# Anthropic Claude Published articles for Anthropic Claude. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Prompt Caching Support in Spring AI with Anthropic Claude DevFeed: [Prompt Caching Support in Spring AI with Anthropic Claude]() Original publisher: [Read original article]() Author: Stelios Anastasakis Published: 2026-09-16T07:49:56Z Content type: tutorial Language: en Sources: [Baeldung]() Topics: [Spring AI](), [Anthropic Claude](), [Caching]() Tags: [anthropic](), [anthropic-claude](), [artificial-intelligence](), [artificial-intelligence-anthropic-spring-ai-chatclient](), [caching](), [how-to](), [spring-ai](), [spring-ai-chatclient]() ### AI overview This tutorial explains how prompt caching works in Spring AI with Anthropic Claude. It covers dependencies, model-specific requirements and limitations, configuration options, caching hierarchy, and practical considerations. Prompt caching can reduce latency and input-token costs when prompt prefixes are reused. ### Source excerpt Learn how prompt caching works, the limitations for different Claude models, and how to use it in Spring AI. The post Prompt Caching Support in Spring AI with Anthropic Claude first appeared on Baeldung. ## The generative AI customization spectrum: From prompt engineering to custom models on AWS DevFeed: [The generative AI customization spectrum: From prompt engineering to custom models on AWS]() Original publisher: [Read original article]() Author: Bhavya Sruthi Sode Published: 2026-09-14T15:47:12Z Content type: tutorial Language: en Sources: [Artificial Intelligence]() Topics: [Generative AI](), [Amazon Web Services](), [Prompt Engineering](), [Fine-tuning](), [Retrieval Augmented Generation (RAG)](), [Amazon Bedrock](), [foundation-models](), [Anthropic Claude](), [Nova](), [llama]() Tags: [advanced-300](), [ai](), [amazon-bedrock](), [anthropic-claude](), [aws](), [best-practices](), [chain-of-thought](), [fine-tuning](), [foundation-models](), [generative-ai](), [llama](), [nova](), [optimization](), [prompt-engineering](), [rag]() ### AI overview This AWS article presents an eight-step decision framework for customizing generative AI workloads. It compares progressively more involved approaches, including prompt engineering, Retrieval Augmented Generation (RAG), fine-tuning, continued pre-training, and custom models such as Amazon Nova Forge, emphasizing that teams should start with the simplest approach and escalate when greater control or domain specificity is required. ### Source excerpt Pick the right generative AI customization approach on AWS with an 8-step decision framework, from prompt engineering and RAG to fine-tuning, continued pre-training, and Amazon Nova Forge. Start simple and escalate only when you must. ## How Heurist Finance built an AI-native investment workbench on Amazon Bedrock AgentCore DevFeed: [How Heurist Finance built an AI-native investment workbench on Amazon Bedrock AgentCore]() Original publisher: [Read original article]() Author: JW Wang Published: 2026-09-09T18:11:12Z Content type: article Language: en Sources: [Artificial Intelligence]() Topics: [Amazon Bedrock](), [Claude](), [Orchestration](), [tracing]() Tags: [advanced-300](), [agent](), [ai](), [amazon-bedrock-agentcore](), [anthropic-claude](), [customer-solutions](), [identity](), [memory](), [observability](), [sandbox](), [tracing]() ### AI overview Heurist Finance uses Amazon Bedrock AgentCore to run a conversational investment workbench that accesses premium data per query and produces auditable, personalized research responses. ### Source excerpt Learn how Heurist built Heurist Finance, a conversational AI investment workbench, on Amazon Bedrock AgentCore. This customer story shows how AgentCore payments, Identity, Memory, Code Interpreter, and Observability let a small team buy premium market data per query, isolate analysis in a sandbox, and keep every action auditable. ## Three identity vendors shipped the same agent access pattern in eight days DevFeed: [Three identity vendors shipped the same agent access pattern in eight days]() Original publisher: [Read original article]() Author: WorkOS Published: 2026-09-04T16:24:43Z Content type: article Language: en Sources: [WorkOS Blog]() Topics: [Auth0](), [Authorization](), [MCP](), [OpenID connect (OIDC)](), [Single sign-on (SSO)](), [Model Context Protocol]() Tags: [anthropic-claude](), [api](), [auth0](), [authorization](), [cross-app-access](), [identity](), [mcp](), [oidc](), [okta](), [sso]() ### AI overview Okta, Auth0, and Descope shipped implementations of the Cross App Access agent-access pattern between August 24 and September 1. The pattern uses a Client ID Metadata Document to identify calling software and an ID-JAG token exchange to let an identity provider control application access. ### Source excerpt Okta, Auth0, and Descope all shipped Cross App Access between August 24 and September 1. The two-layer pattern underneath it outlasts whichever vendor wins. ## Beyond the $1 AI era: How federal