# tools Published articles for tools. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Anthropic merges Claude Chat and Cowork into one interface DevFeed: [Anthropic merges Claude Chat and Cowork into one interface]() Original publisher: [Read original article]() Author: Amanda Caswell Published: 2026-09-16T16:46:36Z Content type: news Language: en Sources: [The New Stack]() Topics: [anthropic](), [Claude]() Tags: [ai-agents](), [ai-infrastructure](), [anthropic](), [chat](), [claude](), [connectors](), [context](), [developer-tools](), [tools](), [workflow]() ### AI overview Anthropic is merging Claude Chat and Cowork into a single interface, allowing conversations to handle simple questions, multi-step projects, connected tools, and background execution. Claude Docs and Claude Slides are launching in beta on paid plans, while Claude Design is moving into conversations. ### Source excerpt Using Claude for anything beyond a quick question has always started with a routing decision to use Chat or Cowork? The post Anthropic bet users were choosing wrong. So it removed the choice. appeared first on The New Stack. ## Getting started with AI governance 🔒 DevFeed: [Getting started with AI governance 🔒]() Original publisher: [Read original article]() Author: Luca Rossi Published: 2026-09-16T13:03:30Z Content type: article Language: en Sources: [Refactoring]() Topics: [ai-governance](), [Artificial Intelligence](), [Tool]() Tags: [agents](), [ai](), [ai-governance](), [governance](), [tools]() ### AI overview A primer on governing people, agents, tools, and actions in AI systems. ### Source excerpt A primer on how you should think about governing people, agents, tools, and actions. ## ADLX 2.0: Extending graphics control to AI agents and agentic apps DevFeed: [ADLX 2.0: Extending graphics control to AI agents and agentic apps]() Original publisher: [Read original article]() Author: Pete Vagiakos; Alexander Blake-Davies Published: 2026-09-16T10:00:00Z Content type: article Language: en Sources: [AMD GPUOpen]() Topics: [GPU](), [Artificial Intelligence](), [Hardware](), [SDK](), [Model Context Protocol]() Tags: [agents](), [ai](), [amd-device-library-extra](), [amd-device-library-extra-adlx](), [announcement](), [developers](), [gpu](), [hardware](), [mcp](), [ml](), [product-blogs](), [product-release](), [tools]() ### AI overview AMD ADLX 2.0 adds an AI extension framework and MCP servers that connect AI applications with AMD graphics technologies, enabling developers to build apps that monitor, manage, and optimize AMD graphics hardware. ### Source excerpt AMD ADLX 2.0 adds AI extensions and MCP servers to help developers build intelligent apps that can monitor, manage, and optimize AMD graphics hardware. ## What do software architects at Khan Academy do? DevFeed: [What do software architects at Khan Academy do?]() Original publisher: [Read original article]() Author: Khan Academy Published: 2018-05-14T22:00:00Z Content type: opinion Language: en Sources: [Khan Academy]() Topics: [Software](), [Development](), [coding]() Tags: [architecture](), [article](), [collaboration](), [decision-making](), [engineering](), [infrastructure](), [news](), [platforms](), [process](), [software](), [standards](), [tools]() ### AI overview Kevin Dangoor explains how software architects at Khan Academy view their role: as product managers for the system in which software is built. The article emphasizes improving coding standards, tools, platforms, and processes by collaborating with engineers and engineering management, with DACI used to structure architecture-change decisions. ### Source excerpt By Kevin Dangoor "Architect" is a new role in Khan Academy's engineering team this year, and my colleague, ... Read more ## Gemini Live audio DevFeed: [Gemini Live audio]() Original publisher: [Read original article]() Author: Simon Willison Published: 2026-09-15T22:47:07Z Content type: tutorial Language: en Sources: [Simon Willison's Weblog]() Topics: [speech-to-speech](), [WebSocket](), [Google AI](), [browser](), [Playback](), [implementation](), [API]() Tags: [api](), [audio](), [browser](), [gemini](), [gemini-196](), [generative-ai](), [generative-ai-1-982](), [google](), [google-416](), [llm-release](), [llm-release-231](), [llms](), [llms-1-948](), [model](), [models](), [openai](), [playback](), [release](), [speech-to-speech](), [speech-to-text](), [speech-to-text-21](), [tools](), [tools-78](), [ui](), [voice](), [websocket](), [websockets](), [websockets-21]() ### AI overview The article describes a browser-based web UI for trying Google's Gemini 3.8 Live and Gemini 3.8 Live Extended Thinking speech-to-speech models. The implementation supports model and voice selection, an optional system prompt, voice