Atlassian's Tamar Yehoshua explains what the AI builder era looks like for product managers in a company of more than 10,000 people. Using Confluence, RovoClaw, and Jira as examples, she covers when PMs should write code and prototypes, when they should steer the team instead, and how Atlassian is building AI fluency in its PM organization.
Robby Stein, drawing on his work on Instagram Stories, Reels, and AI-era Google Search, argues that the core craft of product management is making great decisions. He lays out a three-part process: understand people's underlying needs through jobs to be done, diagnose why a product is failing through root cause analysis and iteration, and refine the craft details that make a product work well and feel delightful.
Zixuan Li, head of Z.ai, introduces GLM-5.2 and explains the company's history, from the 2021 GLM paper to a new high thinking level and near-frontier benchmark results. He makes the case for open weights through enterprise on-prem deployment, domain fine-tuning and community co-design, and closes by announcing Z Code, an open-sourced coding harness that works with other frontier models.
Charlie Holtz, co-founder and CEO of Conductor, shares six principles he has observed among the best AI-assisted builders, including staying near the frontier, creating slop-free zones, and giving agents cloud sandboxes. He also demos real-time collaboration and phone-initiated agents, and argues for orchestras over factories, with humans at the center of AI-driven development.
Ryan Cooke of WorkOS argues that software factories should be judged by customer outcomes rather than PR counts or AI-generated code volume. He describes how WorkOS embedded its product engineering process into its factory through TARS, Horizon, a PM agent that drafts Hilltop docs, and an internal MCP gateway connected to Snowflake, along with bug triage, owned sandboxes, a memory layer and outcome-focused metrics.
Tereza Tížková of Factory explains what a software factory really is: an autonomous, end-to-end software lifecycle that goes far beyond coding agents. She covers three guiding principles of agnosticism, autonomy, and continuous improvement, including automatic model routing, long-running multi-agent missions with sequential workers and independent validators, a deferred context engine, and agent readiness checks, and closes with a view on what remains for humans.