# SDK Published articles for SDK. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## 16 September 2026 Artifact Wave DevFeed: [16 September 2026 Artifact Wave]() Original publisher: [Read original article]() Author: CommonsWare Published: 2026-09-16T19:33:36Z Content type: release Language: en Sources: [The CommonsBlog]() Topics: [Jetpack]() Tags: [2026](), [blog](), [jetpack](), [sdk](), [september-2026]() ### AI overview A brief update on the new Jetpack artifacts released during the week of September 16, 2026. ### Source excerpt Here is what we got as new Jetpack artifacts this week! ## Auth0 launches v0 integration for production application authentication DevFeed: [Auth0 launches v0 integration for production application authentication]() Original publisher: [Read original article]() Author: Juan Cruz Martinez Published: 2026-09-16T00:00:00Z Content type: release Language: en Sources: [Auth0 Blog]() Topics: [Auth0](), [Authentication](), [app builder](), [Access Control](), [Artificial Intelligence](), [Vercel](), [MFA]() Tags: [access-control](), [ai](), [app-builder](), [auth0](), [authentication](), [mfa](), [sdk](), [vercel]() ### AI overview Auth0 is launching an integration with v0 that lets users configure authentication directly from prompts, including login, protected routes, B2B use cases, enterprise identity providers, and MFA. The integration can create Auth0 applications, configure connections, and wire the SDK into generated code. ### Source excerpt Stop relying on generic login forms. Use the new Auth0 v0 integration on the Vercel identity platform to build secure, production-ready AI app authentication. ## TypeSafe AI's Jev now available on AI Gateway DevFeed: [TypeSafe AI's Jev now available on AI Gateway]() Original publisher: [Read original article]() Author: Jerilyn Zheng Published: 2026-09-16T00:00:00Z Content type: release Language: en Sources: [Vercel News]() Topics: [gateway](), [SDKs](), [API](), [Software]() Tags: [ai-gateway](), [api](), [evaluation](), [gateway](), [model](), [queue](), [retry](), [sdk](), [software](), [support](), [typesafe-ai]() ### AI overview Vercel's AI Gateway now supports TypeSafe AI's Jev, a probabilistic decision model that returns typed Choice, Score, and Boolean answers with probabilities. The changelog describes parallel evaluation, AI SDK 7 integration through an experimental evaluate API, example routing and review workflows, and provider options including Zero Data Retention and No Training. ### Source excerpt Jev from TypeSafe AI is now available on AI Gateway. Jev is a probabilistic decision model for software: state goes in, typed Choice, Score, and Boolean answers come out. Regular language models generate text one token at a time, which the application then parses and validates. Jev evaluates all declared questions in parallel and returns typed answers plus probabilities directly. That removes unnecessary text generation and makes it straightforward to automate clear cases while routing uncertain ones to review. TypeSafe reports Jev was up to 193.6x faster and 444.6x cheaper than LLMs on its workflow evaluations. Example use cases include: Choosing the next tool or subagent in an agent loop Deciding whether to continue, retry, ask the user, or stop Scoring urgency or risk before an action Verifying model outputs and enforcing guardrails. AI SDK 7 exposes Jev through the experimental evaluate API. Choice selects an option, Score grades an ordered rubric, and Boolean estimates the probability of true. Install the current AI SDK (AI SDK 7.0.105 onwards supports the evaluate API): Each evaluation specifies: model: the evaluation model to call, state: the shared string, object, or array to evaluate, and questions: a map of named decisions to make about that state. Call the model with typesafe-ai/jev. This example turns one support case into a queue, priority, and refund-review decision, with uncertain routing sent for manual review: The result preserves question IDs and Choice keys. TypeSafe reports separate Choice and Score confidence in result.providerMetadata.typesafe.confidence. Calibrate probabilities and confidence against labeled examples from your workflow. Jev supports Zero Data Retention and No Training, enabled per request in the example. Evaluation calls also appear in logs and custom