Sep 18, 2025

Upwork's Radical Bet on Reinforcement Learning: Building RLEF from Scratch | Andrew Rabinovich (CTO)

Andrew Rabinovich, CTO and Head of AI at Upwork, details their strategy for building AI agents for digital work. He introduces a custom reinforcement learning approach called RLEF (Reinforcement Learning from Experience), explains why digital work marketplaces are ideal training grounds, and shares his vision for a future where AI delivers finished projects, orchestrated by a meta-agent named Uma.

Sep 15, 2025

Building Advanced Agents Over Complex Data // Jerry Liu

Jerry from LlamaIndex explains why naive Retrieval-Augmented Generation (RAG) fails in production and dives deep into advanced data quality techniques—from parsing complex documents and hierarchical indexing to chunking best practices—required to build robust, high-quality LLM applications.

Sep 14, 2025

The ultimate guide to AEO: How to get ChatGPT to recommend your product | Ethan Smith (Graphite)

Ethan Smith, CEO of Graphite, explains Answer Engine Optimization (AEO), the new frontier of getting your product featured in answers from LLMs like ChatGPT. He shares his playbook for ranking in these new systems, revealing why AEO traffic can convert 6x better than traditional search and how even early-stage startups can achieve immediate wins by focusing on landing pages, YouTube videos, and authentic Reddit engagement.

Sep 11, 2025

Beyond the Chatbot: What Actually Works in Enterprise AI

Jay Alammar, Director at Cohere, discusses the practical adoption of Large Language Models in the enterprise. He covers the evolution of Retrieval-Augmented Generation (RAG) from a simple anti-hallucination tool to complex, agentic systems, the critical role of evaluation as intellectual property, and future trends like text diffusion and the increasing capability of smaller models for specialized business tasks.

Sep 10, 2025

Recall.ai: Unlocking the World’s Conversations

Co-founder David shares Recall.ai's journey from a YC hackathon project to the essential data infrastructure for over 1,000 AI companies. He covers their strategic pivot to an API, the fundraising grind, and the lessons learned in building a lean, high-agency team that powers the future of AI with conversation data.

Sep 09, 2025

Ethics in AI: Biases & Responsibilities • Michelle Frost & Hannes Lowette

AI advocate Michelle Frost and consultant Hannes Lowette discuss the complex ethical landscape of AI development. They cover the value alignment problem, balancing competing values like accuracy versus fairness, the impact of recent US regulatory changes, and market disruptions from innovations like Deep Seek, ultimately calling for individual and corporate accountability to develop AI responsibly.