# ChatGPT ChatGPT is an AI chat assistant that supports coding, including writing, debugging, and improving code. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Report says OpenAI contractors review ChatGPT conversations under Project Lily DevFeed: [Report says OpenAI contractors review ChatGPT conversations under Project Lily]() Original publisher: [Read original article]() Author: techno\_mot (Selectel) Published: 2026-09-17T07:07:15Z Content type: news Language: ru Sources: [Tagir Valeev]() Topics: [ChatGPT](), [OpenAI](), [privacy-filter]() Tags: [chatgpt](), [openai](), [privacy](), [privacy-filter](), [selectel](), [tag-11019b84ba80](), [tag-32a5d252bf88](), [tag-61cd5a476b1d](), [tag-9fe8963de219](), [tag-b427c8ac240b]() ### AI overview The article reports on 404 Media's investigation into Project Lily, an internal OpenAI program in which external contractors review real ChatGPT prompts and conversations to assess model responses. It says a Privacy Filter removes some direct identifiers but may miss rare or context-dependent identifying details. ### Source excerpt 404 Media опубликовала расследование о внутренней программе OpenAI под названием Project Lily. Сотни внешних подрядчиков, нанятых через рекрутингового посредника Crossing Hurdles и получающих выплаты через платформу Mercor, читают настоящие промпты и целые диалоги пользователей ChatGPT, оценивают ответы модели и пишут разборы. Сама практика такой "человеческой" оценки известна давно. Новостью стал масштаб и то, насколько мало о ней знают пользователи. У ChatGPT больше 900 млн пользователей. Читать далее ## OpenAI's sponsored agents help advertisers create ChatGPT ads DevFeed: [OpenAI's sponsored agents help advertisers create ChatGPT ads]() Original publisher: [Read original article]() Author: Brandon Vigliarolo Published: 2026-09-16T18:32:37Z Content type: news Language: en Sources: [www.theregister.com - Articles]() Topics: [ChatGPT](), [OpenAI](), [Bot]() Tags: [advertising](), [agentic-ai](), [agents](), [ai-and-ml](), [chatgpt](), [openai]() ### AI overview The article reports that advertisers can ask ChatGPT's sponsored agents to help create advertisements for ChatGPT. ### Source excerpt Advertisers can now ask ChatGPT to help create their ChatGPT ads, because what's more relatable than an ad crafted by a bot? ## Helping older adults use AI in everyday life DevFeed: [Helping older adults use AI in everyday life]() Original publisher: [Read original article]() Published: 2026-09-16T16:00:00Z Content type: release Language: en Sources: [OpenAI News]() Topics: [Artificial Intelligence](), [ChatGPT](), [OpenAI Academy](), [OpenAI]() Tags: [ai](), [chatgpt](), [education](), [free](), [global-affairs](), [openai](), [openai-academy](), [safety](), [scam](), [workshops]() ### AI overview OpenAI Academy and AARP's Older Adults Technology Services are hosting free, in-person ChatGPT workshops to help older adults build practical AI skills, use the technology safely, and spot possible scams. The program will reach 10 communities in the United States. ### Source excerpt OpenAI and AARP are bringing free, hands-on ChatGPT workshops to 1,000 older adults across 10 U.S. cities to build practical AI skills safely. ## Reimagining advertising with AI DevFeed: [Reimagining advertising with AI]() Original publisher: [Read original article]() Published: 2026-09-16T13:00:00Z Content type: release Language: en Sources: [OpenAI News]() Topics: [Artificial Intelligence](), [OpenAI](), [ChatGPT](), [hubspot](), [Shopify]() Tags: [advertising](), [agents](), [ai](), [chatgpt](), [hubspot](), [integrations](), [openai](), [plugin](), [product](), [shopify](), [sponsored]() ### AI overview OpenAI introduces AI-powered advertising features in ChatGPT, including Sponsored Agents that let users converse with select business-sponsored agents after viewing an ad. It also adds prompt-based campaign tools and AI creative tools, plus integrations with HubSpot and Shopify. ### Source excerpt Explore new AI-powered advertising experiences from OpenAI, including Sponsored Agents, tools for marketers, and integrations with HubSpot and Shopify. ## How to connect AI usage to business value DevFeed: [How to connect AI usage to business value]() Original publisher: [Read original article]() Published: 2026-09-16T12:00:00Z Content type: tutorial Language: en Sources: [OpenAI News]() Topics: [Artificial Intelligence](), [ChatGPT](), [codex](), [data](), [Software