Tool · Models & evaluation
Usage Capture
Read visible usage values with separate Codex and Ollama adapters and preserve uncertain readings.
Repository: CinvanaAI/usage-capture
Recorded synthetic example. Source with a complete offline example.
See the idea in action.
five_hour=73, weekly=41, strategy=labels; no account or desktop needed.
5h: 73%; Weekly: 41% (synthetic labeled OCR).
{
"five_hour": 73.0,
"weekly": 41.0,
"strategy": "labels"
}Try the example.
From the repository root, follow the dependency requirements in the README. This example uses supplied synthetic material.
python -m pip install -e ./codex codex-usage parse-text codex/examples/synthetic-ocr.txt
five_hour=73, weekly=41, strategy=labels; no account or desktop needed.
Complete setup and instructions ↗The interesting part.
OCR output can be useful evidence if capture, parse strategy and uncertainty are kept together.
Where it came from.
Two separately authored Windows usage-display scripts, for Codex and Ollama.
Keeps distinct installable adapters and original APIs together around one visitor job.
They are peer adapters with different screen assumptions, not one derived from the other.
Source 1 ↗, Source 2 ↗, Source 3 ↗
Follow the family: Two usage-display adapters