Your AI coding, measured.

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Where you stand

Daily rhythm

Every day in range. Click a day to filter the page to it; click again to clear.

The long view

Every day since collection began, one square each. Click a week to filter to it.

Where the effort goes

Repository first, then the folders inside. Click a bar to filter.

By repository

Inside

Model mix

Output by model

Turns by reasoning effort

When you work, and what it costs

Your week against the meter

Your own hours, with every vendor's peak window drawn over them.

Tools, delegation and the heaviest sessions

Tool calls

Named subagents

Heaviest sessions

Writes/reads is the produce-to-explore ratio. Peak context is the largest single-turn context.

What to change

Ordered by severity, then by what it is worth. Computed over the whole window — the filters above do not change it.

highYou are buying tokens at peak rates you did not have to pay≈$21.88/mo

66.9% of your time-priced spend (deepseek, zhipu) ran inside a peak window, where the same tokens cost up to twice the off-peak rate. That timing cost about $39.39 over the period.

  1. Queue unattended work for an off-peak hour — test generation, migrations, doc sweeps, bulk refactors.
  2. Leave interactive work where it is: the premium buys your attention, and a batch job does not need it.
  3. The windows are narrow — DeepSeek 01:00-04:00 and 06:00-10:00 UTC, GLM 14:00-18:00 UTC+8 on weekdays, so weekends are free of it entirely.

Confidence: high

mediumSome sessions carry a very large context per turn

116 session(s) peaked above 300,000 tokens on a single turn (worst: 942,469), and every later turn re-reads it.

  1. Finish the thread and start the next piece of work in a fresh session.
  2. Carry forward only what that work needs — an exploration transcript dragged into unrelated work is what makes context this expensive.

Confidence: medium

lowSessions that paid to cache and then ended≈$0.18/mo

7 session(s) wrote 933,593 tokens to cache and never read one back — the 1.25x write premium paid for nothing.

Ask the follow-ups in the same session — the second turn is where caching starts paying. These were closed right after the first big context load.

Real but immaterial at this volume (~$0.18/month). Kept so you can see the pattern before it grows.

Confidence: high

lowSessions that read a lot and changed nothing≈$0.11/mo

1 session(s) made 7 read-type tool calls with no edit or write, at 0.67 USD. Some is real research; some is hunting for what a targeted search would have found.

  1. Delegate 'where is X' to a search subagent — the answer comes back without the file bodies.
  2. Ask the question you want answered rather than reading toward it.

Real but immaterial at this volume (~$0.11/month). Kept so you can see the pattern before it grows.

Confidence: medium

infoCache is doing its job

89.1% of everything the models read came from cache rather than being re-billed at full input rate.

No action. Watch this number — a fall means sessions are being restarted more often.

Confidence: high

infoSubagent delegation looks proportionate

13.1% of output came from subagents — enough to keep bulk reading out of the main context without runaway fan-out.

No action.

Confidence: medium

infoWhere your AI investment is concentrated

checkout-service took 34.7% of estimated spend across 81 sessions; the top three take 82.1%.

Compare this ranking against where you would say your priorities are. A workspace high on this list and low on your own priority list is the finding.

Confidence: high

infoWhen you actually use AI

Peak hour is 03:00 (379 turns). 42.6% of turns fall outside 06:00-22:00.

Useful as a check on whether AI use is displacing focus time or filling gaps. A high out-of-hours share is worth knowing about for its own sake.

Confidence: high

infoWork is spread across more than one vendor

deepseek is your largest at 36.9% of output tokens, across 5 vendors in total.

No action. A mix is what lets you re-price or re-route without re-learning a workflow.

Confidence: medium