# bedrock Published articles for bedrock. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## AWS Weekly Roundup: OpenAI GPT-6 Astra on Amazon Bedrock, Amazon Quick desktop GA, Kiro for students, and more (September 14, 2026) DevFeed: [AWS Weekly Roundup: OpenAI GPT-6 Astra on Amazon Bedrock, Amazon Quick desktop GA, Kiro for students, and more (September 14, 2026)]() Original publisher: [Read original article]() Author: Micah Walter Published: 2026-09-14T15:56:33Z Content type: news Language: en Sources: [AWS News Blog]() Topics: [Amazon Bedrock](), [Frontier Model](), [OpenAI](), [context window](), [browser](), [ChatGPT](), [codex](), [macOS](), [Windows](), [Mobile]() Tags: [agents](), [ai](), [ai-agents](), [amazon](), [amazon-bedrock](), [amazon-elastic-block-store-amazon-ebs](), [amazon-opensearch-service](), [amazon-quick-suite](), [aws](), [aws-lambda](), [aws-outposts](), [aws-transform](), [bedrock](), [browser](), [chatgpt](), [codex](), [context-window](), [frontier-model](), [google-play](), [gpt](), [kiro](), [launch](), [macos](), [mobile](), [openai](), [week-in-review](), [windows]() ### AI overview This AWS Weekly Roundup highlights the general availability of OpenAI GPT-6 Astra on Amazon Bedrock, describing its reasoning, writing, design, computer-use, browser-use, and million-token context-window capabilities. It also covers the Amazon Quick desktop app for macOS and Windows, including synchronized conversations and agents across desktop and mobile, plus other AWS launches and updates. ### Source excerpt There's a particular energy to mid-September in New York. Pumpkin spice lattes are flowing, temperatures are dropping, and it's nearly sweater weather. The city is back at full speed, and so is the AWS launch calendar. This week that energy showed up in a new frontier model on Amazon Bedrock, a desktop app for Amazon [...] ## GPT-6 Astra: A new generation of intelligence DevFeed: [GPT-6 Astra: A new generation of intelligence]() Original publisher: [Read original article]() Published: 2026-09-03T11:00:00Z Content type: release Language: en Sources: [OpenAI News]() Topics: [computer-use](), [coding](), [Software Engineering](), [Cybersecurity](), [Amazon Bedrock](), [hugging face]() Tags: [api](), [aws](), [azure](), [bedrock](), [browser](), [chatgpt](), [coding](), [computer-use](), [cybersecurity](), [evaluation](), [gpt](), [latency](), [model](), [openai](), [reinforcement-learning](), [research](), [safety](), [software-engineering]() ### AI overview OpenAI introduces GPT-6 Astra, a model positioned for computer use, coding, cybersecurity, science, and professional work. The article highlights alignment evaluations, benchmark results, availability through ChatGPT and cloud/API channels, and simulated computer-use performance versus GPT-5.6 Sol. ### Source excerpt Introducing GPT-6 Astra, our most intelligent and aligned model yet, with state-of-the-art capabilities across computer use, coding, cybersecurity, and science. ## CoreBreak proves agent guardrails need to live outside the agent DevFeed: [CoreBreak proves agent guardrails need to live outside the agent]() Original publisher: [Read original article]() Author: Tyler Akidau Published: 2026-08-31T00:00:00Z Content type: opinion Language: en Sources: [Redpanda]() Topics: [Artificial Intelligence](), [AI Agent](), [Security](), [Securing AI](), [Amazon Web Services](), [vercel ai sdk](), [Amazon Bedrock](), [Google](), [Vercel]() Tags: [agent](), [agentic-ai](), [ai](), [ai-agent](), [architecture](), [aws](), [bedrock](), [cve](), [google](), [policy](), [security](), [thought-leadership](), [vercel]() ### AI overview The article argues that CoreBreak exposed a structural security flaw in major AI agent stacks and that agent guardrails should be enforced outside the agent's control. ### Source excerpt At Black Hat 2026, CoreBreak exposed the same structural flaw across AWS, Google, and Vercel. Here's why AI agent security enforcement should live beyond the agent's reach. ## AWS Weekly Roundup: EC2 application status checks, IAM role manager, OpenAI Daybreak on Bedrock, and more (August 17, 2026) DevFeed: [AWS Weekly Roundup: EC2 application