# ai 2,236 Published articles for ai 2,236. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## 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 ## The contagion of fear DevFeed: [The contagion of fear]() Original publisher: [Read original article]() Author: Simon Willison Published: 2026-09-14T21:18:13Z Content type: opinion Language: en Sources: [Simon Willison's Weblog]() Topics: [anthropic](), [Artificial Intelligence](), [trust](), [Large Language Model]() Tags: [ai](), [ai-2-236](), [ai-ethics](), [ai-ethics-343](), [anthropic](), [anthropic-336](), [bryan-cantrill](), [bryan-cantrill-13](), [ethics](), [trust]() ### AI overview Bryan Cantrill argues that sweeping claims about AI causing catastrophic harm rely on vague extrapolation, including unsupported references to critical infrastructure and bioweapons. The article calls on domain experts to make alarmist claims more carefully and avoid abusing public trust. ### Source excerpt The contagion of fear Bryan Cantrill responds to the tweet by former Anthropic employee Jacob Coxon confirming that many Anthropic researchers believe AI "could kill us all by the end of the decade". Bryan shares a story of his own youthful mistakes causing unjustified panic among less technical peers, and warns against doing the same: These ghoulish claims strike brazenly at the hearth, and given the obvious importance of AI, it is unsurprising that they have leapt into the mainstream, with people asking the natural question: how would that happen? The answers always rely on hand-wavy extrapolation into the future; for example, Jacob Coxon cites "hacking critical infrastructure" and "extinction-level bioweapons" without further elaboration. But Coxon is not an expert on critical infrastructure, nor on bioweapons -- nor, for that matter, on extinction. [...] That said, we should not expect the public to understand LLMs, critical infrastructure, bioweapons, extinction biology, etc. -- that burden must lie with those making the claim. The lesson that I learned (shamefully) decades ago is that domain experts, by way of their expertise, implicitly hold the public's trust -- and we must not abuse it. It is incumbent upon us to be circumspect in our claims -- and maximally so when raising the alarm. Bryan talked about his doubts about the bioweapons concerns in the recent episode of Oxide and Friends that I joined. You can hear more of his thoughts on that starting at 51m44s in that episode. Here's 57m04s: I really think we need to be careful because it's so easy to be overcome with fear when we kind of make up these... it can give you biological weapons. Like, how? I mean, can we please have a biologist weigh in on this? Or can we have like someone who's got experience with bioweapons? [...] The bioweapon thing just gets under my fingernails because it leaves so much to the imagination that we insert with fear. Via Lobste.rs Tags: ai, anthropic, bryan-cantrill, ai-ethics ## Quoting Laurie Voss DevFeed: [Quoting Laurie Voss]() Original publisher: [Read original article]() Author: Simon Willison Published: 2026-09-14T14:34:29Z Content type: opinion Language: en Sources: [Simon Willison's Weblog]() Topics: [Code](), [Software]() Tags: [agentic](), [agentic-engineering](), [agentic-engineering-63](), [ai](), [ai-2-236](), [careers](), [careers-83](), [deep](), [deep-blue](), [deep-blue-12](), [generative](), [generative-ai](), [generative-ai-1-982](), [laurie-voss](), [laurie-voss-6](), [llms](), [llms-1-948]() ### AI overview A quotation from Laurie Voss argues that the cost of writing code has collapsed and that reviewing, fixing, and operating software may follow. It suggests that identifying user needs, defining them precisely, and making software pleasant to use could become the dominant remaining work as software production expands. ### Source excerpt The cost of writing code collapsed, and the cost of reviewing, fixing and operating it is following, and I'm assuming it gets there. What's left of making software is finding out what people actually want, defining it precisely, and making it pleasant to use. That cost is per piece of software and doesn't transfer, so as the amount of software goes to infinity, which it will because there's no ceiling on demand, that cost becomes the whole job. -- Laurie Voss, We are all Product Engineers now Tags: laurie-voss, generative-ai, agentic-engineering, ai, llms, deep-blue, careers ## 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 ## OpenRouter provider fallbacks can cause inconsistent model behavior DevFeed: [OpenRouter provider fallbacks can cause inconsistent model behavior]() Original publisher: [Read original article]() Author: Simon Willison Published: 2026-09-11T22:49:18Z Content type: opinion Language: en Sources: [Simon Willison's Weblog]() Topics: [API](), [Large Language Model](), [Software]() Tags: [ai](), [ai-2-236](), [api](), [cost](), [generative-ai](), [generative-ai-1-982](), [llms](), [llms-1-948](), [model](), [models](), [news](), [openrouter](), [openrouter-32](), [providers](), [reasoning](), [serve]() ### AI overview OpenRouter's automatic provider fallbacks can produce inconsistent behavior for the same model because providers use different serving software, optimizations, and