# SDKs Software development kits are collections of software tools, libraries, APIs, and related resources used to build applications for a platform or technology. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## SDK vs CLI vs MCP, a dev story DevFeed: [SDK vs CLI vs MCP, a dev story]() Original publisher: [Read original article]() Author: Anthony Viard Published: 2026-09-17T16:00:00Z Content type: opinion Language: en Sources: [Postman Blog]() Topics: [SDKs](), [MCP Server](), [Command-line interface](), [GitHub API](), [Claude Code](), [GitHub]() Tags: [ai-agents](), [claude](), [cli](), [code](), [general](), [mcp](), [mcp-server](), [model-context-protocol](), [postman](), [sdk](), [sdk-generation]() ### AI overview A developer compares SDKs, command-line tools, and MCP servers as ways for an agent to access the GitHub API. Using an internal release-readiness tool as a case study, the article explains that SDKs suit scheduled, predefined code while interactive agent use may require more improvisation. ### Source excerpt SDK, CLI, or MCP server for your agent? I measured the token cost and auth tradeoffs of all three against one real API. See which to pick. The post SDK vs CLI vs MCP, a dev story appeared first on Postman Blog. ## 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 ## 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. ## 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 ## 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 ## Appwrite 2.1 is now available for self-hosting DevFeed: [Appwrite 2.1 is now available for self-hosting]() Original publisher: [Read original article]() Author: Atharva Deosthale Published: 2026-09-14T00:00:00Z Content type: article Language: en Sources: [Appwrite Blog]() Topics: [Appwrite](), [Amazon S3](), [hosting](), [API](), [API keys](), [Command-line interface](), [SDKs](), [Compression](), [Encryption]() Tags: [api](), [api-documentation](), [aws](), [cli](), [compression](), [encryption](), [hosting](), [products](), [s3](), [self-hosting](), [sign-in]() ### AI overview Appwrite 2.1 is available for self-hosted deployments. It adds the S3-compatible Storage API and AutoGravity image previews, along with TikTok and Kakao sign-in and Appwrite Console fixes. The release uses the existing upgrade command and migration process from Appwrite 2.0. ### Source excerpt Appwrite 2.1 brings the S3 API and AutoGravity to self-hosted instances, adds TikTok and Kakao sign-in, and fixes for Appwrite Console. ## Company's Building AI Agent Sandbox SDKs & APIs DevFeed: [Company's Building AI Agent Sandbox SDKs & APIs]() Original publisher: [Read original article]() Author: iHateReading Published: 2026-09-12T13:15:15Z Content type: opinion Language: en Sources: [iHateReading]() Topics: [AI Agent](), [SDKs](), [Artificial Intelligence](), [Development]() Tags: [ai](), [ai-agent](), [ai-sandboxes](), [ai-sandboxes-vercel-aws-google-daytona](), [apis](), [aws](), [backend](), [blockchain](), [coding](), [company-s-building-ai-agent-sandbox-sdks-apis](), [daytona](), [developer](), [development](), [frontend](), [google](), [ihatereading](), [javascript](), [node-js](), [product](), [programming](), [react](), [sdks](), [typescript](), [vercel](), [web-development]() ### AI overview An opinion article discusses companies building AI agent sandboxes, SDKs, and APIs, in the context of an ongoing competition around AI sandboxes. ### Source excerpt Company's Building AI Agent Sandbox SDKs & APIs I wrote about the fight for AI sandboxes back in March -- the short version was that every AI agent that wri... ## Visual playback of the user journey: Introducing Session Replay in Grafana Cloud Frontend Observability DevFeed: [Visual playback of the user journey: Introducing Session Replay in Grafana Cloud Frontend Observability]() Original publisher: [Read original article]() Author: Lukasz Gut Published: 2026-09-12T11:22:06.456390Z Content type: article Language: en Sources: [Grafana Labs blog on Grafana Labs]() Topics: [session replay](), [Grafana Cloud Frontend Observability](), [Frontend observability](), [web applications](), [telemetry](), [SDKs]() Tags: [analytics](), [frontend-observability](), [grafana](), [grafana-cloud-frontend-observability](), [instrumentation](), [logs](), [metrics](), [monitoring](), [observability](), [privacy](), [session-replay](), [telemetry](), [traces](), [web-applications]() ### AI overview The article introduces Session Replay, a public-preview add-on for Grafana Cloud Frontend Observability. It visually reconstructs how a web application appeared and changed during a user session, linking that playback to session timelines and technical telemetry so engineering teams can investigate frontend problems more directly. ### Source excerpt Grafana Cloud Frontend Observability helps engineering teams quantify the end user experience by bringing metrics, logs, traces, and user session context to client-side