# responses Published articles for responses. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Cypress Cloud MCP adds test details, filters, and compact responses DevFeed: [Cypress Cloud MCP adds test details, filters, and compact responses]() Original publisher: [Read original article]() Published: 2026-09-15T16:21:48Z Content type: release Language: en Sources: [Cypress Blog]() Topics: [Cypress](), [MCP](), [Test coverage]() Tags: [cypress](), [mcp](), [release](), [responses](), [september-2026]() ### AI overview Cypress Cloud MCP now provides compact responses by default, new per-spec and per-test tools, filters by run, spec, and test status, and duration data across multiple levels. It also adds deeper links for accessibility and UI coverage reports. ### Source excerpt New tools, more data, smaller responses: what's new in Cypress Cloud MCP as of September 2026. ## One or two nameservers? DevFeed: [One or two nameservers?]() Original publisher: [Read original article]() Author: Geoff Huston Published: 2026-09-15T06:01:49Z Content type: article Language: en Sources: [APNIC Blog]() Topics: [Internet](), [Query (disambiguation)](), [Server](), [Caching]() Tags: [2026](), [africa](), [article](), [china](), [dns](), [europe](), [experiment](), [india](), [internet](), [ipv4](), [ipv6](), [measurement](), [measurements](), [recursive-resolver](), [repeat](), [responses](), [server](), [tech-matters]() ### AI overview This article reports an experiment testing whether serving a DNS zone with two authoritative dual-stack nameservers changes repeated queries. Compared with one nameserver, two nameservers increased the single-query completion rate from 58% to 71% and reduced the average queries per test from 3.43 to 2.57. ### Source excerpt Do multiple dual-stack nameservers increase or decrease repeat DNS queries? The results of this experiment were a complete surprise. ## OpenAI's safety system is already cutting off API responses mid-task DevFeed: [OpenAI's safety system is already cutting off API responses mid-task]() Original publisher: [Read original article]() Author: Amanda Caswell Published: 2026-09-11T17:52:56Z Content type: news Language: en Sources: [The New Stack]() Topics: [releases](), [AI Bots]() Tags: [agents](), [ai](), [ai-models](), [ai-strategy](), [api](), [cybersecurity](), [developers](), [model-development](), [models](), [openai](), [reinforcement-learning](), [release](), [responses](), [safety](), [software-development]() ### AI overview OpenAI is reportedly considering slower development of its most advanced AI systems as safety concerns could delay releases and limit access. The article cites pauses in model work and restrictions following cybersecurity evaluations and an AI-agent containment incident. ### Source excerpt AI companies have spent the last few years competing to build the best models, faster than the other, with each The post OpenAI's safety system is already cutting off API responses mid-task appeared first on The New Stack. ## 2026 APNIC Survey report released DevFeed: [2026 APNIC Survey report released]() Original publisher: [Read original article]() Author: Dale Roberts Published: 2026-09-10T03:21:38Z Content type: article Language: en Sources: [APNIC Blog]() Topics: [Internet]() Tags: [apnic-62](), [apnic-survey](), [blog](), [community](), [executive](), [india](), [internet](), [open](), [research](), [responses](), [september-2026](), [survey]() ### AI overview The 2026 APNIC Survey report is available, presenting feedback from 1,385 valid responses about APNIC's performance, priorities, and future direction. A summary will be discussed at APNIC 62 in Mumbai, with the findings and a later response from the Executive Council forming part of APNIC's strategic planning. ### Source excerpt The 2026 APNIC Survey report is now available, providing valuable feedback from Members and the wider Internet community on APNIC's performance, priorities, and future direction. ## DeepSeek V4.1 Flash now available on AI Gateway DevFeed: [DeepSeek V4.1 Flash now available on AI Gateway]() Original publisher: [Read original article]() Author: Jerilyn Zheng Published: 2026-09-09T00:00:00Z Content type: release Language: en Sources: [Vercel News]() Topics: [deepseek](), [Artificial