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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A Guide for Effective Context Engineering for AI Agents
Anthropic just lately launched a guide on effective Context Engineering for AI Agents — a reminder that context is a important but restricted useful resource. The high quality of an agent typically relies upon much less on the mannequin itself and extra on how its context is structured and managed. Even a weaker LLM can…
Why agentic AI pilots fail and how to scale safely
At the AI Accelerator Institute Summit in New York, Oren Michels, Co-founder and CEO of Barndoor AI, joined a one-on-one discussion with Alexander Puutio, Professor and Author, to explore a question facing every enterprise experimenting with AI: Why do so many AI pilots stall, and what will it take to unlock real value? Barndoor AI…
Anthropic Releases Claude Opus 4.7: A Major Upgrade for Agentic Coding, High-Resolution Vision, and Long-Horizon Autonomous Tasks
Anthropic has launched Claude Opus 4.7, it’s newest frontier mannequin and a direct successor to Claude Opus 4.6. The launch is positioned as a centered enchancment moderately than a full generational leap, however the good points it delivers are substantial within the areas that matter most to builders constructing real-world AI-powered functions: agentic software program…
Alibaba Qwen Team Releases Qwen3.5 Omni: A Native Multimodal Model for Text, Audio, Video, and Realtime Interaction
The panorama of multimodal massive language fashions (MLLMs) has shifted from experimental ‘wrappers’—the place separate imaginative and prescient or audio encoders are stitched onto a text-based spine—to native, end-to-end ‘omnimodal’ architectures. Alibaba Qwen staff newest launch, Qwen3.5-Omni, represents a big milestone on this evolution. Designed as a direct competitor to flagship fashions like Gemini 3.1…
Agent0: A Fully Autonomous AI Framework that Evolves High-Performing Agents without External Data through Multi-Step Co-Evolution
Large language fashions want big human datasets, so what occurs if the mannequin should create all its personal curriculum and train itself to make use of instruments? A workforce of researchers from UNC-Chapel Hill, Salesforce Research and Stanford University introduce ‘Agent0’, a totally autonomous framework that evolves high-performing brokers without exterior information through multi-step co-evolution…
