Posts

The Latency Goldilocks Zone Explained

The Latency Goldilocks Zone Explained

Rafael Borger and Daniel Wolbert from iFood discuss the engineering and product strategy behind ILO-Agent, their conversational AI for 200 million users. They cover hyper-personalized recommendation systems, the "Latency Goldilocks Zone" where AI responses can be too fast for users to trust, and the architectural challenges of building multi-channel agents for text and voice.

Building MCP Before MCP Existed: Inside Despegar's Sofia Agent

Building MCP Before MCP Existed: Inside Despegar's Sofia Agent

A deep dive into Despegar's GenAI travel agent, Sofia. Explore its multi-agent architecture, the custom orchestration layer 'Chappi' built before MCP was a standard, and the strategy of decentralizing agent development across company squads to cover the entire five-phase travel arc.

The Future of AI – Key Trends Shaping What’s Next • Ekaterina Sirazitdinova • YOW! 2025

The Future of AI – Key Trends Shaping What’s Next • Ekaterina Sirazitdinova • YOW! 2025

Ekaterina Sirazitdinova from NVIDIA provides a high-level overview of the latest trends shaping the future of AI, covering the evolution from early deep learning to the rise of agentic and physical AI, and diving deep into the critical optimization techniques required to deploy these powerful models efficiently.

OpenAI’s Daybreak and Mistral’s Mythos competitor

OpenAI’s Daybreak and Mistral’s Mythos competitor

This week's podcast delves into the rapidly evolving landscape of AI-powered vulnerability management, discussing OpenAI's Daybreak, Microsoft's MDASH, and Mistral's Mythos competitor. The panel analyzes the measured real-world results of Anthropic's Mythos on the curl project and explores the implications of the notorious Shai-Hulud npm worm going open source.

Inference, not prediction — Prof. Michael I. Jordan on what modern AI is still missing

Inference, not prediction — Prof. Michael I. Jordan on what modern AI is still missing

Michael I. Jordan, a leading figure in machine learning and statistics, argues for reframing AI from a race for disembodied superintelligence to the design of collective economic systems. He critiques the AGI hype, advocates for integrating economic principles and robust uncertainty quantification into ML, and proposes a new intellectual framework for building technology that augments, rather than replaces, human systems.

How to Build a Self-Improving Company with AI

How to Build a Self-Improving Company with AI

YC General Partner Tom Blomfield explains how to move beyond the 'copilot' mindset and restructure companies as series of recursive, self-improving AI loops. He details how to make company knowledge legible to AI, creating systems that improve overnight with minimal human intervention, ultimately rendering traditional middle management obsolete.