
We’re introducing new Gemini models, including Gemini 3.6 Flash, 3.5 Flash-Lite and 3.5 Flash Cyber.
Google DeepMind has introduced three new versions of its Gemini AI models designed to improve efficiency and reduce costs for developers building AI agents. Gemini 3.6 Flash uses 17% fewer output tokens than the previous version while maintaining better performance in coding and knowledge work tasks, with pricing reduced to $1.50 per million input tokens and $7.50 per million output tokens. The other two models, 3.5 Flash-Lite and 3.5 Flash Cyber, offer faster processing speeds and specialized cybersecurity capabilities respectively. These updates address developer needs for higher efficiency, lower latency, and more reliable performance when scaling AI agent applications.

Long-horizon agents accumulate context faster than they resolve tasks. Every tool output, observation, and intermediate reasoning step stays in the window, and the two capabilities that matter — holding that context and staying coherent across it — have so far been available almost exclusively from cloud endpoints. That excludes regulated industries, public-sector institutions, and on-device applications, where the data is not permitted to leave the boundary at all. Pokee AI released Pokee-Isaa

Mistral AI has released Shieldstral 1.0 3B, an open-weights, policy-adaptive multimodal safety classifier that treats content moderation as a single yes/no question rather than a fixed taxonomy of harm categories. Most guardrail models bake their category list into the weights, so re-targeting one to a new deployment context means retraining — and the same content can be acceptable on a cybersecurity research tool while being harmful on a mental-health platform. Shieldstral inverts that: operat

NVIDIA Labs has open-sourced NOOA (NVIDIA Object-Oriented Agents), a model-agnostic Python framework for building AI agents. Agent development today is split across prompt templates, tool schemas, callback code, and workflow graphs. NOOA collapses all of it into one Python class. Methods are the actions the model can take. Fields are agent state. Docstrings are prompts. Type annotations are contracts the runtime enforces. A method whose body is ... is completed at runtime by an LLM-driven loop,
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