Intelligence](), [API](), [Language models](), [AI-assisted coding](), [Claude Code](), [codex](), [cursor]() Tags: [ai](), [ai-gateway](), [api](), [caching](), [codex](), [context-window](), [cursor](), [deepseek](), [responses](), [tool](), [vercel](), [vision]() ### AI overview DeepSeek V4.1 Flash is now available through Vercel's AI Gateway, offering native image understanding, a 1 million token context window, responses up to 384,000 tokens, reasoning, tool use, and prompt caching. Developers can use it in Claude Code, Codex, Cursor, and other coding agents with the model name deepseek/deepseek-v4.1-flash. ### Source excerpt DeepSeek V4.1 Flash is now available on AI Gateway with native image understanding. V4.1 Flash has vision support and accepts text and images in the same request, so you can ask questions about screenshots, read charts, and extract information from visual content. The model has a 1 million token context window and supports responses up to 384,000 tokens, along with reasoning, tool use, and prompt caching. Its new architecture processes input and generates output with separate components, reducing the active computation needed for each stage. Use deepseek/deepseek-v4.1-flash as the model name: To use it in Claude Code, Codex, Cursor, and more, install the latest Vercel CLI and run setup: Then select deepseek/deepseek-v4.1-flash in the agent. See the coding agents guide for details. Try DeepSeek V4.1 Flash 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, routing rules, 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. You can view all language models available on AI Gateway. Read more ## The Evolution of HTTP, Clearly Explained DevFeed: [The Evolution of HTTP, Clearly Explained]() Original publisher: [Read original article]() Author: Nikki Siapno Published: 2026-09-04T16:39:02Z Content type: article Language: en Sources: [Level Up Coding System Design Newsletter]() Topics: [HTTP](), [client](), [servers](), [Caching](), [Web]() Tags: [browser](), [caching](), [encoding](), [evolution](), [http](), [http-3](), [protocol](), [request](), [responses](), [server](), [tcp]() ### AI overview An explanatory article traces HTTP from versions 0.9 through 1.1, describing how headers, status codes, persistent connections, caching, and chunked transfer encoding addressed the web's growing scale and performance needs. It introduces HTTP/3 as part of that broader evolution. ### Source excerpt From 0.9 to 3.0: What changed and why? ## From Preferences to Principles: Rubric-Based Alignment for Grounded Knowledge Answers DevFeed: [From Preferences to Principles: Rubric-Based Alignment for Grounded Knowledge Answers]() Original publisher: [Read original article]() Published: 2026-08-27T00:00:00Z Content type: article Language: en Sources: [Apple Machine Learning Research]() Topics: [Large Language Model](), [LLM evaluation / benchmarking](), [Natural language processing](), [datasets]() Tags: [datasets](), [evaluation](), [models](), [post-training](), [research](), [responses](), [search]() ### AI overview The article introduces a rubric-based reward framework for grounded open-domain question answering. It generates query-specific rubrics from retrieved evidence and decomposes them into quality dimensions for fine-grained post-training supervision. The method improves composition, grounding, and instruction-following results over the stated baselines and evaluation datasets. ### Source excerpt Designing effective reward signals for open-domain question answering is challenging because high-quality responses must simultaneously satisfy multiple aspects of answer quality that are difficult to capture with a holistic scalar objective. We introduce a rubric-based reward framework that generates query-specific rubrics grounded in retrieved evidence and decomposed into multiple quality dimensions, providing fine-grained supervision during post-training. Averaged across three evaluation axes (composition, grounding, and instruction-following), our approach improves over the... ## Chat SDK adds Instagram adapter DevFeed: [Chat SDK adds Instagram adapter]() Original publisher: [Read original article]() Author: Ben Sabic Published: 2026-08-19T00:00:00Z Content type: release Language: en Sources: [Vercel News]() Topics: [AI Bots]() Tags: [api](), [bots](), [media](), [messaging](), [meta](), [responses]() ### AI overview Chat SDK has added an Instagram adapter for building bots that handle DMs, media, quick replies, typing indicators, reactions, and story replies. It uses Meta's Instagram Messaging API and is subject to account and 24-hour reply-window requirements. ### Source excerpt You can now build bots for Instagram with the new Instagram adapter for Chat SDK. Bots can send and receive DMs and media, render cards as quick replies and link buttons, show typing indicators, receive reactions, and handle story replies. The adapter connects through Meta's Instagram Messaging API and requires a professional Business or Creator account. Messages are buffered, so streamed responses send as one message when the stream completes. Meta enforces a 24-hour messaging window, so bots can only reply within a day of the user's last message. Read the documentation to get started or browse the adapter directory. Read more ## The builder's guide to GPT-5.6 DevFeed: [The builder's guide to GPT-5.6]() Original publisher: [Read original article]() Published: 2026-08-13T11:00:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [Artificial Intelligence](), [Model Routing](), [API](), [Orchestration](), [Benchmark]() Tags: [agentic](), [agents](), [ai](), [ai-agents](), [api](), [applied-ai](), [benchmark](), [build-faster](), [compute](), [cost](), [efficiency](), [frontier-model](), [gpt](), [latency](), [model](), [performance](), [responses](), [search](), [tokens](), [workflows]() ### AI overview A technical guide to using GPT-5.6 in production AI agents. It covers smarter model selection, cost-efficient reasoning, Responses API controls, multi-agent orchestration, programmatic tool calling, and the use of smaller models for high-volume or latency-sensitive workflows. ### Source excerpt Learn how startups use GPT-5.6 to build faster, more cost-efficient AI agents with smarter model selection and new Responses API capabilities. ## Improving GPT-5.6 Sol in ChatGPT--and expanding access to GPT-5.6 Luna for free users DevFeed: [Improving GPT-5.6 Sol in ChatGPT--and expanding access to GPT-5.6 Luna for free users]() Original publisher: [Read original article]() Published: 2026-08-06T10:00:00Z Content type: release Language: en Sources: [OpenAI News]() Topics: [ChatGPT](), [Web]() Tags: [chatgpt](), [gpt](), [model](), [product](), [responses](), [update](), [updates](), [web]() ### AI overview OpenAI announces ChatGPT updates centered on GPT-5.6 Sol, including more focused and reliable answers, adjustable reasoning effort, and a more consistent experience between Instant and Thinking modes. Free users receive expanded access to GPT-5.6 Luna, including unlimited text chats and a new Think button for harder questions. ### Source excerpt ChatGPT introduces improved GPT-5.6 Sol with better accuracy and consistency, plus expanded access for free users and unlimited everyday chats with GPT-5.6 Luna. ## New release of LLM adds support for reasoning traces, OpenAI Responses, server-side tools, and smarter logging DevFeed: [New release of LLM adds support for reasoning traces, OpenAI Responses, server-side tools, and smarter logging]() Original publisher: [Read original article]() Author: Simon Willison Published: 2026-08-04T23:58:24Z Content type: release Language: en Sources: [Simon Willison]() Topics: [Tool](), [Command-line interface](), [API](), [Large Language Model](), [SQLite](), [Logging](), [OpenAI](), [Claude](), [Model Context Protocol](), [gemma4]() Tags: [ai](), [ai-2-235](), [anthropic](), [anthropic-336](), [cli](), [gemma](), [generative-ai](), [generative-ai-1-981](), [llm](), [llm-631](), [llm-reasoning](), [llm-reasoning-103](), [llm-tool-use](), [llm-tool-use-75](), [llms](), [llms-1-947](), [logging](), [mcp](), [model-context-protocol](), [model-context-protocol-35](), [openai](), [openai-463](), [projects](), [projects-553](), [release](), [releases](), [releases-31](), [responses](), [server](), [sqlite]() ### AI overview LLM 0.32 adds visible reasoning traces, server-side provider tools, redesigned content-addressable SQLite logs, support for the GPT-5.6 model family, and features enabled by the OpenAI Responses API. The release also updates