settings. The article notes differences in vision support and reasoning-effort handling, and points to provider.only and /endpoints for controlling or inspecting routing. ### Source excerpt So you want to use OpenRouter? One of OpenRouter's selling points is that it "handles fallbacks automatically and picks the most cost-effective option for each request", so you can call a single API endpoint for a model and get routed to the best available backend provider. Mohamed Moustafa points out a whole set of ways that this can cause you problems. Different providers run different serving software with different optimizations and settings, which means that the same OpenRouter endpoint can serve model requests that behave in different ways. Some providers even lack vision capability for vision models, and the way the reasoning effort option is processed can differ as well. Thankfully you can control which provider is routed to using the provider.only option. The /endpoints method returns the list of available providers for a specific model ID. Via Hacker News Tags: ai, generative-ai, llms, openrouter ## Boris Cherny on guardrails for Claude-written production code DevFeed: [Boris Cherny on guardrails for Claude-written production code]() Original publisher: [Read original article]() Author: Simon Willison Published: 2026-09-11T17:47:11Z Content type: opinion Language: en Sources: [Simon Willison's Weblog]() Topics: [Claude](), [anthropic](), [AI-assisted coding](), [Security]() Tags: [agentic-engineering](), [agentic-engineering-63](), [ai](), [ai-2-236](), [ai-assisted-programming](), [ai-assisted-programming-407](), [anthropic](), [anthropic-336](), [boris-cherny](), [boris-cherny-4](), [claude](), [claude-310](), [claude-code](), [claude-code-127](), [coding-agents](), [coding-agents-248](), [generative-ai](), [generative-ai-1-982](), [llms](), [llms-1-948](), [security]() ### AI overview Boris Cherny says production code written by Claude should meet a higher standard than human-written code. He describes Anthropic's guardrails, including lint rules, tests, Claude-driven end-to-end tests, daily Claude-powered fuzzing, automated code and security reviews, and automated refactoring. ### Source excerpt Production code written by Claude should have a higher bar than if it was written by a human. At Anthropic, we have many guardrails in place to make sure this is happening: lots of lint rules, lots of tests, Claude-driven end to end tests, Claude-powered fuzzers running daily, automated code reviews and security reviews, automated code refactoring, and so on. Without these, you can end up with a mess that is hard to maintain down the line. -- Boris Cherny Tags: claude, ai, claude-code, llms, coding-agents, ai-assisted-programming, generative-ai, agentic-engineering, boris-cherny, anthropic ## Software Engineers Adapting to AI Coding Agents DevFeed: [Software Engineers Adapting to AI Coding Agents]() Original publisher: [Read original article]() Author: Simon Willison Published: 2026-09-11T17:28:37Z Content type: opinion Language: en Sources: [Simon Willison's Weblog]() Topics: [Artificial Intelligence](), [Software Engineering]() Tags: [ai](), [ai-2-236](), [deep-blue](), [deep-blue-12](), [generative-ai](), [generative-ai-1-982](), [llms](), [llms-1-948]() ### AI overview An opinion piece arguing that software engineers can adapt to AI coding agents by applying their experience to broader problems beyond translating a specification into code. ### Source excerpt My comment on Feeling sad about AI -- Hacker News. I'm not sure how useful it is to say this, but I think a lot of people (myself included, a few years ago now) have been through this moment of existential crisis and come out the other side. The initial reaction, when some coding agent does a piece of work that would have taken you a week in an hour and does it well, is to be very disheartened by it. Once you come to terms with the idea that translating an exact specification into decent code isn't a unique skill any more, you can start looking at the larger set of problems that you face as a software engineer and realize that there is so much left, and your existing skill and experience mean you can master these new tools, provide value, and execute at a level far greater than anyone who is just getting started building software using agents without any of your depth. If you don't want your profession to change at all then you're going to have a tough time with this - but that's surely been true for the history of software engineering? Has there ever been any stability to the tools and language we use beyond about a five year time horizon? These changes are happening a bit faster, but if you chose software development as a passion you've opted into pretty frequent radical change from the start. Tags: ai, generative-ai, llms, deep-blue ## Datasette 1.0a39 and 0.65.4 security releases DevFeed: [Datasette 1.0a39 and 0.65.4 security releases]() Original publisher: [Read original article]() Author: Simon Willison Published: 2026-09-11T03:27:16Z Content type: release Language: en Sources: [Simon Willison's Weblog]() Topics: [releases](), [Security](), [audit](), [AI-assisted coding](), [ai security](), [agentic-engineering](), [Generative AI](), [Large