# Skills Published articles for Skills. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## How 8 AI Skills Automate a Content Workflow and Save 5+ Hours per Week DevFeed: [How 8 AI Skills Automate a Content Workflow and Save 5+ Hours per Week]() Original publisher: [Read original article]() Author: Peter Yang Published: 2026-09-16T14:36:57Z Content type: tutorial Language: en Sources: [Behind the Craft]() Topics: [Artificial Intelligence]() Tags: [ai](), [ai-skills](), [automate](), [skills](), [workflow]() ### AI overview The article describes a three-step system that turns 15 manual content-workflow steps into 8 AI skills, reportedly saving more than 5 hours per week. ### Source excerpt How I turned 15 manual steps into 8 AI skills that save me 5+ hours a week, and how you can do the same for your own work ## Building an AI-native data & insights operating system at Webflow DevFeed: [Building an AI-native data & insights operating system at Webflow]() Original publisher: [Read original article]() Author: Ashwini Chaube Published: 2026-09-16T00:00:00Z Content type: article Language: en Sources: [Webflow Blog]() Topics: [data](), [Artificial Intelligence](), [Platform Engineering](), [Data Science](), [decision-making](), [Authorization]() Tags: [agentic](), [ai](), [data](), [data-insights](), [inside-webflow](), [permissions](), [review](), [self-service](), [skills]() ### AI overview Webflow describes how its Data & Insights team built an AI-native operating system for trusted self-service analytics. The approach combines governed data, encoded business context, reusable skills and agents, permissions, architectural controls, review practices, and human judgment, while also changing how the team works through agent-first workflows, learning, and experimentation. ### Source excerpt How we built the governed foundations for trusted self-service analytics while transforming the way our own team works. ## Presentation: Decision Models in Agentic Architectures: From Production to Agent Skills DevFeed: [Presentation: Decision Models in Agentic Architectures: From Production to Agent Skills]() Original publisher: [Read original article]() Author: Alex Porcelli Published: 2026-09-14T11:00:00Z Content type: article Language: en Sources: [InfoQ]() Topics: [Agent Skills](), [Artificial Intelligence](), [Large Language Model](), [NeMo]() Tags: [agent](), [agent-skills](), [agentic](), [agentic-ai-architecture](), [agents](), [ai](), [ai-architecture](), [ai-ml-data-engineering](), [architecture](), [architecture-design](), [architectures](), [artificial-intelligence](), [business](), [decision-models-agentic-ai](), [development](), [enterprise](), [enterprise-architecture](), [governance](), [infoq](), [large-language-models](), [llms](), [ml-data-engineering](), [models](), [nemo](), [presentation](), [production](), [qcon-ai-boston-2026](), [qcon-software-development-conference](), [skills](), [transcripts]() ### AI overview Alex Porcelli explains how DMN decision models can be integrated with LLMs, agent skills, and NeMo guardrails to create auditable and deterministic agentic architectures for high-stakes enterprise decisions. ### Source excerpt Alex Porcelli discusses the critical gap in enterprise AI: non-deterministic output and lack of accountability in high-stakes decisions. He shares how integrating DMN decision models with LLMs, agent skills, and NeMo guardrails creates auditable, deterministic agentic architectures - allowing business leaders to own decision logic while engineers maintain robust architectural governance. By Alex Porcelli ## Generating running routes with GPT-6 Astra and ChatGPT Work DevFeed: [Generating running routes with GPT-6 Astra and ChatGPT Work]() Original publisher: [Read original article]() Author: Simon Willison Published: 2026-09-12T23:56:42Z Content type: opinion Language: en Sources: [Simon Willison](), [Simon Willison's Weblog]() Topics: [ChatGPT](), [Large Language Model](), [GeoJSON](), [Code](), [d3]() Tags: [agent](), [ai](), [ai-2-235](), [chatgpt](), [chatgpt-204](), [code](), [d3](), [d3-15](), [generative-ai](), [generative-ai-1-981](), [geospatial](), [geospatial-85](), [gpt](), [gpt-6-astra](), [gpt-6-astra-9](), [llms](), [llms-1-947](), [map](), [openai](), [openai-463](), [python](), [skills](), [skills-15](), [work]() ### AI overview The article describes using ChatGPT Work with GPT-6 Astra to generate 5K and 10K running routes from OpenStreetMap data, producing an embedded visualization and downloadable GPX and GeoJSON files. It also criticizes the lack of visibility into the generated Python code and the loss