# generative Published articles for generative. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Photoshop Elements 2027 Adds Generative Expand, AI Upscaling and a Beta AI Assistant DevFeed: [Photoshop Elements 2027 Adds Generative Expand, AI Upscaling and a Beta AI Assistant]() Original publisher: [Read original article]() Author: Alex Harper Published: 2026-09-16T14:26:34Z Content type: article Language: en Sources: [Web Designer Depot]() Topics: [Artificial Intelligence](), [Generative AI](), [Graphics]() Tags: [adobe](), [adobe-elements](), [adobe-firefly](), [adobe-photoshop](), [ai](), [ai-assistant](), [ai-image-editing](), [ai-tools](), [artificial-intelligence](), [beta](), [creative-tools](), [design-tools](), [generative](), [generative-ai](), [generative-expand](), [generative-upscale](), [graphic-design](), [image-editing](), [photo-editing](), [photoshop-elements](), [photoshop-elements-2027](), [premiere-elements](), [visual-ui-design](), [web-design]() ### AI overview Adobe's Photoshop Elements 2027 update adds Generative Expand, Generative Upscale and a beta AI Assistant, along with photo organization features and a 25-credit monthly limit for generative features. ### Source excerpt Photoshop Elements is getting a serious AI upgrade, with Generative Expand, AI upscaling and a new assistant that helps you edit simply by describing what you want. ## 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 ## Still: From Akira to Ink Wash, Building a Generative Garden in WebGPU DevFeed: [Still: From Akira to Ink Wash, Building a Generative Garden in WebGPU]() Original publisher: [Read original article]() Author: Ming Jyun Hung Published: 2026-09-09T14:11:57Z Content type: article Language: en Sources: [Codrops]() Topics: [webgpu](), [procedural flowers]() Tags: [3d](), [articles](), [case-study](), [creative-coding](), [deep-dive](), [flower-animation](), [generative](), [generative-art](), [generative-garden](), [generative-tendrils](), [gpu-instancing](), [ink-wash-effect](), [interactive-3d](), [japanese-art](), [japanese-print-style](), [procedural](), [procedural-animation](), [procedural-art](), [procedural-flowers](), [real-time](), [real-time-3d](), [three-js](), [three-js-case-study](), [three-js-shading-language](), [toon-shading](), [tsl](), [tsl-shaders](), [vat](), [vertex-animation-textures](), [webgpu](), [webgpu-shaders]() ### AI overview A technical deep dive into a WebGPU-based, real-time 3D generative garden. It describes combining toon shading, ink-wash shadows, silk-like grain, and procedural flowers and tendrils to translate Japanese print-inspired visual principles into an interactive web scene. ### Source excerpt A technical deep dive into Still, exploring how WebGPU, procedural systems, and Japanese art influences come together to create a living generative garden. ## Blizzard union workers ratify historic contract covering 1,900 employees DevFeed: [Blizzard union workers ratify historic contract covering 1,900 employees]() Original publisher: [Read original article]() Author: Chris Kerr Published: 2026-09-09T12:59:56Z Content type: news Language: en Sources: [gamedeveloper]() Topics: [Microsoft](), [Generative AI](), [Xbox]() Tags: [ai](), [company](), [generative](), [layoff](), [microsoft](), [news](), [remote](), [teams](), [work](), [xbox]() ### AI overview Blizzard union workers ratified a contract covering 1,900 employees. The agreement includes generative AI usage safeguards, wage increases, hybrid and remote-work benefits, layoff protections, severance, and recall rights. ### Source excerpt Union members have secured guardrails around generative AI usage, layoff protections, remote and hybrid work benefits, and more. ## Introducing WeatherNext 3, our most advanced and accurate global weather AI model DevFeed: [Introducing WeatherNext 3, our most advanced and accurate global weather AI model]() Original publisher: [Read original article]() Author: The WeatherNext team Published: 2026-09-03T15:02:08Z Content type: article Language: en Sources: [Google DeepMind News]() Topics: [Artificial Intelligence](), [Google](), [data]() Tags: [ai](), [cloud](), [gemini](), [generative](), [google](), [model](), [none](), [search]() ### AI