reporting, count toward budgets, and accept other Gateway provider options in the same providerOptions.gateway object. Read the documentation on evaluation models on AI Gateway f ## Is Agentic now tailors its audit by site type DevFeed: [Is Agentic now tailors its audit by site type]() Original publisher: [Read original article]() Author: Rich Haines Published: 2026-09-16T00:00:00Z Content type: release Language: en Sources: [Vercel News]() Topics: [audit](), [App](), [API](), [Authentication](), [Error Handling](), [SDKs]() Tags: [api](), [app](), [audit](), [authentication](), [error-handling](), [sdk]() ### AI overview Is Agentic reports now support four site-type views: Docs & content, Business, App, and Commerce. The selected view changes which checks are highlighted, while scores remain comparable and unchanged. ### Source excerpt Is Agentic reports now let you view your checks through one of four site types: Docs & content, Business, App, or Commerce. For example, the Commerce view highlights payment and checkout standards like x402, UCP, and ACP, while the App view highlights API discovery, authentication, error handling, and SDK support. Your score stays the same in every view, and you can change views on an existing report without rescanning. Only the highlighted checks change, so you can focus on what matters for your kind of site while keeping scores comparable across sites. To set the default view, add a single meta tag to your page's : Set the value to match your site type: content for Docs & content business for Business app for App store for Commerce The tag only sets which view readers see first. It doesn't add points or change your score. If no type is declared, Is Agentic infers one and shows which type it chose. Run a check at is-agentic.com or read the documentation. Read more ## Build an AI-powered product tagging system with Amazon SageMaker serverless model customization DevFeed: [Build an AI-powered product tagging system with Amazon SageMaker serverless model customization]() Original publisher: [Read original article]() Author: Linpo Guo Published: 2026-09-15T16:11:36Z Content type: tutorial Language: en Sources: [Artificial Intelligence]() Topics: [Artificial Intelligence](), [Amazon SageMaker AI](), [Fine-tuning](), [rlvr](), [grpo](), [Inference](), [model-deployment](), [Reinforcement learning](), [SDK](), [Serverless]() Tags: [ai](), [amazon-sagemaker](), [amazon-sagemaker-ai](), [aws](), [compute](), [cost](), [customization](), [expert-400](), [fine-tuning](), [inference](), [jobs](), [reinforcement-learning](), [rlvr](), [sdk](), [technical-how-to]() ### AI overview This walkthrough shows how to build a product tagging system by customizing Qwen3-8B with supervised fine-tuning and reinforcement learning with verifiable rewards on Amazon SageMaker serverless model customization. It then deploys the optimized model for asynchronous inference to enrich retail catalogs. ### Source excerpt Manually tagging thousands of catalog products is slow and inconsistent. This walkthrough shows how to customize Qwen3-8B with supervised fine-tuning (SFT) and reinforcement learning with verifiable rewards (RLVR) on Amazon SageMaker serverless model customization, then deploy it for asynchronous inference to build a cost-efficient product tagging system. ## Build an interview coach app with the GitHub Copilot SDK DevFeed: [Build an interview coach app with the GitHub Copilot SDK]() Original publisher: [Read original article]() Author: Justin Yoo Published: 2026-09-15T16:00:11Z Content type: tutorial Language: en Sources: [Developer Blogs]() Topics: [GitHub Copilot](), [SDK](), [Model Context Protocol](), [.NET](), [Blazor]() Tags: [agent-framework](), [ai](), [blazor](), [github-copilot](), [github-copilot-sdk](), [mcp](), [microsoft-agent-framework](), [microsoft-foundry](), [model-context-protocol](), [net](), [sdk]() ### AI overview This tutorial demonstrates how to build an interview coach application with the GitHub Copilot SDK in .NET. The sample uses a Blazor chat interface, Copilot instructions and callable tools, and Model Context Protocol services for document handling and interview records. It emphasizes giving Copilot application-specific tools rather than access to shell commands or source files. ### Source excerpt An interview coach has to do more