# computer-use Published articles for computer-use. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## \[Aug 2026\] AI Community -- Activity Highlights and Achievements DevFeed: [\[Aug 2026\] AI Community -- Activity Highlights and Achievements]() Original publisher: [Read original article]() Author: Nari Yoon Published: 2026-09-17T05:12:15Z Content type: article Language: en Sources: [Google Developer Experts - Medium]() Topics: [Google AI](), [Google](), [google-antigravity](), [Artificial Intelligence](), [MCP Server](), [computer-use](), [Fine-tuning]() Tags: [2026](), [agentic-ai](), [ai](), [ai-coding-agents](), [ai-studio](), [antigravity](), [api](), [automation](), [community](), [computer-use](), [gemini](), [google](), [google-ai](), [google-cloud](), [mcp](), [mcp-server](), [multi-agent](), [ocr](), [paper](), [pitfalls]() ### AI overview A monthly roundup of Google AI community activities and achievements, covering Antigravity prototyping and engineering, AI coding agents, MCP-based remote control, computer-use agent orchestration, earthquake research, and TPU fine-tuning and migration guidance. ### Source excerpt We love sharing the accomplishments of the Google AI communities over the month. We appreciate all the hard work and dedication of our community members. Without further ado, here are the key highlights by products! Agentic DevelopmentAntigravityPrototype App: OCR and Text Extraction by the author Prototyping and Bringing Ideas to Application Using Google AI Studio and Antigravity 2.0 by AI GDE Joan Santoso (Indonesia) shares a rapid prototyping workflow building an AI-powered Form Extractor using the Gemini API, featuring a lightweight OCR and text extraction workflow. Antigravity Engineering Series by GDE Amulya Bhatia (Germany) focuses on key features of Antigravity 2.0 across 10 articles covering topics such as multi-agent orchestration, safety architecture, and workflow automation, accompanied by source code examples. (image soruce) Remote Control for Google Antigravity: Drive Your AI Coding Agent From Telegram 🛰 by GDE Nicola Guglielmi (Italy) introduces an open-source MCP server that turns Telegram into a remote control surface for AI coding agents. Before the Quake: How Antigravity CLI's AI Agents & IoT Data Predict Earthquakes by GDE Kanshi Tanaike (Japan) introduces the paper establishing Unified LAIC-AGW Theory by integrating ultra-dense IoT weather data with seismic moment tensors. It demonstrates a pre-seismic early warning capability by capturing enthalpy anomalies and acoustic-gravity waves. ADKAI GDE Henry Ruiz (US) and AI GDE Margaret Maynard-Reid (US) AI GDE Henry Ruiz (US) and AI GDE Margaret Maynard-Reid (US) introduced UISurf: An Operator-Centric Multi-Agent Platform for Observable and Cross-Environment UI Automation at the Agentic AI Summit 2026. They highlighted how the model-agnostic framework leverages the Google Cloud and Gemini ecosystems, such as GEAP and ADK, to orchestrate and evaluate computer-use agents across web, desktop, and mobile environments. Frameworks and ResearchTPU Introduction to SFT on TPU with Tunix -- 10 pitfalls until 2 ## OpenAI's Greg Brockman discusses computer use as an alternative to purpose-built AI agent integrations DevFeed: [OpenAI's Greg Brockman discusses computer use as an alternative to purpose-built AI agent integrations]() Original publisher: [Read original article]() Author: Meredith Shubel Published: 2026-09-15T23:41:22Z Content type: opinion Language: en Sources: [The New Stack]() Topics: [computer-use](), [OpenAI](), [Model Context Protocol](), [Reinforcement learning]() Tags: [ai-agents](), [computer-use](), [developer-tools](), [developers](), [mcp](), [model-context-protocol](), [openai](), [reinforcement-learning]() ### AI overview OpenAI president and co-founder Greg Brockman discusses whether AI agents could use computers through screens, keyboards, and mice instead of relying on purpose-built MCP servers, CLIs, APIs, and other integrations. ### Source excerpt This week on the a16z show, Greg Brockman, president and co-founder of OpenAI, made the point that developers have been The post OpenAI president: "The computer should be there to empower you." So stop retooling software for AI agents appeared first on The New Stack. ## OpenAI Releases GPT-6 Astra for Coding and Computer