MCP) framework.

Manage multi-tenant Amazon Bedrock costs using application inference profiles

This post explores how to implement a robust monitoring solution for multi-tenant AI deployments using a feature of Amazon Bedrock called application inference profiles. We demonstrate how to create a system that enables granular usage tracking, accurate cost allocation, and dynamic resource management across complex multi-tenant environments.

Open Vercel documentation pages in AI providers

Copy Vercel documentation pages as markdown, or open them in AI providers, such as v0, Claude, or ChatGPT.

Arc Virtual Cell Challenge: A Primer

We’re on a journey to advance and democratize artificial intelligence through open source and open science.

A $50 million fund to build with communities

OpenAI is launching an initial $50 million fund that supports nonprofit and community organizations, informed by the independent OpenAI Nonprofit Commission report

Evaluating generative AI models with Amazon Nova LLM-as-a-Judge on Amazon SageMaker AI

Evaluating the performance of large language models (LLMs) goes beyond statistical metrics like perplexity or bilingual evaluation understudy (BLEU) scores. For most real-world generative AI scenarios, it’s crucial to understand whether a model is producing better outputs than a baseline or an earlier iteration. This is especially important for applications such as summarization, content generation, […]

Building cost-effective RAG applications with Amazon Bedrock Knowledge Bases and Amazon S3 Vectors

In this post, we demonstrate how to integrate Amazon S3 Vectors with Amazon Bedrock Knowledge Bases for RAG applications. You'll learn a practical approach to scale your knowledge bases to handle millions of documents while maintaining retrieval quality and using S3 Vectors cost-effective storage.

Invideo AI uses OpenAI models to create videos 10x faster

Built on GPT-4.1, image generation in the API, and text-to-speech models, invideo AI turns OpenAI models into a full video production team.

Implementing on-demand deployment with customized Amazon Nova models on Amazon Bedrock

In this post, we walk through the custom model on-demand deployment workflow for Amazon Bedrock and provide step-by-step implementation guides using both the AWS Management Console and APIs or AWS SDKs. We also discuss best practices and considerations for deploying customized Amazon Nova models on Amazon Bedrock.

Measuring heart rate with consumer ultra-wideband radar

Microsoft Azure AI Foundry Models and Microsoft Security Copilot achieve ISO/IEC 42001:2023 certification

Microsoft has achieved ISO/IEC 42001:2023 certification—a globally recognized standard for Artificial Intelligence Management Systems for both Azure AI Foundry Models and Microsoft Security Copilot. Learn more.

Grep a million GitHub repositories via MCP

Search 1M+ GitHub repositories from your AI agent using Grep's MCP server. Your agent can now reference coding patterns and solutions used in open source projects to solve problems.

Building enterprise-scale RAG applications with Amazon S3 Vectors and DeepSeek R1 on Amazon SageMaker AI

Organizations are adopting large language models (LLMs), such as DeepSeek R1, to transform business processes, enhance customer experiences, and drive innovation at unprecedented speed. However, standalone LLMs have key limitations such as hallucinations, outdated knowledge, and no access to proprietary data. Retrieval Augmented Generation (RAG) addresses these gaps by combining semantic search with generative AI, […]

Introducing ChatGPT agent

ChatGPT now thinks and acts, proactively choosing from a toolbox of agentic skills to complete tasks for you using its own computer.

ChatGPT agent System Card

ChatGPT agent System Card: OpenAI’s agentic model unites research, browser automation, and code tools with safeguards under the Preparedness Framework.

Five Big Improvements to Gradio MCP Servers

We’re on a journey to advance and democratize artificial intelligence through open source and open science.

Back to The Future: Evaluating AI Agents on Predicting Future Events

We’re on a journey to advance and democratize artificial intelligence through open source and open science.

How Kestral Uses Mastra to Turn Company Knowledge Into Action

Kestrel leverages Mastra's multi-agent workflows to transform company knowledge into actionable tasks and projects.

Mastra Changelog 2025-07-17

New memory improvements, new CLI templates, reasoning display in playground, and major improvements across the board.

OpenAI nonprofit jam

Bringing together 1,000 nonprofit leaders across the US to build with AI

Statement from the OpenAI Board of Directors on the Nonprofit Commission Report

Agent bio bug bounty call

Testing universal jailbreaks for biorisks in ChatGPT Agent

Command GitHub's Coding Agent from VS Code

VS Code's integration with GitHub Copilot Coding Agent allows you to delegate tasks to the agent and let it handle them in the background.