conversations, and interruption while the model is speaking. It uses no libraries, connecting to a WebSocket endpoint and using the Web Audio API for capture and playback. ### Source excerpt Tool: Gemini Live audio Google released Gemini 3.8 Live and 3.8 Live Extended Thinking today - two new speech-to-speech models that are a similar shape to OpenAI's GPT-Live family. I pointed GPT-6 Astra Extra High at the documentation and had it build me this web UI for trying out the new models. You can select a model and voice preset, enter an optional system prompt and then start a voice conversation through your browser, including the ability to interrupt the model while it is talking. The implementation uses no libraries. It connects to the wss://generativelanguage.googleapis.com/ws/google.ai.generativelanguage.v1alpha.GenerativeService.BidiGenerateContent?key=... WebSocket endpoint and uses a Web Audio API AudioContext for both capture and playback. Here's the Gemini Live tutorial for getting started with that WebSockets API. Tags: google, tools, websockets, generative-ai, llms, gemini, llm-release, speech-to-text ## Introducing Gemini 3.8 Live and 3.8 Live Extended Thinking DevFeed: [Introducing Gemini 3.8 Live and 3.8 Live Extended Thinking]() Original publisher: [Read original article]() Author: Tom Ouyang Published: 2026-09-15T17:05:57Z Content type: release Language: en Sources: [Google DeepMind News]() Topics: [Artificial Intelligence](), [real-time](), [speech-to-speech](), [Google](), [API]() Tags: [ai](), [api](), [audio](), [cost](), [efficiency](), [gemini](), [google](), [none](), [real-time](), [speech-to-speech](), [tools]() ### AI overview Google introduces Gemini 3.8 Live and Gemini 3.8 Live Extended Thinking, two models designed for near-real-time voice interaction and reasoning. The release describes visual grounding, multilingual conversation, background tool and API execution, and deeper reasoning for complex workflows. ### Source excerpt Gemini 3.8 Live and Gemini 3.8 Live Extended Thinking are our most advanced live dialogue models yet, built for natural conversation. ## Tools DevFeed: [Tools]() Original publisher: [Read original article]() Author: Kaushik Gopal Published: 2026-09-15T04:00:36.743345Z Content type: article Language: en Sources: [Kaushik Gopal's Site]() Topics: [Programming](), [Cloudflare](), [Terminal](), [Vim](), [Hugo](), [Firefox](), [macOS](), [Obsidian](), [Font]() Tags: [cloudflare](), [firefox](), [hugo](), [mac](), [macos](), [programming](), [programming-font](), [terminal](), [tools](), [vim]() ### AI overview A personal tools page listing services, programming tools, macOS applications, audio equipment, desk items, and physical products. It mentions Buttondown, a customized Hugo site deployed on Cloudflare Pages, Ghostty, tmux, Vim, Zed, Obsidian, Raycast, Firefox, CleanShot, and Logic Pro. ### Source excerpt Bits ## Services ### Newsletter via Buttondown Statically generate site with Henry (highly customized Hugo theme) Deployed on Cloudflare Pages Programming ### Berkeley Mono is my programming font of choice Ghostty for terminal stuff (along with tmux) Within the terminal I'm almost entirely using Vim. Zed as my regular text editor MacOS ### Obsidian for notes & todos Raycast is my launcher Firefox for browsing CleanShot for screenshots on my mac Logic Pro for editing AI Stuff ### With AI evolving so fast, my tag ai-model-choice is a better place to track what I'm using. Atoms ## Desk ### Nordik Leather Desk Mat LEUCHTTURM1917 Soft cover journal dotted Uniball Deluxe 0.5mm Micro pens Audio ### Earthworks Ethos - 400 microphone USBPre 21 audio interface AirPods Max (yes for podcasting!) Physical ### I drive a Tesla Model Y (and eagerly waiting to move to an R3) These are no longer sold but are built like a tank. I'd probably pick the Motu 2 if my USBPre 2 ever fails ↩︎ ## Inside the AI Stack of an $8.3B AI Company's Product Team | Together AI DevFeed: [Inside the AI Stack of an $8.3B AI Company's Product Team | Together AI]() Original publisher: [Read original article]() Author: Aakash Gupta Published: 2026-09-14T23:05:28Z Content type: article Language: en Sources: [Product Growth]() Topics: [Artificial Intelligence](), [context](), [repo](), [context window](), [Developer experience](), [GitHub](), [Linear]() Tags: [ai](), [context](), [context-window](), [developer-experience](), [github](), [linear](), [product](), [repo](), [team](), [tools]() ### AI overview The article examines Together AI's AI-oriented product team and describes four parts of its working stack: a shared context repository, reusable skills for research and product documentation, an orchestrator for product leaders, and agent evaluations. It explains how shared context files and workflows