When I started using AI coding tools more seriously, I thought the problem was cost tracking.
That was only partly true.
The bigger problem was timing.
A normal dashboard tells you what happened after the session. That is useful for accounting, but it is not very useful while you are deciding whether to keep asking Claude Code, Codex, Cursor, or another agent to take one more swing at a bug.
By the time I check a usage page, the expensive part already happened.
The useful moment is during the work
The moment I actually need usage visibility is not at the end of the day.
It is right before I do one of these:
- ask the model to regenerate a large file
- paste a huge error log again
- let an agent continue after it already missed the bug twice
- start a new feature near a reset window
- switch tools because one provider feels close to the limit
Those are small decisions, but they stack up.
If usage is hidden, I usually make the lazy choice. I keep going.
If usage is visible, I pause for two seconds and decide whether the next prompt is worth it.
That is a much better product loop than a weekly report.
Why I put it in the menu bar
A browser dashboard has a problem: you have to remember to open it.
That sounds minor, but it matters. AI coding sessions are already full of context switching. You have the editor, terminal, docs, browser tabs, logs, and maybe a local app running.
Usage tracking should not become another destination.
So the design rule I used for TokenBar was simple:
Put the useful number where the user already glances.
On macOS, that is the menu bar.
Not because the menu bar is fancy. Because it is passive. It can sit there while the work is happening.
The metric is not just dollars
A lot of AI usage tools focus on spend, which makes sense.
But for coding agents, the more useful question is often:
How much runway do I have left in this session?
That can mean cost, tokens, reset timing, provider usage, or just a rough sense that the current loop is getting wasteful.
If I am debugging something and the usage keeps climbing while the output quality stays flat, that is a signal to stop and change strategy.
Maybe I need to write a smaller prompt.
Maybe I need to inspect the code manually.
Maybe I need to start a fresh context.
The usage number is not the answer. It is the cue to think.
The product lesson
I used to think the best version of an AI usage tracker would be a detailed dashboard.
Now I think the best version is closer to a fuel gauge.
A fuel gauge does not give you a spreadsheet. It gives you just enough information at the moment you can still change your behavior.
That is the bar I am using for TokenBar: make AI coding usage visible while the work is happening, not after the damage is already done.
TokenBar is free to try here: https://tokenbar.site/
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