# Generative AI Generative AI is artificial intelligence that creates original content such as text, images, video, audio, or software code in response to prompts. 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 To Write With An LLM DevFeed: [How To Write With An LLM]() Original publisher: [Read original article]() Author: Simon Willison Published: 2026-09-17T23:37:27Z Content type: article Language: en Sources: [Simon Willison's Weblog]() Topics: [Large Language Model](), [Generative AI](), [prompt]() Tags: [ai](), [ai-2-239](), [generative-ai](), [generative-ai-1-985](), [how-to](), [llm](), [llms](), [llms-1-951](), [prompt](), [thomas-ptacek](), [thomas-ptacek-21](), [writing](), [writing-34]() ### AI overview The article discusses using large language models as copyeditors rather than writing assistants. It describes a rule against adopting wording suggested by an LLM, while permitting uses such as fact-checking, spelling, grammar, and occasional thesaurus help. It also mentions a personal copyediting tool and a prompt for building one. ### Source excerpt How To Write With An LLM Thomas Ptacek on using LLMs as copyeditors, not as writing assistants: Rule Number One: You may not use a single word an LLM suggests to you. [...] I think that as a form of intellectual personal protective equipment you should adopt the rule that any specific turn of phrase an LLM suggests is off limits. Be strict about the rule! I won't let LLMs write content for my blog, but I use them for fact-checking, spelling and grammar and as an occasional thesaurus (see my proofreading prompt). The rule to never use a turn of phrase suggested by an LLM feels good to me. The text has that weird smell to it, and it's also a good principle to help stay disciplined. Later in this piece Thomas shows a screenshot of his personal LLM copyediting tool (see also this Twitter thread), and provides a prompt to help kickstart building your own. Tags: thomas-ptacek, writing, ai, generative-ai, llms ## Run open weight models on Amazon Bedrock in AWS European Sovereign Cloud DevFeed: [Run open weight models on Amazon Bedrock in AWS European Sovereign Cloud]() Original publisher: [Read original article]() Author: Marta Taggart Published: 2026-09-17T21:19:20Z Content type: release Language: en Sources: [AWS Security Blog]() Topics: [Amazon Bedrock](), [Amazon Web Services (AWS)](), [gemma4](), [Inference](), [Generative AI](), [digital sovereignty](), [Cloud](), [API](), [SDK](), [Access Control](), [Security]() Tags: [access-control](), [amazon](), [amazon-bedrock](), [api](), [apis](), [artificial-intelligence](), [aws](), [digital-sovereignty](), [europe](), [gemma-4](), [generative-ai](), [inference](), [least-privilege](), [security-blog](), [security-identity-compliance]() ### AI overview AWS announces that Gemma 4 open-weight models are generally available through Amazon Bedrock in the AWS European Sovereign Cloud, allowing European organizations to run generative AI workloads with EU data residency and digital sovereignty controls. The article also describes the inference engine, OpenAI-compatible APIs, SDK compatibility, and access controls. ### Source excerpt European organizations can run AI workloads on Amazon Web Services (AWS) while keeping data within the European Union (EU) and meeting regulatory requirements. You can now run generative AI workloads on open weight models on Amazon Bedrock in the AWS European Sovereign Cloud. We're excited to announce the general availability of the first open weight [...] ## How DHI Group accelerates generative AI workloads from idea to production using hackathons DevFeed: [How DHI Group accelerates generative AI workloads from idea to production using hackathons]() Original publisher: [Read original article]() Author: Umesh Kalaspurkar Published: 2026-09-17T15:13:42Z Content type: article Language: en Sources: [AWS Architecture Blog]() Topics: [Artificial Intelligence](), [Generative AI](), [Hackathon](), [AI Development](), [Amazon Bedrock AgentCore](), [Amazon Web Services](), [Kiro](), [sdlc]() Tags: [ai](), [amazon-bedrock](), [amazon-bedrock-agentcore](), [aws](), [customer-solutions](), [development](), [experimentation](), [generative-ai](), [hackathons](), [kiro](), [sdlc]() ### AI overview DHI Group partnered with AWS to use a structured Hackathon Acceleration Package to move generative AI workloads from experimentation toward production. The post describes preparation, enablement, and workshops covering Amazon Bedrock AgentCore, the AI-driven development lifecycle, and Kiro. ### Source excerpt Learn how DHI Group partnered with AWS to move generative AI