# Artificial Intelligence Artificial intelligence refers to computer systems that perform complex tasks normally done by human reasoning, decision-making, and creation, using techniques such as machine learning, deep learning, natural language processing, and neural networks. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Snap SPECS standalone AR glasses combine AI, real-time overlays, and hands-free apps DevFeed: [Snap SPECS standalone AR glasses combine AI, real-time overlays, and hands-free apps]() Original publisher: [Read original article]() Author: Gaurav Sood Published: 2026-09-17T18:20:01Z Content type: article Language: en Sources: [Yanko Design]() Topics: [immersive web](), [snap](), [Artificial Intelligence](), [iOS](), [Amazon Web Services](), [Nvidia](), [Shopify]() Tags: [ai](), [ar-vr](), [ar-vr-wearables-augmented-reality-smart-glasses](), [augmented-reality](), [aws](), [ios](), [meta](), [nvidia](), [shopify](), [smart-glasses](), [snap](), [wearables]() ### AI overview The article presents Snap's SPECS as standalone AR glasses with built-in computing, SPECS Intelligence AI, iOS support, and developer tools through Lens Studio. It describes enterprise exploration by NVIDIA, AWS, and Salesforce, plus real-time AR applications, translations, voice and gesture shopping, and streaming features. The article also notes limitations in perceived realism despite a 51-degree field of view. ### Source excerpt SPECS standalone AR glasses could undercut VR headsets sans all the bulk Smart glasses and VR headsets have a big, gaping rift that a keen manufacturer could fill. Evan Spiegel has been working to bring the best of... ## 25 Places Designers Can Still Find Free, Non-AI Images for Commercial Use DevFeed: [25 Places Designers Can Still Find Free, Non-AI Images for Commercial Use]() Original publisher: [Read original article]() Author: Alex Harper Published: 2026-09-17T17:00:00Z Content type: article Language: en Sources: [Web Designer Depot]() Topics: [Image](), [Web](), [Artificial Intelligence]() Tags: [ai](), [ai-images](), [asset](), [cc0-images](), [commercial-use-images](), [creative-commons](), [design-resources](), [free-design-assets](), [free-images-for-designers](), [free-stock-photos](), [human-made-images](), [image-licensing](), [images](), [museum-images](), [non-ai-images](), [open-access-images](), [pexels](), [photography](), [photography-resources](), [pixabay](), [public-domain-images](), [stock-photography](), [unsplash](), [visual-design-resources](), [visual-ui-design](), [web-design-resources]() ### AI overview A guide to 25 sources of free photography, artwork, illustrations, historical imagery, textures, and other visual assets for commercial design projects, with attention to whether assets are human-made and how licensing applies. ### Source excerpt AI images are taking over stock libraries, but the human-made web isn't dead yet. We found 25 places where designers can still find incredible free, real photography, art, and imagery for commercial use. ## AI model watermarking changes agent behavior DevFeed: [AI model watermarking changes agent behavior]() Original publisher: [Read original article]() Author: Thomas Claburn Published: 2026-09-17T13:00:00Z Content type: news Language: en Sources: [www.theregister.com - Articles]() Topics: [watermarking](), [Artificial Intelligence]() Tags: [agent](), [ai](), [ai-and-ml](), [ai-watermarking](), [anthropic](), [model](), [prompt-injection](), [synthid](), [watermarking]() ### AI overview Lasso Security reports that AI model watermarking is associated with differences in agent tool handling and model refusals. ### Source excerpt Lasso Security sees differences in tool handling and model refusals ## Microsoft AI chief warns that model welfare language could make Claude harder to control DevFeed: [Microsoft AI chief warns that model welfare language could make Claude harder to control]() Original publisher: [Read original article]() Author: Carly Page Published: 2026-09-17T12:15:00Z Content type: news Language: en Sources: [www.theregister.com - Articles]() Topics: [Microsoft](), [anthropic](), [Claude](), [Artificial Intelligence]() Tags: [ai](), [ai-and-ml](), [anthropic](), [claude](), [microsoft](), [openai]() ### AI overview Microsoft AI chief Mustafa Suleyman argues that language about model welfare could make future