AIAI New York, 2025
Catch up on every session from the AIAI New York with sessions across 3 co-located summit featuring the likes of Meta, Bank of America, Google DeepMind and many more.
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Zhipu AI Unveils ComputerRL: An AI Framework Scaling End-to-End Reinforcement Learning for Computer Use Agents
Within the quickly evolving panorama of AI-driven automation, Zhipu AI has launched ComputerRL, a groundbreaking framework designed to empower brokers with the power to navigate and manipulate advanced digital workspaces. This innovation addresses a core problem in AI agent improvement: the disconnect between pc brokers and human-designed graphical person interfaces (GUIs). By integrating programmatic API…
5 Most Popular Agentic AI Design Patterns Every AI Engineer Should Know
As AI brokers evolve past easy chatbots, new design patterns have emerged to make them extra succesful, adaptable, and clever. These agentic design patterns outline how brokers suppose, act, and collaborate to unravel complicated issues in real-world settings. Whether it’s reasoning via duties, writing and executing code, connecting to exterior instruments, and even reflecting on…
Building Advanced MCP (Model Context Protocol) Agents with Multi-Agent Coordination, Context Awareness, and Gemini Integration
In this tutorial, we’re strolling by the method of constructing a sophisticated MCP (Model Context Protocol) Agent that runs easily inside Jupyter or Google Colab. We are designing the system with real-world practicality in thoughts, specializing in multi-agent coordination, context consciousness, reminiscence administration, and dynamic device utilization. As we progress, we see how every agent…
Build a Low-Footprint AI Coding Assistant with Mistral Devstral
In this Ultra-Light Mistral Devstral tutorial, a Colab-friendly guide is provided that is designed specifically for users facing disk space constraints. Running large language models like Mistral can be a challenge in environments with limited storage and memory, but this tutorial shows how to deploy the powerful devstral-small model. With aggressive quantization using BitsAndBytes, cache…
Alibaba’s Qwen3-Max: Production-Ready Thinking Mode, 1T+ Parameters, and Day-One Coding/Agentic Bench Signals
Alibaba has launched Qwen3-Max, a trillion-parameter Mixture-of-Experts (MoE) mannequin positioned as its most succesful basis mannequin up to now, with a direct public on-ramp through Qwen Chat and Alibaba Cloud’s Model Studio API. The launch strikes Qwen’s 2025 cadence from preview to manufacturing and facilities on two variants: Qwen3-Max-Instruct for normal reasoning/coding duties and Qwen3-Max-Thinking…