status checks, IAM role manager, OpenAI Daybreak on Bedrock, and more (August 17, 2026)]() Original publisher: [Read original article]() Author: Channy Yun (윤석찬) Published: 2026-08-17T16:02:36Z Content type: news Language: en Sources: [AWS News Blog]() Topics: [Amazon Web Services](), [Amazon EC2](), [AWS IAM](), [Amazon OpenSearch Service](), [Open Source](), [Amazon Bedrock](), [Linux](), [OpenAI](), [MCP]() Tags: [amazon-bedrock](), [amazon-ec2](), [amazon-elasticache](), [amazon-opensearch-service](), [amazon-sagemaker-jumpstart](), [aws](), [aws-certificate-manager](), [aws-client-vpn](), [aws-identity-and-access-management-iam](), [bedrock](), [iam](), [linux](), [mcp](), [news](), [open-source](), [openai](), [oracle-database-aws](), [week-in-review]() ### AI overview AWS Weekly Roundup highlights EC2 application status checks, automatic IAM role setup, OpenAI Daybreak on Amazon Bedrock, and updates involving OpenSearch, Valkey, and open source community events. ### Source excerpt Last week, AWS contributors joined the OpenSearch and Valkey communities at Open Source Summit Korea 2026 and MCP DevSummit Seoul 2026 to meet open source developers and contributors. At the four-day event, community leaders and users of these Linux Foundation open source projects gathered to share knowledge, collaborate on solutions, and push the projects forward. [...] ## Track generative AI costs with Amazon Bedrock inference profiles DevFeed: [Track generative AI costs with Amazon Bedrock inference profiles]() Original publisher: [Read original article]() Author: Erik Mack Published: 2026-08-13T15:59:39Z Content type: tutorial Language: en Sources: [AWS Architecture Blog]() Topics: [Amazon Bedrock](), [Generative AI](), [Inference](), [foundation-models](), [Amazon Web Services](), [AWS IAM]() Tags: [ai](), [amazon-bedrock](), [architecture](), [aws](), [bedrock](), [cost](), [generative](), [generative-ai](), [how-to](), [iam](), [inference](), [intermediate-200](), [technical-how-to]() ### AI overview This tutorial explains how to track generative AI costs by department when multiple teams share a foundation model through Amazon Bedrock. It uses tagged application inference profiles, AWS cost allocation tags, department-based request routing, and AWS Cost Explorer to produce separate cost breakdowns. ### Source excerpt Learn how to track generative AI costs by department using Amazon Bedrock application inference profiles and AWS cost allocation tags. Create tagged profiles for each team and view per-department cost breakdowns in AWS Cost Explorer. ## Daybreak models are now available on AWS DevFeed: [Daybreak models are now available on AWS]() Original publisher: [Read original article]() Published: 2026-08-11T10:00:00Z Content type: release Language: en Sources: [OpenAI News]() Topics: [Amazon Bedrock](), [Cybersecurity](), [Amazon Web Services](), [OpenAI](), [Frontier AI](), [Detection engineering](), [Incident response]() Tags: [ai](), [amazon-bedrock](), [aws](), [bedrock](), [cybersecurity](), [incident-response](), [openai](), [product](), [validation](), [workflows]() ### AI overview OpenAI and AWS are making Daybreak Blue and Daybreak Red available through Amazon Bedrock for authorized defensive security work, including vulnerability research, exploit validation, detection engineering, and incident response. ### Source excerpt OpenAI and AWS are making Daybreak cybersecurity capabilities available through Amazon Bedrock to support enterprise security workflows. ## AWS Weekly Roundup: AWS Heroes Summit, Web Search on Amazon Bedrock, Dogwood, Kiro Crew, and more (August 10, 2026) DevFeed: [AWS Weekly Roundup: AWS Heroes Summit, Web Search on Amazon Bedrock, Dogwood, Kiro Crew, and more (August 10, 2026)]() Original publisher: [Read original article]() Author: Channy Yun (윤석찬) Published: 2026-08-10T15:45:03Z Content type: article Language: en Sources: [AWS News Blog]() Topics: [Amazon Bedrock](), [Amazon Web Services](), [Artificial Intelligence](), [Amazon Bedrock AgentCore](), [Amazon DynamoDB](), [AWS Transform](), [Embeddings](), [Containers](), [Serverless](), [Mainframe](), [OpenAI]() Tags: [ai](), [amazon](), [amazon-bedrock](), [amazon-bedrock-agentcore](), [amazon-dynamodb](), [applications](), [aws](), [aws-lambda](), [aws-transform](), [bedrock](), [containers](), [embeddings](), [kiro](), [modernization](), [news](), [open-source](), [product](), [serverless](), [week-in-review]() ### AI overview This AWS Weekly Roundup covers the AWS Heroes Summit and selected launches, including web search for OpenAI models in Amazon Bedrock, dedicated runtime instances for agents in Amazon Bedrock AgentCore, vector search in Amazon DynamoDB, and general availability of AWS Transform continuous modernization. ### Source excerpt Last week, we brought together AWS Heroes from around the world to connect, collaborate, and celebrate the builders who go above and beyond for the AWS community. The AWS Heroes Summit, an invite-only annual gathering, brings global experts specializing in fields like AI, serverless, and containers together for direct collaboration, technical deep-dives, and feedback sessions [...] ## Why every engineering org needs an agentic registry DevFeed: [Why every engineering org needs an agentic registry]() Original publisher: [Read original article]() Author: Matar Peles Published: 2026-08-10T11:43:10Z Content type: article Language: en Sources: [Developer Experience & Platform Engineering Blog | Port]() Topics: [Agent Skill](), [MCP](), [cursor](), [Azure](), [bedrock](), [site-reliability-engineering]() Tags: [agent-skill](), [agentic](), [agents](), [azure](), [bedrock](), [coding](), [governance](), [mcp](), [slack](), [workflows]() ### AI overview The article argues that engineering organizations need an agentic registry to manage the rapid growth of agents and skills. It describes registries for SKILL.md files, deployed agents, and approved MCP servers, with capabilities for discovery, standardization, governance, and workflow composition. ### Source excerpt Why every engineering org needs an agentic registry ## Agentic SDLC in Practice: Insights from Engineering Leaders DevFeed: [Agentic SDLC in Practice: Insights from Engineering Leaders]() Original publisher: [Read original article]() Author: Matar Peles Published: 2026-08-10T11:42:34Z Content type: article Language: en Sources: [Developer Experience & Platform Engineering Blog | Port]() Topics: [sdlc](), [Artificial Intelligence](), [AI Agent](), [Platform Engineering](), [Model Context Protocol](), [MCP Server](), [incident](), [Data Quality](), [Amazon Bedrock](), [Slack](), [cursor](), [Pull Request]() Tags: [agent](), [agentic](), [agents](), [ai](), [ai-agent](), [bedrock](), [coding](), [cursor](), [data-quality](), [engineering](), [incident](), [mcp](), [mcp-server](), [platform-engineering](), [python](), [review](), [sdlc](), [server](), [slack](), [workflow]() ### AI overview Engineering leaders share practical examples of an agentic SDLC, including autonomous Jira ticket resolution through pull request creation and AI-powered incident triage. The article highlights adoption challenges such as siloed agents, missing governance, and the need for a shared registry of agents, skills, and MCPs. ### Source excerpt Engineering leaders share how agentic SDLC works in practice: what scales adoption, where teams get stuck, and how to maximize AI ROI. ## Agent Registry vs Agent Hub: Which one do you need? DevFeed: [Agent Registry vs Agent Hub: Which one do you need?]() Original publisher: [Read original article]() Author: Matar Peles Published: 2026-08-10T11:40:18Z Content type: comparison Language: en Sources: [Developer Experience & Platform Engineering Blog | Port]() Topics: [AI Agent](), [Microsoft](), [Security](), [bedrock](), [Claude](), [cursor]() Tags: [agent](), [agentic](), [agents](), [ai](), [apis](), [bedrock](), [claude](), [cursor](), [enterprise](), [governance](), [hub](), [microsoft](), [policy](), [registry](), [workflow]() ### AI overview The article compares AI agent registries and agent hubs. Registries provide governance by tracking agent identity, ownership, access boundaries, versions, lifecycle state, and policy approvals, while hubs help teams discover and reuse existing agents. It argues that organizations need both capabilities and can build them as