the llm-anthropic plugin and adds Python API changes. ### Source excerpt I released LLM 0.32 this morning, the most significant new version of LLM since the initial launch of the project. The new version includes support for visible reasoning traces, server-side provider tools, redesigned content-addressable SQLite logs, new models, and new features enabled by the OpenAI Responses API. I also released a new version of the llm-anthropic plugin with substantial updates of its own. Headline features for LLM CLI users Running LLM against reasoning models now displays their reasoning traces to standard error, so you can see what they are "thinking" without that information being included in the standard output that you might pipe to another tool. Add -R/--hide-reasoning to turn this off. LLM includes support out-of-the-box for the GPT-5.6 model family, and the new default model used with llm "prompt" is now the inexpensive but capable GPT-5.6 Luna. LLM calls can now use server-side tools from various providers. OpenAI provide a code execution environment as a server-side tool; LLM can now run prompts that benefit from that like so: llm --tool CodeInterpreter 'Show current python and SQLite versions' OpenAI also gets a WebSearch tool. The llm-anthropic plugin adds WebSearch, WebFetch, CodeExecution, and AnthropicMCP, which looks like this: llm -m claude-sonnet-5 -T 'AnthropicMCP("https://datasette.simonwillison.net/-/mcp")' \ 'how many rows in the blog_blogmark table?' That causes Anthropic to execute MCP calls against my new datasette-mcp plugin as part of a single request/response interaction with their API. The new llm openai endpoint command provides a tool for executing prompts against any OpenAI compatible endpoint as a one-liner. These aren't logged, which makes this a handy tool for running one-off prompts against anything that speaks the lingua franca of the LLM API world. Here's how I use that to run prompts against Gemma 4 12B running in my localhost LM Studio API, via uvx (no LLM installation required) and mixing in the llm-tools-q ## Protect AWS Strands Agents with Datadog AI Guard DevFeed: [Protect AWS Strands Agents with Datadog AI Guard]() Original publisher: [Read original article]() Author: Kola Akinnibi; Vijay George; Emmanuelle Lejeail; Alexa Levine Published: 2026-07-08T00:00:00Z Content type: article Language: en Sources: [Datadog | The Monitor blog]() Topics: [Strands Agents](), [Application Security](), [Security](), [Amazon Web Services](), [Orchestration](), [agent observability]() Tags: [agent](), [agent-observability](), [agentic](), [agents](), [ai](), [ai-agents](), [ai-security](), [app-api-protection](), [application-security](), [attacks](), [aws](), [monitor](), [orchestration](), [prompt-injection](), [responses](), [security](), [strands-agents](), [threat-detection](), [tool](), [tools]() ### AI overview Datadog AI Guard integrates with AWS Strands Agents through a Strands plugin that evaluates prompts, model responses, and tool interactions during agent execution. It uses Strands lifecycle hooks to monitor or block unsafe behavior, centralize enforcement, and detect multistep attacks in the context of a full agent session. ### Source excerpt Monitor and help protect AWS Strands Agents by using Datadog AI Guard to evaluate prompts, model responses, and tool calls inline. ## Tail latency: why the slowest requests matter most DevFeed: [Tail latency: why the slowest requests matter most]() Original publisher: [Read original article]() Author: Jim Allen Wallace Published: 2026-07-04T00:00:00Z Content type: article Language: en Sources: [Redis Blog]() Topics: [Latency](), [Back end](), [servers](), [API](), [App]() Tags: [app](), [architecture](), [article](), [backend](), [latency](), [performance](), [responses](), [server](), [tech-de]() ### AI overview Explains tail latency, the slowest requests hidden by averages, and why percentile measurements such as p95, p99, and p99.9 better reveal user-impacting performance problems. It also describes how fan-out across backend servers amplifies the effect of a single slow sub-response. ### Source excerpt Tail latency is the handful of requests that take far longer than the rest, hiding inside an average that looks perfectly fine. Your dashboard shows