Show HN: CodeBurn – Analyze Claude Code token usage by task

That's not how it works

You've never actually hit the limit have you? If you have you would know it's a hard limit.

I used the API version for quite a while before using a subscription, which I now have used extensively for many months.

So, is your claim that they just slow down and queue the subscription version, or are you accusing them of using nerfed models, or is it something else? The only time I ever get some slowness has to do with the models being overloaded and has nothing to do with limits. Those are two separate concepts you seem to be confusing. And luckily, this is pretty rare for me since I don't work during US time zones.

The 56% conversation vs 21% coding split is a really interesting finding — it lines up with trajectory studies on SWE-bench where ~38% of an agent's actions are pure exploration (grep, find, file reads). The remaining "no-tool" turns are likely the agent digesting what it read and planning its next move. These two costs are linked: the less efficiently the agent localizes, the more thinking turns it needs to piece things together. PatchPilot (ICML 2025) quantified this — localization capability accounts for ~47% of an agent's total improvement. One thing that would be really interesting in your tool: separating exploration turns (grep/find/read) from pure thinking turns, and seeing how the ratio scales with project size. On large monorepos, exploration should blow up non-linearly.

The 56% on conversation turns with no tool usage matches what I've seen too. A lot of that is Claude thinking out loud, context management, and planning before it touches anything. It feels wasteful until you realize the alternative is it immediately starts editing files without understanding the codebase first.

The JSONL transcript parsing is clever. I've been reading those same files for a different purpose (rendering conversation history in a menu bar app) and the format is more reliable than I expected. Each tool call has enough metadata to reconstruct what happened without needing to re-parse the full conversation.

One thing that would be interesting to see: cost broken down by session when you're running multiple sessions in parallel. Right now I have no idea which of my 4 running sessions is burning the most tokens.

Installed it just now. The most surprising line for me was "Conversation $225 / 496 turns". Basically half my turn count this month was chat, not building. Had no idea that ratio was so off.

Cache hit rate is another metric I wouldn't have looked at otherwise. 98.2% on Opus 4.6 here. Apparently that's the difference between a $2k month and something much worse.

Activity classification is the actually useful feature though. Most token trackers just tell you total spend. This tells you what kind of work the spend went to.

Nice work!