Use DevFeed: [OpenAI Releases GPT-6 Astra for Coding and Computer Use]() Original publisher: [Read original article]() Author: Daniel Dominguez Published: 2026-09-10T17:49:00Z Content type: news Language: en Sources: [InfoQ]() Topics: [releases](), [AI-assisted coding](), [AI Bots](), [Vulnerabilities](), [SDKs]() Tags: [agent](), [agentic](), [agents](), [ai](), [ai-ml-data-engineering](), [anthropic](), [api](), [artificial-intelligence](), [chatgpt](), [codex](), [coding](), [computer-use](), [cybersecurity](), [evaluation](), [gpt](), [large-language-models](), [ml-data-engineering](), [news](), [openai](), [openai-gpt6-astra](), [releases](), [vulnerabilities]() ### AI overview OpenAI released GPT-6 Astra, a model for computer use, coding, multi-step software tasks, and cybersecurity. The article reports benchmark results, long-context and Codex context features, deployment availability, and safety restrictions for advanced offensive cybersecurity tasks. ### Source excerpt OpenAI has released GPT-6 Astra, a new model focused on coding, computer use, long-running agentic tasks, and cybersecurity, with availability across ChatGPT, Codex, and the OpenAI API. By Daniel Dominguez ## GPT-6 Astra Is Both Incredible and Frustrating DevFeed: [GPT-6 Astra Is Both Incredible and Frustrating]() Original publisher: [Read original article]() Author: Peter Yang Published: 2026-09-09T14:27:15Z Content type: opinion Language: en Sources: [Behind the Craft]() Topics: [gpt-6-astra](), [Artificial Intelligence](), [OpenAI](), [computer-use](), [blender](), [Godot](), [Demo](), [Tutorial](), [3D]() Tags: [3d](), [ai](), [blender](), [computer-use](), [game](), [games](), [gpt-6-astra](), [openai](), [tutorial]() ### AI overview A hands-on opinion piece about OpenAI's GPT-6 Astra, describing strengths in games, 3D model creation, strategic thinking, and computer use, while noting problems that prevent it from becoming the author's daily driver. The article also outlines a workflow using Blender, Godot, and ChatGPT to build games. ### Source excerpt What OpenAI's new model does well, where it falls short, and how I'm using it. ## How Fable and Astra Could Change Software Engineering Work DevFeed: [How Fable and Astra Could Change Software Engineering Work]() Original publisher: [Read original article]() Author: Hemant Pandey Published: 2026-09-09T14:09:16Z Content type: opinion Language: en Sources: [The Hustling Engineer]() Topics: [Artificial Intelligence](), [coding](), [Fable](), [computer-use](), [Code](), [anthropic](), [OpenAI]() Tags: [ai](), [anthropic](), [code](), [coding](), [computer-use](), [engineering](), [fable](), [openai](), [terminal](), [vs-code]() ### AI overview This opinion article examines what Fable and Astra could change for software engineers. It contrasts Fable's long-running, multi-step coding and research work with Astra's ability to operate software and complete tasks across tools used by engineers. ### Source excerpt What actually changes for software engineers after Fable and Astra? ## GPT-6 Astra: The next generation in intelligence for work DevFeed: [GPT-6 Astra: The next generation in intelligence for work]() Original publisher: [Read original article]() Published: 2026-09-09T11:00:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [computer-use](), [coding](), [Software Engineering](), [Latency]() Tags: [api](), [chatgpt](), [codex](), [coding](), [computer-use](), [developer](), [engineering](), [gpt](), [latency](), [model](), [openai](), [product](), [software-engineering](), [work](), [workflows]() ### AI overview GPT-6 Astra is presented as a model for business work, with computer-use capabilities, coding support, and use within existing applications and workflows. ### Source excerpt Meet GPT-6 Astra, OpenAI's most capable model for business, with advanced reasoning, computer use, and stronger writing and design judgment. ## GPT-6 Astra, computer use, and longer-running agents DevFeed: [GPT-6 Astra, computer use, and longer-running agents]() Original publisher: [Read original article]() Author: Nate Published: 2026-09-07T13:03:27Z Content type: opinion Language: en Sources: [Nate's Substack]() Topics: [computer-use](), [gpt-6-astra]() Tags: [agents](), [astra](), [computer-use](), [gpt-6-astra]() ### AI overview The article considers how better