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daimon turns a Discord question into real analysis — Bayesian models fit with PyMC, charts drawn, notebooks delivered. One click to add. $5 of credit on us. Every feature unlocked.
Watch what happens.
# prompt
How has Verstappen's overtaking changed since 2021?
In [1]:
import fastf1, pandas as pd ses = fastf1.get_session(2021, 'Canada', 'R') ses.load()
In [2]:
overtakes = compute_position_changes(ses.laps) plot_overtaking_trend(overtakes)
Out [2]:
2021 → 2024 · F1 timing data
Verstappen — on-track overtakes per race
Field median Verstappen
Everything it can reach into.
01Fit Bayesian modelsRun PyMC models in-thread — posterior inference and honest uncertainty, not just point estimates.
02Generate notebooksAsk a question, get a runnable marimo notebook back — code and charts included.
03Read your DiscordSearch channels, messages, and history to ground its work in your server.
04Pull from your toolsGoogle Drive, Notion, and YouTube transcripts as live data sources.
05Bind a GitHub repoPoint an agent at a repository and let it work against your code.
06Attach Skills & MCPAdd knowledge bundles and connect any MCP server to extend what it can do.
07Schedule runsSet agents to run on a recurring schedule — reports that arrive on their own.
It doesn't chat. It does the work.
The offer
One click.
No API key. No setup.
$5 of Anthropic credit, on us.
Every feature unlocked.
It's yours. And it's open.
- Built by PyMC Labs — the team behind the PyMC project.
- Every server is isolated — one tenant, its own data.
- Your data stays scoped to your server.
- Fully open source.