Distinguished Engineer Chris Hay demonstrates how to run and program Large Language Models (LLMs) locally in just two lines of Python code. The tutorial covers setting up a local environment with Ollama and UV, using a custom library for simplified interaction, and explores advanced topics like asynchronous streaming, persona customization with system prompts, and managing multi-turn conversations.
Qinge Xie from Georgia Tech presents a large-scale evaluation of modern website privacy policies using a novel LLM-based framework. The research systematizes privacy practices from 10 major US and EU regulations into 34 clauses and analyzes over 100,000 websites to reveal current trends in data collection, sharing, and consumer rights disclosure.
A reflection on Richard Sutton's "Bitter Lesson," arguing that while his critique of LLMs' inefficiency and lack of continual learning is valid, imitation learning is a complementary and necessary precursor to true reinforcement learning, much like fossil fuels were to renewable energy.
Dr. Ilia Shumailov, former DeepMind AI Security Researcher, explains why traditional security fails for AI agents. He details the unique threat model of agents, the dangers of supply chain attacks and architectural backdoors, and proposes a system-level solution called CAML to enforce security policies by design, separating model reasoning from data execution.
Yoni Leitersdorf, CEO of Solid, explains that directly applying Large Language Models (LLMs) to databases for text-to-SQL fails due to a lack of business context. He introduces the concept of a semantic layer as a critical "Rosetta Stone" that translates raw data into a meaningful format AI can understand, enabling reliable and accurate data interaction.
Dhanji Prasanna, CTO of Block, discusses the company's AI transformation, centered on their open-source agent, Goose. He details how Goose leverages the Model Context Protocol (MCP) to automate complex workflows, saving engineers 8-10 hours weekly. Prasanna also explains Block's strategic shift to a functional organizational structure to accelerate AI adoption and shares his vision for the future, where swarms of smaller AI models will outperform today's monolithic LLMs.