Oliver Kieselbach
4d0a76336c
Cold starts spent 35-55s (measured across 8 boots today) embedding the static corpus — 119 knowledge docs + 144 event types — through MiniLM on the 0.25 vCPU container before the server even started listening. The corpus is baked into the image, so its embeddings now are too: - precompute-embeddings.ts runs in the Docker build stage (also serving as the model prefetch), writing search-index.json with per-section sha256 corpus hashes. - At boot, index.ts hydrates both vector indexes from that file in milliseconds (135 ms measured locally). Model name + corpus hash are validated first; any mismatch (rules changed without rebuild, model bump, malformed file) logs loudly and falls back to computing at boot — slow but never stale. SEARCH_BACKEND=fuse and the fuse boot fallback are unaffected. - The query embedder is no longer needed at boot; it warms in the background after the indexes are ready. getEmbedder now memoizes the promise instead of the resolved pipeline — a query racing the warmup previously could have constructed the model twice (~2x model RAM on a 0.5 Gi container). - 14 new model-free unit tests: hash stability/sensitivity, validation rejection paths, serialize/hydrate roundtrip. Expected cold start drops from ~60-80s to infra-only (~15-25s). Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Autopilot Monitor
Advanced monitoring and troubleshooting solution for Windows Autopilot deployments. Gain full visibility into every enrollment session with a detailed event timeline, fleet health dashboards, and session reporting. Define custom analysis rules to automatically detect issues and gather rules to collect targeted evidence. Retrieve diagnostics packages on demand, configure agent settings like auto-reboot behavior and automatic timezone adjustment — all managed centrally from the web dashboard.
Private Preview
Autopilot Monitor is currently running as a Private Preview. Visit autopilotmonitor.com to request access and learn more.
Overview
Autopilot Monitor provides real-time tracking, intelligent diagnostics, and automated troubleshooting for Windows Autopilot enrollment processes. It consists of:
- Bootstrap Script — PowerShell script deployed via Intune that starts monitoring early in the enrollment process
- Monitoring Agent — Lightweight .NET application that collects telemetry and evidence during enrollment
- Backend API — Azure Functions-based ingestion and processing pipeline
- Web Dashboard — Next.js application for real-time monitoring and fleet analytics
Architecture
For detailed information about the system architecture, components, and data flow, see Architecture Documentation.
Documentation
Full documentation is available at autopilotmonitor.com/docs
License
This project uses a split licensing model:
- MIT License — Agent (
src/Agent/) and Shared library (src/Shared/) — unrestricted use on end-user devices - AGPL-3.0 — Backend (
src/Backend/), Web Dashboard (src/Web/), and MCP Server (src/McpServer/) — server-side components remain open source
See LICENSE for full details.