one system to address agent sprawl. ### Source excerpt Compare agent registries and agent hubs to understand their roles, key differences, and which one your team actually needs. ## Runtime instances: persistent compute for production AI agents on Amazon Bedrock AgentCore DevFeed: [Runtime instances: persistent compute for production AI agents on Amazon Bedrock AgentCore]() Original publisher: [Read original article]() Author: Sébastien Stormacq Published: 2026-08-06T22:58:00Z Content type: release Language: en Sources: [AWS News Blog]() Topics: [Amazon Bedrock](), [Artificial Intelligence](), [Amazon EC2](), [Amazon Web Services](), [GPU](), [observability]() Tags: [agents](), [ai](), [ai-agents](), [amazon](), [amazon-bedrock](), [amazon-bedrock-agentcore](), [apis](), [aws](), [bedrock](), [compute](), [developers](), [framework](), [infrastructure](), [launch](), [memory](), [model](), [monitoring](), [news](), [os](), [production](), [storage](), [tools]() ### AI overview Amazon Bedrock AgentCore runtime instances provide persistent, AWS-managed EC2 infrastructure for production AI agents. The feature supports multi-agent collaboration, shared sessions lasting up to 14 days, GPU acceleration, containerized deployments, session stop/restart, and integration with EBS, AgentCore Memory, existing APIs, identity controls, and observability. ### Source excerpt Announcing runtime instances in Amazon Bedrock AgentCore--persistent, managed EC2 infrastructure for production AI agents with multi-agent collaboration, GPU support, and sessions lasting up to 14 days. ## AWS Weekly Roundup: Price reduction of GPT models in Bedrock, CloudWatch managed collectors for Prometheus metrics, and more (August 3, 2026) DevFeed: [AWS Weekly Roundup: Price reduction of GPT models in Bedrock, CloudWatch managed collectors for Prometheus metrics, and more (August 3, 2026)]() Original publisher: [Read original article]() Author: Micah Walter Published: 2026-08-03T16:12:30Z Content type: news Language: en Sources: [AWS News Blog]() Topics: [AI, ML & Data Engineering](), [telemetry](), [networking](), [Amazon EC2](), [Amazon Elastic Kubernetes Service](), [Amazon OpenSearch Service]() Tags: [amazon-bedrock](), [amazon-cloudwatch](), [amazon-s3-tables](), [aws](), [aws-iam-identity-center](), [bedrock](), [cloud-networking](), [gpt](), [inference](), [observability](), [openai](), [prometheus](), [week-in-review]() ### AI overview AWS weekly roundup covering lower Amazon Bedrock pricing for OpenAI GPT-5.6 models, managed Prometheus metric collection in CloudWatch, and Oracle Cloud Infrastructure connectivity through AWS Interconnect. ### Source excerpt Last week I had the joy of participating in Amazon's "Bring Your Kids to Work Day" with my 7 year old son. We commuted together into the New York City office, his first real rush hour train ride, and spent the day exploring how Amazon uses AI, machine learning, and robotics to deliver packages to [...] ## How we secure Figma's internal systems with agents DevFeed: [How we secure Figma's internal systems with agents]() Original publisher: [Read original article]() Author: Matthew Sullivan; Brad Girardeau Published: 2026-07-29T20:44:00Z Content type: article Language: en Sources: [Figma Blog]() Topics: [AI Agent](), [Security](), [SIEM, Security](), [cloud-infrastructure](), [Amazon Bedrock](), [Cloud](), [Pull Request](), [Code](), [Vulnerabilities](), [Large Language Model]() Tags: [agents](), [ai-agent](), [bedrock](), [cloud](), [cloud-infrastructure](), [code](), [llm](), [pull-request](), [security](), [vulnerabilities]() ### AI overview Figma's security team built an AI agent that triages alerts, investigates incidents, queries security data, writes fixes, opens pull requests, and retains knowledge from prior investigations. The system reduced alert time to resolution by 71% and changed how on-call engineers handle security work. ### Source excerpt Our security team built an AI agent that triages alerts, conducts forensic investigations, queries our security data lake, writes code to fix issues--and remembers what it learns. Here's how we cut alert time-to-resolution by 71% and fundamentally changed how our on-call engineers work. ## What is an Agentic Data Plane? DevFeed: [What is an Agentic Data Plane?]