workloads from idea to production using a structured hackathon. This post covers the Hackathon Acceleration Package, the winning ClearanceJobs and AgileATS agentic architecture on Amazon Bedrock AgentCore, and the principles that make hackathons a repeatable path to production. ## Claude Cowork and chat are now one Claude DevFeed: [Claude Cowork and chat are now one Claude]() Original publisher: [Read original article]() Author: Simon Willison Published: 2026-09-16T18:09:49Z Content type: opinion Language: en Sources: [Simon Willison's Weblog]() Topics: [Claude](), [anthropic](), [Artificial Intelligence](), [Generative AI](), [Large Language Model]() Tags: [ai](), [ai-2-237](), [anthropic](), [anthropic-337](), [claude](), [claude-311](), [general-agents](), [general-agents-13](), [generative-ai](), [generative-ai-1-983](), [llms](), [llms-1-949]() ### AI overview Anthropic is merging Claude Cowork and Claude chat into one Claude experience. The change is rolling out first to Pro and Max users in the Claude app across web, desktop, and mobile over the coming weeks. The author notes that the practical feature and surface boundaries remain unclear. ### Source excerpt Claude Cowork and chat are now one Claude In hopefully good news for anyone who, like me, was increasingly confused at Cowork v.s. Claude v.s. Claude Code: Starting today, Claude Cowork and chat are merging into one Claude. Bring a quick question, or hand over a report due at noon, and Claude takes it from there, even after you've closed your laptop. [...] This is rolling out to Pro and Max plans first, in the Claude app on web, desktop, and mobile over the coming weeks to existing and new users on these plans. I guess this means Claude is becoming a general agent in its own right. Echoes of OpenAI renaming their Codex desktop app to ChatGPT a few weeks ago. On the one hand, this saves me some work, in that I was planning to finally figure out the boundaries between Cowork and regular Claude and write a follow-up to my piece on Understanding ChatGPT Work. I have a hunch that figuring out what this actually means in terms of features and surfaces is still going to take quite a bit of work. Via Hacker News Tags: ai, generative-ai, llms, anthropic, claude, general-agents ## Mustafa Suleyman argues against treating AI models as sentient or entitled to rights DevFeed: [Mustafa Suleyman argues against treating AI models as sentient or entitled to rights]() Original publisher: [Read original article]() Author: Simon Willison Published: 2026-09-16T16:00:54Z Content type: opinion Language: en Sources: [Simon Willison's Weblog]() Topics: [Artificial Intelligence](), [Generative AI](), [Large Language Model](), [Microsoft]() Tags: [ai](), [ai-2-236](), [ai-ethics](), [ai-ethics-343](), [ethics](), [generative-ai](), [generative-ai-1-982](), [llms](), [llms-1-948](), [microsoft](), [microsoft-134]() ### AI overview The article quotes Mustafa Suleyman arguing that AI models should not be treated as having feelings, preferences, rights, or entitlement to human welfare. He says the evidence does not justify granting models such rights and warns that doing so could make AI containment and alignment more difficult. ### Source excerpt We should not treat models as though they have feelings, preferences, rights, or any entitlement to our welfare. Consciousness is the foundation of our ethical, legal, and political systems. To invite another entity to share any flavor of these rights isn't justified by the evidence and will make the AI containment and alignment challenge even harder. -- Mustafa Suleyman, A warning about 'model welfare' Tags: ai-ethics, generative-ai, ai, microsoft, llms ## Photoshop Elements 2027 Adds Generative Expand, AI Upscaling and a Beta AI Assistant DevFeed: [Photoshop Elements 2027 Adds Generative Expand, AI Upscaling and a Beta AI Assistant]() Original publisher: [Read original article]() Author: Alex Harper Published: 2026-09-16T14:26:34Z Content type: article Language: en Sources: [Web Designer Depot]() Topics: [Artificial Intelligence](), [Generative AI](), [Graphics]() Tags: [adobe](), [adobe-elements](), [adobe-firefly](), [adobe-photoshop](), [ai](), [ai-assistant](), [ai-image-editing](), [ai-tools](), [artificial-intelligence](), [beta](), [creative-tools](), [design-tools](), [generative](), [generative-ai](), [generative-expand](), [generative-upscale](), [graphic-design](), [image-editing](), [photo-editing](), [photoshop-elements](), [photoshop-elements-2027](), [premiere-elements](), [visual-ui-design](), [web-design]() ### AI overview Adobe's Photoshop Elements 2027 update adds Generative Expand, Generative