agencies can build the evidence for FY27 renewals DevFeed: [Beyond the $1 AI era: How federal agencies can build the evidence for FY27 renewals]() Original publisher: [Read original article]() Author: Jimmy Cesario; Chris Leffler; Sophie Wang Published: 2026-08-26T00:00:00Z Content type: article Language: en Sources: [Datadog | The Monitor blog]() Topics: [AI Chat](), [cursor]() Tags: [ai](), [amazon-bedrock](), [anthropic-claude](), [chatgpt](), [cloud](), [cloud-cost-management](), [cost](), [cursor](), [finops](), [gemini](), [government](), [kubernetes]() ### AI overview Datadog explains how federal agencies can establish AI cost, usage, and value baselines before OneGov promotional pricing expires and FY27 renewal decisions are made. ### Source excerpt Learn how federal agencies can use Datadog Cloud Cost Management to build the cost, usage, and value evidence needed for FY27 AI renewals as OneGov promotions expire. ## Build Your First AI Agent in Python -- A Hands-On Guide to the Claude Agent SDK DevFeed: [Build Your First AI Agent in Python -- A Hands-On Guide to the Claude Agent SDK]() Original publisher: [Read original article]() Author: Geeta Kakrani Published: 2026-08-21T09:14:00Z Content type: tutorial Language: en Sources: [Google Developer Experts - Medium]() Topics: [SDKs](), [Claude](), [Python](), [AI Agent](), [anthropic](), [Claude Code](), [API keys](), [Authentication]() Tags: [agentic-ai](), [ai](), [ai-agent](), [anthropic](), [anthropic-claude](), [api](), [authentication](), [claude](), [claude-code](), [guide](), [python](), [sdk](), [tutorial]() ### AI overview This step-by-step tutorial explains how to build an AI agent in Python with Anthropic's Claude Agent SDK. It covers project setup, SDK installation, API-key configuration, and the SDK's agent loop, tools, MCP servers, and subagents. ### Source excerpt A step-by-step, no-hype tutorial using Anthropic's official Agent SDK By Geeta Kakrani -- AI Consultant | Google Developer Expert (AI) If you've been writing simple "call the API, get a response" scripts with an LLM, you already know the limitation: every call is a one-shot Q&A. You ask, it answers, the conversation is over. There's no planning, no tool use, no "keep working until the task is actually done." The Claude Agent SDK -- Anthropic's official, open-source Python and TypeScript library -- solves exactly this. It gives you the same agent loop, tool execution engine, and context management that powers Claude Code, but as a library you can call from your own Python program. No need to build your own tool-calling loop from scratch. In this tutorial, we'll install it, set it up, and build a working agent -- step by step, using only what's documented and verified. What you'll need Python 3.10 or later An Anthropic API key (from the Claude Console) 15-20 minutes Step 1: Set up your project Create a fresh folder for this project. The SDK, by default, has access to files in this folder and its subfolders -- so keep it clean and dedicated. bash mkdir my-agent && cd my-agent python3 -m venv .venv source .venv/bin/activate # on Windows: .venv\Scripts\activateStep 2: Install the SDK bash pip install claude-agent-sdk That's it -- no separate CLI install needed. The package bundles the Claude Code CLI binary internally and uses it automatically. Note: If pip throws an externally-managed-environment error (common on newer Ubuntu/Debian/Homebrew Python), make sure you're inside the virtual environment you just activated in Step 1.Step 3: Set your API key Create a .env file in your project folder: ANTHROPIC_API_KEY=your-api-key-here (If you're on AWS, Google Cloud, or Azure, the SDK also supports Bedrock, Vertex AI, and Azure Foundry authentication -- but for this tutorial, a plain API key is simplest.) The architecture, before you write any code It helps to see the whole picture b ## Claude Fable 5 access restored on AI Gateway DevFeed: [Claude Fable 5 access restored on AI Gateway]() Original publisher: [Read original article]() Author: Jerilyn Zheng Published: 2026-07-01T07:01:00Z Content type: release Language: en Sources: [Vercel News]() Topics: [Fable](), [Anthropic Claude](), [API](), [Artificial Intelligence](), [debugging]() Tags: [anthropic-claude](), [api](), [coding](), [debugging](), [docs](), [fable](), [government](), [model](), [models](), [real-time](), [retention](), [safety]() ### AI overview Access to the Claude Fable 5 Mythos-class model has been restored on AI Gateway after the US Government lifted export controls. The release updates its safety classifiers and documents model fallbacks, API usage, and 30-day retention of prompts and completions. ### Source excerpt Access to Claude Fable 5, the Mythos-class model, has now been restored on AI Gateway following the US Government's decision to lift the export controls. Fable 5 is the same model that was available between June 9 and June 12. What has changed is the safety classifiers, which are now updated and more robust. In the near term, some routine tasks