of that code after thread compaction. ### Source excerpt Here's a neat thing I had ChatGPT Work with GPT-6 Astra (Max) do this morning: I live at . Figure out 5K and 10K running routes from me that loop from my house. Use OSM data. It worked for 27 minutes and produced exactly what I'd asked for, as both an embedded visualization and downloadable GPX file and GeoJSON files. Here's that 5K route: When I asked it how it had created the route, it replied: I used Nominatim to locate the address and Overpass to download local OpenStreetMap roads and trails, then calculated the loops locally. Frustratingly, the actual code it ran and exact details of what it did weren't visible to me in the ChatGPT UI. I see this lack of transparency is an anti-feature. By the time I thought to ask for a copy of the Python code it had used, ChatGPT was unable to provide it. This appears to be because the thread had been compacted. I think any LLM system that uses compaction needs to both preserve the pre-compacted text and make that text available via agent tool calls, to protect against this kind of problem. As for displaying the map to me, that used the visualize skill. It created a file called /workspace/el-granada-5k-share.html to embed directly into the ChatGPT UI. Here's a copy of that HTML, which starts like this:

El Granada harbor loop

5.1 km
Map data © OpenStreetMap contributors
) Original publisher: [Read original article]() Author: Kevin Hou; Haoyu Wang; Alan Blount Published: 2026-09-12T11:04:33.891311Z Content type: article Language: en Sources: [Google Developers Blog]() Topics: [Agent Skills](), [Model Context Protocol (MCP)](), [MCP Server](), [Google](), [amazon](), [Microsoft](), [OpenAI](), [Vercel]() Tags: [agent-skills](), [amazon](), [google](), [maintainers](), [manifest](), [mcp](), [mcp-server](), [microsoft](), [openai](), [plugin](), [portable](), [skills](), [vercel]() ### AI overview The article introduces Agent Plugins 1.0.0, a vendor-neutral specification for packaging Agent Skills and MCP servers into portable plugins. It standardizes the manifest and directory layout so developers can distribute one package across clients while preserving client-specific flexibility. Google is joining the Core Maintainers and beginning support in its products. ### Source excerpt Agent Plugins 1.0.0 is a new, vendor-neutral directory specification--backed by Google, Amazon, Microsoft, and others--for packaging Agent Skills and MCP servers into a single portable unit. By standardizing the manifest (plugin.json) and utilizing a fixed directory layout, it eliminates the need for developers to maintain separate wrappers or configurations to support different AI coding agents and IDEs. Google has officially joined as a Core Maintainer and already rolled out support in the Agents CLI and Data Agent Kit, allowing developers to start building and distributing interoperable plugins today. ## Enable on-demand expertise with Agent Skills in Genkit Go DevFeed: [Enable on-demand expertise with Agent Skills in Genkit Go]() Original publisher: [Read original article]() Author: Daniela Petruzalek Published: 2026-09-12T11:04:33.891311Z Content type: tutorial Language: en Sources: [Google Developers Blog]() Topics: [Agent Skills](), [Agent Harness](), [context window](), [Go](), [Script]() Tags: [agent](), [agent-skills](), [architecture](), [context-window](), [developers](), [go](), [skills](), [workflows]() ### AI overview This article explains how Agent Skills in Genkit Go provide on-demand specialized expertise through progressive disclosure. Skills package instructions, references, and scripts in modular SKILL.md bundles, exposing only metadata initially and loading full content when a task requires it. ### Source excerpt To prevent context window bloat and reduce token consumption, Genkit Go introduces Agent Skills based on a progressive disclosure architecture. Developers can package specialized instructions, scripts, and references into modular SKILL.md bundles where only the frontmatter metadata is initially exposed to the agent's system prompt. When a task matches the skill's description, Genkit's middleware dynamically loads the full instruction body and associated assets, ensuring the model accesses precise workflows exactly when needed. ## Put Redis data and engineering guidance to work in ChatGPT Work DevFeed: [Put Redis data and engineering guidance to work in ChatGPT Work]() Original publisher: [Read original article]() Author: Olga Lopaci Published: 2026-09-11T00:00:00Z Content type: release Language: en Sources: [Redis Blog]() Topics: [Developer Tools](), [AI Chat](), [AI