overview Google DeepMind and Google Research introduce WeatherNext 3, a global weather AI model that uses real-time observations and raw satellite data for hourly, high-resolution forecasts. ### Source excerpt WeatherNext 3, our most advanced global weather AI model, is now in Search, Gemini, Maps, Google Maps Platform, and Cloud. ## Behind the build: Generative plugins and shaders at Figma DevFeed: [Behind the build: Generative plugins and shaders at Figma]() Original publisher: [Read original article]() Author: Rogie King Published: 2026-09-01T14:39:43.296000Z Content type: article Language: en Sources: [Figma Blog]() Topics: [Figma](), [Artificial Intelligence](), [Model Context Protocol](), [GPU](), [webgpu](), [Large Language Model](), [Code](), [React Native]() Tags: [agent](), [ai](), [code](), [collaboration](), [community](), [design](), [figma](), [generative](), [gpu](), [llms](), [mcp](), [mcp-server](), [product](), [react](), [shaders](), [tools](), [webgpu]() ### AI overview Figma describes how generative plugins and shaders expand creative control through prompting, custom tools, richer context, animated effects, code access, community publishing, and MCP integration. The article connects GPU-based real-time pixel manipulation with AI-assisted movement between design and code. ### Source excerpt Starting today, you'll have more creative control over generative plugins and shaders, and a way to share what you've made with the community. Product Designer Rogie King tells the story of how we got here. ## Gemini Omni 1.1 Flash lets you build with more control DevFeed: [Gemini Omni 1.1 Flash lets you build with more control]() Original publisher: [Read original article]() Author: Anish Nangia Published: 2026-08-27T16:11:32Z Content type: article Language: en Sources: [Google DeepMind News]() Topics: [Google AI](), [Artificial Intelligence](), [API](), [Deployment]() Tags: [ai](), [api](), [cost](), [developer](), [gemini](), [generative](), [none](), [production](), [prototyping](), [video]() ### AI overview Google introduces Gemini Omni 1.1 Flash, adding controllable generative video features for developers through the Gemini API in Google AI Studio. The update supports scene extension, keyframe-based transitions, and faster, lower-cost 360p previews for prototyping and creative workflows. ### Source excerpt Gemini Omni 1.1 Flash brings a new suite of creative controls and generative video capabilities to developers. ## IDEA Prune: An Integrated Enlarge-and-Prune Pipeline in Generative Language Model Pretraining DevFeed: [IDEA Prune: An Integrated Enlarge-and-Prune Pipeline in Generative Language Model Pretraining]() Original publisher: [Read original article]() Published: 2026-08-26T00:00:00Z Content type: article Language: en Sources: [Apple Machine Learning Research]() Topics: [Language models](), [compression and generalization](), [Training AI Models](), [Inference Performance]() Tags: [compression](), [generative](), [inference](), [language-models](), [large-language-models](), [model](), [training]() ### AI overview This paper presents IDEA Prune, an integrated enlarge-and-prune pipeline for generative language model pretraining. It combines enlarged-model training, iterative structured pruning, and recovery under one cosine annealing learning-rate schedule, with experiments compressing 2.8B models to 1.3B. ### Source excerpt Recent advancements in large language models have intensified the need for efficient and deployable models within limited inference budgets. Structured pruning pipelines have shown promise in token efficiency compared to training target-size models from scratch. In this paper, we advocate incorporating enlarged model pretraining, which is often ignored in previous works, into pruning. We study the enlarge-and-prune pipeline as an integrated system to address two critical questions: whether it is worth pretraining an enlarged model even when the model is never deployed, and how to optimize the... ## How Generative Recommenders Are Redefining RecSys at Scale DevFeed: [How Generative Recommenders Are Redefining RecSys at Scale]() Original publisher: [Read original article]() Author: Elizabeth Goodman Published: 