Language Model](), [Claude](), [gpt-6-astra]() Tags: [agentic-engineering](), [agentic-engineering-63](), [ai](), [ai-2-236](), [ai-security-research](), [ai-security-research-42](), [coding-agents](), [datasette](), [datasette-1-542](), [generative-ai](), [generative-ai-1-982](), [llms](), [llms-1-948](), [releases](), [releases-31](), [security](), [security-634](), [tests]() ### AI overview Datasette 1.0a39 and 0.65.4 are security patch releases for the alpha and stable series. They address issues found during an extensive audit, including instances that mix public and private tables. The audit used Claude Fable 5.1, GPT-5.6, and GPT-6 Astra alongside human review and automated tests. ### Source excerpt Datasette 1.0a39 and 0.65.4 security releases Today we're releasing two new security patch versions of Datasette: 1.0a39 and 0.65.4 - one for the current alpha series and one for the stable 0.65.x family. These are security fixes which you should apply if you are running a Datasette instance on the public web - in particular if that instance mixes both public and private tables. Following issues reported by Sevban Dönmez, Alex Garcia and I ran an extensive audit of Datasette using Claude Fable 5.1, GPT-5.6, and GPT-6 Astra. We then spent almost a week collaborating on and reviewing the fixes. They helped find some very subtle bugs. We'll be incorporating security audits by frontier models into all of our development work going forward. Alex came up with a way of splitting the work which I found extremely productive: Alex Garcia and I worked together running and then responding to the audit, working in a shared private repository. For most of the issues we split the work: one of us would create the automated tests highlighting the issue, then the other would implement the fix. This ensured that two separate humans had eyes on each of the issues, in addition to our coding agents running different models. Tags: releases, security, ai, datasette, generative-ai, llms, agentic-engineering, ai-security-research ## 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 ## Calif Research Claims AI-Assisted WeWorm Zero-Click WeChat Worm DevFeed: [Calif Research Claims AI-Assisted WeWorm Zero-Click WeChat Worm]() Original publisher: [Read original article]() Author: Simon Willison Published: 2026-09-10T00:56:41Z Content type: news Language: en Sources: [Simon Willison's Weblog]() Topics: [ai security](), [Exploit](), [Artificial Intelligence](), [Android](), [iOS]() Tags: [ai](), [ai-2-236](), [ai-security-research](), [ai-security-research-42](), [generative-ai](), [generative-ai-1-982](), [llms](), [llms-1-948](), [security](), [security-634]() ### AI overview A post quoting Calif Research's claims about a WeWorm demo: a zero-click worm targeting WeChat calls on iOS and Android. The quoted researchers say AI helped them find a bug and develop a remote code execution exploit. ### Source excerpt Today, we're releasing a demo of WeWorm, the first zero-click worm to spread through WeChat calls across iOS and Android. [...] The victim does not need to answer the call, or interact with their phone at all. Even if they do answer, they hear nothing, and the exploit still succeeds. [...] Working with AI, our team found the bug and wrote the first remote code execution (RCE) exploit in about two days. Building the worm took one more week. A worm at this scale used to be the kind of thing that took a larger team months. AI can already do most of the work here. Our team provided the judgment about what to target and how to test it safely. -- Calif Research, WeWorm Tags: ai-security-research, ai, llms, security, generative-ai ## .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 ## Quoting Terence Tao DevFeed: [Quoting Terence Tao]() Original publisher: [Read original article]() Author: Simon Willison Published: 2026-09-09T00:20:17Z Content type: opinion Language: en Sources: [Simon Willison's Weblog]() Topics: [ai-ethics](), [Mathematics](), [Artificial Intelligence]() Tags: [ai](), [ai-2-236](), [ai-ethics](), [ai-ethics-343](), [ethics](), [mathematics](), [mathematics-22]() ### AI overview The article quotes Terence Tao on the risk that AI-powered efforts could rapidly exhaust promising open mathematical problems after even rumors of active research become public. It argues that these incentives may discourage researchers from sharing directions openly, potentially harming long-standing traditions of open science and the future of the field. ### Source excerpt I wrote recently about how the collection of good, fruitful open problems is now being mined in a non-renewable fashion, leading to the potential scenario of these problems becoming scarce. [...] We have now seen that even the rumor of someone working on a problem can trigger a massive amount of AI-powered effort to flatten it before the original research project has time to reach its full potential. The incentives may now be pointing in the direction of no longer sharing any promising research directions with the broader community, which would reverse centuries of traditions of open science and do serious long-term damage to the future of the field. -- Terence Tao Tags: ai-ethics, mathematics, ai ## 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