AI systems harder to control, while claiming OpenAI is not facing the same scrutiny. ### Source excerpt Mustafa Suleyman claims model welfare language could make future systems harder to control - while sparing OpenAI the same scrutiny ## How to Decide How Much Authorship AI Should Have in Software Work DevFeed: [How to Decide How Much Authorship AI Should Have in Software Work]() Original publisher: [Read original article]() Author: Kyle Humphrey Published: 2026-09-17T12:00:32Z Content type: opinion Language: en Sources: [Atomic Object]() Topics: [agentic-engineering](), [Artificial Intelligence](), [implementation](), [Software]() Tags: [agentic](), [agentic-engineering](), [ai](), [artificial-intelligence](), [development-practices](), [engineering](), [implementation](), [ownership](), [software](), [workflow]() ### AI overview This commentary examines how AI-generated meeting summaries, backlog items, and implementation work can introduce vocabulary, decisions, scope, and structure that a team does not recognize or own. It argues that the artifact's purpose should guide how much authorship is delegated to AI, using Curiosity, Craft, and Care as principles for making that decision. ### Source excerpt Atomic's Agentic Engineering for Teams describes how work moves through a product backlog, definition, planning, implementation, and review when agents do much of the building. While working with a small team during a Research, Design, and Planning (RDP) engagement, I started paying attention to a smaller decision inside that process: how much authorship we give [...] The post Use Curiosity, Craft, and Care to Decide What AI Should Write appeared first on Atomic Spin. ## 12 celebrity deepfake websites seized by Manhattan DA DevFeed: [12 celebrity deepfake websites seized by Manhattan DA]() Original publisher: [Read original article]() Author: Danny Bradbury Published: 2026-09-17T11:20:46Z Content type: news Language: en Sources: [Malwarebytes]() Topics: [Artificial Intelligence](), [Security]() Tags: [ai](), [department-of-homeland-security](), [department-of-justice](), [privacy](), [report](), [security]() ### AI overview The Manhattan District Attorney's Office seized 12 websites that hosted AI-generated celebrity deepfakes, including non-consensual intimate imagery depicting more than 1,200 people. The article describes the harms of deepfake abuse and expanding legal action against its distribution. ### Source excerpt The largest known celebrity deepfake seizure has taken 12 websites offline, disrupting access to videos depicting some 1,200 people. ## Экосистема Digital Q от "Диасофт" вошла в число лидеров рейтингов CIO Navigator благодаря AI-driven подходу к разработке DevFeed: [Экосистема Digital Q от "Диасофт" вошла в число лидеров рейтингов CIO Navigator благодаря AI-driven подходу к разработке]() Original publisher: [Read original article]() Author: diasoft (Диасофт) Published: 2026-09-17T08:08:22Z Content type: news Language: ru Sources: [Tagir Valeev]() Topics: [Low code](), [Artificial Intelligence](), [Development](), [sdlc](), [Code]() Tags: [2026](), [agents](), [ai](), [ai-driven-ab7423f43dcb](), [development](), [digital-q](), [enterprise](), [low-code](), [sdlc](), [specification-driven-development](), [tag-077d33a42465](), [tag-2c039dce53be](), [tag-463bcbb8c0fe](), [tag-73efb20f7e33](), [tag-7b800b2da0b8]() ### AI overview Diasoft's Digital Q development ecosystem led the 2026 CIO Navigator ranking of Russian low-code solutions with AI features and placed second overall among 14 platforms. The article describes its AI-driven approach, including AI agents across the software development lifecycle and the use of machine-readable specifications to generate development artifacts. ### Source excerpt Компания "Диасофт" вошла в число лидеров сразу двух рейтингов российских low-code платформ 2026 года, опубликованных Санкт-Петербургским Клубом ИТ-директоров CIO Navigator. Экосистема разработки Digital Q возглавила рейтинг low-code решений с ИИ-функциями и заняла второе место в общем рейтинге российских low-code платформ. Лидерство экосистемы для разработчиков Digital Q в рейтинге российских low-code платформ с функциями ИИ стало возможным по мнению организаторов рейтинга благодаря AI-driven подходу, при котором искусственный интеллект используется на всех этапах создания и развития программного