Bots]() Tags: [agent-skills](), [chatgpt](), [codex](), [engineering](), [observability](), [rag](), [redis](), [review](), [security](), [skills](), [tech](), [work]() ### AI overview Redis launched a development plugin for ChatGPT Work and Codex that supplies current Redis guidance for writing, reviewing, and troubleshooting code. It also describes connecting Redis data to ChatGPT Work's Data agent for plain-language exploration and investigation. ### Source excerpt Redis has launched a development plugin that brings current Redis engineering guidance into ChatGPT Work and Codex. It helps teams write, review, and troubleshoot Redis code without switching between documentation and development tools. Alongside Ope... ## (Re)introducing Developer Story DevFeed: [(Re)introducing Developer Story]() Original publisher: [Read original article]() Author: Philippe Beaudette Published: 2026-09-10T18:01:54Z Content type: news Language: en Sources: [Stack Overflow Blog]() Topics: [Programming](), [Code](), [Large Language Model](), [datasets]() Tags: [career](), [community](), [company](), [cv](), [datasets](), [developer](), [identity](), [llms](), [news](), [privacy](), [profile](), [programming](), [se-stackoverflow](), [se-tech](), [skills](), [work]() ### AI overview Stack Overflow is reintroducing Developer Story, a profile feature designed to showcase developers' careers, specialties, contributions, and technical identity. The article also introduces Stack Identity, a broader vision for verified proof of developer work, with privacy controls and integrations for verified contributions from other sites. ### Source excerpt For the past few years, we've been looking at ways to bring a little more of the individual developer back to Stack. ## 47,000 job listings reveal the engineering roles that AI is creating DevFeed: [47,000 job listings reveal the engineering roles that AI is creating]() Original publisher: [Read original article]() Author: Jennifer Riggins Published: 2026-09-10T13:09:28Z Content type: news Language: en Sources: [The New Stack]() Topics: [Machine Learning & Artificial Intelligence](), [AI Development](), [Machine learning]() Tags: [ai](), [ai-engineering](), [ai-strategy](), [andela](), [engineering](), [jobs](), [machine-learning](), [post](), [skills](), [sponsor-andela](), [sponsored](), [sponsored-post](), [tech-careers](), [work]() ### AI overview Andela's analysis of 47,000 Fortune 500 engineering job postings identifies emerging AI-related roles formed by combining established skill sets. The article argues that organizations should use AI to delegate suitable work while retaining human expertise and specialization. ### Source excerpt Every major transformation in tech has led to roles merging, then new ones emerging. Friction between developers and operations drove The post 47,000 job listings reveal the engineering roles that AI is creating appeared first on The New Stack. ## Using 10 Claude Skills to Run an Online Content Website DevFeed: [Using 10 Claude Skills to Run an Online Content Website]() Original publisher: [Read original article]() Author: iHateReading Published: 2026-09-09T10:43:29Z Content type: opinion Language: en Sources: [iHateReading]() Topics: [Claude](), [Website]() Tags: [ai](), [backend](), [blockchain](), [claude](), [claude-skills](), [coding](), [development](), [founderos](), [founderos-claude-claude-skills-gpt-ai](), [frontend](), [gpt](), [i-gave-claude-10-plus-saas-growth-skills-and-let-it-run-my-own-platform](), [ihatereading](), [javascript](), [node-js](), [product](), [programming](), [react](), [skills](), [typescript](), [web-development]() ### AI overview The article describes using 10 Claude Skills to run an online content website. ### Source excerpt Run entire online content website using 10 Claude Skills ## APNIC and NIXI partner to strengthen routing security and technical capacity in India DevFeed: [APNIC and NIXI partner to strengthen routing security and technical capacity in India]() Original publisher: [Read original article]() Author: Dan Fidler Published: 2026-09-09T09:01:06Z Content type: news Language: en Sources: [APNIC Blog]() Topics: [Routing Security](), [Security](), [Internet](), [Resilience](), [networking]() Tags: [apnic-62](), [collaboration](), [development](), [india](), [infrastructure](), [internet](), [ipv6](), [knowledge-sharing](), [nixi](), [partnership](), [resilience](), [routing](), [routing-security](), [rpki](), [security](), [skills](), [support]() ### AI overview APNIC and NIXI have signed an MoU to strengthen routing security and technical capacity in India. The