masterda/my-personal-codex-data
Coding Agent Conversation Logs This is a performance art project. Anthropic built their models on the world's freely shared information, then introduced increasingly dystopian data policies to stop anyone else from doing the same with their data - pulling up the ladder behind them. DataClaw lets you throw the ladder back down. The dataset it produces is yours to share. Exported with DataClaw. Tag: dataclaw - Browse all DataClaw datasets Stats Metric Value… See the full description on the dataset page: https://huggingface.co/datasets/masterda/my-personal-codex-data.
Coding Agent Conversation Logs
This is a performance art project. Anthropic built their models on the world's freely shared information, then introduced increasingly dystopian data policies to stop anyone else from doing the same with their data - pulling up the ladder behind them. DataClaw lets you throw the ladder back down. The dataset it produces is yours to share.
Exported with DataClaw.
Tag: `dataclaw` - Browse all DataClaw datasets
Stats
Models
Projects
Schema
Each line in conversations.jsonl is one session:
{
"session_id": "abc-123",
"project": "my-project",
"model": "claude-opus-4-6",
"git_branch": "main",
"start_time": "2025-06-15T10:00:00+00:00",
"end_time": "2025-06-15T10:30:00+00:00",
"messages": [
{
"role": "user",
"content": "Fix the login bug",
"content_parts": [
{"type": "image", "source": {"type": "base64", "media_type": "image/png", "data": "..."}}
],
"timestamp": "..."
},
{
"role": "assistant",
"content": "I'll investigate the login flow.",
"thinking": "The user wants me to look at...",
"tool_uses": [
{
"tool": "bash",
"input": {"command": "grep -r 'login' src/"},
"output": {
"text": "src/auth.py:42: def login(user, password):",
"raw": {"stderr": "", "interrupted": false}
},
"status": "success"
}
],
"timestamp": "..."
}
],
"stats": {
"user_messages": 5, "assistant_messages": 8,
"tool_uses": 20, "input_tokens": 50000, "output_tokens": 3000
}
}messages[].content_parts is optional and preserves structured user content such as attachments when the source provides them. The canonical human-readable user text remains in messages[].content.
tool_uses[].output.raw is optional and preserves extra structured tool-result fields when the source provides them. The canonical human-readable result text remains in tool_uses[].output.text.
Load
from datasets import load_dataset
ds = load_dataset("masterda/my-personal-codex-data", split="train")Export your own
pip install -U dataclaw
dataclaw