Ai agents

Context Engineering & Agentic Search with the CEO of Chroma

Context Engineering & Agentic Search with the CEO of Chroma

Jeff Huber, CEO of Chroma, discusses "context rot," the degradation of AI performance in large context windows, and outlines a new vision for retrieval infrastructure. He covers the evolution of search, the importance of a two-stage recall-then-precision pipeline, and the challenges of agentic memory, advocating for a shift from AI "alchemy" to reliable engineering.

Zai GLM 4.6: What We Learned From 100 Million Open Source Downloads — Yuxuan Zhang, Z.ai

Zai GLM 4.6: What We Learned From 100 Million Open Source Downloads — Yuxuan Zhang, Z.ai

Zhang Yuxuan from Z.ai details the technical roadmap behind the GLM-4.6 model series, which has achieved top performance on the LMSYS Chatbot Arena. The summary covers their 15T token data recipe, the SLIME framework for efficient agent RL, key lessons in single-stage long-context training, and the architecture of the multimodal GLM-4.5V model.

Production Ready AI Agents

Production Ready AI Agents

Sam Partee, CTO of Arcade, explains the critical gap between AI agents that gather context and those that take secure, real-world actions. He introduces Arcade as a middleware platform that solves complex challenges like user authorization, fine-grained permissions, and token management, enabling developers to build scalable, enterprise-ready agents.

The Power of AI Agents and Agentic AI Explained

The Power of AI Agents and Agentic AI Explained

AI agents represent a paradigm shift from traditional reactive AI models. This summary explores their proactive, goal-driven nature, detailing how they autonomously plan and execute complex workflows by interacting with a diverse ecosystem of models, APIs, hardware, and even other agents to solve real-world problems.

I’m Teaching AI Self-Improvement Techniques

I’m Teaching AI Self-Improvement Techniques

Aman Khan from Arize discusses the challenges of building reliable AI agents and introduces a novel technique called "metaprompting". This method uses continuous, natural language feedback to optimize an agent's system prompt, effectively training its "memory" or context, leading to significant performance gains even for smaller models.

941: Multi-Agent Human Societies — with Dr. Vijoy Pandey

941: Multi-Agent Human Societies — with Dr. Vijoy Pandey

Dr. Vijoy Pandey of Cisco's Outshift incubator discusses his vision for a future where multi-agent human societies collaborate to solve major scientific and physical challenges. He introduces AGNTCY, an open-source project for the "Internet of Agents," designed to address the critical hurdles of agent interoperability and trust through a Zero Trust framework and Task-Based Access Control (T-BACK).