() Original publisher: [Read original article]() Author: Marc Millstone Published: 2026-07-09T00:00:00Z Content type: article Language: en Sources: [Redpanda]() Topics: [Artificial Intelligence](), [agent observability](), [Amazon API Gateway](), [AWS IAM](), [observability](), [SIEM, Security](), [Amazon Web Services]() Tags: [agentic](), [agents](), [ai](), [ai-agents](), [api](), [api-gateway](), [aws](), [bedrock](), [enterprise](), [governance](), [iam](), [identity](), [identity-management](), [llm](), [models](), [observability](), [siem](), [thought-leadership](), [token](), [tool](), [tools]() ### AI overview The article defines an Agentic Data Plane as a governed governance and runtime layer between enterprise AI agents and the data, tools, identities, and models they access. It explains why existing IAM, API gateways, and observability or SIEM systems do not adequately govern agent-specific actions, identity propagation, model selection, token spending, policy enforcement, and end-to-end tracing. ### Source excerpt The Agentic Data Plane is the governance and runtime layer that connects your AI agents to everything they act on. Learn what it does, why existing tools can't replace it, and what to look for in an enterprise-grade one. ## AWS Bedrock AgentCore, S3 File Mounts, Python & Install Improvements DevFeed: [AWS Bedrock AgentCore, S3 File Mounts, Python & Install Improvements]() Original publisher: [Read original article]() Author: Serverless Team Published: 2026-05-27T00:00:00Z Content type: release Language: en Sources: [Serverless Blog]() Topics: [Amazon Bedrock](), [Amazon S3](), [Python](), [Framework](), [Serverless](), [Security]() Tags: [aws](), [aws-lambda](), [bedrock](), [bug](), [bug-fixes](), [cloud-computing](), [faas](), [framework](), [function-as-a-service](), [improvements](), [infrastructure-as-code](), [install](), [news](), [python](), [s3](), [security](), [serverless](), [serverless-architecture](), [serverless-framework](), [update]() ### AI overview Serverless Framework's May 2026 update covers built-in AWS Bedrock AgentCore support, S3 file system mounts for Lambda, faster Framework installs, Python improvements, bug fixes, and security updates. ### Source excerpt Serverless Framework's May 2026 update covering built-in AWS Bedrock AgentCore support, S3 file system mounts for Lambda, faster Framework installs, Python improvements, bug fixes, and security updates. ## How Bitovi Built a Durable Customer Support AI Agent with Temporal and AWS Bedrock DevFeed: [How Bitovi Built a Durable Customer Support AI Agent with Temporal and AWS Bedrock]() Original publisher: [Read original article]() Author: Mark Repka Published: 2026-05-19T00:00:00Z Content type: article Language: en Sources: [Temporal Blog]() Topics: [AI Agent](), [Amazon Bedrock](), [Amazon Web Services](), [Platform Engineering](), [Large Language Model](), [Inference]() Tags: [ai-agent](), [architecture](), [aws](), [bedrock](), [community](), [customer](), [durability](), [observability](), [retries](), [sessions]() ### AI overview Bitovi built a customer support AI agent that remembers users across sessions and handles issues without losing conversation context. Its architecture uses a ReAct agent loop in a Temporal Workflow, with workflow state for conversation context, AWS Bedrock Java SDK calls in Activities, and Activity-backed tool execution for retries and observability. ### Source excerpt How Bitovi's team built a customer service AI agent on Temporal and AWS Bedrock, and why durability was never optional. ## Introducing SyGra Studio DevFeed: [Introducing SyGra Studio]() Original publisher: [Read original article]() Author: Surajit Dasgupta; Bidyapati Pradhan; Amit Kumar Saha; Vipul Mittal; Sriram Puttagunta Published: 2026-02-05T16:52:28Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [ai observability](), [debugging](), [dashboards]() Tags: [azure-openai](), [bedrock](), [cost](), [debugging](), [hugging-face](), [json](), [latency](), [llm](), [observability](), [openai](), [vertex](), [vllm](), [workflow]() ### AI