AIAI Silicon Valley, 2025
Catch up on every session from the AIAI Silicon Valley with sessions across 3 co-located summit featuring the likes of Anthropic, Open AI, Meta and many more.
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AI Interview Series #2: Explain Some of the Common Model Context Protocol (MCP) Security Vulnerabilities
In this half of the Interview Series, we’ll have a look at some of the frequent safety vulnerabilities in the Model Context Protocol (MCP) — a framework designed to let LLMs safely work together with exterior instruments and knowledge sources. While MCP brings construction and transparency to how fashions entry context, it additionally introduces new…
Qwen AI Releases Qwen-Scope: An Open-Source Sparse AutoEncoders (SAE) Suite That Turns LLM Internal Features into Practical Development Tools
Large language fashions are remarkably succesful, but frustratingly opaque. When a mannequin misbehaves — producing responses within the mistaken language, repeating itself endlessly, or refusing protected requests — AI devs have only a few instruments to diagnose why it occurred on the stage of inside computations. That’s the issue Qwen-Scope is constructed to unravel. Qwen…
AWS Open-Sources an MCP Server for Bedrock AgentCore to Streamline AI Agent Development
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How to Design an Advanced Multi-Agent Reasoning System with spaCy Featuring Planning, Reflection, Memory, and Knowledge Graphs
In this tutorial, we construct an superior Agentic AI system utilizing spaCy, designed to permit a number of clever brokers to purpose, collaborate, replicate, and be taught from expertise. We work by means of your complete pipeline step-by-step, observing how every agent processes duties utilizing planning, reminiscence, communication, and semantic reasoning. By the top, we…
How to Create Reliable Conversational AI Agents Using Parlant?
Parlant is a framework designed to assist builders construct production-ready AI brokers that behave persistently and reliably. A typical problem when deploying giant language mannequin (LLM) brokers is that they usually carry out nicely in testing however fail when interacting with actual customers. They might ignore rigorously designed system prompts, generate inaccurate or irrelevant responses…