support collaboration across product areas, including automated weekly status updates using Linear, GitHub, and strategy documents. ### Source excerpt I got a truly AI-pilled product team to demo the 4 key tools in their stack ## What It Takes to Build a Production Agent Harness DevFeed: [What It Takes to Build a Production Agent Harness]() Original publisher: [Read original article]() Author: Avi Chawla Published: 2026-09-14T19:50:37Z Content type: article Language: en Sources: [Daily Dose of Data Science]() Topics: [Agent Harness](), [LangChain](), [Artificial Intelligence](), [Persistence](), [Graphs]() Tags: [agent](), [ai](), [ai-engineer](), [engineering](), [evaluation](), [langchain](), [langgraph](), [memory](), [persistence](), [tools](), [tracing]() ### AI overview A hands-on series chapter explains how to build a production agent harness with LangChain and LangGraph. It covers model, message, prompt, and tool interactions; tool-call execution; state transitions; persistence; failure handling; tracing; evaluation; human approval; and resumable execution. ### Source excerpt A hands-on nanodegree for production agent engineering. ## Claude Managed Agents: How They Work and Where They Fit DevFeed: [Claude Managed Agents: How They Work and Where They Fit]() Original publisher: [Read original article]() Author: Matar Peles Published: 2026-09-14T11:29:41Z Content type: article Language: en Sources: [Developer Experience & Platform Engineering Blog | Port]() Topics: [Agent Harness](), [Claude](), [Artificial Intelligence](), [sdlc](), [context window](), [Network](), [Processes]() Tags: [agent](), [agents](), [ai](), [claude](), [context-window](), [infrastructure](), [network](), [platform](), [sandbox](), [sdlc](), [software](), [tools]() ### AI overview This article explains Claude Managed Agents, a hosted runtime and managed agent harness operated by Anthropic. It describes how the harness coordinates tools and execution, the sandbox provides isolated command and file access, and the session preserves durable task history outside the model's context window. It also discusses the additional platform layer needed to connect multiple agents across a business process and the SDLC. ### Source excerpt What Claude Managed Agents are, how the runtime works, and how platform teams connect several agents across the SDLC. ## What's New for C++ Developers in Visual Studio 2026 (18.7 - 18.10) DevFeed: [What's New for C++ Developers in Visual Studio 2026 (18.7 - 18.10)]() Original publisher: [Read original article]() Author: Augustin Popa Published: 2026-09-14T06:38:16Z Content type: article Language: en Sources: [C++ Team Blog]() Topics: [Visual Studio 2026](), [C++](), [GitHub Copilot](), [MSVC](), [AI-assisted coding](), [CMake](), [Git](), [Submodules]() Tags: [2026](), [build-optimization](), [c-plus-plus](), [cmake](), [copilot](), [git](), [modernization](), [msvc](), [submodules](), [testing](), [tools](), [visual-studio](), [visual-studio-2026]() ### AI overview This Microsoft C++ Team Blog post recaps Visual Studio 2026 versions 18.7 through 18.10 for C++ developers. It covers C++ reliability fixes, GitHub Copilot modernization for upgrading MSBuild and CMake projects to newer MSVC Build Tools, improved code navigation, discoverable MSVC Build Tools installations, Git worktrees and submodules in the IDE, and debugging improvements. ### Source excerpt Over the past few months, we have continued shipping monthly Visual Studio 2026 releases, delivering improvements across all stages of the development cycle. In this blog post, we'll recap everything that changed from version 18.7 through 18.10 (released this month) that is relevant for C++ developers. This includes new tools for maintaining and navigating C++ [...] The post What's New for C++ Developers in Visual Studio 2026 (18.7 - 18.10) appeared first on C++ Team Blog. ## Are Browser-Based Apps Enough for Web Designers? DevFeed: [Are Browser-Based Apps Enough for Web Designers?]() Original publisher: [Read original article]() Author: Eric Karkovack Published: 2026-09-13T19:09:47Z Content type: opinion Language: en Sources: [Speckyboy Design Magazine]() Topics: [web applications](), [Web platform](), [web-standards](), [Figma](), [Google]() Tags: [apps](), [browser](), [collaboration](), [design](), [design-business](), [freelance-career](), [freelance-design](), [privacy](), [software](), [tools](), [web](), [web-apps](), [web-design](), [web-designers](), [workflows]() ### AI overview Browser-based apps have matured enough to handle serious design, editing, and productivity work. The article considers their convenience and collaboration benefits, while also examining privacy concerns and cases where desktop tools remain preferable. ### Source