Sep 13, 2026

Essential Skills for Becoming an AI Engineer: RAG, AI Agents, & More

Cedric Clyburn outlines the essential skills for AI engineering, distinguishing it from ML research. He details a three-tier skill stack covering foundational programming (Python, Git, APIs), AI-specific techniques (embeddings, RAG, AI agents), and deployment strategies (containerization, observability). The discussion emphasizes judgment over code generation and highlights key use cases like RAG systems and agent-based solutions.

Aug 29, 2026

Agentic Sites: Building Hyper Personalized Websites — Carlos Sanchez, Adobe

Carlos Sanchez from Adobe presents "Agility Sites," a novel approach to building hyper-personalized websites in real-time. By dynamically generating specific content blocks and grounding responses in the site's corpus, these sites achieve an "audience of one" experience. Key innovations include rapid LLM inference (1.1s average latency with Cerebras), continuous model evaluation for speed and accuracy, and leveraging user intent to deliver tailored content, as demonstrated by an on-the-fly camping coffee machine page and an AI conference comparison site.

Aug 26, 2026

Knowledge Systems: The New GTM Stack — Jeffrey Wang, Exa

Jeffrey Wang, co-founder of Exa, details how "go to market" (GTM) is transforming into an AI engineering problem. He showcases Exa's agent-first approach, using tools like an ICP dashboard for market intelligence and a personal AI clone (Jeffbot) to automate and optimize sales, emphasizing the need for robust APIs and arbitrarily customizable systems in this new AI-driven landscape.

Aug 15, 2026

Exo: Harnesses should see their own code and logs — Alex Krentsel

An introduction to Exo, a fully recursive AI agent harnessing a novel three-layer architecture (Executor, Harness, Sandbox) designed for autonomous self-improvement. It delves into how Exo surpasses current agent limitations by allowing the agent to edit its own code and policy at runtime, ensuring protected state and isolated execution, and discusses practical implications and the underlying systems philosophy enabling this paradigm shift.

Aug 11, 2026

Agents, codebases, and teams — Aditya Khandelwal, Amazon AGI Lab

Aditya Khandelwal argues that scaling AI agent adoption within engineering teams is a leadership challenge, not an individual contributor problem. He highlights common pitfalls like agent "babysitting" and "slop," and provides a playbook emphasizing progressive disclosure, high-value automation, robust feedback loops, and a critical mindset shift to successfully integrate agents into team workflows.

Aug 09, 2026

Velocity Sickness: What Happens When Your Whole Team Gets 10x Faster — Matt Dailey, Ref.

Matt Dailey introduces "velocity sickness" – the stress of increased AI output without impact. He proposes shifting from ephemeral chat-based agent interactions to durable, shared documents as the "decision layer" to separate planning from implementation, enabling teams to own their code and prioritize ideas effectively.