2026-08-20T16:00:00Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [recommendation systems](), [AI, ML & Data Engineering](), [Training AI Models](), [Language models]() Tags: [agentic-ai-generative-ai](), [ai-inference](), [data-center-cloud](), [developer-tools-techniques](), [embedding](), [featured](), [generative](), [gpu](), [inference](), [latency](), [llms](), [machine-learning](), [machine-learning-artificial-intelligence](), [recommenders-personalization](), [scale]() ### AI overview The article examines the shift toward generative recommender systems and the challenges of training and serving them at large scale. ### Source excerpt Recommender systems (RecSys) are one of the most ubiquitous machine learning problems in the consumer internet industry yet notoriously difficult to train and... ## Results from the Backblaze Generative AI Media Hackathon DevFeed: [Results from the Backblaze Generative AI Media Hackathon]() Original publisher: [Read original article]() Author: Jeronimo De Leon Published: 2026-08-14T15:52:05Z Content type: article Language: en Sources: [Backblaze Blog | Cloud Storage & Cloud Backup]() Topics: [Generative AI](), [Orchestration](), [cloud-infrastructure](), [For the Love of Code](), [Open Source](), [SDKs]() Tags: [2026](), [ai-ml](), [b2cloud](), [cloud](), [cloud-storage](), [developer](), [featured](), [featured-cloud-storage](), [generative](), [hackathon](), [object-storage](), [open-source](), [orchestration](), [sdk](), [tech-lab](), [technical]() ### AI overview The article examines the strongest projects from the Backblaze Generative AI Media Hackathon and argues that production-ready generative media applications depend on more than generating images, video, or audio. It highlights storage design, provenance, verification, correction, deletion protection, and orchestration across multiple providers. ### Source excerpt See what the strongest projects from the Backblaze Generative AI Media Hackathon had in common. These five generative media apps show how storage and orchestration become part of production-ready design. The post Results from the Backblaze Generative AI Media Hackathon appeared first on Backblaze Blog | Cloud Storage & Cloud Backup ## Track generative AI costs with Amazon Bedrock inference profiles DevFeed: [Track generative AI costs with Amazon Bedrock inference profiles]() Original publisher: [Read original article]() Author: Erik Mack Published: 2026-08-13T15:59:39Z Content type: tutorial Language: en Sources: [AWS Architecture Blog]() Topics: [Amazon Bedrock](), [Generative AI](), [Inference](), [foundation-models](), [Amazon Web Services](), [AWS IAM]() Tags: [ai](), [amazon-bedrock](), [architecture](), [aws](), [bedrock](), [cost](), [generative](), [generative-ai](), [how-to](), [iam](), [inference](), [intermediate-200](), [technical-how-to]() ### AI overview This tutorial explains how to track generative AI costs by department when multiple teams share a foundation model through Amazon Bedrock. It uses tagged application inference profiles, AWS cost allocation tags, department-based request routing, and AWS Cost Explorer to produce separate cost breakdowns. ### Source excerpt Learn how to track generative AI costs by department using Amazon Bedrock application inference profiles and AWS cost allocation tags. Create tagged profiles for each team and view per-department cost breakdowns in AWS Cost Explorer. ## NVIDIA Cosmos-H-Dreams: Bringing Real-Time Generative Simulation to Surgical Robotics DevFeed: [NVIDIA Cosmos-H-Dreams: Bringing Real-Time Generative Simulation to Surgical Robotics]() Original publisher: [Read original article]() Author: Lukas Zbinden; Javier Gamazo; Mostafa Toloui; Sean Huver Published: 2026-07-27T09:32:20Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [Cosmos](), [Robotics](), [foundation-models](), [real-time](), [Nvidia](), [Synthetic Data Generation](), [synthetic-data](), [data](), [datasets](), [GPU](), [Streaming](), [Inference]() Tags: [cosmos](), [data](), [foundation-models](), [generation](), [generative](), [gpu](), [inference](), [nvidia](), [real-time](), [robotics](), [streaming](), [synthetic-data](), [synthetic-data-generation](), [training]() ### AI overview NVIDIA introduces