a 50ms average, everyone seems happy, and then support tickets start piling up about the app feeling s... ## xAI Grok audio models now available on Vercel AI Gateway DevFeed: [xAI Grok audio models now available on Vercel AI Gateway]() Original publisher: [Read original article]() Author: Carlton Aikins Published: 2026-06-29T00:00:00Z Content type: release Language: en Sources: [Vercel News]() Topics: [vercel ai sdk](), [voice ai](), [Artificial Intelligence](), [SDKs](), [API](), [WebSocket](), [Vercel](), [React](), [browser]() Tags: [ai](), [ai-gateway](), [api](), [audio](), [browser](), [models](), [observability](), [react](), [release](), [responses](), [routing](), [sdk](), [server](), [speech](), [text-to-speech](), [vercel](), [voice]() ### AI overview xAI Grok audio models are now available through Vercel AI Gateway and the AI SDK 7 release. The integration supports realtime voice, text-to-speech, and speech-to-text, with routing, observability, and spend controls. ### Source excerpt xAI's audio models are now live on AI Gateway. Realtime voice, text to speech, and speech to text are all available through the AI SDK with the same routing, observability, and spend controls as your other models. These capabilities are available on the AI SDK 7 release. Available models Capability Models Realtime voice xai/grok-voice-think-fast-1.0 Text to speech xai/grok-tts Speech to text xai/grok-stt Realtime A voice agent has two pieces: a server route that mints a short-lived token, so your API key never reaches the client, and a browser component that connects with it. Add the token route: this example sets model to xai/grok-voice-think-fast-1.0: Then connect from the browser. The useRealtimehook from @ai-sdk/react fetches that route and manages the WebSocket connection, microphone capture, and audio playback: Text to speech Generate spoken audio from text with generateSpeech. Pass a voice and an output format, then write the result to a file with xai/grok-tts: Speech to text Transcribe recordings into text with transcribe. This example uses xai/grok-stt: Playground You can also try the xAI audio models directly in the AI Gateway playground. Open the models list and click into any of the models to use them directly in the browser. The xai/grok-voice-think-fast-1.0 playground here allows you to talk to the agent and see responses instantly: More information Realtime quickstart Speech quickstart See all xAI models Read more ## How we used DSPy to turn AI evaluations into better responses in Dash chat DevFeed: [How we used DSPy to turn AI evaluations into better responses in Dash chat]() Original publisher: [Read original article]() Author: Yasmin McDowell,Lawrence Good,Ilya Yakovlev,Andrew Cheung,Binoy Dash,Simran Jumani,Dmitriy Meyerzon Published: 2026-06-25T16:30:00Z Content type: article Language: en Sources: [Dropbox Tech Blog]() Topics: [DSPy](), [AI Chat](), [Large Language Model](), [Artificial Intelligence](), [Optimization](), [Algorithms](), [Open Source]() Tags: [agent](), [agents](), [ai](), [dash](), [dspy](), [evaluation](), [llm](), [open-source](), [optimization](), [performance](), [reasoning](), [responses](), [tools]() ### AI overview Dropbox describes using DSPy and evaluation feedback to improve Dash chat. Human-labeled examples calibrate LLM judges, whose scalable signals then optimize the chat agent's system prompt, reducing incomplete answers and token usage without compromising answer quality. ### Source excerpt We used DSPy to improve LLM judges and optimize our chat experience, creating an evaluation-driven feedback loop that produced better outputs. ## Conversation design: How to make your AI Agent communicate like your team DevFeed: [Conversation design: How to make your AI Agent communicate like your team]() Original publisher: [Read original article]() Author: Fred Walton Published: 2026-06-18T16:55:07Z Content type: article Language: en Sources: [The Intercom Blog]() Topics: [AI Agent](), [Artificial Intelligence](), [Support]() Tags: [agent](), [ai](), [ai-agent](), [ai-agent-blueprint](), [ai-automation](), [communication](), [context](), [customer](), [customer-experience](), [customer-service](), [customers](), [designer](), [experience](), [process](), [responses](), [structure](), [support](), [teams](), [trust](), [voice]() ### AI