partnership will expand IPv6 and RPKI deployment, pilot an RPKI repository mirror, promote routing security practices, and support technical training and knowledge sharing. ### Source excerpt The partnership will expand IPv6 and RPKI deployment including piloting an RPKI Repository Mirror to support a more secure and resilient Internet ecosystem. ## How we built data-driven AI Golden Paths at Datadog DevFeed: [How we built data-driven AI Golden Paths at Datadog]() Original publisher: [Read original article]() Author: Addie Beach; Rui Martins Lacerda Published: 2026-09-09T00:00:00Z Content type: article Language: en Sources: [Datadog | The Monitor blog]() Topics: [AI Bots]() Tags: [agent](), [agent-observability](), [ai](), [cli](), [cost](), [dashboards](), [development](), [documentation](), [experiments](), [performance](), [security](), [skills](), [workflows]() ### AI overview Datadog describes a data-driven process for establishing AI Golden Paths: standardized AI-agent workflows governed by controls such as skills, hooks, and tests. The approach evaluates controls for their effects on code quality, security, token usage, cost, and agent performance. ### Source excerpt See how a Datadog guild achieved 13% faster agent runs by building Golden Paths for AI-assisted development using controls, experiments, and dashboards. ## v0 adds one-click integrations for email, auth, search, and databases DevFeed: [v0 adds one-click integrations for email, auth, search, and databases]() Original publisher: [Read original article]() Author: Jathin Singaraju Published: 2026-09-09T00:00:00Z Content type: release Language: en Sources: [Vercel News]() Topics: [AI Chat]() Tags: [agent-skills](), [auth](), [code](), [databases](), [integration](), [react](), [search](), [skills](), [vercel]() ### AI overview Vercel's v0 adds inline Marketplace integrations for providers including Resend, Amazon OpenSearch, MongoDB Atlas, Algolia, and Clerk. Connected providers can be configured automatically, and provider skills can guide generated code. ### Source excerpt We're working toward bringing parity across Vercel integration and v0, starting with Resend, Amazon OpenSearch, MongoDB Atlas, Algolia and Clerk. Prompt v0 with what you want to build, and when your prompt requires a provider, v0 renders a connect card in the chat. With Marketplace integrations in v0, you get: Connect as you build: Prompt v0 with what you want to create, and connect the required provider inline. Automatic setup: Once connected, v0 handles the required environment variables and configuration. Provider skills loaded automatically: When you connect a provider that publishes agent skills, v0 loads them and generates code that follows the provider's recommended patterns. Provider-specific capabilities: Resend, for example, adds transactional email with React Email components. To get started, open a v0 chat and prompt what you want to add to your stack. Read more ## AI Coding Tip 035 - Write Skill Descriptions in Three Sentences DevFeed: [AI Coding Tip 035 - Write Skill Descriptions in Three Sentences]() Original publisher: [Read original article]() Author: Maxi Contieri Published: 2026-09-06T01:34:09Z Content type: tutorial Language: en Sources: [Maximiliano Contieri - Software Design]() Topics: [ai-coding](), [coding]() Tags: [agent](), [ai](), [ai-coding](), [context](), [how-to](), [skills]() ### AI overview This tutorial recommends writing each skill description in three sentences: when to read it, the situation that triggers it, and what the skill does. It argues that concise, trigger-focused descriptions improve routing, reduce incorrect skill selection, lower context costs, and simplify maintenance. ### Source excerpt TL;DR: Split every skill description into three sentences: when to read it, when to use it, and what it does. Common Mistake ❌ You write a skill description as one long paragraph. It explains everyth ## Your Agent Speaks MCP. Give It a Computer. DevFeed: [Your Agent Speaks MCP. Give It a Computer.]