Cosmos-H-Dreams, a real-time, action-conditioned generative simulator for surgical robotics. The model generates future surgical video from an initial RGB frame and live robot kinematics, enabling interactive closed-loop control, offline policy evaluation, and synthetic data generation. ### Source excerpt World foundation models offer a different path. Instead of manually authoring every object and physical interaction, they learn visual dynamics directly from synchronized video and robot kinematics. NVIDIA's Cosmos-H-Surgical-Simulator demonstrated this approach by generating future surgical video from an initial scene and a sequence of robot actions. It enabled faster-than-physical evaluation and synthetic data generation across the Open-H-Embodiment ecosystem. ## How to Build a Domain Expert AI DevFeed: [How to Build a Domain Expert AI]() Original publisher: [Read original article]() Author: Louis-François Bouchard Published: 2026-07-17T11:00:54Z Content type: tutorial Language: en Sources: [System Design Newsletter]() Topics: [Generative AI](), [Artificial Intelligence]() Tags: [ai](), [generative](), [generative-ai](), [how-to](), [masterclass]() ### AI overview Part 4 of a Generative AI Masterclass about how to build a domain expert AI. ### Source excerpt #162: Part 4 - Generative AI Masterclass ## Newer Models, Same Advantage DevFeed: [Newer Models, Same Advantage]() Original publisher: [Read original article]() Author: Erick Lachmann; Gabriel Pimenta de Freitas Cardoso; Francisco de Almeida Rocha Alves; Victor Gabriel Ferreira Barbosa Published: 2026-07-16T11:49:48Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [Fine-tuning](), [Inference](), [Language models](), [Benchmark](), [datasets](), [model-deployment](), [Optimization]() Tags: [article](), [benchmark](), [cost](), [data](), [dpo](), [errors](), [fine-tuning](), [generative](), [inference](), [model](), [models](), [multimodal](), [ocr](), [open](), [optimization](), [production](), [technology](), [training]() ### AI overview DharmaOCR is presented as a Brazilian Portuguese OCR model that outperformed newer alternatives through domain specialization and targeted training. Its two-stage pipeline combines supervised fine-tuning on Portuguese-language documents with Direct Preference Optimization, improving extraction quality, stability, inference efficiency, and production reliability. ### Source excerpt Despite newer architectures, DharmaOCR outperformed Mistral OCR4 and Unlimited-OCR on Brazilian Portuguese through domain specialization and targeted training. This article presents the evidence and the mechanism behind that advantage. Three months ago, we published a paper on DharmaOCR and open-sourced one of the models. The objective was specific: optical character recognition engineered for Brazilian Portuguese. The training pipeline was built in two stages. ## Exploring Hierarchical Interest Representation For Meta Ads Deep Funnel Optimization DevFeed: [Exploring Hierarchical Interest Representation For Meta Ads Deep Funnel Optimization]() Original publisher: [Read original article]() Author: Yuhui Ouyang; Di Wang; Sreedal Menon; Jie Tian Published: 2026-07-15T17:00:52Z Content type: article Language: en Sources: [Engineering at Meta](), [Meta AI Research]() Topics: [Optimization](), [Embeddings](), [Graphs](), [recommendation systems](), [LLMs](), [Artificial Intelligence](), [Transformer](), [data]() Tags: [ads](), [ai](), [ai-research](), [data](), [embeddings](), [engineering](), [frontier-ai](), [generative](), [learning](), [meta](), [optimization](), [personalization](), [reasoning](), [research](), [retrieval](), [scale]() ### AI overview Meta describes Hierarchical Interest Representation, an upstream system that learns unified embeddings for users, advertisers, products, and services. It combines graph learning, multimodal content processed through LLMs, engagement signals, and self-supervised distillation to improve personalization, retrieval, ranking, and deep-funnel advertising optimization. ### Source excerpt Hierarchical Interest Representation is a research area for Meta Ads. We're exploring an upstream representation layer over the universe of Ads entities - users, advertisers, products, services - learning unified embeddings that connect users' inferred interests with the breadth of what advertisers offer in their deep funnel ads. The innovations in Hierarchical Interest Representation are [...] Read More... The post Exploring Hierarchical Interest Representation For Meta Ads Deep Funnel Optimization appeared first on Engineering at Meta. ## How Expedia Group Builds AI That Lasts at Scale DevFeed: [How Expedia Group Builds AI That Lasts at Scale]() Original publisher: [Read original article]() Author: Xavier Amatriain Published: 2026-07-14T11:01:01Z Content type: opinion Language: en Sources: [Expedia]() Topics: [Artificial Intelligence](), [AI Strategy](), [AI Development](), [Development](), [sdlc](), [Monitoring]() Tags: [agentic-ai](), [ai](), [data](), [evaluation](), [generative](), [governance](), [innovation](), [machine-learning](), [monitoring](), [recommendations](), [sdlc](), [software-engineering](), [tooling](), [use-cases]() ### AI overview Expedia Group describes a framework for building, deploying, and evolving AI systems that remain reliable and scalable over time. The article emphasizes principles covering business value, ownership, governance, evaluation, safe rollout, and monitoring, and describes Agentic Release tollgates that translate those principles into launch checks integrated with the SDLC. ### Source excerpt Expedia Group Technology -- InnovationA framework for how we build, deploy, and evolve AI systems for impact and scalePhoto by Florian Wehde on Unsplash There's an important distinction between Artificial Intelligence (AI) that just works today and AI that lasts at scale. Many companies optimize hard for the first one without ever asking whether they're building the second. Velocity without discipline and strategic direction is a liability, not an asset. The hardest part of building AI at scale isn't getting a model to work once. It's building systems that continue to work, scale beyond individual teams and use cases, and improve consistently over time. Today's AI systems do more than just predict and optimize. They converse, reason, and increasingly take action. An autonomous system making decisions on a traveler's behalf creates a very different set of expectations around reliability, governance, and accountability. As AI takes on more of those roles, the principles behind how these systems operate matter more than ever. At Expedia Group™, we have spent years applying AI and machine learning across the traveler journey from personalization, ranking, and recommendations, to fraud prevention, customer support, and, more recently, generative and agentic AI experiences. That depth of experience is what led us to develop a set of machine learning and AI principles to guide how we build, deploy, and evolve AI systems across the company. The goal is simple: make sure the systems we build create real business value, scale across the company, and operate safely. These principles define how we measure, design, govern, and operate the systems we use. From principles to practice Publishing principles is the easy part. The harder and more important work is turning them into operating mechanisms: recommendations, requirements, tooling, and release processes that teams actually use. At Expedia Group, we have started doing this through Agentic Release tollgates: a set of recommend ## New method aims to keep kids safe from illegal AI-generated content DevFeed: [New method aims to keep kids safe from illegal AI-generated content]() Original publisher: [Read original article]() Author: Adam Zewe | MIT News Published: 2026-07-13T04:00:00Z Content type: news Language: en Sources: [MIT AI News]() Topics: [Artificial Intelligence](), [ai safety](), [AI Research](), [Open Source]() Tags: [ai](), [ai-deepfakes](), [ai-safety](), [algorithms](), [artificial-intelligence](), [ashia-wilson](), [computer-science-and-technology](), [csam](), [ethics](), [generative](), [generative-ai](), [human-computer-interaction](), [institute-for-medical-engineering-and-science-imes](), [laboratory-for-information-and-decision-systems-lids](), [machine-learning](), [marzyeh-ghassemi](), [mit-schwarzman-college-of-computing](), [models](), [online-child-safety](), [open-source](), [privacy](), [public-health](), [research](), [safety](), [school-of-engineering](), [vinith-suriyakumar]() ### AI