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Chinese technology company Baidu unveiled its latest foundation model, ERNIE 5.5 Flash Preview, and launched a new marketplace called "Token Exchange" at its annual World Conference on November 12, 2026. The announcements included major upgrades to its agent product line and a vision for the "intelligent organization" outlined by Co-founder and CEO Robin Li.

ERNIE 5.5 Flash Preview is a Mixture-of-Experts (MoE) model with 280 billion total parameters and 14 billion active parameters per token, supporting a 1 million token context window. Baidu claims strong performance in coding, 3D modeling, and content creation, optimized by its proprietary recursive self-improvement technology, ERNIE Evolve. Notably, Baidu disclosed specific API pricing: RMB 1 per million input tokens and RMB 4 per million output tokens, with cached input at just RMB 0.04. Model weights will be open-sourced by November 25. This aggressive pricing strategy suggests Baidu aims to expand its market share by lowering barriers to entry for developers.

Beyond the model itself, Baidu's Token Exchange serves as a neutral marketplace connecting AI model providers with customers. Users can compare model services side-by-side based on price and performance, accessing them through a single interface. According to Baidu, the platform has onboarded over 100 model resources in its first month, handling an average of more than 50 million calls and 700 billion tokens daily. This initiative addresses the difficulty of discovering high-quality model services and may help utilize underused compute resources.

Regarding internal applications, Robin Li disclosed concrete progress in Baidu's transformation into an "intelligent organization." Over the past year, the number of daily active agents (DAA) within Baidu surged from 5,000 to 37,000, an increase of over 600%. Weekly tasks completed by agents rose from 84,000 to more than 1 million. These figures provide investors with a quantifiable view of how Baidu is converting AI technology into operational efficiency. Additionally, Baidu upgraded its general-purpose agent DuMate and digital human agent Baidu Yijing, and presented a new roadmap for its Kunlunxin chips.

Phota Labs, a startup backed by Andreessen Horowitz (a16z), launched its iOS application on November 12, 2026. The app serves as a personal AI photo assistant designed to transform everyday snapshots into images that resemble professional photography. Unlike traditional filters or manual retouching tools, Phota Labs emphasizes "identity preservation," ensuring that adjustments to lighting, composition, and lens effects do not distort or replace the natural appearance of the people in the photo.

The company was founded by Cecilia Zhang and Zach Xia, who previously worked together at Adobe on computational photography products including Project Indigo, Lightroom, and Photoshop. They identified a gap where existing generative AI tools often altered a person's appearance when fixing blurry or low-quality images. Phota Labs addresses this by building a proprietary personalization model. "For Phota, we built our own tech," Zhang told TechCrunch. "We built this personalization technology that actually learns about people’s identity and is able to preserve them in any transformation we do to a photo."

The app is launching as a free service to build an initial user base before exploring a freemium model, with plans for an Android version in the future. Users can select various photography styles ranging from subtle edits to dramatic transformations like fisheye lenses or alternative angles. All images edited with Phota Labs feature invisible watermarks to identify them as AI-altered content when shared on social platforms.

Scott Hanselman, Microsoft Vice President and software developer, has released GPU Taskman, an open-source task manager specifically designed to monitor graphics processing unit (GPU) video memory usage. The tool runs on Windows, Linux, and macOS, combining process lists with device data such as VRAM occupancy, load, and temperature via a terminal interface to help developers understand which processes are consuming memory.

For locally running Large Language Models (LLMs), GPU Taskman identifies loaded runtime environments and model files, estimating the storage required for weights and the Key-Value Cache (KV-Cache). The KV-Cache is a critical data structure in Transformer architectures used to accelerate inference, growing in size with context length. The tool distinguishes between measured data and estimates, avoiding the misidentification of unused disk models as active processes.

The latest version, v0.4.0, adds support for additional Intel GPU metrics on Linux and device monitoring for Huawei Ascend NPUs. Developers explicitly state that the software is in an early experimental stage, noting that model attribution and memory allocation calculations are not direct measurements, advising users to treat estimated data with caution.