Engineering]() Tags: [ai](), [analytics](), [business-value](), [chatgpt](), [codex](), [product](), [training]() ### AI overview This tutorial explains how ChatGPT Work and Codex analytics help administrators connect AI adoption, usage, spending, supported tasks, and outcomes to business decisions. It covers views for identifying training needs, evaluating workflows, reviewing model and tool usage, and monitoring Codex contributions and code-review activity. ### Source excerpt Learn how ChatGPT Work and Codex analytics help teams understand AI usage and spend, identify training needs, and connect adoption to business outcomes. ## Agents at Large | Tracing Illicit OpenAI Agent Activity on Hugging Face DevFeed: [Agents at Large | Tracing Illicit OpenAI Agent Activity on Hugging Face]() Original publisher: [Read original article]() Author: Tom Hegel Published: 2026-09-16T10:00:34Z Content type: article Language: en Sources: [SentinelLabs - We are hunters, reversers, exploit developers, and tinkerers shedding light on the world of malware, exploits, APTs, and cybercrime across all platforms.]() Topics: [Threat Research](), [Incident response](), [hugging face](), [OpenAI](), [ChatGPT](), [spaces](), [Flask](), [OAuth](), [HTTP]() Tags: [agentic-ai](), [agents](), [chatgpt](), [flask](), [http](), [hugging-face](), [incident-response](), [oauth](), [openai](), [research](), [spaces](), [token]() ### AI overview SentinelLABS traces activity associated with two Hugging Face accounts, 0Time and Nyx9, that appears to extend OpenAI's published chronology. The report describes relay-code commits, a workbook containing unexecuted-looking external probes, and a Flask-wrapped tool that could potentially provision ChatGPT identities or OAuth credentials if deployed and invoked. ### Source excerpt Two Hugging Face accounts reveal that OpenAI's agents staged relay code, internal probes and ChatGPT account registration beyond the published timeline. ## OpenAI research examines how workers incorporate AI tasks beyond their usual occupations DevFeed: [OpenAI research examines how workers incorporate AI tasks beyond their usual occupations]() Original publisher: [Read original article]() Published: 2026-09-16T09:00:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [Artificial Intelligence](), [OpenAI](), [ChatGPT]() Tags: [ai](), [chatgpt](), [global-affairs](), [openai](), [report](), [research](), [tasks](), [work](), [workflow]() ### AI overview OpenAI Economic Research analyzes more than 1.5 million work-related ChatGPT messages to examine whether workers repeatedly use AI for tasks outside their usual occupations. The findings suggest that some cross-occupation tasks become recurring parts of workers' AI use and that work design matters alongside access to AI tools. ### Source excerpt New OpenAI Economic Research shows how workers use AI beyond traditional roles and which new activities become recurring parts of their work. ## MCP Apps: Your UI, Their Chat DevFeed: [MCP Apps: Your UI, Their Chat]() Original publisher: [Read original article]() Author: James Strong Published: 2026-09-16T07:00:00Z Content type: article Language: en Sources: [Scott Logic]() Topics: [Model Context Protocol (MCP)](), [ui](), [ChatGPT](), [App]() Tags: [ai](), [app](), [artificial-intelligence](), [chatgpt](), [mcp](), [ui]() ### AI overview The article examines MCP Apps, an extension of the Model Context Protocol, alongside the protocol's 2026-07-28 changes. It explains how MCP Apps let servers return isolated, interactive, stateful UIs that clients such as ChatGPT can embed in agent responses, with examples including dashboards, data tables, and forms. ### Source excerpt A lot of focus has been put on the statelessness introduced as part of the new MCP specification, but the formalisation of extensions, including MCP Apps, is also worth attention. Using FastMCP and Prefab, I put together an MCP app and show it running in ChatGPT. ## How to opt out of AI chatbot training DevFeed: [How to opt out of AI chatbot training]() Original publisher: [Read original article]() Author: Pieter Arntz Published: 2026-09-15T15:41:44Z Content type: tutorial Language: en Sources: [Malwarebytes]() Topics: [ChatGPT](), [OpenAI](), [online privacy](), [Artificial Intelligence]() Tags: [ai](), [anthropic](), [chatgpt](), [how-to](), [human-review](), [news](), [openai](), [perplexity](), [privacy](), [privacy-filter]() ### AI overview This tutorial explains