than ask questions. It needs to read a resume, follow up on an incomplete answer, and save enough context to give useful feedback at the end. Some of that work is conversation. Some of it requires calling an application service. The GitHub Copilot SDK lets you use the [...] The post Build an interview coach app with the GitHub Copilot SDK appeared first on Microsoft for Developers. ## New in Symfony 8.2: KeyManagement Component DevFeed: [New in Symfony 8.2: KeyManagement Component]() Original publisher: [Read original article]() Author: Javier Eguiluz Published: 2026-09-15T07:11:00Z Content type: release Language: en Sources: [Symfony Blog]() Topics: [Symfony](), [Encryption](), [Amazon Web Services](), [Azure](), [Google]() Tags: [2026](), [api](), [article](), [aws](), [azure](), [backend](), [encryption](), [features](), [google](), [hashicorp-vault](), [kms](), [new-features](), [openssl](), [sdk](), [symfony]() ### AI overview Symfony 8.2 introduces an experimental KeyManagement component that provides one API for AWS KMS, Azure Key Vault, Google Cloud KMS, and HashiCorp Vault. It supports direct and envelope encryption, local development backends, provider bridges, console commands, debug-toolbar integration, and Doctrine support. ### Source excerpt This is the first article in a series showcasing the most important new features introduced by Symfony 8.2, which will be released at the end of November 2026. Contributed... ## Microsoft Releases .NET 11 RC1 with Go-Live Support, C# 15 and F# 11 as Default Language Versions DevFeed: [Microsoft Releases .NET 11 RC1 with Go-Live Support, C# 15 and F# 11 as Default Language Versions]() Original publisher: [Read original article]() Author: Almir Vuk Published: 2026-09-15T07:11:00Z Content type: news Language: en Sources: [InfoQ]() Topics: [.NET 11](), [C# 15](), [Microsoft](), [releases](), [ASP.NET Core](), [F#](), [SignalR](), [SDKs](), [.NET MAUI](), [native aot]() Tags: [android](), [api](), [asp-net-core](), [c-sharp-15](), [development](), [dotnet-11-rc-1-release](), [f-sharp](), [ios](), [macos](), [microsoft](), [native-aot](), [net](), [net-10](), [net-11](), [net-9](), [net-core](), [net-maui](), [news](), [releases](), [sdk](), [signalr]() ### AI overview Microsoft released .NET 11 Release Candidate 1 with go-live support for production use. It makes C# 15 and F# 11 the default language versions and stabilizes features from the preview cycle. The release also extends testing across mobile and desktop targets, updates SignalR authentication refresh APIs, and adds SDK, ASP.NET Core, Blazor, and Native AOT-related improvements. ### Source excerpt Last week, Microsoft has released .NET 11 Release Candidate 1, the first release candidate of the cycle, shipping with a go-live support license for production use. The release makes C# 15 and F# 11 the default language versions, extends dotnet test to mobile and desktop targets, finalizes the SignalR authentication refresh APIs, and improves Android build times in .NET MAUI. By Almir Vuk ## How to Connect Your Twilio Agent to External APIs DevFeed: [How to Connect Your Twilio Agent to External APIs]() Original publisher: [Read original article]() Author: Amanda Lange Published: 2026-09-15T00:00:00Z Content type: tutorial Language: en Sources: [Twilio Blog]() Topics: [AI Agent](), [API](), [C#](), [.NET 9](), [REST API](), [SDKs](), [OpenAI]() Tags: [agent](), [api](), [apis](), [c-sharp](), [developer-insights](), [environment-variables](), [how-to](), [net](), [net-9](), [ngrok](), [openai](), [rest-api](), [sdk]() ### AI overview This tutorial explains how to build a voice AI agent with C# and .NET 9 using Twilio Conversation Relay. It demonstrates LLM tool calling to fetch live data from an external REST API and covers project setup, required packages, environment variables, and API key storage. ### Source excerpt How to Connect Your Twilio Agent to External APIs ## Introducing Encore x E2B DevFeed: [Introducing Encore x E2B]() Original publisher: [Read original article]() Author: Ivan Cernja Published: 2026-09-15T00:00:00Z Content type: release Language: en Sources: [Encore Updates]() Topics: [AI-assisted coding](), [Cloud](), [backends](), [SDKs](), [Databases](), [Deployment](), [API](), [Code]() Tags: [agents](), [api](), [backend](), [cloud](), [code](), [coding](), [coding-agents](), [database](), [deployment](), [pull-request](), [sandbox](), [sdk]() ### AI overview Encore and E2B can provide coding agents with isolated sandboxes containing complete local backends. The integration lets agents build and verify end-to-end changes against infrastructure such as databases, queues, and APIs, while supporting deployment in the user's AWS or GCP account. ### Source excerpt Give coding agents an isolated sandbox with a complete backend inside it -- and keep both in your own cloud. ## Run Zephyr on ESP32 with QEMU: no board required DevFeed: [Run Zephyr on ESP32 with QEMU: no board required]() Original publisher: [Read original article]() Author: John Lee Published: 2026-09-15T00:00:00Z Content type: tutorial Language: en Sources: [Blog on Developer Portal]() Topics: [Zephyr RTOS](), [ESP32](), [qemu](), [Espressif](), [Emulator](), [Embedded Software Dev](), [SDK]() Tags: [blog](), [boot](), [bootloader](), [build](), [debugger](), [emulator](), [esp32](), [esp32-c3](), [esp32-c6](), [esp32-s3](), [espressif](), [firmware](), [flash](), [gdb](), [install](), [qemu](), [sdk](), [toolchain](), [tutorial](), [zephyr]() ### AI overview This tutorial explains how to install and use the Espressif QEMU fork to build and run Zephyr firmware for emulated ESP32, ESP32-S3, ESP32-C3, and ESP32-C6 targets without physical boards. It covers Simple Boot and MCUboot/sysbuild workflows, debugger attachment through GDB, and the relevant Zephyr SDK toolchain setup. ### Source excerpt Install the Espressif QEMU fork and run Zephyr on emulated ESP32, ESP32-S3, ESP32-C3, and ESP32-C6 targets without a physical board. ## Gemini 3.8 Live models now available on AI Gateway DevFeed: [Gemini 3.8 Live models now available on AI Gateway]() Original publisher: [Read original article]() Author: Jerilyn Zheng Published: 2026-09-15T00:00:00Z Content type: release Language: en Sources: [Vercel News]() Topics: [real-time](), [Artificial Intelligence](), [API](), [Google](), [WebSocket](), [SDKs]() Tags: [ai](), [api](), [cost](), [failover](), [gemini](), [google](), [real-time](), [realtime](), [sdk](), [websocket]() ### AI overview Vercel announces that Google's Gemini 3.8 Live and Gemini 3.8 Live Extended Thinking models are available on AI Gateway. The models support real-time spoken interactions, audio and visual grounding, multilingual switching, background tool calls, and parallel reasoning through the AI SDK's realtime API. ### Source excerpt Gemini 3.8 Live and Gemini 3.8 Live Extended Thinking from Google are now available on AI Gateway. Both models support real-time spoken interactions for voice assistants, conversational experiences, and applications that respond through audio. google/gemini-3.8-live supports real-time audio, visual grounding, automatic switching across 97 languages, and background tool calls while the conversation continues. google/gemini-3.8-live-extended-thinking adds multi-step reasoning that runs in parallel with speech, allowing it to acknowledge requests and narrate progress without interrupting the conversation. Use either model through the AI SDK's realtime API. Install the Gateway provider and a WebSocket client: Mint a short-lived token, open the WebSocket, and use the model adapter to serialize and parse realtime events: See the realtime quickstart for more details on realtime events and WebSocket connections. Try Gemini 3.8 Live or Gemini 3.8 Live Extended Thinking 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. Read more ## Calling the WorkOS API from server-side Swift DevFeed: [Calling the WorkOS API from server-side Swift]() Original publisher: [Read original article]() Author: WorkOS Published: 2026-09-15T00:00:00Z Content type: tutorial Language: en Sources: [WorkOS Blog]() Topics: [Swift](), [API](), [Back end](), [SDK](), [Vapor]() Tags: [api](), [async](), [backend](), [sdk](), [swift]() ### AI overview This article explains how to call the WorkOS API from trusted server-side Swift applications. It covers typed resource clients, structured errors, automatic retries for transient failures, and AsyncSequence-based pagination. ### Source excerpt Typed resources, structured errors, automatic retries, and AsyncSequence pagination for server-side Swift apps. ## AI SDK harness layer now supports native subscription