() Original publisher: [Read original article]() Published: 2026-09-03T00:00:00Z Content type: article Language: en Sources: [The Fly Blog]() Topics: [MSP MCP](), [AI Bots]() Tags: [agents](), [api](), [auth](), [cdn](), [claude](), [claude-code](), [cli](), [close-to-users](), [cloud](), [codex](), [cursor](), [deploy-app-servers](), [docker](), [elixir](), [filesystems](), [fly](), [fly-io](), [gemini](), [heroku-alternative](), [heroku-competitor](), [hosting](), [i](), [mcp](), [mcp-server](), [networking](), [postgresql-clusters](), [servers](), [skills]() ### AI overview The article presents Sprites as disposable cloud computers for coding agents and explains using their API through MCP. It argues that MCP transport and progressive capability disclosure can work together, including through plugins and skills. ### Source excerpt Sprites are disposable cloud computers. They appear instantly, always include durable filesystems, and cost practically nothing when idle. They're the best and safest place on the Internet to run agents and we want you to create dozens of them. Sprites are a place to run agents; the first thing you should think to do with a new Sprite is to type claude (or gemini or codex). We've put a lot of effort into making sure coding agents feel safe and happy when they're on Sprites, because, to (probably) quote John von Neumann, "happy agents are productive agents." What's less obvious about Sprites is that they're great tools for agents. Want three different versions of a new feature? A test environment? An ensemble of cooperating services? It's super handy to be able to start your prompts, "On a new Sprite, do...". The Sprites API is simple, discoverable, and designed for this use case. The only real question is how your agent reaches it. For most of you the answer is MCP, and the setup is already written. You Don't Have To Pick There's an argument going around that MCP is the wrong way to extend an agent, and that command line tools and discoverable APIs are the Right Way. Half of that argument is correct, and it's the important half, so let's take it seriously. Dumping thirty tool descriptions into a context window is a bad way to teach anything. Not every Sprite command matters in every session, and cramming them all in signals to the model that they all matter to you. If you're not using network policies, gemini shouldn't burn a single token learning to configure them. Capabilities should reveal themselves progressively, the way they do when an agent works out a CLI one subcommand at a time. The wrong half is treating that as a case against MCP. Progressive disclosure is a question of what you say to the model. MCP is a question of how the bytes get there: transport, auth, structured results, a tool the model can call instead of a command whose flags it has to guess. Tho ## Decoding the new AI lingo: Loops, harnesses, squads, hill climbing... oh my! DevFeed: [Decoding the new AI lingo: Loops, harnesses, squads, hill climbing... oh my!]() Original publisher: [Read original article]() Author: Cassidy Williams Published: 2026-09-02T21:00:00Z Content type: article Language: en Sources: [GitHub Engineering]() Topics: [AI Development](), [Software Engineering](), [Open Source Models & Datasets]() Tags: [agent](), [agentic-ai](), [agents](), [ai](), [ai-ml](), [engineering](), [github-podcast](), [loops](), [models](), [observability](), [open-source](), [routing](), [skills](), [validation](), [workflow]() ### AI overview A guide to emerging AI-development terminology, explaining loop engineering and Ralph loops, multi-agent squads and fleets, and related concepts such as open weights and open source models. ### Source excerpt From loop engineering to harnesses, squads, and open weights, the GitHub Podcast breaks down the AI terms showing up in developer conversations. The post Decoding the new AI lingo: Loops, harnesses, squads, hill climbing... oh my! appeared first on The GitHub Blog. ## New Trailer Reveals Project Hail Mary: Journey Among The Stars Gameplay DevFeed: [New Trailer Reveals Project Hail Mary: Journey Among The Stars Gameplay]() Original publisher: [Read original article]() Author: James Tocchio Published: 2026-09-02T14:47:20Z Content type: news Language: en Sources: [UploadVR]() Topics: [Android XR](), [Meta]() Tags: [2026](), [android-xr](), [engineering](), [experience](), [launch](), [meta](), [platforms](), [skills](), [vr-gaming]() ### AI overview A new trailer offers the first extended look at gameplay for Project Hail Mary: Journey Among the Stars, a mixed reality experience developed with Andy Weir. Players take the role of Ryland Grace and work with Rocky to diagnose failing ship systems and protect the mission. ### Source excerpt A new trailer reveals gameplay of Project Hail Mary: Journey Among The Stars, a mixed reality experience launching on Quest, Pico, and Android XR this fall. ## REFACTOR-VLA: Unsupervised Library Learning of Typed Motor Programs DevFeed: [REFACTOR-VLA: Unsupervised Library Learning of Typed Motor Programs]() Original publisher: [Read original article]() Published: 2026-09-02T00:00:00Z Content type: article Language: en Sources: [Apple Machine Learning Research]() Topics: [World models](), [Large