About BlueRobin
BlueRobin is a homelab where I build production-grade AI on self-hosted infrastructure — and write about every decision along the way. It began with one question and grew into two projects that now run side by side on the same cluster.
Two projects
The Archives — document intelligence
It started in 2022 with a frustrating question: “Why does it take so long to find anything in my family’s medical records?” Years of lab results, doctor’s notes, prescriptions and imaging reports had piled up, and searching by filename was useless for medical content.
The Archives is the answer: upload any document, ask any question, and get an answer cited directly from your own archive. Under the hood it’s a complete pipeline — OCR and structure extraction, named-entity recognition into a knowledge graph, multi-model vector search, and retrieval-augmented generation that answers in plain language with citations to the exact source paragraphs.
The Debug Agent — autonomous root-cause analysis
Running the Archives in production created a second problem: when something broke at 2 a.m., I was the on-call engineer. The Debug Agent is the answer to that — an LLM agent that investigates incidents on its own.
It traverses a graph world-model of the whole platform, ranks likely causes with correlation-first scoring, runs a bounded tool-using investigation against real observability data, and only opens a pull request when hard evidence supports it. It’s deliberately advisory: a human always merges. The interesting parts — a persistent graph, an externalized verification gate, and bi-temporal incident memory — are written up as papers in the Debug Agent section.
The journey
Phase 1 — Cloud-first (2022). Like many developers, I started on managed cloud services. It worked, but the monthly bill for AI features climbed fast. For a personal project, that felt excessive.
Phase 2 — The homelab pivot (2023). I had a server collecting dust. Moving everything local forced honest thinking about resource constraints — which, it turned out, led to better architecture: lightweight Kubernetes (k3s), GitOps with Flux, and a service mesh for mTLS.
Phase 3 — AI integration (2024–2025). Accessible LLMs changed what was possible: local OCR, local embeddings, a real RAG pipeline, and a hybrid model strategy that keeps cost under a strict monthly ceiling.
Phase 4 — The agent (2025–2026). With the platform stable, the focus shifted from building the system to operating it — and to teaching an agent to debug it. That’s the Debug Agent.
The platform
| Component | Technology | Why |
|---|---|---|
| Frontend | Blazor Server | Real-time UI, C# everywhere |
| API | FastEndpoints | High-performance, clean contracts |
| Messaging | NATS JetStream | Lightweight, durable, KV store |
| Database | PostgreSQL | Relational metadata + lifecycle |
| Vectors | Qdrant | Multi-model semantic search |
| Graph | FalkorDB | Entity & world-model graphs |
| Inference | Ollama + Claude | Local-first, gateway for the rest |
| Orchestration | k3s + Flux | GitOps on a homelab cluster |
Everything runs under a strict cost ceiling — the constraint that shapes nearly every design decision on this blog.
Why this blog
A few years of building and operating BlueRobin taught me patterns I wish I’d known earlier: Domain-Driven Design in a real .NET codebase, event-driven architecture with NATS, GitOps with Flux, practical RAG, and — most recently — how to make an LLM agent reason about failures without letting it hallucinate a fix. This blog documents those learnings with real code and honest evaluation from a production system.
About me
I’m Victor Robin. BlueRobin is my playground for the patterns I don’t always get to use elsewhere: DDD, event-driven design, LLM integration, graph reasoning, and homelab DevOps. The best way to learn is to build; the second best is to teach. This blog is both.
Questions, or want to compare notes on any of this? Reach out on LinkedIn.