Upscale and a beta AI Assistant, along with photo organization features and a 25-credit monthly limit for generative features. ### Source excerpt Photoshop Elements is getting a serious AI upgrade, with Generative Expand, AI upscaling and a new assistant that helps you edit simply by describing what you want. ## Try these 5 Weave tools--and share your own DevFeed: [Try these 5 Weave tools--and share your own]() Original publisher: [Read original article]() Author: Moran Michelle Dankner Published: 2026-09-16T12:00:00Z Content type: article Language: en Sources: [Figma Blog]() Topics: [Figma](), [Figma Community](), [Generative AI](), [Large Language Model]() Tags: [figma](), [figma-community](), [generative-ai](), [llm]() ### AI overview Figma announces community publishing for Weave tools and highlights five examples, including tools for room rendering, headshot creation, embroidery-style illustrations, and branded seals. The article also describes how creators build tools for their workflows. ### Source excerpt Community publishing is now live for Weave tools. Here are a few tools that stand out, from turning a moodboard into a realistic room mockup to previewing your next nail set. ## AMD's GAIA Local AI Now Able To Transcribe & Summarize Meeting Recordings DevFeed: [AMD's GAIA Local AI Now Able To Transcribe & Summarize Meeting Recordings]() Original publisher: [Read original article]() Author: Michael Larabel Published: 2026-09-16T10:19:10Z Content type: news Language: en Sources: [Phoronix]() Topics: [gaia](), [Local AI](), [Generative AI](), [Artificial Intelligence]() Tags: [agents](), [ai](), [bug-fixes](), [desktop-linux](), [gaia](), [generative-ai](), [github](), [linux](), [linux-benchmarking](), [linux-hardware-benchmarks](), [linux-hardware-reviews](), [linux-how-to](), [linux-performance](), [linux-server-benchmarks](), [local-ai](), [macos](), [open-source](), [open-source-graphics](), [phoronix](), [phoronix-test-suite](), [release](), [security](), [ubuntu-benchmarks](), [ubuntu-hardware](), [user-interface](), [windows]() ### AI overview AMD GAIA 0.24 adds local transcription and summarization of meeting recordings, including multi-speaker recognition. The release also improves inbox triage and its text interface, supports an embedded local Lemonade server, and includes security and bug fixes. ### Source excerpt AMD's GAIA software for building AI agents on your PC and leveraging generative AI locally with the power of AMD Ryzen and Radeon hardware continues becoming more featureful. Out today is AMD GAIA 0.24/0.24.1 and with it comes the ability to transcribe and summarize meeting recordings locally along with other functionality... ## AWS agents will suggest your new flights. Code decides what gets booked. DevFeed: [AWS agents will suggest your new flights. Code decides what gets booked.]() Original publisher: [Read original article]() Author: Meredith Shubel Published: 2026-09-15T21:27:21Z Content type: news Language: en Sources: [The New Stack]() Topics: [AWS Step Functions](), [Amazon Bedrock AgentCore](), [Orchestration](), [Generative AI](), [Amazon Web Services]() Tags: [ai-agents](), [ai-engineering](), [airline](), [amazon-bedrock-agentcore](), [aws](), [aws-step-functions](), [cloud-services](), [generative-ai](), [orchestration]() ### AI overview AWS published a Step Functions pattern for airline rebooking in which Amazon Bedrock AgentCore agents propose itineraries and compensation messages, while deterministic workflow steps validate proposals before reservations change or payments are issued. ### Source excerpt AWS published a new Step Functions pattern this week that gives AI agents a role in airline rebooking while keeping The post AWS agents will suggest your new flights. Code decides what gets booked. appeared first on The New Stack. ## What is CIAM in 2026 and why does it matter? DevFeed: [What is CIAM in 2026 and why does it matter?]() Original publisher: [Read original article]() Author: Ravleen Kaur Published: 2026-09-15T00:00:00Z Content type: tutorial Language: en Sources: [Twilio Blog]() Topics: [identity and access management](), [trust](), [ai-governance](), [Security](), [Artificial Intelligence](), [IAM](), [Generative AI]() Tags: [ai-agents](), [ai-governance](), [email](), [fraud](), [fraud-detection](), [identity](), [identity-and-access-management](), [industry-insights](), [messaging](), [mobile](), [trust]() ### AI overview This article explains how customer identity and access management (CIAM) is evolving from point-in-time password checks into a continuous trust and control layer. It describes CIAM's role in