authentication DevFeed: [AI SDK harness layer now supports native subscription authentication]() Original publisher: [Read original article]() Author: Felix Arntz Published: 2026-09-14T21:28:00Z Content type: release Language: en Sources: [Vercel News]() Topics: [vercel ai sdk](), [Authentication](), [OAuth](), [codex](), [Claude Code]() Tags: [ai](), [authentication](), [claude-code](), [codex](), [coding-agents](), [copilot](), [cursor](), [gateway](), [oauth](), [opencode](), [sdk]() ### AI overview The AI SDK harness layer now supports authenticating supported coding-agent harnesses through their native subscriptions. It uses explicit provider credentials when available, otherwise falls back to a host-native subscription without requiring code or configuration changes. ### Source excerpt The AI SDK harness layer now supports authenticating harnesses through their native subscriptions, where the underlying harness supports them. The harness layer runs different coding agents through the same HarnessAgent interface, so you can switch agents without changing your application code. No code changes or new settings are required. The direct authentication mode uses explicit provider environment credentials when they are present, and otherwise a native subscription found on the host. The default auto mode does the same when no AI Gateway credentials are set. The ai-gateway mode never reads native subscriptions. Native subscription credentials stay on the host. Just like with API keys, credentials are resolved at the host boundary. OAuth access tokens are refreshed there as needed. Where the sandbox supports it, the harness receives placeholder credentials and the real token is injected into outbound requests on the host. Native subscriptions work across harness adapters wherever the harness itself supports subscription login. These include Claude Code, Cline, Codex, Cursor, fx, GitHub Copilot, Grok Build, OpenCode, and Pi. Read the harness documentation to learn more. Read more ## I Had Never Built A VR App - 8 Days Later, Mine Was On The Quest Store DevFeed: [I Had Never Built A VR App - 8 Days Later, Mine Was On The Quest Store]() Original publisher: [Read original article]() Author: Craig Storm Published: 2026-09-14T15:12:36Z Content type: article Language: en Sources: [UploadVR]() Topics: [ai-coding](), [codex](), [ChatGPT](), [coding](), [Code](), [App](), [Meta](), [APK](), [SDKs]() Tags: [ai](), [ai-coding](), [apk](), [app](), [app-development](), [chatgpt](), [code](), [codex](), [coding](), [meta](), [openxr](), [sdk]() ### AI overview The article describes an eight-day attempt to build and publish Punchable Face, a simple VR game for Meta Quest, despite having little prior development experience. Using Unity, OpenXR, Meta XR SDK components, ChatGPT, and Codex, the project progressed from an empty Unity project through coding, headset testing, store asset creation, submission, and approval on the Meta Quest Store. ### Source excerpt Until a little over a week ago, Craig Storm had never built a VR app. Eight days later, his AI-assisted Quest game Punchable Face was live on Meta's store. ## Не трогая веса модели: как мы построили исследовательского агента Алисы AI и в разы сократили потребление GPU DevFeed: [Не трогая веса модели: как мы построили исследовательского агента Алисы AI и в разы сократили потребление GPU]() Original publisher: [Read original article]() Author: prohor33 (Яндекс) Published: 2026-09-14T08:01:22Z Content type: article Language: ru Sources: [Яндекс - Как мы делаем Яндекс / Статьи]() Topics: [Artificial Intelligence](), [Retrieval Augmented Generation (RAG)](), [SDKs](), [Large Language Model]() Tags: [agent](), [ai](), [ai-867179ebf949](), [deepresearch](), [llm](), [rag](), [research](), [sdk](), [tag-14fcd5db6179](), [tag-1605473766c5](), [tag-6237da65686b]() ### AI overview The article describes how the Alice AI team built and deployed a Deep Research agent without changing the model weights. It traces the evolution from a reasoning mode with a multi-query RAG pipeline into a full agent capable of planning research, searching the web, handling dynamic JavaScript content, running Python code, and working with downloaded files. It also discusses production rollout, answer quality, latency, and GPU consumption. ### Source excerpt Меня зовут