overview SyGra Studio provides a guided interface for building and running LLM workflows. It connects data sources, configures models and structured outputs, and exposes execution status, costs, latency, guardrail outcomes, logs, and breakpoints. ### Source excerpt - Configure and validate models with guided forms (OpenAI, Azure OpenAI, Ollama, Vertex, Bedrock, vLLM, custom endpoints). - Connect Hugging Face, file-system, or ServiceNow data sources and preview rows before execution. - Configure nodes by selecting models, writing prompts (with auto-suggested variables), and defining outputs or structured schemas. - Design downstream outputs using shared state variables and Pydantic-powered mappings. ## Building Real-Time AI: Highlights from the AWS MCP Hackathon in San Francisco DevFeed: [Building Real-Time AI: Highlights from the AWS MCP Hackathon in San Francisco]() Original publisher: [Read original article]() Author: Zoe Steinkamp Published: 2025-11-20T00:00:00Z Content type: article Language: en Sources: [ClickHouse Blog]() Topics: [AI Bots](), [Kafka](), [incident](), [Orchestration](), [Temporal data]() Tags: [agents](), [ai](), [analytics](), [aws](), [bedrock](), [clickhouse](), [hackathon](), [incident](), [kafka](), [latency](), [mcp](), [monitoring](), [observability](), [orchestration](), [real-time](), [streaming](), [time-series]() ### AI overview A ClickHouse recap of three AWS MCP Hackathon projects that use live and streaming data for ad bidding, incident response, and glucose monitoring. ### Source excerpt How teams used ClickHouse to power agents with streaming data, sub-second analytics, and production-ready dashboards at the AWS MCP Hackathon. ## Redpanda open-sources top 16 AI connectors DevFeed: [Redpanda open-sources top 16 AI connectors]() Original publisher: [Read original article]() Author: Mike Broberg Published: 2025-08-18T00:00:00Z Content type: article Language: en Sources: [Redpanda]() Topics: [Redpanda-Connect](), [Open Source](), [Artificial Intelligence](), [stream-processing](), [OpenAI](), [Streaming](), [Amazon Bedrock](), [Embeddings](), [Retrieval Augmented Generation (RAG)](), [cohere](), [Ollama]() Tags: [ai](), [ai-connector-platforms](), [ai-connectors-for-commercial-products](), [ai-connectors-open-source](), [ai-data-streaming](), [ai-integration-tools](), [announcement](), [bedrock](), [cloud-native-ai-services](), [cohere](), [embeddable-ai-capabilities](), [embeddings](), [ollama](), [open-source](), [open-source-ai-for-business](), [openai](), [openai-connectors](), [rag](), [real-time-ai-streaming](), [redpanda-ai-connectors](), [redpanda-connect](), [stream-processing](), [streaming](), [streaming-ai-processors]() ### AI overview Redpanda announces the open-source release of its top AI connectors under the Apache 2.0 license. The connectors integrate Redpanda Connect with destinations and models including OpenAI, Cohere, Amazon Bedrock, Ollama, and Google Cloud Vertex AI, supporting streaming pipelines and use cases such as generation, summarization, classification, translation, and text embeddings for RAG. ### Source excerpt Redpanda open-sources top AI connectors to the most used destinations, including OpenAI, Cohere, Bedrock, Ollama, and Vertex AI. Learn more. ## From siloed DataOps, MLOps, and LLMOps to a unified data-intelligence platform DevFeed: [From siloed DataOps, MLOps, and LLMOps to a unified data-intelligence platform]() Original publisher: [Read original article]() Author: Rajit Saha Published: 2025-08-04T18:03:19Z Content type: opinion Language: en Sources: [Udemy Engineering]() Topics: [DataOps](), [MLOps](), [Generative AI](), [Apache Spark](), [Amazon Bedrock](), [Amazon S3](), [Amazon Redshift](), [Amazon SageMaker](), [apache-flink]() Tags: [ai](), [aiops](), [amazon-s3](), [analytics](), [apache-flink](), [apache-spark](), [bedrock](), [dataops](), [large-language-models](), [llmops](), [mlops]() ### AI overview The article describes how DataOps, MLOps, and AI/LLM Ops commonly rely on separate systems and teams for data processing, model deployment, inference, evaluation, orchestration, governance, and monitoring. It then introduces Databricks' Data