Fredrik Wisløff

Palantir CEO Alex Karp’s secret to staying physically fit enough to run Palantir: “Copy the Norwegians.” “Run like a snail—run at the pace you walk at, get a band, stay in Zone 1.” “Do that as much as possible and over a long period of time, you will transform your health.”

Introducing open-slide - The slide framework built for agents. Prompt your agent, get a polished deck. $ npx @⁠open-slide/cli init 👇

Doing this well requires more than just querying a database. Historically we relied on the expertise of the data owner to know which metric to pull. Now it requires an agent understanding which metrics actually matter and selecting the right ones as they evolve over time. At Glean, when we analyze structured data from systems like Salesforce or Databricks, we look at usage patterns, who produced the metric, and how it’s already being used to infer the likely canonical metric. After all, this is fundamentally a search problem. That said, I think one thing missing from the discussion is that the "data agent" is usually just one component of a larger workflow. Take a prompt like: “Analyze my sales pipeline to understand what’s at risk.” Yes, some of that analysis comes from structured data in systems like Databricks or Snowflake. But a large part of understanding “risk” actually lives in unstructured data. Things like team conversations, comments on documents or spreadsheets, and notes from forecasting review meetings. That's why the context layer can’t just operate on structured data. Context graphs are rising in importance because they recognize that agents need a new kind of data structure to understand how work actually happens and how decisions get made. That's what we're working on here at Glean too.