Repos
What a tracked panel of 66,704 developers actually put their hours into. Not stars. Pull requests and issues, counted monthly against 1,639 AI-tooling repositories, each labeled by a language model with the problem it's trying to solve.
The half-year in one picture
Share of classified panel attention, February against July. A line sloping up means the problem took a bigger cut of the crowd’s work, not that more people arrived.
What the half-year shows
The crowd stopped building harnesses and started running fleets. agent-harness is still the largest single problem in the set — 425 repositories, 48% of classified attention in July — but its share has fallen steadily. The work did not leave; it moved one layer up.
fleet-ops grew its share 3.2× between February and July, the largest move in the data, and memory grew 1.8×. Those two are the story of the half-year: once one agent works, the problem becomes many agents and what they remember between runs.
The clearest decline is privacy, down to 31% of its February share. Read that as attention rather than concern — the opening cohort carried a burst of that work which has not repeated.
These readings come from a rolling panel of 66,704 accounts, all polled across the full window; 20,330 recorded any activity inside it. How this is measured