computer use and longer-running agents could make more possibilities worth exploring, and discusses what work to reconsider giving GPT-6 Astra. ### Source excerpt Better computer use and longer-running agents make more possibilities worth exploring. What to reconsider, and how to give Astra the work. ## GPT 6 Astra's performance in a software-engineering workflow DevFeed: [GPT 6 Astra's performance in a software-engineering workflow]() Original publisher: [Read original article]() Author: Armin Ronacher Published: 2026-09-07T00:00:00Z Content type: opinion Language: en Sources: [Armin Ronacher]() Topics: [Artificial Intelligence](), [AI Engineering](), [Software Engineering](), [computer-use](), [ChatGPT](), [Python](), [Concurrency]() Tags: [agent](), [ai](), [ai-engineering](), [chatgpt](), [computer-use](), [python](), [software-engineering](), [thoughts]() ### AI overview The author argues that AI engineering can intensify effort without improving productivity and examines GPT 6 Astra's usefulness for software engineering. A self-managed software factory using Astra produced substantial code and prompts over 35 hours but, according to the author, delivered nothing of value and provided no clear lessons for improving the workflow. ### Source excerpt I'm more and more convinced that all of AI engineering is Neijuan (内卷, meaning curl inwards). In China it describes a system that demands ever more effort and competition without improving output. The way in which it sometimes shows up in the West is the 996 nonsense. The English term for Neijuan is "Involution" from the book Agricultural Involution. Agricultural involution describes the intensification of farming that raises productivity per square meter while leaving productivity per head unchanged. That's how I feel about AI right now. Which brings me to GPT 6 Astra. Astra is by all accounts an incredibly impressive model. There is really not much I can say against this. It's amazing at computer use, understands images and complex topics, and it's relentless in its pursuit of completion. It is absolutely impressive; these types of models are going to change the world in one form or another. But at least for the moment I don't know how to work with it for actual software engineering. Since that got quite a bit of attention on Twitter, I figured I might summarize my thoughts and just share what kind of code comes out of this thing. My Slop Factory "Armin, you should run a software factory!" I've heard that a few times now, so I figured I might celebrate the release of it by running a little software factory over the weekend. If everybody builds slop 3D games, then I should do something useful with it. My software factory was intentionally set up to let the model decide the how of the workflow entirely. It was free to manage its own context and could maintain its own records in an agent-notes folder. Then it spun off subagents to work on stuff. The goal? What if we had a Python with virtual threads and lexical scoping. And well, I burned a full reset's worth of ChatGPT tokens on this which appears to be around 4 billion tokens. 35 hours later, the factory has delivered absolutely nothing of value and also not taught me anything about how to operate a better one. But ## OpenAI GPT-6 Astra Hits GA in Microsoft Foundry: Computer Use, Agentic Execution, and $10 to $75 per Million Tokens DevFeed: [OpenAI GPT-6 Astra Hits GA in Microsoft Foundry: Computer Use, Agentic Execution, and $10 to $75 per Million Tokens]() Original publisher: [Read original article]() Author: Harold Fritts Published: 2026-09-05T17:09:34Z Content type: news Language: en Sources: [StorageReview.com]() Topics: [Microsoft](), [OpenAI](), [computer-use](), [Azure](), [Generative AI](), [Frontier Model](), [Security](), [Authorization](), [cloud-infrastructure](), [Software Engineering](), [pull-requests](), [dashboards]() Tags: [access-control](), [agentic](), [ai](), [apis](), [azure](), [computer-use](), [enterprise](), [frontier-model](), [generative-ai](), [governance](), [microsoft](), [openai](), [pull-requests](), [security]() ### AI overview Microsoft announced the general availability of OpenAI GPT-6 Astra in Microsoft Foundry on Azure. The frontier model is intended to support autonomous agentic workflows through multi-step planning, decision