such as coding and debugging may trigger safety classifiers. To ensure requests are still serviced when the safety classifiers are triggered, use model fallbacks. AI Gateway will try each model in models in the stated order if Anthropic refuses the request to Fable 5. To call Fable 5, use model name anthropic/claude-fable-5: Model fallbacks work on every API format: for more information on how to configure these, see the docs. Anthropic does not support Zero Data Retention for the model, because some misuse patterns are only visible across cumulative requests, which real-time filters cannot catch on their own. Prompts and completions are retained for 30 days and are not used to train Claude. Read more in the data retention whitepaper. Read more ## Claude Sonnet 5 now available on Vercel AI Gateway DevFeed: [Claude Sonnet 5 now available on Vercel AI Gateway]() Original publisher: [Read original article]() Author: Jerilyn Zheng Published: 2026-06-30T00:00:00Z Content type: news Language: en Sources: [Vercel News]() Topics: [AI, ML & Data Engineering](), [vercel ai sdk](), [long-context](), [SDKs]() Tags: [agentic](), [ai-gateway](), [ai-models](), [anthropic](), [anthropic-claude](), [api](), [api-keys](), [claude](), [coding](), [cost](), [inference](), [leaderboard](), [long-context](), [memory](), [model](), [models](), [performance](), [playground](), [vercel]() ### AI overview Claude Sonnet 5 is available through Vercel AI Gateway, with claimed improvements in coding, agentic work, document parsing, and long-context memory. The announcement lists launch and standard pricing and describes Gateway features for model access, usage tracking, cost controls, retries, and failover. ### Source excerpt Claude Sonnet 5 from Anthropic is now available on AI Gateway. Sonnet 5 improves on Sonnet 4.6 across coding and agentic work, reaching outcomes on many tasks that previously needed an Opus model, at Sonnet pricing. The model is more agentic and follows instructions more closely. Document parsing and long-context memory use are also stronger. Sonnet 5 also uses an updated tokenizer, like the recent Opus models, which can map the same input to more tokens. Launch pricing of $2 per million input tokens and $10 per million output tokens runs through August 31, 2026. Standard list price will be $3/M input tokens, $15/M output tokens. To use Sonnet 5, set model to anthropic/claude-sonnet-5 in the AI SDK: You can also try Sonnet 5 in the model playground. AI Gateway provides a unified API for calling models, tracking usage and cost, and configuring retries, failover, and performance optimizations for higher-than-provider uptime. It includes built-in custom reporting, Zero Data Retention support, budgets for API keys, and more. AI Gateway reflects provider pricing with no markup and does not charge a platform fee on inference, including on Bring Your Own Key (BYOK) requests. Read more ## From Figma to code in minutes: How I built UI Studio with AI DevFeed: [From Figma to code in minutes: How I built UI Studio with AI]() Original publisher: [Read original article]() Author: Jayant Kumar Published: 2026-06-18T07:01:03Z Content type: tutorial Language: en Sources: [Amex GBT Technology]() Topics: [Figma](), [ui](), [React](), [Anthropic Claude](), [Prompt Engineering](), [repo](), [Terminal]() Tags: [ai](), [anthropic-claude](), [figma](), [frontend-development](), [prompt-engineering](), [react](), [repo](), [software-development](), [terminal](), [ui](), [ui-design]() ### AI overview This tutorial explains how the author built UI Studio, a tool that generates UI code using Egencia's internal UITK React components from a prompt, a Figma URL, or a screenshot. It describes the original feasibility-checking workflow and the tool's progression from a local prototype to a deployable application. ### Source excerpt Whether you're a developer checking feasibility or a designer wanting to see your Figma come to life in real code, UI Studio is built for both. If you've ever worked on a product team, you know the dance, a designer hands over a Figma file, but before anyone writes a single line of production code, a developer has to answer one question: Is this buildable with our component library? At Egencia by Amex GBT, we use UITK (UI Toolkit), our internal component library built on React. It gives developers a set of standardized components to build consistent UIs across our products. As part of Egencia, that question kicks off a process that can take days. We open the doc site, scan the components, write some test code in the React playground or our own repo, and eventually come back to the designer with a verdict. If something isn't feasible, the design goes back for revisions. Then we check again and finally start building. It works. But it's slow. And I couldn't help but wonder whether there was a better way. The idea Earlier this year, I was doing the Anthropic Claude course and somewhere between learning about prompt engineering and working through the hands-on