#Google

Explore Google's Universal Commerce Protocol, aimed at revolutionizing AI-driven purchasing experiences.

Explore how FunctionGemma optimizes function calling for edge devices.

Google's A2UI allows agents to define UIs in JSON, enhancing interaction without executable code.

Easily access Kaggle datasets, models, and competitions directly from Google Colab with the new Data Explorer.

'Antigravity makes the IDE a control plane for autonomous agents, using artifacts to surface task level work and supporting multiple foundation models within a Visual Studio Code fork.'

'OpenAI's Sora and Google's Veo 3 are carving different paths in AI video creation: Sora for playful, fast inspiration, Veo 3 for studio-grade control and polished output.'

'A joint team from Anthropic and Thinking Machines Lab generated 300k+ value tradeoff scenarios to stress-test model specs, finding that high cross-model disagreement flags spec contradictions, coverage gaps and provider-level value differences.'

OpenAI's Sora hit one million downloads in days, sparking excitement for fast AI-driven filmmaking and fresh worries about likeness abuse, deepfakes and copyright

'The Bank of England warned that surging AI investments may be inflating into a fragile bubble, urging investors to weigh valuations, infrastructure costs and uneven productivity gains.'

'Google's RLM treats regression as language modeling, letting compact LLMs predict cluster performance directly from serialized logs and configs with high accuracy and uncertainty estimates.'

Google released Mangle, a Datalog-inspired Go library that unifies querying and reasoning across fragmented data sources for tasks like vulnerability detection and dependency analysis.

'Google released a report estimating that a median text prompt to Gemini uses 0.24 Wh, about one second of microwave power, plus 0.26 ml of water and 0.03 g CO2, and provided a detailed breakdown of infrastructure contributions.'

Google DeepMind introduced LSM-2 with AIM, a novel framework allowing AI models to learn effectively from incomplete wearable sensor data, enhancing health-related predictions and robustness.