authenticating users, governing delegated authority for AI agents, evaluating risk across channels, and balancing low-friction access with fraud detection. It also distinguishes customer identity from workforce IAM. ### Source excerpt Discover how customer identity and access management (CIAM) uses continuous trust, deepfake defense, and agentic AI governance to protect users. ## Validating multi-agent decisions with Step Functions and Bedrock AgentCore DevFeed: [Validating multi-agent decisions with Step Functions and Bedrock AgentCore]() Original publisher: [Read original article]() Author: Ben Freiberg Published: 2026-09-14T16:47:23Z Content type: article Language: en Sources: [AWS Compute Blog]() Topics: [Amazon Bedrock AgentCore](), [AWS Step Functions](), [Artificial Intelligence](), [Generative AI](), [Automation]() Tags: [ai-agents](), [amazon-bedrock-agentcore](), [audit-trail](), [automation](), [aws-step-functions](), [generative-ai](), [regulatory](), [technical-how-to](), [workflow]() ### AI overview The article presents a pattern for validating multi-agent airline rebooking decisions. Amazon Bedrock AgentCore agents propose options, while AWS Step Functions applies deterministic validation, supports large-scale parallel processing, enables human review, and maintains an execution history for auditing. ### Source excerpt Orchestrating specialized Amazon Bedrock AgentCore agents with AWS Step Functions gives you the reasoning power of generative AI with the guardrails of deterministic validation. Agents propose options, and deterministic code validates them before any action is taken, demonstrated here with an airline rebooking workflow. ## From AI demos to real work: How Engineering and Operations learn side by side DevFeed: [From AI demos to real work: How Engineering and Operations learn side by side]() Original publisher: [Read original article]() Author: Nubank Editorial Published: 2026-09-14T16:28:23Z Content type: article Language: en Sources: [Nubank]() Topics: [Artificial Intelligence](), [Generative AI](), [AI Development](), [Development](), [observability ai agents]() Tags: [agents](), [ai](), [ai-research](), [applied-ai](), [data-science-machine-learning](), [development](), [enablement](), [technical]() ### AI overview Nubank's Ops AI Acceleration Program uses workshops to connect technical AI knowledge with operational expertise. A workshop with PJ Operations introduced generative AI, agents, and the building blocks of AI systems before applying that shared understanding to a real operational challenge. ### Source excerpt Building shared technical understanding so operational expertise can turn AI into practical improvements The post From AI demos to real work: How Engineering and Operations learn side by side appeared first on Building Nubank. ## From AI demos to real work: How Engineering and Operations learn side by side DevFeed: [From AI demos to real work: How Engineering and Operations learn side by side]() Original publisher: [Read original article]() Author: Nubank Editorial Published: 2026-09-14T16:28:23Z Content type: article Language: en Sources: [Nubank]() Topics: [Artificial Intelligence](), [Generative AI](), [AI Development](), [Software Engineering](), [Development]() Tags: [agents](), [ai](), [ai-research](), [applied-ai](), [data-science-machine-learning](), [development](), [engineering](), [mcps]() ### AI overview This article describes Nubank's Ops AI Acceleration Program and an applied AI workshop with PJ Operations. The workshop combined foundational technical concepts about generative AI, agents, models, tools, context, skills, and MCPs with a real operational challenge to connect engineering knowledge with operational expertise. ### Source excerpt Building shared technical understanding so operational expertise can turn AI into practical improvements The post From AI demos to real work: How Engineering and Operations learn side by side appeared first on Building Nubank. ## The generative AI customization spectrum: From prompt engineering to custom models on AWS DevFeed: [The generative AI customization spectrum: From prompt engineering to custom models on AWS]() Original publisher: [Read original article]() Author: Bhavya Sruthi Sode Published: 2026-09-14T15:47:12Z Content type: tutorial Language: en Sources: [Artificial Intelligence]() Topics: [Generative AI](), [Amazon Web Services](), [Prompt Engineering](), [Fine-tuning](), [Retrieval Augmented Generation (RAG)](), [Amazon Bedrock](), [foundation-models](), [Anthropic Claude](), [Nova](), [llama]() Tags: [advanced-300](), [ai](), [amazon-bedrock](), [anthropic-claude](), [aws](), [best-practices](), [chain-of-thought](), [fine-tuning](), [foundation-models](), [generative-ai](), [llama](), [nova](), [optimization](), [prompt-engineering](), [rag]() ### AI overview This AWS article presents an eight-step decision framework for customizing generative AI workloads. It compares progressively more involved approaches, including prompt engineering, Retrieval Augmented Generation (RAG), fine-tuning, continued pre-training, and custom models such as Amazon Nova Forge, emphasizing that teams should start with the simplest approach and escalate when greater control or domain specificity is required. ### Source excerpt Pick the right generative AI customization approach on AWS with an 8-step decision framework, from prompt engineering and RAG to fine-tuning, continued pre-training, and Amazon Nova Forge. Start simple and escalate only when you must. ## MIT researchers develop a generative AI method for enforcing hard constraints in safety-critical applications DevFeed: [MIT researchers develop a generative AI method for enforcing hard constraints in safety-critical applications]() Original publisher: [Read original article]() Author: Adam Zewe | MIT News Published: 2026-09-14T04:00:00Z Content type: news Language: en Sources: [MIT AI News]() Topics: [Generative AI](), [AI Models](), [Algorithm](), [Requirements](), [Computer vision](), [Robotics]() Tags: [ai](), [ai-models](), [algorithm](), [algorithms](), [artificial-intelligence](), [computer-science-and-technology](), [computer-vision](), [diffusion-models](), [electrical-engineering-and-computer-science-eecs](), [flow-matching](), [generative-ai](), [hard-constrained-sampling](), [hardflow](), [idss](), [kaveh-alim](), [laboratory-for-information-and-decision-systems-lids](), [machine-learning](), [mechanical-engineering](), [mit-schwarzman-college-of-computing](), [navid-azizan](), [optimal-control](), [paper](), [requirements](), [research](), [robotics](), [safe-ai](), [school-of-engineering](), [trajectory-optimization](), [zeyang-li]() ### AI overview MIT researchers developed a deployment-time technique that lets pretrained generative AI models explore solutions while enforcing hard constraints on final outputs. Experiments in robotics, physical-process control, and computer vision found that the method satisfied required constraints and identified better solutions than existing techniques. ### Source excerpt The "HardFlow" algorithm could help generative AI models produce high-quality outputs that obey strict requirements when "pretty close" doesn't cut it. ## Paul Ford on AI, software development, and the limits of making coding widely accessible DevFeed: [Paul Ford on AI, software development, and the limits of making coding widely accessible]() Original publisher: [Read original article]() Author: Simon Willison Published: 2026-09-12T18:00:21Z Content type: opinion Language: en Sources: [Simon Willison's Weblog]() Topics: [Artificial Intelligence](), [Generative AI]() Tags: [ai](), [ai-2-236](), [deep](), [deep-blue](), [deep-blue-12](), [generative-ai](), [generative-ai-1-982](), [llms](), [llms-1-948](), [paul-ford](), [paul-ford-17]() ### AI overview Paul Ford argues that AI can produce very good software while also making it easier for people to do other jobs poorly. He suggests that developing truly cutting-edge software still depends on humans collaborating and practicing their respective crafts. ### Source excerpt For a while, I must admit, it looked as if software developer roles like mine were done for. How could we fight against tireless robots? But our industry is slowly realizing that making truly cutting-edge software still requires humans to think and work together, to maximize their skill sets and to practice their respective crafts. A.I. can write very good software, but it also makes it easy to do someone else's job badly, which is part of why all those projects fail. Now that everyone can code, it's become clearer why many shouldn't. -- Paul Ford, A.I. Was Supposed to Give Us New Killer Apps. What Happened? Tags: paul-ford, generative-ai, deep-blue, ai, llms ## AI Can Help Write an Article, but It Can't Stand Behind It DevFeed: [AI Can Help Write an Article, but It Can't Stand Behind It]() Original publisher: [Read original article]() Author: Raluca Budiu Published: 2026-09-11T17:00:00Z Content type: article Language: en Sources: [NN/g latest articles and announcements]() Topics: [Artificial Intelligence](), [Generative AI](), [User experience (UX)](), [Microsoft]() Tags: [ai](), [ai-authored](), [ai-content](), [ai-editing](), [ai-writing](), [article](), [copilot](), [genai](), [generative-ai](), [microsoft](), [ux](), [writing]() ### AI