labs, it clicked. What if I could skip the whole feasibility loop and just generate the UI directly using our actual UITK components, from a prompt, a Figma link, or even a screenshot? That was the seed. A few weeks later, UI Studio was live. Figure 1: UI Studio, describe what you want, drop an image, or paste a Figma URLHow it got built It took about three to four weeks to build, and it wasn't linear. It went through three very different versions before becoming what it is today. Phase 1: The hacky local version The first version was built to prove the idea, not to share it. I cloned the UITK doc site repo locally, fetched component data directly from it, and created a proxy that passed all user input to the model via terminal. It worked on my machine. But getting anyone else to use it would have meant cloning re ## Opus 4.8 on AI Gateway DevFeed: [Opus 4.8 on AI Gateway]() Original publisher: [Read original article]() Author: Jerilyn Zheng Published: 2026-05-28T07:00:00Z Content type: release Language: en Sources: [Vercel News]() Topics: [Claude](), [Anthropic Claude](), [Vercel](), [vercel ai sdk](), [API](), [coding](), [Latency](), [model-deployment]() Tags: [agentic](), [ai](), [ai-gateway](), [anthropic](), [anthropic-claude](), [api](), [claude](), [coding](), [cost](), [latency](), [leaderboard](), [performance](), [playground](), [pricing](), [retention](), [sdk](), [uptime](), [vercel]() ### AI overview Claude Opus 4.8 is now available through Vercel AI Gateway and the AI SDK. It is designed for long-horizon agentic execution, complex multi-step coding tasks, and knowledge work. AI Gateway offers unified model access, usage and cost tracking, retries, failover, performance optimization, reporting, Zero Data Retention support, and provider selection based on latency and cost without adding inference fees. ### Source excerpt Claude Opus 4.8 is now available on Vercel AI Gateway. Claude Opus 4.8 is built for long-horizon agentic execution and handles complex, multi-step coding tasks like refactors that previously required human correction mid-task. The model also produces clearer, less hedgy prose for knowledge work like drafting documents, analyzing data, and building presentations. To use Opus 4.8, set model to anthropic/claude-opus-4.8 in the AI SDK. AI Gateway provides a unified API for calling models, tracking usage and cost, and configuring retries, failover, and performance optimizations for higher-than-provider uptime. It includes built-in custom reporting, Zero Data Retention support, dynamic provider sorting by latency & cost, and more. AI Gateway reflects provider pricing with no markup and does not charge a platform fee on inference, including on Bring Your Own Key (BYOK) requests. Learn more about AI Gateway, view the AI Gateway model leaderboard or try it in our model playground. Read more ## Snyk announces Anthropic updates: Evo integrates with Claude Enterprise, and Snyk Desk comes to Claude Desktop DevFeed: [Snyk announces Anthropic updates: Evo integrates with Claude Enterprise, and Snyk Desk comes to Claude Desktop]() Original publisher: [Read original article]() Author: Ranko Cupovic Published: 2026-05-21T17:00:00Z Content type: news Language: en Sources: [Blog RSS Feed | Snyk]() Topics: [Anthropic Claude](), [Security](), [Artificial Intelligence](), [Extension](), [MCP](), [API](), [macOS](), [Windows]() Tags: [anthropic-claude](), [api](), [extension](), [integrations](), [macos](), [mcp](), [security](), [windows]() ### AI overview Snyk announces two Anthropic integrations: Evo adds Claude Enterprise inventory, risk, and compliance visibility, while the Snyk Security Desktop Extension brings real-time scanning and vulnerability context to Claude on macOS and Windows. ### Source excerpt Snyk announces two new integrations with Anthropic that cover both sides of AI-assisted development. Evo by Snyk now integrates with Anthropic's Claude Enterprise, and the Snyk Security Desktop Extension is now available in Claude for macOS and Windows. ## Anthropic Claude API Pricing: Build a Margin-Safe AI Billing Model DevFeed: [Anthropic Claude API Pricing: Build a Margin-Safe AI Billing Model]() Original publisher: [Read original article]() Author: Ayush Agarwal Published: 2026-04-30T00:00:00Z Content type: tutorial Language: en Sources: [Dodo Payments Blog]() Topics: [Claude](), [API](), [Artificial Intelligence](), [anthropic]() Tags: [ai](), [anthropic](), [anthropic-claude](), [api](), [billing](), [pricing](), [usage-based-billing]() ### AI overview This guide explains Anthropic Claude API pricing by model and token type, then outlines how AI founders can calculate per-user costs and design customer pricing that protects margins as usage and model prices change. It covers token markups and other billing patterns for handling variable usage. ### Source excerpt Anthropic Claude API pricing explained per model and per token. How to design customer pricing that protects margin as Claude prices change. Real math for AI founders.