Oct 23, 2026

Don't Trust Your AI Agents. Test Them Like a Scientist — Stephanie Jarmak, Omni Analytics

Stephanie Jarmak, who moved from astronomy into building coding benchmarks and agent evals, argues that most agent evaluations are easy to misread. She covers how harness choice can swamp model differences, why public benchmarks serve shortlisting rather than decisions, how to design experiment-style evals with realistic tasks, repeated runs, and full traces, and how to set quality gates, manage human review load, verify agent records, and treat production as the final test.

Oct 22, 2026

Better AI Models Won't Fix Your Engineering Team — Nicholas Arcolano, Jellyfish

Nicholas Arcolano of Jellyfish uses data from about 50,000 engineers across 447 companies to test what really drives AI coding output. A natural experiment forcing users onto Grok barely moved merged PRs. The analysis breaks throughput into tool, skill use, token consumption, company and team, and individual engineer, finding the individual and organization matter far more than the model.

Oct 22, 2026

The Frontier Ideas a16z Wants to Fund Now | Marc Andreessen, Ben Horowitz & Chris Dixon

Chris Dixon, Marc Andreessen, Ben Horowitz, and Erik Torenberg discuss the expansion of a16z crypto into a16z Frontier, a mandate for founders building at the edges of technology. They explore why AI could pull decades of progress forward across hardware, robotics, entertainment, and science, the coming internet of AI agents, why open source matters for a decentralized AI future, and what the breakdown of the internet's traffic economy means for creators and businesses.

Oct 22, 2026

How to Test AI Agents When No Benchmark Exists — Nathanael Weill, Air Canada

Nathanael Weill of Air Canada explains how to evaluate AI agents when no public benchmark exists and expert time is scarce: start from a known answer, generate the input from it, and check whether the agent recovers it. He covers lessons from a telecom RAG project that moved from one expert-graded test a week to dozens a day, and five ways to build test sets for a supplier contract review agent, along with why code-based checks are preferred over LLM judges.

Oct 21, 2026

The AI Content System Behind 200M Views — Sarah Chieng, Cerebras & Cova Lee, OpenAI

Sarah Chieng (Cerebras) and Cova Lee (OpenAI) walk through an agent-powered content system spanning ideation, scripting, recording, editing, repurposing, and publishing, emphasizing swipe files, voice-driven scripts, design systems, API-based editing, and weekly process reviews as ways to free creators to focus on craft over generic AI output.

Oct 21, 2026

The Agentic Automation Revolution • Don Syme • Bug Bash EU 2026

Don Syme, designer of F# and GitHub Next engineer, explains how AI agents are moving from individual productivity tools toward continuous, collaborative automation inside repositories. He introduces continuous AI and agentic ops, GitHub Agentic Workflows with their guardrails, and the factory and farm metaphors for managing software production.