Accenture scales video analysis with Amazon Nova and Amazon Bedrock Agents

This post was written with Ilan Geller, Kamal Mannar, Debasmita Ghosh, and Nakul Aggarwal of Accenture. Video highlights offer a powerful way to boost audience engagement and extend content value for content publishers. These short, high-impact clips capture key moments that drive viewer retention, amplify reach across social media, reinforce brand identity, and open new […]

Deploy conversational agents with Vonage and Amazon Nova Sonic

In this post, we explore how developers can integrate Amazon Nova Sonic with the Vonage communications service to build responsive, natural-sounding voice experiences in real time. By combining the Vonage Voice API with the low-latency and expressive speech capabilities of Amazon Nova Sonic, businesses can deploy AI voice agents that deliver more human-like interactions than traditional voice interfaces. These agents can be used as customer support, virtual assistants, and more.

LangSmith and LangGraph Platform are now available in AWS Marketplace

LangSmith and LangGraph Platform (self-hosted deployments) are now available in AWS Marketplace.

More advanced AI capabilities are coming to Search

For Google AI Pro and AI Ultra subscribers, AI Mode in Search now features the ability to use Gemini 2.5 Pro and do deeper research for you.

Open Deep Research

TL;DR Deep research has broken out as one of the most popular agent applications. OpenAI, Anthropic, Perplexity, and Google all have deep research products that produce comprehensive reports using various sources of context. There are also many open source implementations. We've built an open deep researcher that is simple

Enabling customers to deliver production-ready AI agents at scale

Today, I’m excited to share how we’re bringing this vision to life with new capabilities that address the fundamental aspects of building and deploying agents at scale. These innovations will help you move beyond experiments to production-ready agent systems that can be trusted with your most critical business processes.

Google France hosted a hackathon to tackle healthcare's biggest challenges

Doctors, developers and researchers gathered in Paris to prototype new medical solutions using Google’s AI models.

Seq vs Seq: the Ettin Suite of Paired Encoders and Decoders

We’re on a journey to advance and democratize artificial intelligence through open source and open science.

Amazon Bedrock Knowledge Bases now supports Amazon OpenSearch Service Managed Cluster as vector store

Amazon Bedrock Knowledge Bases has extended its vector store options by enabling support for Amazon OpenSearch Service managed clusters, further strengthening its capabilities as a fully managed Retrieval Augmented Generation (RAG) solution. This enhancement builds on the core functionality of Amazon Bedrock Knowledge Bases , which is designed to seamlessly connect foundation models (FMs) with internal data sources. This post provides a comprehensive, step-by-step guide on integrating an Amazon Bedrock knowledge base with an OpenSearch Service managed cluster as its vector store.

Monitor agents built on Amazon Bedrock with Datadog LLM Observability

We’re excited to announce a new integration between Datadog LLM Observability and Amazon Bedrock Agents that helps monitor agentic applications built on Amazon Bedrock. In this post, we'll explore how Datadog's LLM Observability provides the visibility and control needed to successfully monitor, operate, and debug production-grade agentic applications built on Amazon Bedrock Agents.

How PayU built a secure enterprise AI assistant using Amazon Bedrock

PayU offers a full-stack digital financial services system that serves the financial needs of merchants, banks, and consumers through technology. In this post, we explain how we equipped the PayU team with an enterprise AI solution and democratized AI access using Amazon Bedrock, without compromising on data residency requirements.

The next wave of AI for content creation includes digital twins

AI and digital twins transform CPG marketing with scalable, cost-effective, personalized content creation. Learn more.

Supercharge generative AI workflows with NVIDIA DGX Cloud on AWS and Amazon Bedrock Custom Model Import

This post is co-written with Andrew Liu, Chelsea Isaac, Zoey Zhang, and Charlie Huang from NVIDIA. DGX Cloud on Amazon Web Services (AWS) represents a significant leap forward in democratizing access to high-performance AI infrastructure. By combining NVIDIA GPU expertise with AWS scalable cloud services, organizations can accelerate their time-to-train, reduce operational complexity, and unlock […]

Accelerate generative AI inference with NVIDIA Dynamo and Amazon EKS

This post introduces NVIDIA Dynamo and explains how to set it up on Amazon EKS for automated scaling and streamlined Kubernetes operations. We provide a hands-on walkthrough, which uses the NVIDIA Dynamo blueprint on the AI on EKS GitHub repo by AWS Labs to provision the infrastructure, configure monitoring, and install the NVIDIA Dynamo operator.

Moonshot AI's Kimi K2 model is now supported in Vercel AI Gateway

You can now access Kimi K2 from Moonshot AI using Vercel's AI Gateway, with no Moonshot AI account required.

