codeburn-rs; CodeBurn but 600x faster in Rust [view coding token usage]
600x speedup over Node.js version, but Cursor support is currently broken.
Local TUI unifies AI token costs across 16 coding tools instantly.
Developers using multiple AI coding assistants
WakaTime · OpenPipe · LangFuse
Recently I found myself using a bunch of different AI coding tools at the same time (Claude Code, Cursor, and some custom API scripts). By the end of the week, I had absolutely no idea how many tokens I was burning or what my real API costs were unless I logged into three separate billing pages.
I built tokemon to solve this for myself. It finds the log and cache files generated by these tools on your machine, parses them, handles deduplication so requests aren't double-counted, and drops the totals into a terminal dashboard. I wrote it in Rust, so it's extremely fast and performant.
The feature I end up using the most is `tokemon top`. It opens a live TUI dashboard with sparklines for daily and weekly trends. I usually just leave it running in a terminal pane as a persistent monitor since it's nice to look at and keeps my API spend top of mind.
You can grab it via cargo: cargo install tokemon
Repo is here if you want to poke around or add parsers for other tools: https://github.com/mm65x/tokemon
Let me know what you think!
600x speedup over Node.js version, but Cursor support is currently broken.
Claude Code usage dashboard reading local files—fills exact gap Anthropic didn't address.
Parses local session files for multi-provider AI cost tracking; Anthropic/OpenAI billing pages don't break it down.
Local indexer with AST + impact graph replaces grepping and cloud RAG for code context.
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.
Finally tracks LLM spending across services without refreshing 27 browser tabs.