A walkthrough of the memory wall, explaining why raw chip compute growth has not translated into proportional AI workload speedups. It covers arithmetic intensity, the roofline model and ridge points for the B200 GPU, why LLM decode is memory-bound while prefill is compute-bound, and how batching and continuous batching shift workloads toward higher efficiency.
Nick Nisi and Zack Proser from WorkOS explore how to move "Beyond Done" with AI agents, ensuring their work is truly complete and reliable. They demonstrate building robust verification loops using custom CLIs, browser tools, and LLM judges, allowing agents to self-check and self-correct, preventing costly mistakes and accelerating development velocity for a technical audience.
This episode of Paper Club addresses AI safety from a scientific perspective, contrasting it with recent media sensationalism. Stanford professors Clark Barrett and Mykel Kochenderfer discuss how automated reasoning can mathematically prove code security, how aircraft stress-testing methods are being adapted for language models, and the potential need for an NTSB-like agency for AI, highlighting the real-world engineering challenges and solutions for AI safety.
Nacho Martinez argues that the model is fixed while the harness (agent loop, memory, context engineering, tools and skills, continual learning) is where engineers can make real gains. He walks through the agent stack and applies it to an autonomous AML agent that flags suspicious bank transactions, then demonstrates the workshop environment and answers questions on memory design, skill retrieval, caching, and hybrid search.
A roundtable of AI power users reviews OpenAI DevDay and asks what actually works in daily use. Topics include personal assistants and their trust, permission, and safety trade-offs; durable agent state and remote execution; the Jev decision model and calibrated classification; model preferences across Anthropic, OpenAI, xAI, Meta, and open models; subagents and context management; and the gap between impressive capabilities and products that hold up.
Grepr CEO Jad Naous talks with a16z's Martin Casado about how coding agents have changed a 10-person engineering team, with projects that once took six months now finishing in about ten days. They examine whether AI can reason about and troubleshoot production distributed systems, why operations is a harder problem than code generation, and where AI adoption is likely to go next.