how to turn off ChatGPT's "Improve the model for everyone" setting to opt out of using chats for model improvement. It also notes that authorized personnel and trusted service providers may still access content for safety, support, troubleshooting, security, abuse investigations, or legal matters. ### Source excerpt ChatGPT contractors are reviewing real users' conversations. Here's how to stop AI companies using your chats for model training. ## From Autocomplete to Intelligence: How Language Became the Operating System of AI DevFeed: [From Autocomplete to Intelligence: How Language Became the Operating System of AI]() Original publisher: [Read original article]() Author: Wix Engineering Published: 2026-09-15T10:17:07Z Content type: opinion Language: en Sources: [Wix Engineering]() Topics: [Artificial Intelligence](), [Language models](), [Large Language Model](), [ChatGPT](), [OpenAI]() Tags: [ai](), [article](), [chatgpt](), [language-models](), [large-language-models-llms](), [openai]() ### AI overview This article examines why language is closely tied to intelligence in both humans and artificial intelligence. It argues that AI became significantly more powerful by learning language exceptionally well, then introduces how large language models acquire intelligence through models that represent the world and reduce uncertainty. ### Source excerpt Introduction Every week brings a new AI model, a new coding assistant, a new agent framework. The pace can feel overwhelming. Yet beneath the churn lies a single, surprisingly simple fact - the fact this whole revolution turns on: AI became so powerful because it learned language exceptionally well. In this article I want to step away from the weekly headlines and ask a more basic question: why is language so tightly bound to intelligence - human intelligence in general, and artificial... ## AWS Weekly Roundup: OpenAI GPT-6 Astra on Amazon Bedrock, Amazon Quick desktop GA, Kiro for students, and more (September 14, 2026) DevFeed: [AWS Weekly Roundup: OpenAI GPT-6 Astra on Amazon Bedrock, Amazon Quick desktop GA, Kiro for students, and more (September 14, 2026)]() Original publisher: [Read original article]() Author: Micah Walter Published: 2026-09-14T15:56:33Z Content type: news Language: en Sources: [AWS News Blog]() Topics: [Amazon Bedrock](), [Frontier Model](), [OpenAI](), [context window](), [browser](), [ChatGPT](), [codex](), [macOS](), [Windows](), [Mobile]() Tags: [agents](), [ai](), [ai-agents](), [amazon](), [amazon-bedrock](), [amazon-elastic-block-store-amazon-ebs](), [amazon-opensearch-service](), [amazon-quick-suite](), [aws](), [aws-lambda](), [aws-outposts](), [aws-transform](), [bedrock](), [browser](), [chatgpt](), [codex](), [context-window](), [frontier-model](), [google-play](), [gpt](), [kiro](), [launch](), [macos](), [mobile](), [openai](), [week-in-review](), [windows]() ### AI overview This AWS Weekly Roundup highlights the general availability of OpenAI GPT-6 Astra on Amazon Bedrock, describing its reasoning, writing, design, computer-use, browser-use, and million-token context-window capabilities. It also covers the Amazon Quick desktop app for macOS and Windows, including synchronized conversations and agents across desktop and mobile, plus other AWS launches and updates. ### Source excerpt There's a particular energy to mid-September in New York. Pumpkin spice lattes are flowing, temperatures are dropping, and it's nearly sweater weather. The city is back at full speed, and so is the AWS launch calendar. This week that energy showed up in a new frontier model on Amazon Bedrock, a desktop app for Amazon [...] ## I Had Never Built A VR App - 8 Days Later, Mine Was On The Quest Store DevFeed: [I Had Never Built A VR App - 8 Days Later, Mine Was On The Quest Store]() Original publisher: [Read original article]() Author: Craig Storm Published: 2026-09-14T15:12:36Z Content type: article Language: en Sources: [UploadVR]() Topics: [ai-coding](), [codex](), [ChatGPT](), [coding](), [Code](), [App](), [Meta](), [APK](), [SDKs]() Tags: [ai](), [ai-coding](), [apk](), [app](), [app-development](), [chatgpt](), [code](), [codex](), [coding](), [meta](), [openxr](), [sdk]() ### AI overview The article describes an eight-day attempt to build and publish Punchable Face, a simple VR game for Meta Quest, despite having little prior development experience. Using Unity, OpenXR, Meta XR SDK components, ChatGPT, and Codex, the project progressed