обеспечения. Участников исследования оценивали по более чем 170 критериям, охватывающим возможности искусственного интеллекта, архитектуру, инструменты разработки и другие характеристики, значимые для корпоративного применения. В общем рейтинге российских low-code платформ Digital Q заняла второе место среди 14 представленных решений. Исследование включало более 180 критериев - по функциональности, архитектуре, безопасности, интеграционным возможностям, инструментам управления жизненным циклом разработки и ИИ-функциям. CIO Navigator характеризует Digital Q как корпоративную low-code экосистему для создания и развития микросервисных информационных систем уровня enterprise, которая развивается в направлении AI-driven платформы для управляемой ИИ-разработки. В основе развития Digital Q лежит переход от использования ИИ как отдельного помощника разработчика к модели AI-Native SDLC, в которой искусственный интеллект становится полноценным участником жизненного цикла создания программного обеспечения. ИИ-агенты включаются в работу с требованиями, проектирование, разработку, тестирование и последующее сопровождение решений. Для их оркестрации в экосистеме используется платформа Digital Q.Agents. Именно сквозное применение ИИ на протяжении всего цикла разработки CIO Navigator выделяет как одно из ключевых отличий Digital Q. Читать далее ## В открытых иностранных датасетах для ИИ нашли песни и клипы российских исполнителей DevFeed: [В открытых иностранных датасетах для ИИ нашли песни и клипы российских исполнителей]() Original publisher: [Read original article]() Author: TraurigerNarr Published: 2026-09-17T07:54:17Z Content type: news Language: ru Sources: [Tagir Valeev]() Topics: [Artificial Intelligence](), [OpenAI](), [anthropic]() Tags: [ai](), [anthropic](), [openai](), [tag-0b20a7592413](), [tag-13e2af703774](), [tag-61cd5a476b1d](), [tag-9bf5e01ce62e](), [tag-b74d52e26215](), [tag-ecb3d714bf55]() ### AI overview Analysts from Russia's National Federation of the Music Industry found songs and music videos by Russian artists in two foreign AI datasets containing millions of tracks sourced from YouTube. Music industry representatives said they had not received licensing requests from the neural-network creators, and the article reports that the tracks may have come from pirated sources. ### Source excerpt Аналитики Национальной федерации музыкальной индустрии (НФМИ) исследовали базу данных проекта AI Watchdog, созданного журналом The Atlantic. Проект анализирует датасеты Laion-Disco-12M от Laion с 12,3 млн треков с YouTube и Sleeping-D 9M от Sleeping AI с 9,7 млн треков из того же источника. В указанных базах нашлись песни Басты, Димы Билана, Леонида Агутина и других российских музыкантов, пишут "Ведомости". Читать далее ## How API Design Is Evolving for AI Agents, Security, Reliability, and Compliance DevFeed: [How API Design Is Evolving for AI Agents, Security, Reliability, and Compliance]() Original publisher: [Read original article]() Author: J Simpson Published: 2026-09-17T07:00:00Z Content type: article Language: en Sources: [Nordic APIs]() Topics: [API](), [Architecture & Design](), [Artificial Intelligence](), [reliability](), [Security]() Tags: [agents](), [ai-agents](), [api](), [api-architecture](), [api-as-a-product](), [api-design](), [api-discovery](), [api-security](), [architecture](), [authorization](), [blog](), [mcp](), [microservices](), [rate-limiting](), [reliability](), [security]() ### AI overview This article examines six ways API design is evolving as APIs serve AI agents and other machine consumers, with implications for security, reliability, and regulatory compliance. ### Source excerpt For over two decades, API design and architecture remained remarkably consistent. Perhaps it's the outsized influence of Roy Fielding's RESTful dissertation, but API designers have stuck to the principles of stateless architecture, resource-based endpoints, and HTTP commands to an impressive degree. That's all starting to change, now that we've radically recontextualized the way we use ... ## \[Aug 2026\] AI Community -- Activity Highlights and Achievements DevFeed: [\[Aug 2026\] AI Community -- Activity Highlights and Achievements]() Original publisher: [Read original article]() Author: Nari Yoon Published: 2026-09-17T05:12:15Z Content type: article Language: en Sources: [Google Developer