overview This article explains how conversation design helps an AI Agent communicate consistently with a support team. It covers voice, tone, response structure, adapting language to customer context, and designing smooth handoffs to human support representatives. An Intercom example reports that a warmer opening message increased CSAT from 72.8% to 78.4%. ### Source excerpt If nobody on your team owns how your AI Agent communicates, it defaults to sounding like an LLM. Conversation design is the discipline that fixes that. ## Announcing the Networking Workgroup DevFeed: [Announcing the Networking Workgroup]() Original publisher: [Read original article]() Author: Franz Busch Published: 2026-06-04T10:00:00Z Content type: news Language: en Sources: [Swift.org]() Topics: [networking](), [Swift](), [API](), [Protocol (disambiguation)](), [client](), [Concurrency](), [TLS (Transport Layer Security)](), [servers](), [WebSocket]() Tags: [announcement](), [api](), [architecture](), [concurrency](), [http](), [networking](), [responses](), [server](), [swift](), [tls]() ### AI overview Swift has announced a community-led Networking workgroup to guide the evolution of networking libraries, protocols, and APIs. Its goals include a unified, layered networking stack, interoperable networking types, modern HTTP client and server APIs built on structured concurrency, and shared implementations of protocols such as TLS, HTTP versions, QUIC, and WebSockets. ### Source excerpt The Swift Ecosystem Steering Group is excited to announce the creation of the Networking workgroup! Workgroups are community-led efforts, formally recognized by the project, to advance key areas of Swift. The primary goal is to guide the evolution of networking libraries, protocols, and APIs in the Swift ecosystem, making networking in Swift excellent everywhere: high-level and safe by default, modular and interoperable, cross-platform, and observable. Networking is one of the most common entry points for Swift developers, and the ecosystem has matured significantly over the years. Foundational libraries like SwiftNIO, AsyncHTTPClient, and swift-http-types, alongside platform stacks such as URLSession and Network.framework, power networking across apps, servers, and beyond. The workgroup will build upon these efforts and pursue the long-term directions outlined in the Networking vision, focusing on work to: Define a unified networking stack with a coherent layered architecture, from shared I/O primitives at the foundation, through common protocol implementations, to ergonomic client and server APIs at the top. Define currency types that let libraries interoperate without coupling to specific implementations, such as IP addresses, hostnames, ports, and HTTP requests and responses. Evolve HTTP APIs by designing and guiding a modern, unified HTTP client and server API built on structured concurrency. Guide the evolution of shared protocol implementations (TLS, HTTP/1.1, HTTP/2, HTTP/3, QUIC, WebSockets) so improvements benefit the entire ecosystem rather than being duplicated across libraries. The new Networking workgroup joins a growing list of Swift workgroups, including the Android workgroup, Windows workgroup, and Build and Packaging workgroup, which were all added in the past year. To learn more and get involved: Discuss this announcement on the forums, and share ideas in the Networking category. Learn more about the Networking workgroup by reading its charter. Th ## Continuous Offensive Security: The Line We've Been Walking DevFeed: [Continuous Offensive Security: The Line We've Been Walking]() Original publisher: [Read original article]() Author: Nuno Loureiro Published: 2026-05-27T04:00:00Z Content type: opinion Language: en Sources: [Blog RSS Feed | Snyk]() Topics: [Vulnerabilities](), [AI Bots]() Tags: [agent](), [ai](), [announcements](), [api](), [apis](), [authentication](), [autonomous](), [benchmark](), [bugs](), [code](), [llm](), [opinion](), [post](), [product](), [reasoning](), [responses](), [sdlc](), [security](), [speed](), [sql]() ### AI overview Snyk argues that continuous offensive security should combine DAST, AI pentesting, and agent red teaming to identify exploitable vulnerabilities before autonomous