We analyzed 1,573 Claude Code sessions to see how AI agents work
First analytics layer for Claude Code revealing 26% session abandonment rate.
See how many tokens your AI coding agents really burned.
Unified local AI cost tracker when native dashboards stay siloed and cloud-based.
Developers using multiple AI coding agents
Wakatime · Cursor · Claude Console
npx whoburnedmore
reads the usage/log files these tools already write locally, sums them up, and prints a dashboard: total tokens, a cost estimate, active days, and your heaviest single day.
There is also a public leaderboard on the webiste which compares your ai usage with all others
Feedback & a upvote is appreciated
github is /amiinwani/whoburnedmore.com product hunt : https://www.producthunt.com/products/whoburnedmore
First analytics layer for Claude Code revealing 26% session abandonment rate.
Parses local JSONL session files from Copilot, Claude Code, and Codex and surfaces an interactive DAG, per-agent/token metrics, and session replay directly in the activity bar — immediately useful for spotting which agents and skills actually do the work. The Claude cache-token breakdown and timeline replay are clever, concrete features that show the author dug into provider internals; adoption looks small today, but the concept and implementation give real observability where previously there was opacity.
Unified cost & activity tracking across 14 AI coding editors, local-only, no signup.
Pulls data straight from ~/.claude/projects and only uploads aggregated metrics (tokens, cost, calls) via a 6-letter invite code flow — nice and surgical. The one-command npx init + join UX and a show-data privacy audit button make adoption trivial, but it’s strictly useful only to groups already using Claude Code and requires trust that aggregated uploads are enough for your threat model.
600x speedup over Node.js version, but Cursor support is currently broken.
Like top for AI agents: tracks token costs across 6 coding agents, 100% offline.