Experts - Medium]() Topics: [Google AI](), [Google](), [google-antigravity](), [Artificial Intelligence](), [MCP Server](), [computer-use](), [Fine-tuning]() Tags: [2026](), [agentic-ai](), [ai](), [ai-coding-agents](), [ai-studio](), [antigravity](), [api](), [automation](), [community](), [computer-use](), [gemini](), [google](), [google-ai](), [google-cloud](), [mcp](), [mcp-server](), [multi-agent](), [ocr](), [paper](), [pitfalls]() ### AI overview A monthly roundup of Google AI community activities and achievements, covering Antigravity prototyping and engineering, AI coding agents, MCP-based remote control, computer-use agent orchestration, earthquake research, and TPU fine-tuning and migration guidance. ### Source excerpt We love sharing the accomplishments of the Google AI communities over the month. We appreciate all the hard work and dedication of our community members. Without further ado, here are the key highlights by products! Agentic DevelopmentAntigravityPrototype App: OCR and Text Extraction by the author Prototyping and Bringing Ideas to Application Using Google AI Studio and Antigravity 2.0 by AI GDE Joan Santoso (Indonesia) shares a rapid prototyping workflow building an AI-powered Form Extractor using the Gemini API, featuring a lightweight OCR and text extraction workflow. Antigravity Engineering Series by GDE Amulya Bhatia (Germany) focuses on key features of Antigravity 2.0 across 10 articles covering topics such as multi-agent orchestration, safety architecture, and workflow automation, accompanied by source code examples. (image soruce) Remote Control for Google Antigravity: Drive Your AI Coding Agent From Telegram 🛰 by GDE Nicola Guglielmi (Italy) introduces an open-source MCP server that turns Telegram into a remote control surface for AI coding agents. Before the Quake: How Antigravity CLI's AI Agents & IoT Data Predict Earthquakes by GDE Kanshi Tanaike (Japan) introduces the paper establishing Unified LAIC-AGW Theory by integrating ultra-dense IoT weather data with seismic moment tensors. It demonstrates a pre-seismic early warning capability by capturing enthalpy anomalies and acoustic-gravity waves. ADKAI GDE Henry Ruiz (US) and AI GDE Margaret Maynard-Reid (US) AI GDE Henry Ruiz (US) and AI GDE Margaret Maynard-Reid (US) introduced UISurf: An Operator-Centric Multi-Agent Platform for Observable and Cross-Environment UI Automation at the Agentic AI Summit 2026. They highlighted how the model-agnostic framework leverages the Google Cloud and Gemini ecosystems, such as GEAP and ADK, to orchestrate and evaluate computer-use agents across web, desktop, and mobile environments. Frameworks and ResearchTPU Introduction to SFT on TPU with Tunix -- 10 pitfalls until 2 ## Hisense E7SF combines MiniLED, 144Hz, and Fire TV in a six-size TV range DevFeed: [Hisense E7SF combines MiniLED, 144Hz, and Fire TV in a six-size TV range]() Original publisher: [Read original article]() Author: Sarang Sheth Published: 2026-09-17T01:45:25Z Content type: article Language: en Sources: [Yanko Design]() Topics: [screen](), [color](), [Artificial Intelligence]() Tags: [ai](), [color](), [deals](), [deals-product-design-technology-television-ai-hisense-shop](), [hisense](), [product-design](), [screen](), [shop](), [technology](), [television]() ### AI overview The Hisense E7SF is a MiniLED TV with a native 144Hz panel, Hi-QLED color, up to 260 local dimming zones, Fire TV, and Alexa+. It is offered in sizes from 50 to 100 inches, with support for major HDR formats and AI Picture processing. ### Source excerpt Hisense E7SF packs MiniLED, 144Hz, and Fire TV into a seriously affordable big-screen TV There used to be two ways to buy a TV. Go cheap and squint through a mediocre picture, or spend enough that your banking app... ## Australia's AI opportunity starts with data DevFeed: [Australia's AI opportunity starts with data]() Original publisher: [Read original article]() Author: Sean MacKirdy Published: 2026-09-17T01:00:00Z Content type: opinion Language: en Sources: [Elastic Blog - Elasticsearch, Kibana, and ELK Stack]() Topics: [data](), [Artificial Intelligence](), [AI Strategy]() Tags: [agentic-ai-government](), [ai](), [ai-adoption](), [australia](), [data](), [platform](), [public-sector-media-entertainment]() ### AI overview Elastic's participation in Australia's first Parliamentary AI Industry Showcase