Android

Computer-Use 2.0: Agents Just Got Multi-Cursor — Francesco Bonacci, Cua

Jul 15, 2026 · 16:41

Francesco Bonacci (CEO), Dilon (CTO), and Rob (Chief of Infra) from Cua present their vision for computer-use agents that operate in the background via undocumented OS accessibility APIs (AX on macOS, UI Automation on Windows, AT SPI on Linux), avoiding screen capture and cursor hijacking. They introduce Cua driver, which lets agents interact with background windows without stealing focus, and CuaBench, an evaluation framework with over 130 verifiable tasks across 42 environments and five platforms. Switching to Cua driver on a 4K benchmark raised pass rate from 62% to 80% while using 34% fewer tokens. Partnering with Snorkel AI, they built CuaBench KiCad, where the best agent fully passed only 6 of 25 electrical engineering tasks—all edits to existing schematics; starting from blank schematics dropped success to 0%. Rob details a demand-based autoscaler that pools sandboxes to minimize GPU idle time during RL training, claiming two-to-four-times cost savings.

Teaching Gemini to Speak YouTube: Adapting LLMs for Video Recommendations to 2B+DAU - Devansh Tandon

Jul 16, 2025 · 22:51

Devansh Tandon, a Product Manager at Google leading YouTube's discovery system, details how YouTube adapted Gemini LLMs to power its recommendation engine for billions of daily active users. The team built SemanticID, a tokenization system that compresses video features into semantically meaningful tokens, creating a new language for YouTube content. They then continued pre-training Gemini on sequences of user watches to make the model bilingual in English and this video language. For generative retrieval, they prompt the adapted model with user demographics and watch history to output video recommendations as SemanticIDs, achieving 95%+ cost savings to serve at scale. Challenges include serving billions of users with low latency and handling video freshness—Taylor Swift's new music video must be recommendable within minutes. Tandon argues LLM-led recommendations are a bigger consumer application than search and hints at future interactive, steerable recommendations and even personalized content creation.

Personal, Local, Private AI Agents: Soumith Chintala

Apr 6, 2025 · 20:32

Soumith Chintala, co-founder of PyTorch, argues that personal AI agents should run locally and privately to maintain trust and control over intimate data. He warns that cloud-based agents, lacking complete context (like access to all messaging or financial accounts), become unreliable and potentially dangerous—catastrophic actions like buying a Tesla instead of Tide Pods are possible. Key technical challenges include slow local inference, immature open-source computer-use models, and poor catastrophic action classification. He is bullish on open models surpassing closed ones through coordinated improvement, citing Linux, Llama, and DeepSeek. Chintala also plugs open reasoning data from gr.ink and PyTorch’s work on enabling local agents, urging AI engineers to tackle these gaps.