Quick answer
Run whoburnedmore with the CLI command npx whoburnedmore. Existing commands and links remain supported.
Search intent
The reader is worried about privacy when tracking local AI coding usage.
Best for
Security-conscious developers and teams evaluating usage trackers.
Know what is needed
A token leaderboard needs totals, dates, and tool names. It does not need proprietary source files or the content of prompts.
- Tokens
- Cost estimate
- Tool
- Date
Prefer local parsing
Local parsing lets the tool inspect logs on your machine and upload only the usage summary. That is a better privacy boundary than sending raw logs.
- Read locally.
- Summarize locally.
- Submit aggregates.
Make public profiles opt-in
Some developers enjoy public ranking; others only need private diagnostics. Both modes should exist.
- Local mode
- Private profile
- Public leaderboard
Review team policy
Teams should document what can be shared and who can see it. A clear policy prevents accidental over-sharing.
- Allowed fields
- Forbidden fields
- Retention
- Access
Short answer for local ai coding logs privacy
The practical answer is to measure the workflow before changing tools or plans. A privacy-aware AI coding tracker reads local logs, extracts usage totals, avoids source code and prompt text, and lets users choose whether to publish a profile. Then review the result against the intended outcome: whether the work shipped, whether the agent got stuck in a loop, and whether the same task should use a smaller prompt, a cheaper model, or a different AI coding product next time.
This is also why the page links to authoritative external sources and to related whoburnedmore guides. Pricing pages explain the vendor unit; your local usage history explains what that unit means in practice. Keep both views together before making a budget, upgrade, or team-policy decision.
Mistakes to avoid
Optimizing before measuring
It is tempting to change plans, switch tools, or clamp down on usage as soon as local ai coding logs privacy becomes a concern. That usually hides the real issue. Measure the current workflow first, then decide whether the problem is volume, scope, model choice, team policy, or one unusually expensive session.
Comparing vendor units directly
A request, credit, ACU, message, token, and quota are not interchangeable units. Convert each tool back to the work it produced: the feature, bug fix, review, prototype, or incident response. That makes cross-tool comparison fair enough to act on.
Treating high burn as automatically bad
A high-burn session can be waste, but it can also be the session that unblocked a release. Add outcome notes before judging the number. The goal is not low usage; the goal is useful, explainable usage that the team can repeat.
Practical playbook
What to measure first
Start with the signal most likely to change behavior for this topic: usage totals. For someone searching local ai coding logs privacy, the useful answer is not a generic definition. It is a repeatable way to decide whether the current workflow is healthy, whether the cost is justified, and which next action will reduce waste without killing useful AI experimentation.
How to turn it into a habit
Use a simple weekly rhythm: measure the biggest burn, label the task, record whether it shipped value, and change one prompt or routing rule. The sections above cover know what is needed, prefer local parsing, make public profiles opt-in, and review team policy. Those are the pieces that make the guide actionable instead of another pricing summary.
How whoburnedmore fits
whoburnedmore is the measurement layer, not the policy layer. It reads local AI coding-agent usage, keeps source code out of the upload path, and gives you a shared burn view. That means this guide can stay focused on decisions: when to upgrade, when to narrow context, when to switch tools, and when a high-burn session was actually worth it.
Decision checklist
Can you explain why local ai coding logs privacy matters for a real task this week?
Do you know which tool, model, project, or workflow created the largest burn?
Is the next action a smaller prompt, a different tool, a plan change, or a team policy update?
Can you review the result without uploading source code or raw prompt content?