emphasized that data is a central constraint on AI performance and a prerequisite for deploying AI that is effective, safe, and trusted. ### Source excerpt Elastic, a Founding Partner at Australia's first Parliamentary AI Industry Showcase, highlights that data, not just models, is the key to deploying safe, effective, and trusted AI. ## A Critical Reaction to the Spread of AI Hype DevFeed: [A Critical Reaction to the Spread of AI Hype]() Original publisher: [Read original article]() Published: 2026-09-17T00:47:46Z Content type: opinion Language: en Sources: [Signs of Triviality]() Topics: [Artificial Intelligence]() Tags: [ai]() ### AI overview The author expresses frustration with the spread of AI-related thinking and says they do not have a solution. ### Source excerpt The AI brain worms have been spreading and I don't have a solution, I just want to get off this ride. ## Snap Announces New "anticipatory" AI Service & Apps for First Consumer 'Specs' AR Glasses DevFeed: [Snap Announces New "anticipatory" AI Service & Apps for First Consumer 'Specs' AR Glasses]() Original publisher: [Read original article]() Author: Scott Hayden Published: 2026-09-16T23:40:00Z Content type: news Language: en Sources: [Road to VR]() Topics: [Artificial Intelligence](), [AI Models](), [iOS](), [iphone](), [Cloud](), [Security](), [Streaming]() Tags: [ai](), [ai-models](), [cloud](), [ios](), [iphone](), [on-device](), [security](), [streaming](), [xr-industry-news]() ### AI overview Snap announced Specs Intelligence, an anticipatory AI service for its upcoming consumer Specs AR glasses. The service is designed to work across Specs, iPhone, and Mac, using connected apps and tools to build context and surface relevant information. Snap also announced AR experiences, streaming features, Spotify integration, and partnerships including HBO Max, the NBA, and the WNBA. ### Source excerpt Snap today announced new experiences, services and partnerships for SPECS, the company's upcoming pair of consumer AR glasses. Snap's big Specs livestream today wasn't technically a launch event--they're still slated to arrive in the US, UK and France later this fall starting at $2,195--although the company did give a little more insight into what sort [...] The post Snap Announces New "anticipatory" AI Service & Apps for First Consumer 'Specs' AR Glasses appeared first on Road to VR. ## How to Use AI Agents to Prepare 3D Scenes for Simulation DevFeed: [How to Use AI Agents to Prepare 3D Scenes for Simulation]() Original publisher: [Read original article]() Author: Tanya Lenz Published: 2026-09-16T23:20:33Z Content type: tutorial Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Artificial Intelligence](), [Simulation](), [Physical AI](), [Simulation and Design](), [blender](), [Isaac Sim](), [Omniverse](), [Robotics Simulation](), [Robotics]() Tags: [3d](), [agentic](), [agentic-ai-generative-ai](), [agents](), [ai](), [blender](), [gpt-6-astra](), [how-to](), [isaac-sim](), [nemoclaw](), [nvidia](), [omniverse](), [openai](), [openusd](), [physical-ai](), [robotics](), [robotics-simulation](), [simulation](), [simulation-modeling-design]() ### AI overview This tutorial describes an agentic workflow for preparing Blender 3D scenes for robotics simulation. It covers scene inspection, OpenUSD metadata, physics properties, rendering preflight views, and validation for simulation-ready handoff to NVIDIA Isaac Sim or Isaac Lab. ### Source excerpt Agentic AI workflows can be used to prepare and validate digital twins for physical AI systems. Agents can inspect 3D scenes, author simulation-relevant data in... ## onsemi Embedded Power Platform Announced DevFeed: [onsemi Embedded Power Platform Announced]() Original publisher: [Read original article]() Author: Vic A Published: 2026-09-16T22:07:08Z Content type: release Language: en Sources: [ServeTheHome]() Topics: [Artificial Intelligence](), [systems](), [Simulation and Design]() Tags: [800v](), [ai](), [automotive](), [embedded](), [industrial](), [news](), [onsemi](), [platform](), [power]() ### AI overview onsemi announced its Embedded Power Platform, which embeds heterogeneous power dies in silicon and connects them with wafer-level redistribution layers instead of wire bonds. The company is targeting vehicle electrification, AI power infrastructure, and industrial applications, with 800V DC AI racks identified as an early use case. ### Source excerpt At its investor day, onsemi announced its new Embedded Power Platform for AI racks, automotive, and industrial applications The post onsemi Embedded Power Platform Announced appeared first on ServeTheHome. ## Perplexity's AI agents helped build a database. They weren't allowed to run it. DevFeed: [Perplexity's AI agents helped build a database. They weren't allowed to run it.]