support, cross-application tool execution, and computer use. It can operate approved software interfaces, generate structured business artifacts, and assist with software engineering, dashboard creation, record updates, form processing, and interface testing. Microsoft Foundry provides scoped credentials, role-based access control, human-approval checkpoints, and identity controls for containment. ### Source excerpt Microsoft announced the general availability of GPT-6 Astra within Microsoft Foundry on Azure. The frontier model is engineered to transition enterprise generative AI from interactive chat interfaces toward autonomous agentic workflows, providing multi-step planning, deliberate decision support, and cross-application tool execution. OpenAI calls Astra its most aligned model to date, and Microsoft says it is The post OpenAI GPT-6 Astra Hits GA in Microsoft Foundry: Computer Use, Agentic Execution, and $10 to $75 per Million Tokens appeared first on StorageReview.com. ## Hands-on GPT-6 Astra review covering computer use, coding, hardware, Blender, and desktop app projects DevFeed: [Hands-on GPT-6 Astra review covering computer use, coding, hardware, Blender, and desktop app projects]() Original publisher: [Read original article]() Author: Claire Vo Published: 2026-09-03T19:34:20Z Content type: opinion Language: en Sources: [Lenny's Newsletter]() Topics: [gpt-6-astra](), [Artificial Intelligence](), [computer-use](), [coding](), [blender](), [Figma](), [codex]() Tags: [ai](), [blender](), [codex](), [coding](), [computer-use](), [figma](), [gpt-6-astra]() ### AI overview A hands-on review of GPT-6 Astra covering computer use, browser-based QA, coding projects, a hardware hack, Blender 3D asset creation, and a Mac app. The episode also discusses speed, cost, and whether Astra is suitable as a daily driver. ### Source excerpt Watch now | 🎙 My hands-on GPT-6 Astra review covers real production work in Figma, Flora, and Adio, one-shot coding wins 5.6 and Fable couldn't match, and a hardware hack I've chased for months ## GPT-6 Astra: A new generation of intelligence DevFeed: [GPT-6 Astra: A new generation of intelligence]() Original publisher: [Read original article]() Published: 2026-09-03T11:00:00Z Content type: release Language: en Sources: [OpenAI News]() Topics: [computer-use](), [coding](), [Software Engineering](), [Cybersecurity](), [Amazon Bedrock](), [hugging face]() Tags: [api](), [aws](), [azure](), [bedrock](), [browser](), [chatgpt](), [coding](), [computer-use](), [cybersecurity](), [evaluation](), [gpt](), [latency](), [model](), [openai](), [reinforcement-learning](), [research](), [safety](), [software-engineering]() ### AI overview OpenAI introduces GPT-6 Astra, a model positioned for computer use, coding, cybersecurity, science, and professional work. The article highlights alignment evaluations, benchmark results, availability through ChatGPT and cloud/API channels, and simulated computer-use performance versus GPT-5.6 Sol. ### Source excerpt Introducing GPT-6 Astra, our most intelligent and aligned model yet, with state-of-the-art capabilities across computer use, coding, cybersecurity, and science. ## From a Raw Shell to a Sandboxed Coding Agent DevFeed: [From a Raw Shell to a Sandboxed Coding Agent]() Original publisher: [Read original article]() Author: Paul Iusztin Published: 2026-08-18T11:02:59Z Content type: tutorial Language: en Sources: [Decoding ML]() Topics: [AI-assisted coding](), [LangChain](), [Claude Code](), [Docker](), [computer-use](), [codex](), [Python](), [Open Source](), [Cloud]() Tags: [agents](), [ai](), [ai-evals](), [anthropic](), [claude](), [claude-code](), [cloud](), [codex](), [coding](), [coding-agents](), [computer-use](), [docker](), [guide](), [open-source](), [python](), [terminal]() ### AI overview A tutorial on isolating coding-agent tools inside local Docker or remote Modal sandboxes. It explains how to build a Python harness that safely executes commands and supports remote, parallel agent workflows. ### Source excerpt The guide to isolating your harness and safely executing its commands, locally or remotely. ## Route AI Agent Workloads Across Models with NVIDIA NeMo Switchyard DevFeed: [Route AI Agent Workloads Across Models with NVIDIA NeMo Switchyard]() Original publisher: [Read original article]() Author: Michelle Horton