attackers do. It contrasts heuristic-detectable flaws with context-dependent and chained vulnerabilities. ### Source excerpt Snyk's Continuous Offensive Security unifies DAST, AI pentesting, and agent red teaming to find exploitable flaws -- not just bugs -- before attackers do. Here's why lineage matters. ## Nuxt MCP Toolkit now supports MCP apps DevFeed: [Nuxt MCP Toolkit now supports MCP apps]() Original publisher: [Read original article]() Author: Ben Sabic Published: 2026-05-19T14:00:00Z Content type: release Language: en Sources: [Vercel News]() Topics: [AI Chat](), [modern web development](), [Web Development]() Tags: [agent](), [apps](), [chatgpt](), [claude](), [html](), [mcp](), [responses](), [tool](), [tools](), [ui](), [vue]() ### AI overview Nuxt MCP Toolkit adds MCP app support, allowing agent tools to return interactive HTML that Claude and ChatGPT can render inline. It provides APIs for declared tools, hydrated data, follow-up prompts, and tool calls from the UI, while bundling Vue SFCs into self-contained HTML at build time. ### Source excerpt The Nuxt MCP Toolkit now supports MCP apps. Your agent tools can return interactive HTML responses that MCP clients like Claude and ChatGPT render inline, rather than plain-text responses. Declare a tool with the defineMcpApp macro, then read pre-hydrated data, trigger follow-up prompts, or call other tools from inside the UI with the useMcpApp composable. The toolkit bundles each Vue SFC into a self-contained HTML file at build time and serves it from your MCP endpoint. Read the Nuxt MCP Toolkit documentation to get started. Read more ## Helping ChatGPT better recognize context in sensitive conversations DevFeed: [Helping ChatGPT better recognize context in sensitive conversations]() Original publisher: [Read original article]() Published: 2026-05-14T00:00:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [ChatGPT](), [Monitoring](), [Model Development](), [systems]() Tags: [awareness](), [chatgpt](), [model](), [monitoring](), [responses](), [safety](), [support](), [training](), [updates]() ### AI overview OpenAI describes safety updates that help ChatGPT recognize evolving signs of risk across sensitive conversations. The updates use conversational context to support more careful responses, including de-escalation, refusal of harmful details, and guidance toward support. ### Source excerpt Learn how new ChatGPT safety updates improve context awareness in sensitive conversations, helping detect risk over time and respond more safely. ## Vercel Sandbox firewall now supports request proxying and filtering DevFeed: [Vercel Sandbox firewall now supports request proxying and filtering]() Original publisher: [Read original article]() Author: Brandon Tuttle Published: 2026-05-11T01:00:00Z Content type: release Language: en Sources: [Vercel News]() Topics: [Firewall](), [Vercel](), [API](), [OpenID connect (OIDC)](), [SDKs]() Tags: [api](), [debugging](), [enterprise](), [features](), [firewall](), [http](), [identity](), [oidc](), [post](), [responses](), [sdk](), [server](), [vercel]() ### AI overview Vercel Sandbox firewall now supports routing selected outbound HTTPS requests through a user-controlled proxy. Matchers can restrict proxy forwarding and credentials brokering by path, method, query string, or headers. The beta features are available for Pro and Enterprise plans through the @vercel/sandbox@beta SDK. ### Source excerpt The Vercel Sandbox firewall now supports forwarding specific HTTP requests to a proxy you control. You can also use matchers to filter forwarding and credentials brokering to only the requests that need it. Requests proxying You can now route outbound sandbox traffic through your own proxy for logging, debugging, or transforming requests and responses. Set a forwardURL on any allowed domain, and the firewall will forward matching HTTPS requests to your server. The proxy receives the original request along with additional headers to identify the source: vercel-forwarded-host: The original request's SNI vercel-forwarded-scheme: The original request's scheme vercel-forwarded-port: The original request's port vercel-sandbox-oidc-token: A Vercel-issued OIDC token that the proxy can use to authenticate the request and identity the source