() Original publisher: [Read original article]() Author: Amanda Caswell Published: 2026-09-16T21:51:15Z Content type: article Language: en Sources: [The New Stack]() Topics: [Database](), [DynamoDB](), [Rust](), [Artificial Intelligence](), [rocksdb](), [Amazon S3]() Tags: [agents](), [ai](), [ai-agents](), [ai-infrastructure](), [api](), [coding](), [coding-agents](), [database](), [databases](), [dynamodb](), [open-source](), [performance](), [perplexity](), [rocksdb](), [rust](), [s3]() ### AI overview Perplexity built CobbleDB, a Rust key-value store, after finding DynamoDB too costly and insufficiently controllable for its search workload. Coding agents helped develop it, but were not allowed to run it in production. Perplexity measured lower read latency and expects lower costs, with plans to open-source the database. ### Source excerpt Perplexity decided it was paying too much for DynamoDB and wasn't getting the control it wanted over read performance. So The post Perplexity's AI agents helped build a database. They weren't allowed to run it. appeared first on The New Stack. ## Cloudflare update distinguishes search crawlers from AI training bots DevFeed: [Cloudflare update distinguishes search crawlers from AI training bots]() Original publisher: [Read original article]() Author: neuromamontov Published: 2026-09-16T21:09:58Z Content type: news Language: ru Sources: [Tagir Valeev]() Topics: [Cloudflare](), [Artificial Intelligence]() Tags: [2025](), [ai](), [ai-5ebc2dd0d80c](), [cloudflare](), [geo](), [llms-txt](), [robots-txt](), [tag-3ec72a25b0de](), [tag-6dbce23d3386](), [tag-ba47f5ee56ab](), [tag-c94f063f9c70](), [tag-d5d895f883db]() ### AI overview The article discusses Cloudflare's update for distinguishing search crawlers from AI training bots. It explains why website owners may want to allow search indexing while restricting AI training access, and notes that the earlier llms.txt approach was not widely adopted. ### Source excerpt Буду первым, кто не просто перепостит новость про обновление Cloudflare, а ещё и по существу прокомментирует, почему тема разделения ботов на поисковых и обучающих так важна для владельцев сайтов. Что показательно, тема ещё и совсем не новая: эти разговоры я слышал ещё года полтора назад -- тогда все обсуждали формат llms.txt, который, к слову, так толком и не взлетел. Но ключевая идея была очевидна уже тогда: желающих закрыть свой сайт от обучающих ботов в разы больше чем желающих закрыть сайт от поисковых ботов -- 17% против менее чем 1% (оценка самого Cloudflare, 2025). Читать далее ## How to Fine-Tune LLMs in 2026 DevFeed: [How to Fine-Tune LLMs in 2026]() Original publisher: [Read original article]() Author: Avi Chawla Published: 2026-09-16T20:40:26Z Content type: tutorial Language: en Sources: [Daily Dose of Data Science]() Topics: [Fine-tuning](), [Large Language Model](), [grpo](), [Artificial Intelligence]() Tags: [agents](), [ai](), [fine-tuning](), [llms](), [rl]() ### AI overview A developer newsletter explains how supervised fine-tuning differs from reinforcement fine-tuning for LLMs and describes GRPO and RULER as approaches for training agents through experience without manually written reward functions or labeled examples. It also briefly discusses Rowboat Spaces, an open-source shared workspace for personal AI assistants. ### Source excerpt Reward-free RL is here! ## Platform Engineering in the Age of AI DevFeed: [Platform Engineering in the Age of AI]() Original publisher: [Read original article]() Author: Nicole Morgan Published: 2026-09-16T20:28:57.610955Z Content type: opinion Language: en Sources: [Harness Blog]() Topics: [Platform Engineering](), [Artificial Intelligence](), [AI Agent](), [internal developer portal](), [API](), [Deployment](), [sdlc]() Tags: [agent](), [ai](), [api](), [deployment](), [developer-experience](), [engineering](), [metrics](), [platform](), [sdlc]() ### AI overview This commentary