Intelligence Platform as a unified environment intended to bring these domains together. ### Source excerpt Introduction In modern data-driven businesses, the pace of innovation in analytics and artificial intelligence has outstripped the capacity of many teams. Three distinct disciplines emerged to handle this expansion: Data platform (DataOps) teams built data lakes on cloud storage such as Amazon S3, processed them with Apache Spark and Hive on EMR, ingested streaming data with Spark Structured Streaming or Apache Flink, and loaded tabular copies into MPP warehouses like Redshift for interactive SQL and BI. Cataloguing and governance were offloaded to external tools such as DataHub, and fine-grained access controls required third-party services like Privacera. This architecture worked, but it required separate workflows for batch and streaming, extra systems for lineage and governance, and a mosaic of operational teams. MLOps teams provided an additional layer. Data scientists used notebook environments (for example, Amazon SageMaker) to preprocess data, train, and evaluate models. Deploying models meant writing integration code to move features into a serving layer, to register models in disparate registries and to build custom APIs for inference. Feature stores and model registries were bought from additional vendors. Updates and monitoring were often manual processes. AI/LLM Ops teams are a new addition because generative AI requires specialized components: LLM gateways (e.g., Amazon Bedrock) to proxy access to foundation models; evaluation tooling to compare large language models; orchestration frameworks for agents; vector databases for retrieval augmented generation; and of course another layer of security, access management and cost control. These tools seldom integrate seamlessly with existing data and ML pipelines. This fragmented state makes it difficult to react quickly when product requirements change. Each new capability requires another system, another integration, and another team. Meanwhile, budgets tighten and go-to-market timelines shrink. The questio ## From Beta to Bedrock: Build Products that Stick. DevFeed: [From Beta to Bedrock: Build Products that Stick.]() Original publisher: [Read original article]() Author: by Liam Nugent Published: 2025-04-23T18:04:31Z Content type: article Language: en Sources: [A List Apart: The Full Feed]() Topics: [bedrock](), [Security]() Tags: [bedrock](), [business](), [business-industry-usability-user-experience-user-research-web-strategy](), [development](), [finance](), [industry](), [security](), [usability](), [user-experience](), [user-research](), [web-strategy]() ### AI overview The article argues that financial products should be built around a stable bedrock of clear customer value rather than an accumulation of features. It discusses the risks of feature-first development, the role of a Minimum Viable Product, and the need to resist internal pressures that can produce confusing, bloated experiences. ### Source excerpt As a product builder over too many years to mention, I've lost count of the number of times I've seen promising ideas go from zero to hero in a few weeks, only to fizzle out within months. Financial products, which is the field I work in, are no exception. With people's real hard-earned money on the line, user expectations running high, and a crowded market, it's tempting to throw as many features at the wall as possible and hope something sticks. But this approach is a recipe for disaster. Here's why: The pitfalls of feature-first development When you start building a financial product from the ground up, or are migrating existing customer journeys from paper or telephony channels onto online banking or mobile apps, it's easy to get caught up in the excitement of creating new features. You might think, "If I can just add one more thing that solves this particular user problem, they'll love me!" But what happens when you inevitably hit a roadblock because the narcs (your security team!) don't like it? When a hard-fought feature isn't as popular as you thought, or it breaks due to unforeseen complexity? This is where the concept of Minimum Viable