web applications. Teams can monitor application health and performance over time, triage errors, and correlate frontend signals with backend telemetry to investigate issues across the stack. Yet some of the hardest frontend problems remain difficult to diagnose. A support ticket might report that a checkout button did nothing, a form unexpectedly reset, or a workflow broke only in one browser or on one device. Metrics can reveal a performance regression, logs can capture an error, and traces can expose a slow request, but no single signal shows what the interface actually looked like to the user. This is exactly why we built Session Replay, an add-on feature in Frontend Observability that provides a visual reconstruction of a user's journey, connected to the telemetry Grafana Cloud already collects. It helps engineering teams move from a reported problem to seeing what happened and knowing exactly where to investigate next. What is Session Replay in Grafana Cloud Frontend Observability? Session Replay, now in public preview, adds visual playback capabilities to Frontend Observability. It reconstructs how a web application appeared and changed as a user navigated and interacted with it, so you can observe the journey as it unfolded instead of inferring it from individual telemetry events. Frontend Observability already brings together a chronological timeline of the events within a user session. That timeline tells you what happened and when. Session Replay adds the missing visual context: what was happening in the interface around those events and how one interaction led to the next. Because both views belong to the same session, you can move between the user experience and the relevant technical signals without losing the thread of your investigation. Session Replay builds on the Grafana Faro Web SD ## 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 ## Scaling AI Agent Infrastructure with the MCP Stateless updates DevFeed: [Scaling AI Agent Infrastructure with the MCP Stateless updates]() Original publisher: [Read original article]() Author: Kurtis Van Gent; Alan Blount Published: 2026-09-12T11:04:33.891311Z Content type: release Language: en Sources: [Google Developers Blog]() Topics: [networking](), [SDKs]() Tags: [agentic](), [ai-agent](), [c-sharp](), [go](), [http](), [infrastructure](), [mcp](), [python](), [routing](), [sdks](), [serverless](), [typescript](), [updates]() ### AI overview The MCP release candidate replaces transport-level session management with a stateless core for HTTP load balancing, cloud-native scaling, and serverless deployments. It also introduces routing headers, caching controls, MRTR, and beta SDKs for Python, TypeScript, Go, and C#. ### Source excerpt The 2026-07-28 Model Context Protocol (MCP) specification replaces legacy stateful constraints with a fully stateless core, enabling cloud-native horizontal scaling, serverless deployments, and standard round-robin load balancing. This architectural shift introduces standardized HTTP headers for efficient routing without deep packet inspection, caching controls, and Multi Round-Trip Requests (MRTR) to handle interactive and long-running tasks without blocking connections. Developers can immediately begin migrating their agentic applications to this highly scalable infrastructure using the newly available beta SDKs for Python, TypeScript, Go, and C#. ## Agent and Model Evaluations in Gemini Enterprise Agent Platform are now GA DevFeed: [Agent and Model Evaluations in Gemini Enterprise Agent Platform are now GA]() Original publisher: [Read original article]() Author: Alex Martin; Dima Melnyk Published: 2026-09-12T11:04:33.891311Z Content type: release Language: en Sources: [Google Developers Blog]() Topics: [ai observability](), [SDKs](), [ci]() Tags: [agent](), [agents](), [ci](), [cli](), [development](), [enterprise](), [evaluation](), [experiments](), [gemini](), [llm](), [metrics](), [model](), [platform](), [production](), [telemetry](), [testing](), [traces]() ### AI overview Gemini Enterprise Agent Platform's evaluation service is generally available. It provides consistent evaluation of agents and models across local experiments and production traffic, with pre-built metrics, adaptive rubrics, custom metrics, simulators, and workflow integrations. ### Source excerpt Agent Platform's evaluation service is now generally available, providing developers with a unified engine to measure agent quality consistently across local development experiments and live production traffic. You can evaluate agents using over 20 pre-built metrics, DeepMind-backed adaptive rubrics, or custom code-based and LLM-as-a-judge metrics stored in a centralized, versioned registry. The service integrates directly into existing workflows via the Agent Platform SDK, agents-cli, and ADK, offering built-in user and environment simulators to automate complex multi-turn testing and streamline CI pipelines. ## Visual Studio Code 1.138 (Insiders) DevFeed: [Visual Studio Code 1.138 (Insiders)]() Original publisher: [Read original article]() Author: Visual Studio Code Team Published: 2026-09-12T11:03:34.159469Z Content type: release Language: en Sources: [Visual Studio Code - Code Editing. Redefined.]