Linear

Your Attention Is the Bottleneck, Not Your Agents — Zack Proser, WorkOS

Jun 11, 2026 · 25:17

Zack Proser from WorkOS argues that human attention, not agent speed, is the real bottleneck in AI-assisted coding. He proposes a sustainable stack: signal layers to filter Slack and Linear, voice-first flows at 184 wpm, remote control of agents from a phone to leverage diffuse thinking, and weekly self-improvement passes over JSONL conversation history. He also integrates an Oura ring via MCP so Claude can nudge him about sleep, emphasizing balance over burnout.

Why Can't Anyone Answer Questions About the Business? — Garrett Galow, WorkOS

Jun 11, 2026 · 19:06

Garrett Galow from WorkOS built Studio, an internal workspace where anyone can ask natural language questions against Snowflake, Linear, and Notion, and get reusable widgets instead of filing a request. The LLM generates declarative JavaScript widgets that call data sources directly, making subsequent runs deterministic and cheap. Three techniques made it reliable: preflight sequencing injects schema context only when a tool is invoked, a layering rule tells the model to distrust its own knowledge about WorkOS and use primary sources, and query validation catches valid SQL that returns zero rows before hardcoding it into a widget.

Self Driving Products: Product Signals to Pull Requests — Joshua Snyder, PostHog

Jun 10, 2026 · 15:39

Joshua Snyder of PostHog explains how they're building a pipeline that turns product signals—errors, Slack messages, session replays—into automated pull requests. He reveals that off-the-shelf embedding models cluster signals by structural similarity, so they embed LLM-generated queries instead. He argues specificity determines whether the agent produces a useful PR, with error tracking being immediately actionable while Slack and replay usually are not. He advises starting with costly agents to discover patterns, then collapsing expensive steps into one-shot calls.