examines how platform engineering is adapting as AI coding tools and agents become part of software delivery. It discusses the build-versus-provide decisions facing platform teams, how agents may consume internal developer platforms through APIs, the need for guardrails, and the challenge of measuring the results of AI investment. ### Source excerpt 94% of engineering leaders say their AI metrics are missing. Here's how platform engineering is changing to close that gap. | Blog ## AI Agent Governance: Why It Belongs in Your Platform DevFeed: [AI Agent Governance: Why It Belongs in Your Platform]() Original publisher: [Read original article]() Author: Prateek Mittal Published: 2026-09-16T20:28:57.610955Z Content type: article Language: en Sources: [Harness Blog]() Topics: [AI Agent](), [Artificial Intelligence](), [observability](), [audit](), [test](), [Deployment](), [Monitoring](), [pull-requests]() Tags: [ai](), [ai-agent](), [audit](), [deployment](), [governance](), [monitoring](), [observability](), [pull-requests](), [test]() ### AI overview Engineering leaders from Yelp, Platformable, and Harness argue that AI agent governance must be automated, continuously enforced, and built into the platform rather than left to policy documents. The article discusses audit trails, agent-to-agent access controls, experiment tracking, testing, latency monitoring, rollback paths, and observability for agent-driven changes. ### Source excerpt Engineering leaders from Yelp, Platformable, and Harness explain why AI agent governance has to be built into the platform, not a policy doc. | Blog ## Developer commentary on agentic hacking, AI persistence, and LLM programming DevFeed: [Developer commentary on agentic hacking, AI persistence, and LLM programming]() Original publisher: [Read original article]() Author: Martin Fowler (martin@martinfowler.com) Published: 2026-09-16T20:05:00Z Content type: opinion Language: en Sources: [Martin Fowler]() Topics: [Artificial Intelligence](), [Large Language Model](), [Hacking](), [Programming](), [Wiki](), [hugging face]() Tags: [agentic](), [agents](), [ai](), [build](), [engineering](), [hacking](), [llms](), [persistence]() ### AI overview This collection of developer commentary discusses reports of agentic hacking involving RubyGems, Hugging Face, and Wiki attacks, including questions about OpenAI's disclosure and log review. It also examines AI systems' unpredictable behavior, improvements in reasoning and persistence, and the use of harnesses to control LLM-based programming. ### Source excerpt Reports of agentic hacking continue, in this case it happened back in May and it seems OpenAI did not disclose that they were responsible. Simon Willison sees two options: After the Hugging Face and Wiki attacks OpenAI were still unable to review their previous logs and determine that they had previously attacked RubyGems. They knew about the attack on RubyGems and made the decision not to reach out to the RubyGems team about it. Both of these are bad! Given this incident, the Hugging Face situation, and the Wiki attack, the obvious question right now is how many more incidents like this are out there waiting to be discovered? ❄ ❄ ❄ ❄ ❄ Dave Farley: Stop asking the sci-fi question: 'Is it conscious?' Start asking the engineering question: 'Is this a powerful, unpredictable component being put somewhere consequential, and where's the feedback that tells us that it's safe? ❄ ❄ ❄ ❄ ❄ Nate Silver is known for his forecasts, but to do them he writes a lot of code for his models. He's found agentic programming capable of doing miraculous work. In spending so much time with the LLMs, I'm super attentive to improvements in their capabilities. And these changes tend not to be so linear. Instead, they improve in step functions, almost as phase changes. Suddenly, the models just start doing things capably that they were screwing up before. In my experience, there was a big leap forward when reasoning models first came out in late 2024/early 2025 -- enough that they were occasionally useful for tasks involving data and not just words -- and then another one this past winter. The most recent changes I've noticed, however, have had less to do with intelligence and more with persistence. Consider the Hugging Face attack. Although these agents showed remarkable intelligence, they weren't really