overview The article explains that NN/G uses AI during its editorial process for writing clarity, formatting, and critique, while humans retain editorial judgment and responsibility for published content. It describes a rigorous review cycle involving topic selection, multiple revisions, and checks for logical coherence, UX accuracy, and copy. AI tools such as Copilot in Microsoft Word and Grammarly help make writing more concise and clear, especially for authors who are not professional writers or speak English as a second language. ### Source excerpt NN/G uses AI for clarity, formatting, and critique, but humans retain editorial judgment and responsibility for every article. ## Native is now the future of mobile at Shopify DevFeed: [Native is now the future of mobile at Shopify]() Original publisher: [Read original article]() Author: Simon Willison Published: 2026-09-10T21:11:15Z Content type: opinion Language: en Sources: [Simon Willison's Weblog]() Topics: [Mobile](), [React Native](), [Shopify](), [AI-assisted coding](), [Kotlin](), [Swift](), [Open Source](), [Android](), [Generative AI](), [iOS]() Tags: [ai](), [ai-2-236](), [ai-assisted-search](), [ai-assisted-search-36](), [android](), [android-14](), [coding-agents](), [coding-agents-248](), [generative-ai](), [generative-ai-1-982](), [ios](), [ios-26](), [kotlin](), [llms](), [llms-1-948](), [mobile](), [mobile-41](), [open-source](), [open-source-320](), [react](), [react-43](), [react-native](), [shopify](), [shopify-8](), [swift](), [swift-12]() ### AI overview Shopify is moving its mobile apps from React Native back to separate Swift and Kotlin codebases. The article explains that AI agents can now handle enough implementation, translation, testing, and review work to reduce the cost of maintaining separate platforms. It also notes the planned relocation or archival of Shopify's React Native libraries. ### Source excerpt Native is now the future of mobile at Shopify Shopify are moving from React Native back to separate Swift and Kotlin codebases for their native apps, for the exact reason you would expect: We decided to switch from native to React Native in 2020 for three reasons: Stop building the same features twice Allow developers to work across the stack Spend less time chasing feature parity and more time shipping value [...] Native still means building and maintaining software on two platforms, that cost has not disappeared. What changed is that agents can now do enough of the implementation, translation, testing, and review work that it's no longer the deciding factor it was in 2020. It's a well-written post, which gives full credit to React Native as a great platform for the six years they were using it. Shopify are the maintainers of three significant React Native libraries: react-native-skia, flash-list, and restyle. The first two are finding new homes; the third "has a smaller user base than our other libraries" and will be archived at the end of 2026. Via Hacker News Tags: android, mobile, open-source, ios, ai, react, generative-ai, llms, ai-assisted-search, coding-agents, swift, shopify ## AI, Disruption, and Standards DevFeed: [AI, Disruption, and Standards]() Original publisher: [Read original article]() Author: L. Jeffrey Zeldman Published: 2026-09-10T16:12:17Z Content type: opinion Language: en Sources: [Jeffrey Zeldman]() Topics: [Artificial Intelligence](), [ai-governance](), [web-standards](), [Generative AI](), [MCP](), [a2ui](), [Accessibility]() Tags: [a2ui](), [accessibility](), [ai](), [ai-governance](), [appearances](), [architecture](), [best-practices](), [design](), [designers](), [ethics](), [formats](), [generative-ai](), [governance](), [mcp](), [open-source](), [standards](), [ux](), [video](), [web-standards](), [webstandards]() ### AI overview This article introduces a video panel on AI and standards featuring Jeffrey Zeldman and Patrick Neeman. The discussion connects lessons from the web standards movement to AI interfaces, focusing on fragmented user experiences, accessibility, interoperability, provenance, and the role of open-source and community efforts. ### Source excerpt Individually, we have very little power, but together we have a great deal of power. In response to a series of Medium articles by Patrick Neeman--like this one that addressed issues of AI governance, fairness, and standardization--publisher Louis Rosenfeld invited Patrick and me to come on his show and talk about AI and standards. A [...] The post AI, Disruption, and Standards appeared first on Jeffrey Zeldman Presents. ## .