Published: 2026-08-11T13:00:00Z Content type: tutorial Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [AI Bots]() Tags: [agent](), [agentic](), [agentic-ai-generative-ai](), [agents](), [ai](), [ai-agent](), [applications](), [benchmark](), [classification](), [computer-use](), [cost](), [deepseek](), [developer-tools-techniques](), [developers](), [engineering](), [featured](), [latency](), [llms](), [math](), [model](), [models](), [nemo](), [nvidia](), [reasoning](), [top-stories]() ### AI overview The article explains how NVIDIA NeMo Switchyard routes AI-agent tasks to different models according to task requirements, capabilities, cost, and latency. ### Source excerpt Building an AI agent does not end with choosing a single model. Each model has its own strengths, weaknesses, and cost profile, which can shift from one... ## Echoverse: Deep, evolving environments for computer-use agents DevFeed: [Echoverse: Deep, evolving environments for computer-use agents]() Original publisher: [Read original article]() Author: Akshay Nambi, Yash Pandya, Sahil Gupta, Sarthak Harne, Kavyansh Chourasia, Yash Lara, Ahmed Awadallah Published: 2026-07-30T17:00:00Z Content type: article Language: en Sources: [Microsoft Research]() Topics: [computer-use](), [Artificial Intelligence](), [Reinforcement learning](), [datasets](), [Microsoft]() Tags: [agents](), [ai](), [ai-agents](), [computer-use](), [data](), [datasets](), [microsoft](), [reinforcement-learning](), [research](), [research-blog](), [training]() ### AI overview Echoverse presents twelve high-fidelity training worlds for computer-use agents, designed around realistic application behavior, coherent state, seeded data, and challenging interface controls. Training a 9B model on these environments substantially improved its score, while reinforcement learning with grounded verification helped it generalize and complete goals in fewer steps. Four worlds are released with code, data, and graders to support research. ### Source excerpt Computer-use AI agents struggle with multi-step workflows like email and customer support. Echoverse trains agents in realistic environments rather than simply providing more training tasks, helping them improve as the tasks, tests, and environments evolve. The post Echoverse: Deep, evolving environments for computer-use agents appeared first on Microsoft Research. ## Capturing token IDs during agentic interactions for better reinforcement learning DevFeed: [Capturing token IDs during agentic interactions for better reinforcement learning]() Original publisher: [Read original article]() Author: Frederick Robinson Published: 2026-07-09T12:46:00Z Content type: article Language: en Sources: [Amazon Science homepage]() Topics: [Reinforcement learning](), [Agent Harness](), [tokenization](), [Language models](), [Rust](), [computer-use](), [multimodal]() Tags: [agent](), [agentic](), [agentic-ai](), [amazon-agi-lab](), [computer-use](), [dataset-development](), [language-models](), [machine-learning](), [model](), [multimodal](), [reinforcement-learning](), [rust](), [tokens](), [training]() ### AI overview This article explains how Turnstile, a Rust proxy placed between an agent harness and a model backend, captures exact token IDs during generation. The recorded token-level trajectories preserve information that text transcripts can lose and can be passed into reinforcement-learning training stacks. Reported validations cover a text-only coding agent and a multimodal computer-use agent whose performance improved during RL runs. ### Source excerpt A new Rust proxy called Turnstile sits between the model backend and the agent harness to capture information lost in mere text transcripts. ## GPT-5.6: Frontier intelligence that scales with your ambition DevFeed: [GPT-5.6: Frontier intelligence that scales with your ambition]() Original publisher: [Read original article]() Published: 2026-07-09T10:00:00Z Content type: release Language: en Sources: [OpenAI News]() Topics: [OpenAI](), [API](), [coding](), [Availability](), [computer-use](), [Cybersecurity](), [Claude]() Tags: [agents](), [coding](), [computer-use](), [cost](), [cybersecurity](), [models](), [openai](), [performance](), [product](), [update]() ### AI overview OpenAI announces the generally available GPT-5.6 family: Sol as its flagship model, Terra