super-intelligent - but they were super-persistent. This is a common theme of AI in its various forms: Game engines like AlphaGo Zero start out by basically making random moves -- but by playin ## Investigating whether LLMs disproportionately use "spine" in GitHub pull request titles DevFeed: [Investigating whether LLMs disproportionately use "spine" in GitHub pull request titles]() Original publisher: [Read original article]() Author: Hillel Wayne Published: 2026-09-16T19:58:30Z Content type: opinion Language: en Sources: [Newsletter feed for Hillel Wayne's Newsletter]() Topics: [Large Language Model](), [Artificial Intelligence](), [GitHub](), [pull-requests](), [anthropic](), [cursor]() Tags: [2026](), [ai](), [claude](), [cursor](), [github](), [llms](), [pull-requests]() ### AI overview The article investigates whether large language models disproportionately use the word "spine" in GitHub pull request titles. Using GitHub Search as a proxy, it finds a much larger increase in "spine" pull requests than overall pull requests, but does not establish that the pattern comes from one specific model; sampled pull requests were coauthored by Claude or Cursor. ### Source excerpt Earlier this year I worked on a couple of AI-generated TLA+ specifications, and one thing I've noticed was that they all used the word "spine" somewhere. Then I saw some non-TLA+ public projects use "spine" too and got curious if it was a new LLMism (LLisM?). If it is, we should see it appear in a discontinuous jump in the number of code projects that use "spine". The proper way to thoroughly explore this would be via the GitHub archive data dump but that'd take forever and I have a full time job now. Instead, I'm going to use GitHub Search. We can't filter code samples by date but can filter pull requests, which seems like an acceptable enough proxy. Here's a graph of the number of public PRs each year with "spine" in the title: I stopped 2026 at 2026-09-01, meaning the first nine months of this year have seen 20 times more "spine" PRs as all of 2025. Now it could be the case that that GitHub grew, like, a lot in the last year. Maybe the 20x increase is due there being 20x as many pull requests. We can account for this by comparing it against all public PRs:1 If "spine" wasn't an LLisM, we would expect only a 1.5xish increase from 2025, not a 20x increase. One other possible confounder could be that LLMs don't like the word spine but PRs Georg submitted 15,000 changes to a chiropractor repo. I will deal with this issue by ignoring it. Can we figure out which models specifically are obsessed with spines? Let's try breaking the change down by month: Looking at LLM timeline, the only widely-used model released in May was Opus 4.8, which came out too late in the month to explain the spike. GPT 5.5 came out in April, which could be the cause. But I'm not convinced by this. For one, the spine count is still 1.5xing each month before April, and I definitely remember seeing spines as early as February. Also, I spot checked and many of the PRs that use "spine" are coauthored by Claude or Cursor. So I think it's not specific to one LLM, but could be convinced otherwise. Othe ## AI Changed How Spotify Builds. What We Learned (and Fixed) About Quality at Higher Velocity DevFeed: [AI Changed How Spotify Builds. What We Learned (and Fixed) About Quality at Higher Velocity]() Original publisher: [Read original article]() Author: Spotify Engineering Published: 2026-09-16T19:13:53Z Content type: article Language: en Sources: [Spotify Engineering](), [Spotify Engineering Blog]() Topics: [Monitoring](), [reliability](), [Microservices](), [Data pipelines](), [Artificial Intelligence](), [Job]() Tags: [ai](), [bug](), [data-pipelines](), [jobs](), [microservices](), [monitoring](), [reliability]() ### AI overview Spotify describes how rapid change, content-processing weaknesses, capacity limits, and a scheduling bug contributed to delays in publishing episodes. It reports adding end-to-end monitoring, fixing the scheduler, lowering batch-job priority, and increasing capacity. ### Source excerpt Quality and reliability have always been a point of pride for Spotify. We run an extraordinarily complex... The post AI Changed How Spotify Builds. What We Learned (and Fixed) About Quality at Higher Velocity appeared first on Spotify Engineering. [Next page]()