AWS doubles investment in AWS Generative AI Innovation Center, marking two years of customer success

In this post, AWS announces a $100 million additional investment in its AWS Generative AI Innovation Center, marking two years of successful customer collaborations across industries from financial services to healthcare. The investment comes as AI evolves toward more autonomous, agentic systems, with the center already helping thousands of customers drive millions in productivity gains and transform customer experiences.

A summer of security: empowering cyber defenders with AI

Here’s what we’re announcing at cybersecurity conferences like Black Hat USA and DEF CON 33.

Intellectual freedom by design

ChatGPT is designed to be useful, trustworthy, and adaptable so you can make it your own.

Build AI-driven policy creation for vehicle data collection and automation using Amazon Bedrock

Sonatus partnered with the AWS Generative AI Innovation Center to develop a natural language interface to generate data collection and automation policies using generative AI. This innovation aims to reduce the policy generation process from days to minutes while making it accessible to both engineers and non-experts alike. In this post, we explore how we built this system using Sonatus’s Collector AI and Amazon Bedrock. We discuss the background, challenges, and high-level solution architecture.

How Rapid7 automates vulnerability risk scores with ML pipelines using Amazon SageMaker AI

In this post, we share how Rapid7 implemented end-to-end automation for the training, validation, and deployment of ML models that predict CVSS vectors. Rapid7 customers have the information they need to accurately understand their risk and prioritize remediation measures.

Build secure RAG applications with AWS serverless data lakes

In this post, we explore how to build a secure RAG application using serverless data lake architecture, an important data strategy to support generative AI development. We use Amazon Web Services (AWS) services including Amazon S3, Amazon DynamoDB, AWS Lambda, and Amazon Bedrock Knowledge Bases to create a comprehensive solution supporting unstructured data assets which can be extended to structured data. The post covers how to implement fine-grained access controls for your enterprise data and design metadata-driven retrieval systems that respect security boundaries. These approaches will help you maximize the value of your organization's data while maintaining robust security and compliance.

Advanced fine-tuning methods on Amazon SageMaker AI

When fine-tuning ML models on AWS, you can choose the right tool for your specific needs. AWS provides a comprehensive suite of tools for data scientists, ML engineers, and business users to achieve their ML goals. AWS has built solutions to support various levels of ML sophistication, from simple SageMaker training jobs for FM fine-tuning to the power of SageMaker HyperPod for cutting-edge research. We invite you to explore these options, starting with what suits your current needs, and evolve your approach as those needs change.

Streamline machine learning workflows with SkyPilot on Amazon SageMaker HyperPod

This post is co-written with Zhanghao Wu, co-creator of SkyPilot. The rapid advancement of generative AI and foundation models (FMs) has significantly increased computational resource requirements for machine learning (ML) workloads. Modern ML pipelines require efficient systems for distributing workloads across accelerated compute resources, while making sure developer productivity remains high. Organizations need infrastructure solutions […]

Intelligent document processing at scale with generative AI and Amazon Bedrock Data Automation

This post presents an end-to-end IDP application powered by Amazon Bedrock Data Automation and other AWS services. It provides a reusable AWS infrastructure as code (IaC) that deploys an IDP pipeline and provides an intuitive UI for transforming documents into structured tables at scale. The application only requires the user to provide the input documents (such as contracts or emails) and a list of attributes to be extracted. It then performs IDP with generative AI.

Build a conversational data assistant, Part 2 – Embedding generative business intelligence with Amazon Q in QuickSight

In this post, we dive into how we integrated Amazon Q in QuickSight to transform natural language requests like “Show me how many items were returned in the US over the past 6 months” into meaningful data visualizations. We demonstrate how combining Amazon Bedrock Agents with Amazon Q in QuickSight creates a comprehensive data assistant that delivers both SQL code and visual insights through a single, intuitive conversational interface—democratizing data access across the enterprise.

Build a conversational data assistant, Part 1: Text-to-SQL with Amazon Bedrock Agents

In this post, we focus on building a Text-to-SQL solution with Amazon Bedrock, a managed service for building generative AI applications. Specifically, we demonstrate the capabilities of Amazon Bedrock Agents. Part 2 explains how we extended the solution to provide business insights using Amazon Q in QuickSight, a business intelligence assistant that answers questions with auto-generated visualizations.

Implement user-level access control for multi-tenant ML platforms on Amazon SageMaker AI

In this post, we discuss permission management strategies, focusing on attribute-based access control (ABAC) patterns that enable granular user access control while minimizing the proliferation of AWS Identity and Access Management (IAM) roles. We also share proven best practices that help organizations maintain security and compliance without sacrificing operational efficiency in their ML workflows.