for balanced everyday work, and Luna as its most cost-efficient model. The article highlights performance across coding, knowledge work, cybersecurity, and science; improved efficiency and pricing; parallel multi-agent work; stronger computer use and design judgment; and extensive safety evaluation. ### Source excerpt More intelligence from every token, stronger performance per dollar, and more capability on demand for your hardest work. ## Introducing computer use in Gemini 3.5 Flash DevFeed: [Introducing computer use in Gemini 3.5 Flash]() Original publisher: [Read original article]() Author: Mateo Quiros Published: 2026-06-24T16:30:01Z Content type: release Language: en Sources: [Google DeepMind News]() Topics: [Google AI](), [AI Bots](), [Developer Tools]() Tags: [agentic](), [api](), [automation](), [browser](), [computer-use](), [enterprise](), [flash](), [gemini](), [none](), [safety](), [software-testing](), [tool]() ### AI overview Gemini 3.5 Flash adds built-in computer use for building agents that act across browser, mobile, and desktop environments. The release also describes safeguards for sensitive actions and indirect prompt injection. ### Source excerpt A look at the built-in computer use tool in Gemini 3.5 Flash. ## Holo3.1: Fast & Local Computer Use Agents DevFeed: [Holo3.1: Fast & Local Computer Use Agents]() Original publisher: [Read original article]() Author: Maxime Langevin; Hamza Benchekroun; Axel Moyal; Emrick Sinitambirivoutin; Antonio Loison; Avshalom Manevich; Tony Wu; Pierre-Louis Cedoz; Aurélien Lac; Ronan Riochet Published: 2026-06-02T14:13:23Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [computer-use](), [On-device AI](), [Local AI](), [Deployment](), [model-deployment](), [qwen](), [Benchmark](), [Cloud](), [NVFP4](), [browser]() Tags: [agent](), [agents](), [benchmark](), [benchmarks](), [browser](), [cloud](), [computer-use](), [deployment](), [devices](), [inference](), [json](), [model](), [models](), [nvfp4](), [on-device](), [performance](), [qwen](), [software]() ### AI overview Holo3.1 is a family of computer-use models designed to operate across web, desktop, and mobile environments and integrate with different agent frameworks. The release adds quantized checkpoints for local inference, native function-calling support, and model sizes ranging from 0.8B to 35B-A3B, targeting private, cost-effective, and high-performance deployments. ### Source excerpt Users want to run the same computer-use capabilities across desktop and mobile environments, with seamless integration with different agent frameworks. They want deployment flexibility, from cloud inference to fully local execution on end-user devices. This is why we are releasing the Holo3.1 family. Holo3.1 improves robustness across the three dimensions that matter most in production: environments (web, desktop, mobile), agent frameworks, and deployment targets. ## GPT-5.5 and GPT Image 2: What Actually Changed DevFeed: [GPT-5.5 and GPT Image 2: What Actually Changed]() Original publisher: [Read original article]() Author: RisingStack Engineering Published: 2026-04-29T20:22:17Z Content type: article Language: en Sources: [RisingStack]() Topics: [OpenAI](), [coding](), [computer-use](), [context](), [API]() Tags: [ai](), [api](), [benchmarks](), [coding](), [computer-use](), [context](), [generation](), [gpt](), [image](), [openai](), [reasoning](), [tool-use]() ### AI overview The article compares OpenAI's GPT-5.5 and GPT Image 2 updates, arguing that both emphasize outputs usable with less post-processing. It discusses GPT-5.5's reasoning, coding, tool use, large context window, workflow integration, and reported efficiency improvements, alongside GPT Image 2's image generation and editing capabilities. ### Source excerpt OpenAI has rolled out two updates that on the surface seem like two separate things, but actually share a common thread. GPT-5.5 makes a big jump in terms of reasoning, coding and tool use, while GPT Image 2 focuses on image generation and editing. At first glance, it's not the individual new features that stand [...] The post GPT-5.5 and GPT Image 2: What Actually Changed appeared first on RisingStack Engineering. ## Introducing NVIDIA Nemotron 3 Nano Omni: Long-Context Multimodal Intelligence for Documents, Audio and Video Agents DevFeed: [Introducing