team / project / sandbox. Learn more about it in the docs Filtering Additionally, you can now use matchers to filter request forwarding or credentials brokering to requests matching a specific path, method, query string, or headers. This gives you fine-grained control over which requests get transformed; for example, only forwarding POST requests to a specific API path while allowing all other traffic through untouched. These features are available in beta for Pro and Enterprise plans. Get started by installing the @vercel/sandbox@beta SDK, and learn more in the docs about requests proxying and matchers. Read more ## Speeding up agentic workflows with WebSockets in the Responses API DevFeed: [Speeding up agentic workflows with WebSockets in the Responses API]() Original publisher: [Read original article]() Published: 2026-04-22T10:00:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [WebSocket](), [API](), [Latency](), [Caching](), [codex](), [Inference](), [Large Language Model]() Tags: [agentic](), [api](), [caching](), [codex](), [deep-dive](), [engineering](), [inference](), [latency](), [llm](), [performance](), [responses]() ### AI overview This deep dive explains how OpenAI reduced end-to-end latency in Codex agentic workflows using the Responses API. WebSockets provide persistent connections, while connection-scoped caching, fewer network hops, and faster safety classification reduce API overhead and help users benefit from much faster model inference. ### Source excerpt A deep dive into the Codex agent loop, showing how WebSockets and connection-scoped caching reduced API overhead and improved model latency. ## Anomaly alerts are now generally available DevFeed: [Anomaly alerts are now generally available]() Original publisher: [Read original article]() Author: Malavika Tadeusz Published: 2026-04-13T15:00:00Z Content type: release Language: en Sources: [Vercel News]() Topics: [telemetry](), [App](), [dashboards]() Tags: [agent](), [applications](), [edge](), [metrics](), [monitor](), [observability](), [real-time](), [responses](), [slack](), [vercel]() ### AI overview Vercel has generally released anomaly alerts for Observability Plus. The alerts detect unusual usage and error patterns, and Vercel Agent can investigate detected issues and suggest remediation steps. ### Source excerpt Anomaly alerts are now generally available. Teams using Observability Plus receive alerts when anomalies are detected in their applications to help quickly identify, investigate, and resolve unexpected behavior. Alerts help monitor your app in real-time by surfacing unexpected changes in usage or error patterns: Usage anomalies: unusual patterns in your application metrics, such as edge requests or function duration. Error anomalies: abnormal error patterns, such as sudden spikes in 5XX responses on a specific route. Once an anomaly is detected, Vercel Agent can automatically investigate the issue, identify the likely root cause, analyze the impact, and suggest next steps for remediation. View alerts directly in your dashboard, or subscribe via email, Slack, or webhooks to get notified wherever your team works. You can also customize what alerts you receive using alert rules. This feature is available for all teams with Observability Plus at no additional cost. Try it out or learn more about Alerts. Read more ## Introducing the Child Safety Blueprint DevFeed: [Introducing the Child Safety Blueprint]() Original publisher: [Read original article]() Published: 2026-04-08T05:00:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [Artificial Intelligence](), [OpenAI](), [AI Strategy](), [genai]() Tags: [ai](), [collaboration](), [legal](), [openai](), [partners](), [policy](), [responses](), [safety]() ### AI overview OpenAI introduces a policy blueprint for strengthening U.S. child protection frameworks in the age of AI. The blueprint emphasizes modernizing laws for AI-generated and altered child sexual abuse material, improving provider reporting and coordination, and embedding safety-by-design measures into AI systems. ### Source excerpt Discover OpenAI's Child Safety Blueprint--a roadmap for building AI responsibly with safeguards, age-appropriate design, and collaboration to protect and empower young people online. [Next page]()