Long-running execution flows now supported in Amazon Bedrock Flows in public preview

We announce the public preview of long-running execution (asynchronous) flow support within Amazon Bedrock Flows. With Amazon Bedrock Flows, you can link foundation models (FMs), Amazon Bedrock Prompt Management, Amazon Bedrock Agents, Amazon Bedrock Knowledge Bases, Amazon Bedrock Guardrails, and other AWS services together to build and scale predefined generative AI workflows.

Fraud detection empowered by federated learning with the Flower framework on Amazon SageMaker AI

In this post, we explore how SageMaker and federated learning help financial institutions build scalable, privacy-first fraud detection systems.

Building intelligent AI voice agents with Pipecat and Amazon Bedrock – Part 2

In Part 1 of this series, you learned how you can use the combination of Amazon Bedrock and Pipecat, an open source framework for voice and multimodal conversational AI agents to build applications with human-like conversational AI. You learned about common use cases of voice agents and the cascaded models approach, where you orchestrate several components to build your voice AI agent. In this post (Part 2), you explore how to use speech-to-speech foundation model, Amazon Nova Sonic, and the benefits of using a unified model.

Uphold ethical standards in fashion using multimodal toxicity detection with Amazon Bedrock Guardrails

In the fashion industry, teams are frequently innovating quickly, often utilizing AI. Sharing content, whether it be through videos, designs, or otherwise, can lead to content moderation challenges. There remains a risk (through intentional or unintentional actions) of inappropriate, offensive, or toxic content being produced and shared. In this post, we cover the use of the multimodal toxicity detection feature of Amazon Bedrock Guardrails to guard against toxic content. Whether you’re an enterprise giant in the fashion industry or an up-and-coming brand, you can use this solution to screen potentially harmful content before it impacts your brand’s reputation and ethical standards. For the purposes of this post, ethical standards refer to toxic, disrespectful, or harmful content and images that could be created by fashion designers.

The EU Code of Practice and future of AI in Europe

OpenAI joins the EU Code of Practice, advancing responsible AI while partnering with European governments to drive innovation, infrastructure, and economic growth.

New capabilities in Amazon SageMaker AI continue to transform how organizations develop AI models

In this post, we share some of the new innovations in SageMaker AI that can accelerate how you build and train AI models. These innovations include new observability capabilities in SageMaker HyperPod, the ability to deploy JumpStart models on HyperPod, remote connections to SageMaker AI from local development environments, and fully managed MLflow 3.0.

Accelerate foundation model development with one-click observability in Amazon SageMaker HyperPod

With a one-click installation of the Amazon Elastic Kubernetes Service (Amazon EKS) add-on for SageMaker HyperPod observability, you can consolidate health and performance data from NVIDIA DCGM, instance-level Kubernetes node exporters, Elastic Fabric Adapter (EFA), integrated file systems, Kubernetes APIs, Kueue, and SageMaker HyperPod task operators. In this post, we walk you through installing and using the unified dashboards of the out-of-the-box observability feature in SageMaker HyperPod. We cover the one-click installation from the Amazon SageMaker AI console, navigating the dashboard and metrics it consolidates, and advanced topics such as setting up custom alerts.

Accelerating generative AI development with fully managed MLflow 3.0 on Amazon SageMaker AI

In this post, we explore how Amazon SageMaker now offers fully managed support for MLflow 3.0, streamlining AI experimentation and accelerating your generative AI journey from idea to production. This release transforms managed MLflow from experiment tracking to providing end-to-end observability, reducing time-to-market for generative AI development.

Amazon SageMaker HyperPod launches model deployments to accelerate the generative AI model development lifecycle

In this post, we announce Amazon SageMaker HyperPod support for deploying foundation models from SageMaker JumpStart, as well as custom or fine-tuned models from Amazon S3 or Amazon FSx. This new capability allows customers to train, fine-tune, and deploy models on the same HyperPod compute resources, maximizing resource utilization across the entire model lifecycle.

Supercharge your AI workflows by connecting to SageMaker Studio from Visual Studio Code

AI developers and machine learning (ML) engineers can now use the capabilities of Amazon SageMaker Studio directly from their local Visual Studio Code (VS Code). With this capability, you can use your customized local VS Code setup, including AI-assisted development tools, custom extensions, and debugging tools while accessing compute resources and your data in SageMaker Studio. In this post, we show you how to remotely connect your local VS Code to SageMaker Studio development environments to use your customized development environment while accessing Amazon SageMaker AI compute resources.