Прохор, я лид команды агента "Исследовать" -- это режим глубокого исследования в чате с Алисой AI. Напомню, про что вообще речь, если никогда не пользовались Deep Research: это специальный режим работы, который строит уникальный план решения задачи пользователя, делает сотни поисков по вашему запросу, умеет ходить на сайты (даже с динамическим JavaScript-контентом), писать и выполнять Python-код (для сложных расчётов), работать со скачанными файлами и так далее. Всё это для того, чтобы дать лучший ответ на ваши сложные запросы, например: "Спланируй мне путешествие в Дагестан на две недели на машине с детьми". За год агент прошёл путь от первого прототипа до продакшена -- вместе с ним менялись качество ответов, скорость работы и потребление GPU. За продуктовую часть отвечал Руслан Илиев, продакт менеджер агента: он сформулировал продуктовые цели, определил набор инструментов и валидационный набор запросов, а затем вёл запуск от закрытого вейтлиста до 100% продакшена. Как мы к этому пришли -- через выброшенный прототип, десятки слоёв обвязки и пару болезненных уроков, -- расскажу по порядку. Добро пожаловать под кат! Читать далее ## How to Orchestrate Multi-Call Conversations with an LLM and Twilio Conversation in Node.js Memory DevFeed: [How to Orchestrate Multi-Call Conversations with an LLM and Twilio Conversation in Node.js Memory]() Original publisher: [Read original article]() Author: Amanda Lange, Dhruv Patel Published: 2026-09-14T00:00:00Z Content type: tutorial Language: en Sources: [Twilio Blog]() Topics: [Node.js](), [Large Language Model](), [Express](), [OpenAI](), [API](), [WebSocket](), [Machine learning]() Tags: [api](), [axios](), [console](), [developer-insights](), [environment-variables](), [express](), [how-to](), [http](), [ide](), [install](), [js](), [json](), [llm](), [ngrok](), [node-js](), [npm](), [openai](), [rest-api](), [sdk](), [visual-studio-code](), [web]() ### AI overview This tutorial shows how to build a Node.js Express service that uses Twilio Conversation Memory, OpenAI, and voice streams to preserve caller context, preferences, and action history across separate inbound calls. It covers project setup, package installation, local webhook exposure with ngrok, and access to Twilio's Conversation Memory REST API. ### Source excerpt Learn how to orchestrate multi-call voice conversations using Node.js, OpenAI, and Twilio Conversation Memory to persist caller context across separate calls. ## The Grafana AI SDK for Go: a shared foundation for building AI applications DevFeed: [The Grafana AI SDK for Go: a shared foundation for building AI applications]() Original publisher: [Read original article]() Author: Luccas Quadros Published: 2026-09-12T11:22:06.456390Z Content type: article Language: en Sources: [Grafana Labs blog on Grafana Labs]() Topics: [vercel ai sdk](), [SDKs](), [Go Language](), [AI Development](), [Large Language Model](), [agent observability](), [Model Context Protocol](), [React]() Tags: [agent-observability](), [ai](), [analytics](), [backend](), [building](), [go](), [grafana](), [logs](), [metrics](), [monitoring](), [observability](), [sdk](), [streaming](), [tools](), [traces]() ### AI overview Grafana Labs introduces the Grafana AI SDK for Go, an open-source shared foundation for building AI applications. The SDK standardizes model calls, streaming, tool execution, structured output, multi-step agents, workflow controls, and operational features such as retries, logging, metrics, and Agent Observability. It also supports streaming Go backends to Vercel AI SDK frontend hooks. ### Source excerpt Starting an experiment with an LLM has never been easier. Keeping a growing collection of those experiments consistent is another matter. Earlier this year, as more teams began exploring AI features here at Grafana Labs, we repeatedly encountered the same pattern: a new experiment would start, move quickly, and build its own client for whichever model provider it needed. The next experiment would do the same, with a slightly different abstraction for streaming, tools, errors, or provider configuration. This was understandable, given the circumstances. Model providers were changing quickly, our teams were learning quickly, and coding agents made it possible to turn an idea into a working integration faster than ever. But that speed