excerpt Browser-based apps can now handle serious design, editing, and productivity work. We take a look at their convenience, collaboration benefits, privacy risks, and where desktop tools still make sense. The post Are Browser-Based Apps Enough for Web Designers? appeared first on Speckyboy Design Magazine. ## 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 ## How to Evaluate Live & Voice Agents in ADK DevFeed: [How to Evaluate Live & Voice Agents in ADK]() Original publisher: [Read original article]() Author: Stephen Allen Published: 2026-09-12T11:04:33.891311Z Content type: tutorial Language: en Sources: [Google Developers Blog]() Topics: [AI Bots](), [CI/CD]() Tags: [agents](), [audio](), [ci-cd](), [cli](), [evaluation](), [gemini](), [how-to](), [json](), [llm](), [production](), [testing](), [tool](), [tools](), [transcripts](), [voice](), [workflows]() ### AI overview The article explains how to evaluate live voice agents in ADK with simulated audio conversations, automated scoring, and recorded results. It covers scenario-based and fixed-conversation test cases, multi-agent workflows, and running evaluations in CI/CD. ### Source excerpt Moving live voice agents from demo to production requires rigorous, automated testing to handle the unpredictability of real multi-turn conversations. ADK now provides native live evaluation, allowing developers to test graph-based agent workflows against LLM-driven simulated users that generate actual audio via Gemini TTS. By defining evaluation scenarios and natural-language rubrics, you can automatically score audio responses and tool executions, inspect the resulting transcripts in ADK Web, or run the CLI directly in your CI/CD pipeline. ## OpenAI's researchers burned $7,000 a day on AI agents -- now it's opening the floodgates DevFeed: [OpenAI's researchers burned $7,000 a day on AI agents -- now it's opening the floodgates]() Original publisher: [Read original article]() Author: Amanda Caswell Published: 2026-09-11T21:27:42Z Content type: news Language: en Sources: [The New Stack]() Topics: [Inference](), [long-context]() Tags: [agents](), [ai](), [ai-agents](), [ai-infrastructure](), [api](), [cloud-services](), [codex](), [compute](), [developers](), [inference](), [openai](), [research](), [sandbox](), [tools]() ### AI overview OpenAI's public-beta Agents API lets developers run long-lived agents with managed job state, context compression, optional tools, parallel subagents, and execution in OpenAI's sandbox or developer-controlled infrastructure. The article highlights the resulting inference and compute costs, citing internal research-agent usage figures. ### Source excerpt OpenAI rolled out its Agents API in public beta Thursday, opening the backend behind Codex to developers looking to run The post OpenAI's researchers burned $7,000 a day on AI agents -- now it's opening the floodgates appeared first on The New Stack. ## What Is an Agent Harness? The Architecture Behind Claude Code, DeepSeek Harness, and Hermes Agent DevFeed: [What Is an Agent Harness? The Architecture Behind Claude Code, DeepSeek Harness, and Hermes Agent]() Original publisher: [Read original article]() Author: Rudrendu Paul Published: 2026-09-11T15:07:18Z Content type: tutorial Language: en Sources: [freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More]() Topics: [AI Bots](), [AI-assisted coding]() Tags: [agent](), [ai-agents](), [architecture](), [artificial-intelligence](), [claude-code](), [deepseek](), [developer-tools](), [llm](), [machine-learning](), [mcp](), [memory](), [python](), [sandbox](), [software-engineering](), [tools]() ### AI overview An explainer and hands-on guide to agent harnesses: the runtime infrastructure around an LLM that manages model calls, tool execution, memory, and filesystem sandboxing. It compares popular harnesses and introduces a small Python implementation. ### Source excerpt On August 13, 2026, DeepSeek published a GitHub repository called deepseek-harness. Within two days, it had passed 95,386 stars and 8,826 forks (a vanity metric on its own, but a spike this fast signa ## How to calculate DevOps platform total cost of ownership DevFeed: [How to calculate DevOps platform total cost of ownership]() Original publisher: [Read original article]() Author: GitLab Published: 2026-09-11T00:00:00Z Content type: tutorial Language: en Sources: [GitLab]() Topics: [CI/CD]() Tags: [ai](), [ci-cd](), [complexity](), [compute](), [cost](), [devops](), [devsecops](), [devsecops-platform](), [drivers](), [how-to](), [infrastructure](), [integration](), [model](), [platform](), [reduce](), [software-delivery](), [tools]() ### AI overview A guide to modeling the total cost of ownership of a DevOps platform, including subscriptions, CI/CD