() Topics: [Visual Studio Code](), [releases](), [SDKs]() Tags: [agents](), [codex](), [release](), [release-notes](), [sandbox](), [vs-code]() ### AI overview Visual Studio Code 1.138 Insiders adds improvements to Agent Host sessions, including shareable links, adjacent sessions, repository context, voice awareness, SDK setup recovery, sandbox-policy consistency, and session cleanup controls. ### Source excerpt Learn what's new in Visual Studio Code 1.138 (Insiders) Read the full article ## How to Build an AI Chat App Interface With the Vercel AI SDK and Shadcn/ui DevFeed: [How to Build an AI Chat App Interface With the Vercel AI SDK and Shadcn/ui]() Original publisher: [Read original article]() Author: Vaibhav Gupta Published: 2026-09-11T16:21:29Z Content type: tutorial Language: en Sources: [freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More]() Topics: [vercel ai sdk](), [AI Chat](), [SDKs](), [Node.js]() Tags: [ai](), [app](), [how-to](), [llm](), [mcp](), [next-js](), [node](), [openai](), [react](), [shadcn-ai-chat-app](), [shadcn-ui](), [streaming](), [tailwind](), [typescript](), [ui](), [vercel](), [vercel-ai-sdk]() ### AI overview Tutorial for building a streaming AI chat interface in Next.js with the Vercel AI SDK and shadcn/ui. ### Source excerpt Every other AI product you open today has the same screen: a message list, a text box at the bottom, and words that stream in one token at a time. It looks simple, but it's not simple to build well. Y ## From zero-shot forecast to purchase order with Amazon Bedrock AgentCore DevFeed: [From zero-shot forecast to purchase order with Amazon Bedrock AgentCore]() Original publisher: [Read original article]() Author: Hyunsoo Kim, Ph.D. Published: 2026-09-11T14:08:01Z Content type: article Language: en Sources: [AWS Architecture Blog]() Topics: [AI Bots](), [Training AI Models](), [SDKs]() Tags: [advanced-300](), [agents](), [amazon-bedrock](), [amazon-bedrock-agentcore](), [automation](), [best-practices](), [orchestration](), [strands-agents](), [time-series](), [training](), [zero-shot]() ### AI overview An architecture article on using Amazon Chronos2 zero-shot forecasting and Bedrock AgentCore multi-agent orchestration to turn demand forecasts into validated purchase orders without per-product model training. ### Source excerpt Combine zero-shot forecasting with Amazon Chronos2 and multi-agent orchestration on Amazon Bedrock AgentCore to turn demand forecasts into validated purchase orders. No per-product model training, with business rules, auditability, and cost that scales to zero. ## How Featured's users make 100K media pitches per month on Vercel DevFeed: [How Featured's users make 100K media pitches per month on Vercel]() Original publisher: [Read original article]() Author: Susan Aziz Published: 2026-09-11T04:00:00Z Content type: article Language: en Sources: [Vercel News]() Topics: [migration](), [vercel ai sdk](), [AI Chat](), [SDKs]() Tags: [ai](), [ai-gateway](), [aws](), [featured](), [infrastructure](), [migration](), [models](), [vercel](), [vercel-ai-sdk](), [workflow]() ### AI overview Featured describes migrating 374 Sanity sites from AWS Elastic Beanstalk to Vercel while using Vercel's AI SDK, AI Gateway, and Workflow SDK to reduce infrastructure work for a three-engineer team. ### Source excerpt Featured on Vercel 3 engineers supporting 3 brands and 100,000+ users on Vercel Migrated 374 Sanity sites from AWS Elastic Beanstalk to Vercel AI SDK and AI Gateway power Featured's chat bot across 17 models Workflow SDK replaced custom long-running job infrastructure Featured is a co-pilot for public relations (PR) that subject matter experts and PR teams use to find media opportunities. Tell Featured's agents what you know, and it surfaces opportunities across journalist requests, podcasts, awards, and GEO, with no PR background required. Founder Brett Farmiloe knows from experience how hard and time consuming getting press is. He spent 10 years running Markitors, a digital marketing agency with 500 small business clients. Every client, from an eyelash extension supplier to an equipment financing company, had real expertise to share, but no way to get it in front of journalists. PR, as Farmiloe puts it, "has always been about who has access to what." He founded Featured to change the question from who has access to who has knowledge. Featured connects one of their users with a journalist or publisher every 6 seconds. Their agents deliver more than 100,000 media pitches per month, and have sent over 100 million Help A Reporter Out (HARO) emails in the past year. Behind it all is an engineering team of just