also made it easier for every integration to develop its own architecture. Eventually, we were maintaining a collection of solutions to what was essentially the same problem. And since most of our backend is written in Go, we built the Grafana AI SDK for Go to give our teams a shared foundation to work from. It provides common interfaces for calling models, streaming responses, executing tools, producing structured output, and running multi-step agents. It also speaks the protocol used by Vercel AI SDK frontend hooks, so a Go backend can stream directly to useChat, useCompletion, and useObject. We built it because we needed it inside Grafana Labs, but we open sourced it last month (alongside a broader collection of tools we released for building, operating, and understanding AI systems during our first Grafana Labs AI Week) because we think other teams building AI applications in Go are likely to encounter many of the same problems. We would like to build the next part together, so in this blog I'll tell you a bit more about the project, including how you can put it to use today, as well as how you can help us improve it. What teams can build with it today The SDK supports both simple model calls and larger application workflows: Generate ## Designing Documentation for LLMs and Coding Agents DevFeed: [Designing Documentation for LLMs and Coding Agents]() Original publisher: [Read original article]() Author: CommonsWare Published: 2026-09-11T22:55:25Z Content type: opinion Language: en Sources: [The CommonsBlog]() Topics: [Documentation](), [Large Language Model](), [AI-assisted coding](), [SDKs]() Tags: [ai](), [blog](), [coding-agents](), [documentation](), [llms](), [markdown](), [sdk](), [sdks](), [software-development](), [vibe-coding]() ### AI overview This opinion article argues that SDK and library documentation should be designed for both human developers and AI systems. It recommends text-based formats such as Markdown, smaller topic-focused sections, and strong indexing to make documentation more efficient for LLMs and coding agents, while retaining formats such as PDFs and videos for human consumption. ### Source excerpt Let's continue to explore how we can succeed with libraries and SDKs at a time when coding agents will be consuming what we create, as will ordinary developers ## Adding Google Login to a React App with Auth0 DevFeed: [Adding Google Login to a React App with Auth0]() Original publisher: [Read original article]() Author: Carla Urrea Stabile Published: 2026-09-11T00:00:00Z Content type: tutorial Language: en Sources: [Auth0 Blog]() Topics: [Auth0](), [React](), [SDK](), [OAuth](), [OpenID connect (OIDC)](), [Vite](), [web applications](), [Node.js]() Tags: [auth0](), [authentication](), [google](), [node-js](), [oauth](), [oidc](), [react](), [sdk](), [tutorial](), [vite](), [web-applications]() ### AI overview A step-by-step tutorial for adding Google login to a Vite-based React application with Auth0. It covers installing the Auth0 React SDK, creating and configuring an Auth0 application, setting callback, logout, and web-origin URLs, and preparing credentials for production Google authentication. ### Source excerpt Add Google login to a React app with Auth0. A step-by-step tutorial from installing the Auth0 React SDK to configuring your own production Google credentials. ## How to Build a Voice AI Agent with OpenAI's GPT-Live, Twilio Agent Connect, and Python DevFeed: [How to Build a Voice AI Agent with OpenAI's GPT-Live, Twilio Agent Connect, and Python]() Original publisher: [Read original article]() Author: Xinghao Huang Published: 2026-09-10T00:00:00Z Content type: tutorial Language: en Sources: [Twilio Blog]() Topics: [voice ai](), [AI Agent](), [Python](), [OpenAI](), [SDKs](), [API]() Tags: [ai](), [ai-agent](), [and-best-practices](), [api](), [backend](), [code](), [developer-insights](), [how-to](), [openai](), [python](), [sdk](), [tutorials](), [voice-ai]() ### AI overview A tutorial for building inbound and outbound phone voice AI agents with Python, Twilio Agent Connect, and OpenAI's GPT-Live. It covers tool calls, proactive greetings, per-call customization, and configuration for Twilio Media Streams. ### Source excerpt Build inbound and outbound voice AI assistants that can handle tool calls using Python, Twilio Agent Connect, and