Few areas have seen as much AI progress this year as video generation. Chinese company Kuaishou is closing out the year with new features for Kling 2.6: a technically impressive update that shows just how tight the AI race has become, both internationally and between major players and lesser-known competitors.
Nvidia has released a new base model for gaming agents. NitroGen is an open vision action model trained on 40,000 hours of gameplay videos from more than 1,000 games. The researchers tapped into a previously overlooked resource: YouTube and Twitch videos with visible controller overlays. Using template matching and a fine-tuned SegFormer model, they extracted player inputs directly from these recordings.
NitroGen builds on Nvidia's GR00T N1.5 robotics model. According to the researchers, it's the first model to demonstrate that robotics foundation models can work as universal agents across virtual environments with different physics engines and visual styles. The model handles various genres—action RPGs, platformers, roguelikes, and more. When dropped into unfamiliar games, it achieves up to 52 percent better success rates than models trained from scratch.
The team, which includes researchers from Nvidia, Stanford, Caltech, and other universities, has made the dataset, model weights, paper, and code publicly available.
Alibaba's AI unit Qwen has released a new image editing model that breaks down photos into separate, editable components. Qwen-Image-Layered splits images into multiple individual layers with transparent backgrounds (RGBA layers), letting users edit each layer independently without affecting the rest of the image.
The model handles straightforward edits like resizing, repositioning, and recoloring individual elements. Users can swap out backgrounds, replace people, modify text, or delete, move, and enlarge objects. Images can be split into either 3 or 8 layers, and the process is repeatable - each layer can be broken down into additional layers as needed. The Qwen team describes this approach as a bridge between standard images and structured, editable representations.
The Qwen team has published the code on GitHub, with models available on Hugging Face and ModelScope. More details are available in the blog post and technical report. For hands-on testing, demos are available on Hugging Face and ModelScope.
OpenAI now lets users customize how ChatGPT communicates. The new "Personalization" settings include options for adjusting warmth, enthusiasm, and formatting preferences like headings, lists, and emojis. Each setting can be toggled to "More" or "Less." Users can also pick a base style - like "efficient" for shorter, more direct responses.
OpenAI says these settings only affect the chatbot's tone and style, not its actual capabilities. The company notes that the new options likely work as an extension of the custom instructions feature available in the same settings window.
OpenAI is significantly expanding the availability of ChatGPT Go, its budget-friendly subscription tier. Following a launch in India in August, the plan is now available in over 70 additional countries—including markets across Europe and South America—according to an updated support page. In Germany, the service costs 8 euros per month. Beyond extended access to the flagship model, the subscription adds capabilities for image generation, file analysis, and data evaluation, along with a larger context window for handling longer conversations. Users can also organize projects and build their own custom GPTs. However, the plan excludes access to Sora, the API, and older models like GPT-4o.
The broader rollout comes alongside a cost-saving adjustment to how the system handles queries. OpenAI recently removed the automatic model router for users on the free tier and the Go subscription. By default, the system now answers requests using the faster GPT-5.2 Instant. Users must manually switch to more powerful reasoning models when needed, as the automatic routing feature is now exclusive to the higher-priced plans.
Anthropic’s autonomous kiosk is finally making money, but not without drama. In the second phase of Project Vend, stronger models, stricter processes, and an AI “CEO” turned losses into profits, while also exposing how easily AI agents can be manipulated, misunderstand authority, or ignore real‑world laws. The experiment shows that structure and guardrails matter more than raw intelligence when AI runs a business.
Meta is developing new AI models for images, videos, and text under the codenames "Mango" and "Avocado." The release is planned for the first half of 2026, according to a report from the Wall Street Journal citing internal statements by Meta's head of AI Alexandr Wang. During an internal Q&A with Head of Product Chris Cox, Wang explained that "Mango" focuses on visual content, while the "Avocado" language model is designed to excel at programming tasks. Meta is also researching "world models" capable of visually capturing their environment.
The development follows a major restructuring where CEO Mark Zuckerberg personally recruited researchers from OpenAI to establish the "Meta Superintelligence Labs" under Wang's leadership. The market for image generation remains fiercely competitive. Google recently released Nano Banana Pro, an impressive model known for its precise prompt adherence, and OpenAI quickly followed up with GPT Image 1.5 a few weeks later. Most recently, Meta introduced the fourth generation of its Llama series in April and is currently collaborating with Midjourney and Black Forest Labs on the Vibes video feed.
Anthropic is releasing "Agent Skills" as an open standard at agentskills.io. The idea is to make these skills interoperable across different platforms, meaning a capability that works in Claude should function just as well in other AI systems. Anthropic likens this strategy to the Model Context Protocol (MCP) and notes that it is already working with ecosystem partners on the project. OpenAI already adopted skills.
The company is also upgrading how skills function within Claude. These skills are basically repeatable workflows that customize the AI assistant for specific jobs. Administrators on Team and Enterprise plans can now manage skills from a central hub and push them to every user in their organization, though individuals still have the option to turn them off.
Creating these skills has also gotten easier: users just describe what they need, and Claude helps configure it. Anthropic has also launched a directory of partner skills from companies like Notion, Canva, Figma, and Atlassian at claude.com/connectors. Developers can find technical documentation at platform.claude.com/docs, and skills are now live across Claude apps, Claude Code, and the developer API.