overview MIT researchers and Thorn developed an auditing technique that assesses whether a generative AI model has been specialized to produce child sexual abuse material without generating illegal outputs. In testing, the procedure identified specialized model variants with 100 percent accuracy. ### Source excerpt Researchers developed an auditing technique to test generative AI models for malicious capabilities, without prompting them for illegal outputs. ## Too big to draw, but yet drawable DevFeed: [Too big to draw, but yet drawable]() Original publisher: [Read original article]() Author: Yehonathan Sharvit Published: 2026-07-06T09:00:00Z Content type: article Language: en Sources: [Klipse]() Topics: [ordering](), [structure]() Tags: [aboulafia](), [caustics](), [generative](), [math](), [permutation](), [permutations](), [random](), [ranking](), [recursion](), [visualization]() ### AI overview The third article in a series explains how Aboulafia's tserouf orders all permutations of a word and visualizes them by placing the permutations around a circle and connecting each word to its reversal. Because the full permutation space becomes too large to draw, the article samples chords and shows that they form recurring caustic curves visible at multiple scales. ### Source excerpt Aboulafia's Tserouf - Part 3 of 4 <- Previous: An elegant formulation, inspired by Bill Gates - Next: A wheel, the same forwards and backwards -> ## Vercel's Agent Framework, Netflix's Generative Homepage, and a Repo That Writes Less Code: The Tokenizer Edition #33 DevFeed: [Vercel's Agent Framework, Netflix's Generative Homepage, and a Repo That Writes Less Code: The Tokenizer Edition #33]() Original publisher: [Read original article]() Author: Sairam Sundaresan Published: 2026-07-05T05:27:18Z Content type: article Language: en Sources: [Gradient Ascent]() Topics: [Artificial Intelligence](), [Agent Skill](), [ZEIT](), [AI-assisted coding](), [Netflix]() Tags: [ai](), [coding](), [coding-agents](), [generative](), [models](), [netflix](), [tools](), [vercel]() ### AI overview This newsletter edition curates AI and machine learning resources, including papers, videos, articles, tools, and learning materials. Topics include Program-as-Weights, bounded-memory long-horizon agents, coding-agent benchmarks, Netflix's model-generated homepage, Vercel's file-first framework for durable agents, and tools for distributed model inference. ### Source excerpt This week's most valuable AI resources ## The good, the bad, and the AI apps DevFeed: [The good, the bad, and the AI apps]() Original publisher: [Read original article]() Author: Phoebe Sajor Published: 2026-07-03T07:40:00Z Content type: article Language: en Sources: [Stack Overflow Blog]() Topics: [Artificial Intelligence](), [Human-AI evaluation](), [App](), [Generative AI](), [Open Source]() Tags: [ai](), [apps](), [eval](), [evaluation](), [generative](), [generative-ai](), [open-source](), [podcast](), [se-stackoverflow](), [se-tech]() ### AI overview Ryan welcomes Benny Chen, co-founder of Fireworks AI, to discuss what makes an AI application good or bad, how qualitative signals and quantitative metrics can be balanced in AI evaluation, and how open-source protocols and community efforts are shaping evaluation standards. ### Source excerpt Ryan welcomes Benny Chen, co-founder of Fireworks AI, to the show to explore what actually makes an AI application good or not, how to balance qualitative signals with quantitative metrics when evaluating AI, and how open-source eval protocols and community efforts are setting the standard for AI evaluation. ## Start building with Nano Banana 2 Lite and Gemini Omni Flash DevFeed: [Start building with Nano Banana 2 Lite and Gemini Omni Flash]() Original publisher: [Read original article]() Author: Alisa Fortin Published: 2026-06-30T16:02:40Z Content type: release Language: en Sources: [Google DeepMind News]() Topics: [Google AI](), [API](), [text-to-image](), [Artificial Intelligence]() Tags: [ai](), [api](), [benchmarks](), [build-faster](), [building](), [cost](), [efficiency](), [gemini](), [generation](), [generative](), [google-ai](), [images](), [latency](), [life](), [media](), [models](), [none](), [performance](), [prototyping](), [scale](), [video]() ### AI overview Google DeepMind announces Nano Banana 2 Lite, a fast and cost-efficient image model for high-throughput developer pipelines, and makes Gemini Omni Flash available to developers for video generation and conversational editing. The models are offered through Google AI Studio, the Gemini API and Gemini Enterprise Agent Platform. ### Source excerpt Scale your ideas with Nano Banana 2 Lite, our fastest, most cost-efficient Gemini Image model, and Gemini Omni Flash for high-quality video and conversational editing. ## GenPage: Towards End-to-End Generative Homepage Construction at Netflix DevFeed: [GenPage: Towards End-to-End Generative Homepage Construction at Netflix]() Original publisher: [Read original article]() Author: Netflix Technology Blog Published: 2026-06-29T13:01:02Z Content type: article Language: en Sources: [Netflix](), [Netflix TechBlog - Medium]() Topics: [Netflix](), [recommendation systems](), [Large Language Model](), [Machine learning](), [Reinforcement learning](), [Transformer]() Tags: [ai](), [app](), [diversity](), [engineering](), [feature-engineering](), [generation](), [generative](), [large-language-models](), [machine-learning](), [model](), [netflix](), [optimization](), [ranking](), [recommendation-system](), [reinforcement-learning](), [rl]() ### AI overview This Netflix developer article introduces GenPage, a generative approach that uses a single autoregressive model to construct a personalized homepage by generating recommendation rows, entities, and layout together. It describes replacing a multi-stage recommendation pipeline with end-to-end modeling and using reinforcement learning to optimize whole-page rewards, including interactions such as diversity and the balance between rows. ### Source excerpt Authors: Lequn Wang, Jiangwei Pan, and Linas Baltrunas Figure 1. Autoregressive homepage generation. GenPage builds a Netflix homepage one row or entity at a time, each one conditioned on what's already on the page and the user's context.Introduction The Netflix homepage is the first thing users see when they open the app and the primary way they discover content to enjoy. Almost every part of it is personalized, including which rows appear, which entities show up within those rows, and how everything is arranged on the page. Constructing that homepage is a genuinely hard problem. It is not simply producing one ranked list. The homepage is a structured, two-dimensional layout, made up of recommendation rows and the entities within them. Here, an entity can be a movie, show, game, live event, or other recommendable item. Each choice can affect the value of the others. Traditionally, it is built through a complex, multi-stage pipeline, with separate components for candidate generation and ranking at both the row and entity levels. We saw an opportunity to rethink this design. Large language models have shown that a single generative model can perform diverse tasks just by generating a response to a prompt. Inspired by this prompt-response paradigm, we trained a single generative model to build the homepage by directly answering one question: Given everything we know about this user and this request, what homepage should we generate to maximize user satisfaction? We call this approach GenPage. It treats the user history and request context as the prompt, and autoregressively generates the entire homepage as the response (Figure 1). Unlike most generative recommenders, such as TIGER, HSTU, and OneRec, which generate flat ranked lists, GenPage generates the rows, entities, and layout together. This shift is motivated by several goals: End-to-end modeling. A single transformer model that constructs the page from raw input signals can replace a complex multi-stage recommen ## Accelerating Transformers Fine-Tuning with NVIDIA NeMo AutoModel DevFeed: [Accelerating Transformers Fine-Tuning with NVIDIA NeMo AutoModel]() Original publisher: [Read original article]() Author: Adil Asif; Alexandros Koumparoulis; Wenwen Gao; Sylendran Arunagiri; David Messina; Bernard Nguyen