compute, AI usage, infrastructure, tools, and internal labor. ### Source excerpt There's nothing like budget pressure to put your DevOps platform under a microscope. But subscription fees and license costs only tell one part of the story. The total cost of ownership (TCO) for a DevOps platform also includes variable costs like CI/CD compute and AI usage, along with the infrastructure, tools, and employee time required to keep software delivery moving. That wider view matters when you're tasked with defending platform spend or comparing options with a head of finance. Designing a useful TCO model can: Make those costs transparent for stakeholders Shine a light on the reasoning (or lack thereof) behind each cost Identify areas to reduce spend without negatively impacting software delivery What total cost of ownership really includes The core challenge of calculating TCO is that DevOps platforms package and price capabilities differently. For example, one platform may bundle CI/CD or AI capabilities into a per-seat subscription, while another could price usage separately. A third may appear less expensive upfront but require additional tools and ongoing integration work. That's why list prices or pricing tiers alone won't give you a useful comparison. Start with the capabilities and workloads your organization actually needs, then calculate what it takes to support them on each platform. Use the same scope and time period for every option -- often one year -- and define which teams, applications, environments, and delivery stages are included. Separate recurring costs from one-time expenses and external spend from internal labor, so finance can audit the assumptions and forecast future years. A useful TCO model, therefore, answers two questions: What does it cost to meet our requirements today? Which variables will cause that cost to rise or fall as our usage changes? The cost categories that drive your bill Most DevOps platform costs fit into the following categories: Cost categoryWhat it includesMain cost driverPlatform accessPaid seats, role-based ## MSVC C++23: constexpr cmath with LLVM Libc DevFeed: [MSVC C++23: constexpr cmath with LLVM Libc]() Original publisher: [Read original article]() Author: Cody Miller Published: 2026-09-10T21:19:59Z Content type: article Language: en Sources: [C++ Team Blog]() Topics: [MSVC](), [C++](), [math]() Tags: [c-plus-plus](), [feature](), [llvm](), [math](), [msvc](), [os](), [performance](), [tools](), [visual-studio]() ### AI overview MSVC is preparing an experimental C++23 implementation of compile-time-evaluable standard math functions, powered by a new math library. The article explains the existing UCRT math-function arrangement and concerns about accuracy, compatibility, performance, and OS-dependent behavior. ### Source excerpt Proposal P0533R9 made numerous math functions in the standard library compile-time evaluable in C++23. Implementing the feature required a good bit of time and effort, but MSVC is preparing its experimental implementation for the 14.52 build tools (compiler version 19.52)! We are still refining the feature, so expect the dust to settle only when this [...] The post MSVC C++23: constexpr cmath with LLVM Libc appeared first on C++ Team Blog. ## Fear Is Not an Argument DevFeed: [Fear Is Not an Argument]() Original publisher: [Read original article]() Author: Daniel Lemire Published: 2026-09-10T18:23:42Z Content type: opinion Language: en Sources: [Daniel Lemire]() Topics: [Artificial Intelligence](), [Large Language Model](), [Language models](), [ChatGPT](), [Tool](), [Embeddings]() Tags: [ai](), [chatgpt](), [embeddings](), [large-language-model](), [openai](), [statement](), [tokens](), [tools]() ### AI overview This opinion argues that fears about AI causing human extinction are vague and unfalsifiable, and compares them with earlier technological and social end-of-the-world predictions. It describes large language models as systems that process and generate tokens using fixed weights, while noting that connecting them to tools makes their capabilities more consequential. ### Source excerpt We are told that AI entities much like ChatGPT might soon kill us all. The statement is vague and unfalsifiable. It might be true, it might be false. People with credentials (e.g., Turing Award recipient Yoshua Bengio) believe it. Many still remember the Year-2000 bug. Our computers used two-digit coding for dates, and some software ... Continue reading Fear Is Not an Argument ## Agent Harness vs Platform Harness: Why Teams Need Both DevFeed: [Agent Harness vs Platform Harness: Why Teams Need Both]() Original publisher: [Read original article]() Author: Zohar Einy Published: 2026-09-10T04:47:42Z Content type: article Language: en Sources: [Developer Experience & Platform Engineering Blog | Port]() Topics: [Agent Harness](), [AI Engineering](), [AI Agent](), [Orchestration](), [Software]() Tags: [agentic](), [ai-agent](), [ai-engineering](), [article](), [governance](), [memory](), [orchestration](), [platform](), [tools]() ### AI overview This article distinguishes between an agent harness, which wraps a model with prompts, tools, orchestration, memory, and guardrails, and a platform harness, which adapts an agent to an organization's systems, standards, skills, tools, and governance requirements. It explains why teams need both layers and why vendor-agent adopters should build the platform harness first. ### Source excerpt Agent harness vs platform harness: what each layer covers, who owns it, what breaks when you have only one, and why you need both. ## 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 ## Your tools work. Will the agent use them right? DevFeed: [Your tools work. Will the agent use them right?]() Original publisher: [Read original article]() Author: Gil Levin Published: 2026-09-10T00:00:00Z Content type: article Language: en Sources: [Webflow Blog]() Topics: [Model Context Protocol (MCP)](), [Artificial Intelligence](), [Claude Code](), [cursor]() Tags: [ai-agents](), [chatgpt](), [claude-code](), [cursor](), [engineering](), [mcp](), [tools]() ### AI overview Webflow's MCP eval harness tests whether autonomous agents use its tools correctly to complete plain-English, multi-step tasks. It runs Claude Code or OpenAI's Codex CLI against disposable Webflow sites, combines deterministic checks with LLM and visual judging, and sends traces and scores to Datadog. ### Source excerpt A passing MCP tool test tells you the JSON is valid. It doesn't tell you an agent will pick the right tool, in order, and get the task right. ## .blend URL Viewer DevFeed: [.blend URL Viewer]() Original publisher: [Read original article]() Author: Simon Willison Published: 2026-09-09T23:58:32Z Content type: article Language: en Sources: [Simon Willison's Weblog]() Topics: [blender](), [3D](), [Artificial Intelligence](), [Generative AI](), [Large Language Model](), [AI-assisted coding](), [browser]() Tags: [3d](), [3d-18](), [ai](), [ai-2-236](), [blender](), [blender-4](), [codex](), [codex-57](), [coding-agents](), [coding-agents-248](), [generative-ai](), [generative-ai-1-982](), [gpt-6-astra](), [gpt-6-astra-9](), [javascript](), [javascript-764](), [llms](), [llms-1-948](), [tools](), [tools-78]() ### AI overview The article presents a .blend URL Viewer built from an existing Blender viewing experiment. It describes using ChatGPT Images 2.5 to generate a Pluribus-themed Fabergé egg image, then using Codex running GPT-6 Astra to create Blender model files that can be viewed in a browser. ### Source excerpt Tool: .blend URL Viewer I'm continuing to have a lot of fun with GPT-6 Astra and Blender (see my TIL). As a big fan of the Imperial Fabergé Easter eggs, I've always thought it would be fun to make some new ones that celebrate popular culture. Yesterday I decided to try out the new ChatGPT Images 2.5 by running this prompt: Generate a photo of a faberge egg that's themed after the TV show Pluribus - research first It gave me this - honestly not bad for a first attempt! Then, just to see what would happen, I pasted that image into Codex running GPT-6 Astra (high) and prompted: Use your blender local skill to create a blender model of this faverge egg (Here's the skill file, which I created like this.) It churned away for 17m51s and built me several .blend files. I already had this vibe-coded Blender viewing experiment lying around, so I added that to my tools collection and now you can use it to see my Pluribus blender model in your browser: Tags: 3d, javascript, tools, ai, generative-ai, llms, blender, coding-agents, codex, gpt-6-astra ## An Effective Python Development Environment DevFeed: [An Effective Python Development Environment]() Original publisher: [Read original article]() Author: Martin Breuss Published: 2026-09-09T14:00:00Z Content type: tutorial Language: en Sources: [Real Python]() Topics: [coding](), [Visual Studio Code](), [Command-line interface](), [debug](), [Syntax Highlighting]() Tags: [development](), [guide](), [python](), [tools](), [tutorials](), [vs-code]() ### AI overview A guide to setting up a practical Python development environment with an editor, terminal, interpreter, and isolated project dependencies. It recommends VS Code and uv as a starting point and explains related tools such as debuggers and syntax highlighting. ### Source excerpt Choose a Python development environment that helps you get coding. Find tutorials and courses on editors, uv, virtual environments, and useful tools. [Next page]()