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 ## Blizzard union workers ratify historic contract covering 1,900 employees DevFeed: [Blizzard union workers ratify historic contract covering 1,900 employees]() Original publisher: [Read original article]() Author: Chris Kerr Published: 2026-09-09T12:59:56Z Content type: news Language: en Sources: [gamedeveloper]() Topics: [Microsoft](), [Generative AI](), [Xbox]() Tags: [ai](), [company](), [generative](), [layoff](), [microsoft](), [news](), [remote](), [teams](), [work](), [xbox]() ### AI overview Blizzard union workers ratified a contract covering 1,900 employees. The agreement includes generative AI usage safeguards, wage increases, hybrid and remote-work benefits, layoff protections, severance, and recall rights. ### Source excerpt Union members have secured guardrails around generative AI usage, layoff protections, remote and hybrid work benefits, and more. ## Introducing ChatGPT Images 2.5 DevFeed: [Introducing ChatGPT Images 2.5]() Original publisher: [Read original article]() Author: Simon Willison Published: 2026-09-08T22:46:33Z Content type: release Language: en Sources: [Simon Willison's Weblog]() Topics: [ChatGPT](), [OpenAI](), [Generative AI](), [text-to-image](), [Command-line interface]() Tags: [ai](), [ai-2-236](), [chatgpt](), [generative-ai](), [generative-ai-1-982](), [openai](), [openai-463](), [text-to-image](), [text-to-image-47](), [tools](), [tools-78](), [uv](), [uv-100]() ### AI overview OpenAI's ChatGPT Images 2.5 release adds improved multi-turn instruction following, faster responses, and better preservation of subjects in reference photos. The API introduces the gpt-image-2.5-sunburst and gpt-image-2.5-flare model IDs, with Sunburst positioned for precise editing and Flare for faster everyday generation. ### Source excerpt Introducing ChatGPT Images 2.5 OpenAI's image generation models are apparently used "more than 3 billion images across ChatGPT Images and the GPT-Image models in the API". This latest release improves their instruction-following ability across multiple turns, responds faster, and "is better at preserving the subjects in your reference photos". There are two new model IDs in the API: gpt-image-2.5-sunburst and gpt-image-2.5-flare. Based on this I think Sunburst is the stronger option: Choose Sunburst for workflows where editing precision matters most, and Flare for fast, high-quality everyday image generation. I upgraded my openai_image.py CLI tool to support passing in one or more reference images, so now this works: uv run https://tools.simonwillison.net/python/openai_image.py \ 'add a raccoon scientist studying the chart thoughtfully' \ -i https://static.simonwillison.net/static/2026/openai-agent-usage.webp \ -m gpt-image-2.5-sunburst This is the original image, and here's what I got back from that prompt to "add a raccoon scientist studying the chart thoughtfully": Tags: tools, ai, openai, generative-ai, uv, text-to-image ## 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. ## On the Ground at VMware Explore 2026: How Lenovo Is Partnering with VMware for Turnkey AI and Taming the Memory Crunch DevFeed: [On the Ground at VMware Explore 2026: How Lenovo Is Partnering with VMware for Turnkey AI and Taming the Memory Crunch]() Original publisher: [Read original article]() Author: Tom Fenton Published: 2026-09-01T18:28:19Z Content type: news Language: en Sources: [StorageReview.com]() Topics: [Generative AI](), [on-prem](), [cloud-infrastructure](), [datacenter](), [Cloud](), [data]() Tags: [2026](), [ai](), [cloud](), [data-centers](), [enterprise](), [enterprise-storage](), [generative-ai](), [infrastructure](), [on-prem](), [technology]() ### AI overview At VMware Explore 2026, StorageReview discusses Lenovo's partnership with VMware around turnkey generative AI infrastructure, the return to on-premises deployments, and memory supply constraints. The article describes moving memory tiering into the VMware Cloud Foundation hypervisor layer, using NVMe drives to reduce dependence on expensive DRAM without requiring application or operating-system rewrites. ### Source excerpt At VMware Explore 2026, we had the chance to sit down with Stuart McRae, Executive Director of Lenovo's Enterprise Storage, Software, and Solutions Offering Group. Rather than focusing on a specific product or technology, as we often do, this conversation took a broader view. We covered everything from how AI is reshaping buyer options and The post On the Ground at VMware Explore 2026: How Lenovo Is Partnering with VMware for Turnkey AI and Taming the Memory Crunch appeared first on StorageReview.com. [Next page]()