AI Product OKRs miss the point when they track accuracy

Jeff Gothelf argues AI product OKRs should measure user actions, not model outputs. Instead of chasing a 95% accuracy target, teams should set key results like “increase the share of meetings where users send AI‑generated data without rewriting it from 40% to 65%.” This shifts focus to real value delivered.

Eddie Kim and a five‑person team delivered Gusto Cofounder, a full AI‑powered payroll assistant, from zero to production in ten weeks. They replaced traditional PM tools with a constant Zoom call and Claude Code for eval‑first coding, proving that tiny, process‑light teams can ship complex products quickly.

Figma Config 2026 adds a "Check designs" panel that flags colour, spacing, typography and component violations across all files. By surfacing design‑system debt everywhere, it forces every team, not just the design‑systems group, to own the problem and act before it compounds.

DoorDash open‑sourced Agentic Orchestrator, a terminal UI that lets a single engineer supervise multi‑step AI coding agents from feature prompt to PR. It adds context building, phased planning, quality gates and parallel worktrees, turning vague requests into reviewable code without manual shepherding. Developers get visibility and control over autonomous coding sessions.

PMB is an open‑source, local‑first memory system that stores decisions, lessons, and project facts in a single SQLite file and injects them into MCP‑aware agents like Claude Code, Cursor, and Codex. By eliminating cloud calls and API keys, it lets agents recall context instantly across restarts, boosting productivity.

Trajeckt adds a runtime firewall that inspects the whole sequence of AI agent actions, preventing multi‑step data exfiltration that per‑action checks miss. It compiles a sealed policy graph, enforces it in ~1.6 ms, and ships a Docker demo that blocks illegal writes while allowing benign reads.