OpenAI's GPT-Live. ## Tako Search is free on AI Gateway through September 30 DevFeed: [Tako Search is free on AI Gateway through September 30]() Original publisher: [Read original article]() Author: Jerilyn Zheng Published: 2026-09-10T00:00:00Z Content type: release Language: en Sources: [Vercel News]() Topics: [vercel ai sdk](), [SDKs](), [API](), [Web](), [data]() Tags: [ai](), [ai-gateway](), [api](), [configuration](), [documentation](), [free](), [integration](), [playground](), [sdk](), [search](), [tools](), [web]() ### AI overview This changelog announces that Tako Search is free on AI Gateway through September 30. It enables AI models to search curated data and the live web, filter results, and provide current answers with citations and visualizations. Tako Search works with any model on AI Gateway and does not require a separate Tako account or API key. ### Source excerpt Tako Search is free exclusively on AI Gateway through September 30. It lets AI models search Tako's curated data and the live web, filter web results by domain or publication date, and use the results to answer questions with current information, citations, and visualizations. After September 30, searches are billed at standard rates. The same integration works with any model on AI Gateway, so you can switch models without changing your search setup. You also don't need a separate Tako account or API key. To use Tako Search with the AI SDK, add gateway.tools.takoSearch() to a generateText or streamText request. The model can then call it when it needs current information: Try Tako Search in the AI Gateway playground. See the web search documentation for configuration and search options. Read more ## Vercel Sandbox is now available in all regions DevFeed: [Vercel Sandbox is now available in all regions]() Original publisher: [Read original article]() Author: Rob Herley Published: 2026-09-10T00:00:00Z Content type: release Language: en Sources: [Vercel News]() Topics: [Vercel](), [cloud-infrastructure](), [Latency](), [Command-line interface](), [SDKs](), [configuration]() Tags: [cli](), [compute](), [configuration](), [cpu](), [enterprise](), [latency](), [memory](), [sdk](), [support](), [update](), [vercel]() ### AI overview Vercel Sandbox is now available across all 20 Vercel compute regions, expanding from four. Users can select primary and failover regions, helping reduce latency and meet data residency requirements. Region selection is available on every plan, while Pro and Enterprise teams can configure failover regions. ### Source excerpt Vercel Sandbox can now run in all 20 Vercel compute regions, up from four. Running sandboxes closer to the databases, storage, and other services they access reduces latency. Teams can also keep sandbox workloads in approved regions to support data residency and regional processing requirements. iad1 remains the default region. Region selection is available on every plan. Pro and Enterprise teams can also configure failover regions. If Vercel can't create a sandbox in the primary region, it tries each configured failover region in order. Teams with data residency requirements can limit primary and failover regions to approved geographies. Set a default region for new sandboxes from your project's Settings > Sandboxes. With the SDK: With the CLI: A region passed at creation overrides the project default. Existing sandboxes remain in the regions where they were created. Active CPU and Provisioned Memory rates vary by region. See Sandbox pricing. If you use the Sandbox SDK or CLI, update it to the latest version before selecting one of the newly supported regions. Learn about region selection, failover, and CLI configuration in the Sandbox regions documentation. Read more ## 9 September 2026 Artifact Wave DevFeed: [9 September 2026 Artifact Wave]() Original publisher: [Read original article]() Author: CommonsWare Published: 2026-09-09T20:32:28Z Content type: release Language: en Sources: [The CommonsBlog]() Topics: [Jetpack](), [iOS]() Tags: [2026](), [blog](), [ios](), [jetpack](), [sdk](), [september-2026]() ### AI overview This release update covers a new Jetpack artifact wave, including Ink Rendering support for iOS and more than 1,300 new artifact versions. ### Source excerpt Here is what we got as new Jetpack artifacts this week! [Next page]()