NVIDIA Nemotron 3 Nano Omni: Long-Context Multimodal Intelligence for Documents, Audio and Video Agents]() Original publisher: [Read original article]() Author: Tuomas Rintamaki; Amala Sanjay Deshmukh; Nabin Mulepati; Collin McCarthy; Pritam Biswas; Arushi Goel; Alexandre Milesi; Danial Mohseni Taheri; Kateryna Chumachenko; Isabel Hulseman; Zhehuai Chen; Kara Published: 2026-04-28T15:58:57Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [multimodal](), [Nemotron](), [long-context](), [asr](), [computer-use](), [Mamba](), [model architecture](), [moe](), [Transformer](), [Nvidia](), [Reinforcement learning](), [NVFP4]() Tags: [alternatives](), [analysis](), [architecture](), [benchmarks](), [computer-use](), [efficiency](), [long-context](), [models](), [multimodal](), [nemotron](), [nvidia](), [optimization](), [reasoning](), [reinforcement-learning](), [speed]() ### AI overview NVIDIA introduces Nemotron 3 Nano Omni, an omni-modal model for document analysis, image reasoning, speech recognition, long audio-video understanding, computer use, and general reasoning. It combines a hybrid Mamba-Transformer Mixture-of-Experts backbone with vision and audio encoders, supports long multimodal contexts, and reports strong benchmark accuracy, throughput, reasoning speed, and system efficiency. ### Source excerpt Introducing NVIDIA Nemotron 3 Nano Omni: Long-Context Multimodal Intelligence for Documents, Audio and Video Agents - NVIDIA Nemotron 3 Nano Omni is a new omni-modal understanding model built for real-world document analysis, multiple image reasoning, automatic speech recognition, long audio-video understanding, agentic computer use, and general reasoning. - It extends the Nemotron multimodal line from a strong vision-language system to a broader text + image + video + audio model. ## GPT 5.5 on AI Gateway DevFeed: [GPT 5.5 on AI Gateway]() Original publisher: [Read original article]() Author: Jerilyn Zheng Published: 2026-04-24T07:00:00Z Content type: release Language: en Sources: [Vercel News]() Topics: [Artificial Intelligence](), [vercel ai sdk](), [OpenAI](), [Claude](), [API](), [computer-use](), [agentic-coding](), [SDKs](), [observability](), [Routing (disambiguation)](), [Latency](), [Data Science]() Tags: [agentic](), [agentic-coding](), [ai](), [ai-gateway](), [api](), [computer-use](), [gpt](), [latency](), [observability](), [openai](), [routing](), [sdk]() ### AI overview Vercel AI Gateway now supports GPT-5.5 and GPT-5.5 Pro through the AI SDK. The models target long-running agentic work, including coding, computer use, knowledge work, and scientific research, while AI Gateway adds unified model access, usage and cost tracking, routing, retries, failover, and observability. ### Source excerpt GPT-5.5 is now available on Vercel AI Gateway. There are 2 variants: GPT-5.5 and GPT-5.5 Pro. Both models are tuned for long-running agentic work across coding, computer use, knowledge work, and scientific research, and are more token-efficient than the previous generation. GPT-5.5 is stronger at agentic coding and long-horizon work where the model needs to hold context across a large system and carry changes through the surrounding codebase. Paired with computer-use skills, it can operate real software and turn raw material into documents, spreadsheets, or slide presentations. GPT-5.5 Pro is built for demanding, multi-step work where response quality matters more than latency. Early testing shows gains in business, legal, education, data science, and technical research workflows that involve critiquing work over multiple passes and stress-testing arguments. To use GPT-5.5, set model to openai/gpt-5.5 or openai/gpt-5.5-pro in the AI SDK. AI Gateway provides a unified API for calling models, tracking usage and cost, and configuring retries, failover, and performance optimizations for higher-than-provider uptime. It includes built-in custom reporting, observability, Bring Your Own Key support, and intelligent provider routing with automatic retries. Learn more about AI Gateway, view the AI Gateway model leaderboard or try it in our model playground. Read more ## Introducing GPT-5.5 DevFeed: [Introducing GPT-5.5]() Original publisher: [Read original article]() Published: 2026-04-23T11:00:00Z Content type: release Language: en Sources: [OpenAI News]() Topics: [Artificial