Sep 01, 2026

Building AI That Optimizes AI

Wafer (S24) is a fast AI inference cloud that runs open-source models at industry-leading speeds by using AI agents to optimize GPUs across the entire stack. This unique approach allows them to deliver the low cost of open source with the latency of much smaller models. The company recently raised a $40 million Series A after achieving rapid growth by making GLM 5.2 run 2-3x faster than competitors, driving significant customer adoption due to speed, not just cost, which is crucial for latency-sensitive applications like voice agents.

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 27, 2026

How Anthropic Builds: Lessons from Labs — Mike Krieger, Anthropic

Mike Krieger, a former CPO at Anthropic, details his transition to an IC role to build directly with AI, advocating for "unreasonable" asks like porting entire codebases. He shares lessons from Instagram on scaling and highlights Anthropic's internal use of Claude as a proactive teammate, flexible lab structure, and critical insights on AI product design, vertical applications, and mental health in the fast-paced AI industry.

Aug 27, 2026

LLM & AI Agent Benchmarks vs Reality: Why AI Applications Break

LLM leaderboard scores often don't reflect real-world performance. This video explains why and outlines a comprehensive approach to evaluate AI systems, focusing on the critical balance of accuracy, latency, and cost. It details model and system evaluation techniques, including handling different inference phases, workload shapes, and specific considerations for AI agents, emphasizing the need for realistic testing over generic benchmarks.

Aug 21, 2026

Building Agents Is Trivial Now, Context Is the Next Frontier — Jeff Ng, Unblocked

Building AI agents is now easier than ever, but they frequently make confident yet incorrect decisions. This is because agents often lack critical context that humans provide, such as historical discussions, postmortems, and tribal knowledge. The solution proposed is a 'context engine' that synthesizes and grounds information from across an organization's documents, code, tickets, and conversations, providing agents with a holistic and reconciled view, thus bridging the 'context gap' that current LLMs often miss.

Aug 20, 2026

Modern Computer Architecture & Organization • Jim Ledin & Maciej «MJ» Jedrzejewski • GOTO 2026

Jim Ledin, author of *Modern Computer Architecture and Organization*, discusses the integration of GPUs and LLMs into his book, emphasizing architectural principles over fleeting trends. He highlights memory bandwidth as the primary bottleneck in AI, explaining the impact of High Bandwidth Memory (HBM) demand on consumer RAM. Ledin also identifies understanding pipelining and cache hierarchy as the most crucial skill for software developers to optimize code performance.