Language Model]() Tags: [models](), [skills](), [training](), [world-model]() ### AI overview REFACTOR-VLA is a vision-language-action system that learns reusable typed motor-program skills through alternating world-model-based clustering and policy optimization. On LIBERO, the article reports that scaling the world model reduced performance across all four benchmark suites, while an InfoNCE auxiliary loss improved skill clustering. ### Source excerpt Most current vision-language-action (VLA) models--such as OpenVLA, π0, RT-2, and RDT-1B--are "monolithic." This means they generate raw motor commands or very short sequences of actions, without organizing behaviors into reusable, well-defined abstractions. As a result, these models perform poorly on long-horizon (multi-step) tasks, and it's difficult to interpret what they have learned. Existing approaches for discovering skills often avoid the core problem of deciding when two action sequences are "behaviorally equivalent." For example, AtomicVLA and AtomSkill group action sequences by... ## UX Conference September Announced (Sep 14 - Sep 25) DevFeed: [UX Conference September Announced (Sep 14 - Sep 25)]() Original publisher: [Read original article]() Published: 2026-09-01T20:37:45Z Content type: article Language: en Sources: [NN/g latest articles and announcements]() Topics: [User interface design](), [AI Strategy](), [AI Chat]() Tags: [ai](), [best-practices](), [collaboration](), [design](), [evaluation](), [event](), [management](), [skills](), [training](), [ux]() ### AI overview NN/G announces live virtual UX training courses running September 14-25, 2026. The program covers UX best practices, design systems, usability testing, AI-related UX strategy and writing, collaboration, and certification pathways. ### Source excerpt Take up to 5 in-depth training courses, teaching user experience best practices for successful design. Training focused on long-lasting skills for UX professionals. September 14 - September 25, 2026. ## AI Skills Adoption: How to Measure Reuse and Engineering Impact DevFeed: [AI Skills Adoption: How to Measure Reuse and Engineering Impact]() Original publisher: [Read original article]() Author: Tomasz Skora Published: 2026-08-31T08:53:02Z Content type: article Language: en Sources: [Developer Experience & Platform Engineering Blog | Port]() Topics: [Artificial Intelligence](), [incident](), [Pull Request](), [migration]() Tags: [ai](), [engineering](), [how-to](), [incident](), [migration](), [projects](), [pull-request](), [review](), [skills](), [workflow]() ### AI overview The article explains how to evaluate the value of AI Skills as they spread through engineering organizations. It recommends measuring adoption first, then examining consistency and reuse, and finally connecting Skill usage to improvements in the engineering work those Skills are intended to support. ### Source excerpt AI Skills adoption is only the starting point. See how consistency, reuse, and engineering context reveal which Skills matter. ## Understanding ChatGPT Work DevFeed: [Understanding ChatGPT Work]() Original publisher: [Read original article]() Author: Simon Willison Published: 2026-08-30T23:59:47Z Content type: article Language: en Sources: [Simon Willison]() Topics: [ChatGPT](), [OpenAI](), [App](), [codex]() Tags: [ai](), [ai-2-235](), [app](), [article](), [chatgpt](), [chatgpt-204](), [code-interpreter](), [code-interpreter-32](), [general-agents](), [general-agents-12](), [generative-ai](), [generative-ai-1-981](), [lethal-trifecta](), [lethal-trifecta-30](), [llms](), [llms-1-947](), [openai](), [openai-463](), [skills](), [skills-15]() ### AI overview An overview of ChatGPT Work, distinguishing its cloud and local versions and describing the cloud version's access requirements and features that differ from ChatGPT Chat. ### Source excerpt OpenAI announced ChatGPT Work on July 9th, and have been furiously iterating on it ever since. It is an extraordinarily confusing and very powerful product. Here's what I've figured out about it so far. ChatGPT Work is actually two products The more interesting version of ChatGPT Work is the one that runs in the cloud. This can be accessed via chatgpt.com or through the ChatGPT mobile apps. Let's call it Work Cloud. If you install the ChatGPT desktop app - the app that used to be called Codex - you gain access to a thing called ChatGPT Work that can access files and run programs directly on your computer. Let's call that one Work Local. This one feels more like regular Codex re-skinned to be less intimidating to non-software-developers. (Update: Work Cloud