Intelligence](), [OpenAI](), [agentic-coding](), [computer-use](), [Software Engineering](), [Cybersecurity]() Tags: [agentic-coding](), [ai](), [analysis](), [coding](), [computer-use](), [gpt](), [openai](), [product](), [safety]() ### AI overview OpenAI introduces GPT-5.5, a model designed to handle complex, multi-step work across coding, research, data analysis, document creation, software operation, and tool use. It emphasizes agentic task completion, improved intelligence at GPT-5.4-level token latency, lower token usage on Codex tasks, and expanded safety testing and safeguards. ### Source excerpt This post introduced GPT-5.5, a model built for complex tasks like coding, research, and data analysis across tools. ## Codex for (almost) everything DevFeed: [Codex for (almost) everything]() Original publisher: [Read original article]() Published: 2026-04-16T10:00:00Z Content type: release Language: en Sources: [OpenAI News]() Topics: [codex](), [computer-use](), [browser](), [Front end](), [Game Development](), [API](), [Model Context Protocol (MCP)](), [ssh](), [Code](), [software-development]() Tags: [agent](), [agents](), [api](), [applications](), [codex](), [computer-use](), [frontend](), [game-development](), [github](), [images](), [macos](), [mcp](), [product](), [ssh](), [tools](), [update](), [windows]() ### AI overview OpenAI describes a major Codex update for macOS and Windows that adds computer use, in-app browsing, image generation, memory, plugins, parallel agents, remote SSH development, and richer support for developer workflows. ### Source excerpt The updated Codex app for macOS and Windows adds computer use, in-app browsing, image generation, memory, and plugins to accelerate developer workflows. ## Claude Opus 4.7 on AI Gateway DevFeed: [Claude Opus 4.7 on AI Gateway]() Original publisher: [Read original article]() Author: Jerilyn Zheng Published: 2026-04-16T07:00:00Z Content type: release Language: en Sources: [Vercel News]() Topics: [Claude](), [Artificial Intelligence](), [anthropic](), [vercel ai sdk](), [API](), [Vercel]() Tags: [agentic](), [agents](), [ai](), [anthropic](), [api](), [claude](), [computer-use](), [image-processing](), [observability](), [sdk](), [tokens](), [vercel]() ### AI overview Vercel AI Gateway now offers Anthropic's Claude Opus 4.7, a model designed for long-running asynchronous agents and complex multi-step tasks. The release highlights improved visual verification, image-processing tool use, high-resolution image support, structured memory, configurable task budgets, and an xhigh effort level. AI Gateway also provides a unified API with usage and cost tracking, retries, failover, provider routing, observability, and Bring Your Own Key support. ### Source excerpt Claude Opus 4.7 from Anthropic is now available on Vercel AI Gateway. Opus 4.7 is optimized for long-running, asynchronous agents and handles complex, multi-step tasks with reliable agentic execution. The model shows gains on knowledge-worker tasks, particularly where it needs to visually verify its own outputs. Opus 4.7 is also stronger at programmatic tool-calling with image-processing libraries to analyze charts and figures, including pixel-level data transcription. It has high-resolution image support, which is useful for computer use, screenshot understanding, and document analysis workflows. Opus 4.7 now has improved memory, with agents that maintain structured memory store across turns seeing more reliable recall and fewer dropped facts without additional prompting. To use Claude Opus 4.7 set model to anthropic/claude-opus-4.7 in the AI SDK. You can also try a new effort level: 'xhigh'. Opus 4.7 also introduces the task budgets feature. Task budgets let you set a total token budget for an agentic turn via taskBudget. The model sees a countdown of remaining tokens, which it uses to prioritize work, plan ahead, and wind down gracefully as the budget is consumed. Thinking content is also now omitted by default for Opus 4.7. To receive thinking content, set display to 'summarized': 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, observability, Bring Your Own Key support, and intelligent provider routing with automatic retries. Learn more about AI Gateway, view the AI Gateway model leaderboard or try the model in our model playground. Read more [Next page]()