Published: 2026-06-24T16:00:13Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [NeMo](), [Fine-tuning](), [AI Models](), [Mixture of Experts (MoE)](), [distributed-training](), [Generative AI](), [Artificial Intelligence](), [GPU](), [PyTorch](), [vllm](), [sglang]() Tags: [ai](), [ai-models](), [building](), [compute](), [distributed-training](), [fine-tuning](), [framework](), [generative](), [generative-ai](), [gpu](), [infrastructure](), [kernels](), [memory](), [model](), [moe](), [nemo](), [nemotron](), [nvidia](), [open](), [transformers]() ### AI overview This article explains how NVIDIA NeMo AutoModel accelerates fine-tuning of mixture-of-experts models by extending Transformers v5 with Expert Parallelism, DeepEP fused all-to-all dispatch, and TransformerEngine kernels. It describes API compatibility, distributed execution, dynamic weight loading, and reported gains of 3.4-3.7x higher training throughput and 29-32% lower GPU memory use. ### Source excerpt NVIDIA NeMo AutoModel is an open library part of the NVIDIA NeMo framework for building custom generative AI models at scale. NeMo AutoModel builds cleanly on top of v5, adding Expert Parallelism, DeepEP fused all-to-all dispatch, and TransformerEngine kernels, and it leans on v5's dynamic weight loading to bring those optimizations to a broad and growing set of model families. ## Toward More Controllable AI Video Editing: An Early Research Exploration at Netflix DevFeed: [Toward More Controllable AI Video Editing: An Early Research Exploration at Netflix]() Original publisher: [Read original article]() Author: Netflix Technology Blog Published: 2026-06-23T00:31:01Z Content type: article Language: en Sources: [Netflix](), [Netflix TechBlog - Medium]() Topics: [Netflix](), [Artificial Intelligence](), [Generative AI]() Tags: [ai](), [generative](), [netflix](), [research](), [technology](), [video](), [workflows]() ### AI overview Netflix explores generative AI video editing methods designed to give artists precise control over changes while preserving source footage, creative intent, and physical continuity. The article discusses challenges including unintended edits and unnatural physics in professional video workflows. ### Source excerpt By Zhuoning Yuan, Ta-Ying Cheng, Benjamin Klein, Bahareh Azarnoush Introduction At Netflix, we build technology to help storytellers bring their creative visions to life and to help members discover the stories they love. To connect stories with diverse audiences around the world, we produce promotional assets, including trailers, teasers, and social short-form videos, that build on and elevate the original footage. Through close collaboration with the teams crafting these assets, we identified a recurring gap in current tools. Transforming raw footage into a polished final asset often requires complex edits like seamlessly adding new visual elements, patching or replacing backgrounds, or removing unwanted objects without breaking the scene's physical continuity. These tasks typically demand hours of specialized manual editing work. While recent generative video editing models show promise, they often struggle to preserve the integrity of the source footage. Many methods regenerate every pixel to make an edit, which can fail to isolate changes and inadvertently alter elements that should remain untouched. To execute these tasks effectively, artists need tools that empower them to dictate exactly what changes and how it changes. Our research goal is to make this process easier for artists. We're deliberate about where and how AI is applied, ensuring that the technology always serves the creative intent. That principle drives our recent work: exploring the benefits of generative AI in ways that protect and expand creative choice, and keeping artists in precise control of their final vision. Recent advancements in AI video editing have demonstrated impressive capabilities in streamlining complex manual editing workflows, but key challenges remain before they can reliably support professional use: Unintended edits: When editing a specific element in a video clip, many methods regenerate the entire video, which can inadvertently alter identity, performance, and other ele [Next page]()