from an empty Unity project through coding, headset testing, store asset creation, submission, and approval on the Meta Quest Store. ### Source excerpt Until a little over a week ago, Craig Storm had never built a VR app. Eight days later, his AI-assisted Quest game Punchable Face was live on Meta's store. ## 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: Daniel Lemire Published: 2026-09-10T18:23:42Z Content type: opinion Language: en Sources: [Daniel Lemire]() Topics: [Artificial Intelligence](), [Large Language Model](), [Language models](), [ChatGPT](), [Tool](), [Embeddings]() Tags: [ai](), [chatgpt](), [embeddings](), [large-language-model](), [openai](), [statement](), [tokens](), [tools]() ### AI overview This opinion argues that fears about AI causing human extinction are vague and unfalsifiable, and compares them with earlier technological and social end-of-the-world predictions. It describes large language models as systems that process and generate tokens using fixed weights, while noting that connecting them to tools makes their capabilities more consequential. ### Source excerpt We are told that AI entities much like ChatGPT might soon kill us all. The statement is vague and unfalsifiable. It might be true, it might be false. People with credentials (e.g., Turing Award recipient Yoshua Bengio) believe it. Many still remember the Year-2000 bug. Our computers used two-digit coding for dates, and some software ... Continue reading Fear Is Not an Argument ## 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 ## The Work Now Within Reach DevFeed: [The Work Now Within Reach]() Original publisher: [Read original article]() Published: 2026-09-08T13:00:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [Artificial Intelligence](), [OpenAI](), [Software Engineering](), [API](), [ChatGPT](), [codex](), [Software]() Tags: [agentic](), [ai](), [api](), [applications](), [codex](), [company](), [developers](), [enterprise](), [openai]() ### AI overview OpenAI describes how more capable and affordable AI could expand the work that people and businesses can accomplish. It highlights GPT-6 Astra's capabilities, OpenAI's full-stack compute strategy, and the reinforcing relationship between research, products, infrastructure, revenue, and adoption. The article also discusses ChatGPT, ChatGPT Work, Codex, API applications, enterprise deployments, developers, and agentic products. ### Source excerpt Explore how more capable, affordable AI can expand the work people and businesses can accomplish--and make growth more economical. ## 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 ## A milestone in expanding access to AI DevFeed: [A milestone in expanding access to AI]() Original publisher: [Read original article]() Published: 2026-08-31T04:00:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [Artificial Intelligence](), [ChatGPT](), [OpenAI]() Tags: [ads](), [advertising](), [ai](), [chatgpt](), [global](), [openai](), [product](), [revenue]() ### AI overview ChatGPT Ads has reached a $1 billion annualized revenue run rate in less than 200 days and is expanding globally. OpenAI says advertising supports a diversified business model and helps sustain an advertising-supported free tier, while ads remain labeled separately from answers and users retain control over personalization. ### Source excerpt ChatGPT Ads reaches $1 billion in annualized revenue run rate and expands globally, supporting broader access to AI through free and affordable options. ## 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's third era: the rise of persistent AI coworkers | Tara Seshan (Product Lead ChatGPT Work) DevFeed: [AI's third era: the rise of persistent AI coworkers | Tara Seshan (Product Lead ChatGPT Work)]() Original publisher: [Read original article]() Author: Lenny Rachitsky Published: 2026-08-30T12:31:30Z Content type: opinion Language: en Sources: [Lenny's Newsletter]() Topics: [Artificial Intelligence](), [codex](), [ChatGPT](), [OpenAI]() Tags: [ai](), [chatgpt](), [codex](), [openai]() ### AI overview A podcast discussion with Tara Seshan, head of Codex and ChatGPT Work at OpenAI, about persistent AI coworkers, ambition as a differentiator, steering versus rowing, and building for expected model progress over the next two to three months. ### Source excerpt Listen now | Tara Seshan, head of Codex and ChatGPT Work at OpenAI, on why ambition is now the differentiator, steering vs. rowing, and