is also available from the ChatGPT desktop app, via a Where should this chat run? dropdown.) For the rest of this article I'm going to talk exclusively about Work Cloud. Work is for paid subscribers only Right now, ChatGPT Work (in both flavors) is available only to $20/month and up subscribers. Free users and $8/month Go users do not have access. Work has features that aren't available in Chat The interface for accessing Work is a tab selector, which presents it as an alternative to Chat: The obvious question is when should I use Chat, and when should I use Work? OpenAI's official answer to that question is: Use Chat when you want an answer, explanation, brainstorm, or short draft. Use ChatGPT Work when you want ChatGPT to complete a task with a clear outcome, such as a brief, deck, analysis, recurring update, workflow, or file you can review and use. I find that almost entirely useless, because I've been using regular ChatGPT Chat for all of those task categories for years! The better question then is what features does Work have that are missing from Chat? After extensive experimentation I think I've mostly figured that out: Options to use Luna and Terra in place of Sol A code execution environment with Interne ## AI Coding Tip 034 - Stop Hoarding Rules in Your AGENTS.md DevFeed: [AI Coding Tip 034 - Stop Hoarding Rules in Your AGENTS.md]() Original publisher: [Read original article]() Author: Maxi Contieri Published: 2026-08-28T18:10:58Z Content type: tutorial Language: en Sources: [Maximiliano Contieri - Software Design]() Topics: [ai-coding](), [coding](), [Code]() Tags: [agents](), [ai-coding](), [coding](), [context](), [developer](), [how-to](), [mcp](), [skills](), [test]() ### AI overview The article advises developers to regularly audit AGENTS.md files and skills, remove rules that no longer matter, and test whether instructions still improve agent performance. It highlights context bloat, stale skills, dead MCP servers, plugins, and hooks as ongoing maintenance costs. ### Source excerpt TL;DR: Audit your AGENTS.md and skills on a schedule, or you keep paying context rent on rules the model has outgrown. Common Mistake ❌ Every time the AI does something wrong, you add a rule to stop ## How to Train a Cross-Embodiment Robot Navigation Policy with AI Agents DevFeed: [How to Train a Cross-Embodiment Robot Navigation Policy with AI Agents]() Original publisher: [Read original article]() Author: Tanya Lenz Published: 2026-08-26T20:05:06Z Content type: tutorial Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Artificial Intelligence](), [Reinforcement learning](), [codex](), [coding](), [Simulation and Design](), [CUDA]() Tags: [agent](), [agentic-ai-generative-ai](), [ai](), [ai-agent](), [ai-agents](), [architecture](), [codex](), [coding](), [cuda](), [development](), [evaluation](), [framework](), [nvidia](), [omniverse](), [physical-ai](), [reinforcement-learning](), [robotics](), [simulation-modeling-design](), [skills](), [testing](), [training](), [tutorial](), [workflow]() ### AI overview This tutorial presents an agent-driven COMPASS workflow for training and evaluating cross-embodiment robot navigation policies. It covers asset preparation, smoke testing, residual reinforcement learning, checkpoint evaluation, runtime integration, and optional reconstructed environments using NVIDIA Omniverse NuRec. ### Source excerpt Navigation enables a robot to turn perception and motion into purposeful autonomy. Unlike locomotion, which produces stable movement, navigation must be used to... ## Webflow is now available in Codex and ChatGPT DevFeed: [Webflow is now available in Codex and ChatGPT]() Original publisher: [Read original article]() Author: Blaire McClure Published: 2026-08-24T00:00:00Z Content type: release Language: en Sources: [Webflow Blog]() Topics: [webflow](), [ChatGPT](), [codex](), [Model Context Protocol (MCP)](), [Artificial Intelligence](), [Content Management System](), [Accessibility](), [Search engine optimization (SEO)]() Tags: [accessibility](), [agents](), [chatgpt](), [code](), [codex](), [developer](), [extensions](), [links](), [mcp](), [optimization](), [performance](), [seo](), [skills](), [software](), [webflow]() ### AI overview Webflow is now available in Codex and ChatGPT through Webflow MCP, expanding access to Webflow sites and workflows. Built-in Skills can help users audit sites, manage CMS content, validate custom code, and accelerate development tasks using natural-language requests. ### Source excerpt Webflow is now available in Codex, alongside a new set of built-in Skills for both ChatGPT and Codex. [Next page]()