three people. With a team that lean, there's no time to manage servers or piece together custom integrations. Every hour spent on infrastructure is an hour taken away from building features what will help their customers land more media placements. The cost of managing infrastructure by hand Before Vercel, Featured's infrastructure work pulled the team away from product development. Hosting lived on AWS Elastic Beanstalk, AI features depended on custom provider integrations, and long-running, multi-step jobs ran on separate orchestration infrastructure. Each layer worked, but each one added operational overhead for a three-person team supporting multiple brands. ## Physical AI Takes the Wheel: How the World's Robotaxi Leaders Are Building With NVIDIA Technologies DevFeed: [Physical AI Takes the Wheel: How the World's Robotaxi Leaders Are Building With NVIDIA Technologies]() Original publisher: [Read original article]() Author: Ali Kani Published: 2026-09-10T16:00:04Z Content type: article Language: en Sources: [NVIDIA Blog]() Topics: [Training AI Models](), [SDKs]() Tags: [ai-models](), [ai-training](), [artificial-intelligence](), [autonomous-vehicles](), [cosmos](), [customer-stories](), [datasets](), [driving](), [mobility](), [nvidia](), [nvidia-blackwell](), [nvidia-dgx](), [nvidia-drive](), [nvidia-halos](), [omniverse](), [physical-ai](), [simulation-and-design]() ### AI overview NVIDIA describes an open robotaxi platform for training AI driving models, simulation and safety validation, and real-time in-vehicle computing. ### Source excerpt The global robotaxi market -- physical AI's first commercial breakthrough -- is projected to reach $400 billion by 2035, with over 6 million commercial vehicles in operation as driverless fleets are already moving people through some of the world's busiest and most complex streets. Deploying a driverless vehicle is one challenge. Scaling a fleet is [...] ## VMware defends ending downloads of SDK that helps VM backups - or migrations to rivals DevFeed: [VMware defends ending downloads of SDK that helps VM backups - or migrations to rivals]() Original publisher: [Read original article]() Author: Simon Sharwood Published: 2026-09-10T02:05:58Z Content type: news Language: en Sources: [www.theregister.com - Articles]() Topics: [SDKs]() Tags: [dev](), [migration](), [software-license](), [virtualization](), [vmware]() ### AI overview The article reports that VMware is defending its decision to end downloads of an SDK used for virtual-machine backups and potentially migrations to rival platforms. VMware says the license was never intended to support moving VMs. ### Source excerpt Says license was never about moving VMs ## Build a support chatbot with Appwrite Functions and VectorsDB DevFeed: [Build a support chatbot with Appwrite Functions and VectorsDB]() Original publisher: [Read original article]() Author: Atharva Deosthale Published: 2026-09-10T00:00:00Z Content type: tutorial Language: en Sources: [Appwrite Blog]() Topics: [Appwrite](), [Chat Bot](), [Retrieval-Augmented Generation](), [Embeddings](), [React](), [SDKs](), [Node.js]() Tags: [api](), [build](), [chat](), [deploy](), [embedding](), [functions](), [node-js](), [rag](), [react](), [retrieval](), [support](), [tutorials]() ### AI overview This tutorial builds Harbor Help, a support chatbot for a fictional project-management product. It uses Appwrite VectorsDB to store help-center article embeddings, an Appwrite Function to retrieve relevant excerpts and generate answers through OpenRouter, and Appwrite Sites to host a React interface. The chatbot uses retrieval-augmented generation and can refuse questions outside the help center. ### Source excerpt Seed a help center, retrieve relevant articles with VectorsDB, and answer customer questions through an Appwrite Function. Deploy the chat interface on Appwrite Sites. ## 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. ## Build Voice AI Experiences with Twilio and GPT-Live-1 in the OpenAI API DevFeed: [Build Voice AI Experiences with Twilio and GPT-Live-1 in the OpenAI API]() Original publisher: [Read original article]() Author: Lenore Files Published: 2026-09-10T00:00:00Z Content type: tutorial Language: en Sources: [Twilio Blog]() Topics: [voice ai](), [real-time](), [API](), [SDKs](), [OpenAI](), [Node.js](), [Python]() Tags: [ai](), [api](), [developer-insights](), [openai](), [real-time](), [tutorials](), [voice](), [voice-ai](), [websocket]() ### AI overview This tutorial article explains how to build real-time voice AI applications by connecting Twilio Agent Connect with OpenAI's GPT-Live-1 through the OpenAI API. It provides tutorials, sample applications, SDK support, and documentation for voice assistants and outbound voice agents using Node.js and Python. ### Source excerpt Build real-time voice AI Agents with Twilio and GPT-Live-1 in the OpenAI API with these tutorials, sample apps, and more. [Next page]()