Llm

BDD, ADR, PRD, WTF: Capturing Decisions for Humans and AI Alike — Michal Cichra, Safe Intelligence

BDD, ADR, PRD, WTF: Capturing Decisions for Humans and AI Alike — Michal Cichra, Safe Intelligence

Michal Cichra from Safe Intelligence explains how to maintain consistency in AI-driven software development by capturing decisions and enforcing rules. He argues for reviving Behavior-Driven Development (BDD) with Cucumber to close the loop left by spec-driven development. The core idea is to enforce architectural and product decisions (ADRs, PRDs) through an automated loop of git hooks and CI, ensuring both human and AI developers adhere to established standards.

Scaling Meta's Multi-Agent Systems to a Billion Videos

Scaling Meta's Multi-Agent Systems to a Billion Videos

Meta's approach to solving modality misalignment and content theft in short-form video using a multi-agent system of smaller, specialized models instead of a single large LLM. The talk covers the architecture (Perceiver, Retriever, Reasoner), evaluation stack, and key cost-saving optimizations.

How Google DeepMind Runs Agents at Scale — KP Sawhney & Ian Ballantyne, Google DeepMind

How Google DeepMind Runs Agents at Scale — KP Sawhney & Ian Ballantyne, Google DeepMind

KP Sawhney from Google DeepMind discusses the internal strategies for scaling agentic AI, including managing token-hungry workflows, curating a 'Darwinian' skills library, and evolving the Deep Research pipeline from large context blobs to a collaborative file system.

CAG vs Long Context: How AI Models Use and Remember Information

CAG vs Long Context: How AI Models Use and Remember Information

Martin Keen explains how Long Context and Cache Augmented Generation (CAG) serve as powerful alternatives to RAG for providing external knowledge to LLMs. This summary details the mechanics of each approach, the role of the KV cache, the practical application through prompt caching, and the trade-offs in performance, cost, and latency for real-world AI workloads.

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.