v0.1.0 — First public release

Whydunit v0.1.0 — First public release

Find the why behind AI pipeline failures — synthetic, offline, deterministic.

What's in the box

• Simulator — 10-stage AI pipeline, 35-signal telemetry catalogue, seeded generator with diurnal seasonality, and a 12-scenario incident library (σ-shift injection with onset/ramp control). CLI: python -m whydunit.simulator
• Forensic engine — robust z-score + EWMA anomaly detection, two-window change-point detection, cross-signal correlation clustering, dependency-DAG origin reasoning, and a signature-scoring hypothesis engine with confidence bands. Fully deterministic, zero LLM calls.
• Streamlit console — ten investigation pages: dashboard, incident explorer, pipeline view, timeline, signal explorer, forensic analysis, evidence graph, incident comparison, case-file export (server-side fallback included), and ground-truth eval.
• Eval harness — python -m whydunit.evaluation grades diagnoses against ground truth, which is firewalled out of the forensic engine (enforced by test).

Report card (1 day @ 1-min resolution, seed 42)

• 12/12 scenarios diagnosed correctly at top-1
• 0 false positives, 0 false negatives
• Detection delays: 10–37 minutes

Under the hood

• Python ≥3.12, uv-managed; pandas 3 / numpy 2.5 / plotly / networkx / scikit-learn
• 140 tests passing, ruff clean
• CI: lint + format + tests + eval smoke on ubuntu/windows × py3.12/3.13

Roadmap

v0.2: optional LLM explanation layer (explains deterministic evidence, never generates it), more scenarios. See README for the full roadmap.

MIT licensed.