Product (MVP) comes in. Jason Fried's book Getting Real and his podcast Rework often touch on this idea, even if he doesn't always call it that. An MVP is a product that provides just enough value to your users to keep them engaged, but not so much that it becomes overwhelming or difficult to maintain. It sounds like an easy concept but it requires a razor sharp eye, a ruthless edge and having the courage to stick by your opinion because it is easy to be seduced by "the Columbo Effect"... when there's always "just one more thing..." that someone wants to add. The problem with most finance apps, however, is that they often become a reflection of the internal politics of the business rather than an experience solely designed around the customer. This means that the focus is on delivering as many features and functionalities as pos ## Guardcraft: A Minecraft Java Server with Zero CVEs DevFeed: [Guardcraft: A Minecraft Java Server with Zero CVEs]() Original publisher: [Read original article]() Published: 2025-02-28T00:00:00Z Content type: article Language: en Sources: [Chainguard: Unchained]() Topics: [chainguard](), [Docker](), [Containers](), [Vulnerabilities](), [grype](), [Docker Hub](), [Security](), [Ubuntu](), [Linux](), [Steam Deck]() Tags: [bedrock](), [chainguard](), [chainguard-containers](), [chainguard-images](), [container](), [container-image](), [containers](), [docker](), [docker-hub](), [grype](), [guardcraft](), [linux](), [security](), [steam-deck](), [vulnerabilities](), [zero-cves]() ### AI overview The article describes building a Minecraft Java server with a Chainguard Image. It compares a popular Ubuntu-based Docker Hub image with the Chainguard approach, highlighting the former's 165 unresolved CVEs and the latter's stated result of zero CVEs. ### Source excerpt We built a Minecraft Java server using a Chainguard Image, resulting in zero CVEs and a whole lot of fun! ## Hugging Face models in Amazon Bedrock DevFeed: [Hugging Face models in Amazon Bedrock]() Original publisher: [Read original article]() Author: Simon Pagezy; Philipp Schmid; Jeff Boudier; Violette Published: 2024-12-09T00:00:00Z Content type: tutorial Language: en Sources: [Hugging Face - Blog]() Topics: [Amazon Bedrock](), [hugging face](), [Amazon SageMaker JumpStart](), [gemma](), [Generative AI](), [Open Source](), [Google]() Tags: [amazon-bedrock](), [amazon-sagemaker](), [amazon-sagemaker-jumpstart](), [api](), [artificial-intelligence](), [aws](), [bedrock](), [cloud](), [deployment](), [gemma](), [generative-ai](), [hugging-face](), [open-source](), [partnerships]() ### AI overview This tutorial explains how to deploy open Hugging Face models, including Google Gemma 2 27B Instruct, through the Amazon Bedrock Marketplace. It covers model selection, deployment, endpoint configuration, Bedrock API usage, and cleanup, with model endpoints managed by Amazon SageMaker JumpStart. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Introducing The AWS AI Stack DevFeed: [Introducing The AWS AI Stack]() Original publisher: [Read original article]() Author: Serverless Team Published: 2024-09-11T00:00:00Z Content type: release Language: en Sources: [Serverless Blog]() Topics: [Artificial Intelligence](), [Amazon Web Services](), [Amazon Bedrock](), [Large Language Model](), [AWS Lambda](), [Boilerplate](), [Claude](), [React](), [Serverless](), [Back end](), [Front end](), [llama3]() Tags: [ai](), [aws](), [aws-lambda](), [back-end](), [bedrock](), [ci-cd](), [claude](), [cloud-computing](), [faas](), [front-end](), [function-as-a-service](), [infrastructure-as-code](), [llama3](), [llms](), [news](), [react](), [serverless](), [serverless-architecture](), [serverless-framework]() ### AI overview The AWS AI Stack is introduced as a full-stack, serverless boilerplate for building AI applications on AWS. It uses Bedrock LLMs including Claude 3.5 Sonnet and Llama3.1, with a React front end, AWS Lambda back end, and built-in CI/CD. ### Source excerpt Full-stack, serverless, boilerplate for AI applications on AWS, featuring Bedrock LLMs like Claude 3.5 Sonnet and Llama3.1, a React front-end, AWS Lambda back-end, built-in CI/CD and more. [Next page]()