why you need to build for where the models will be in two to three months ## Worth Reading 082926 DevFeed: [Worth Reading 082926]() Original publisher: [Read original article]() Author: Russ Published: 2026-08-29T14:47:35Z Content type: article Language: en Sources: [rule 11 reader]() Topics: [cdnjs](), [ChatGPT](), [genai](), [data centers](), [DNSSEC]() Tags: [chatgpt](), [data-centers](), [dnssec](), [genai](), [technology](), [worth-reading]() ### AI overview A developer-oriented reading roundup covering content delivery networks, ChatGPT and scaling toward artificial general intelligence, high-density data-center builds, GenAI-assisted medical image analysis, and the practical difficulty of maintaining DNSSEC. ### Source excerpt The term "content delivery network" reflects the technology's original value proposition. Sam Altman released ChatGPT for free in late 2022. Many users fell in love both with it and Altman's argument that artificial general intelligence could be achieved through scaling. T If you work around data centers and high-density builds, you have probably heard the term "Multi-Core Fiber" thrown around. Ask yourself these questions, assuming you have a serious medical condition and your doctors are going to be using GenAI to scan your images and test results to discover the breadth and depth of your condition and to recommend treatment. Maybe adoption is low not because people don't believe DNSSEC is useful, but because turning it on and keeping it on is still genuinely harder than it should be. ## Better answers, broader thinking: What students gain from ChatGPT and critical-thinking training DevFeed: [Better answers, broader thinking: What students gain from ChatGPT and critical-thinking training]() Original publisher: [Read original article]() Published: 2026-08-27T09:00:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [ChatGPT](), [Artificial Intelligence](), [OpenAI]() Tags: [ai](), [analysis](), [business](), [chatgpt](), [company](), [experiment](), [generate](), [gpt](), [marketing](), [openai](), [performance](), [reasoning](), [research](), [training]() ### AI overview Un randomized experiment with more than 1,000 first-year undergraduate students found that ChatGPT access improved the quality, coherence, logic, and professionalism of work on a real-world business assignment. Separate causal-reasoning training increased the originality and variety of ideas, and students receiving both interventions showed both benefits. ### Source excerpt A randomized study of more than 1,000 students examines ChatGPT, critical thinking, originality, and student performance on a real-world university assignment. ## Expanding OpenAI's presence in Brazil DevFeed: [Expanding OpenAI's presence in Brazil]() Original publisher: [Read original article]() Published: 2026-08-27T03:00:00Z Content type: news Language: en Sources: [OpenAI News]() Topics: [OpenAI](), [Artificial Intelligence](), [ChatGPT](), [Deployment](), [Code]() Tags: [ai](), [ai-adoption](), [brazil](), [chatgpt](), [company](), [deployment](), [developers](), [marketing](), [openai](), [production](), [writing-code]() ### AI overview OpenAI is launching commercial operations in Brazil, where ChatGPT adoption is growing rapidly. The local team will work with businesses, developers, researchers, and public institutions to support responsible AI use and broader economic and social benefits. ### Source excerpt OpenAI is expanding its presence in Brazil, deepening engagement with developers, businesses, and communities to support AI adoption across the country. ## ChatGPT vs. Claude vs. Grok vs. Gemini: The Best AI Across 10 Real Use Cases DevFeed: [ChatGPT vs. Claude vs. Grok vs. Gemini: The Best AI Across 10 Real Use Cases]() Original publisher: [Read original article]() Author: Peter Yang Published: 2026-08-26T15:04:55Z Content type: comparison Language: en Sources: [Behind the Craft]() Topics: [AI Models](), [ChatGPT](), [Claude](), [coding]() Tags: [ai](), [ai-models](), [chatgpt](), [claude](), [coding](), [gemini](), [vs]() ### AI overview A 26-minute tutorial compares ChatGPT, Claude, Grok, and Gemini across 10 use cases, including writing, design, coding, personal agents, and AI video. It presents the author's picks for different tasks and an overall winner. ### Source excerpt My picks for the best AI models and tools across 10 everyday tasks, plus a preview of my upcoming course for paid subscribers [Next page]()