Oct 17, 2026

Memory-Aware Agent Harnesses for Anti-Money Laundering — Nacho Martinez, Oracle

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.

Oct 16, 2026

High Taste Testers: OpenAI Shipped Everything. What Actually Works?

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.

Oct 16, 2026

AI Agents Cut 3-Day Portfolio Builds to Minutes — Jack McDonald, Apollo & Michael Manapat, Rowspace

Jack McDonald of Apollo's Redding Ridge and Michael Manapat of Rowspace discuss where AI becomes mission-critical in structured credit and CLO management. They cover compressing portfolio construction from days to minutes, automating the weekly trade reconciliation, and why embedding engineers with front-office teams, rather than relying on generic LLMs, is what earns trust in financial data.

Oct 16, 2026

Why AI Can Write Code But Can’t Keep It Running (Yet)

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.

Oct 16, 2026

Is AI Holding Kids Back from Learning? (Ep. 1036 with Jon Krohn)

Drawing on a wave of new studies, this piece examines whether AI stops children from learning and argues the answer hinges on how it is used. AI as a shortcut that outsources thinking undermines learning, while AI as a tutor that explains concepts and gives feedback can improve it. It covers a large natural experiment in China, a Turkish tutoring study, and practical takeaways that apply equally to adults using AI at work.

Oct 16, 2026

Decision models take off & Anthropic bans cruelty to Claude

The panel examines the rise of decision models, a family of LLM-based systems that return a single, scored classification in one forward pass, and asks whether they are genuinely new or a smarter use of existing architectures. It then weighs the cost and benefit of multi-agent teams, drawing on a study that found agent teams cost two to five times more without consistently better results, and closes with Anthropic's new policy against sustained cruelty toward its models and what it says about AI safety, ethics, and human behavior.