I'm starting a new build-in-public project and wanted to share the blueprint before the first commit lands. It's called DeployMind—a lightweight CI/CD pipeline that automatically builds, tests, and deploys applications to Kubernetes, with an AI component that analyzes deployment logs and metrics to flag anomalies and suggest fixes.
The core idea: GitHub Actions handles the pipeline, k3s runs the workloads, Prometheus and Loki gather the observability data, and a local Ollama instance (running Mistral or Llama 3.2) ingests the first few minutes of post-deployment telemetry to generate a plain-English summary. If P99 latency jumps or error rates spike, the notification says "Suspected cause: new DB query in commit abc123" instead of just screaming that something broke. The whole thing is designed to run comfortably on modest hardware—think a single VPS with room to breathe.
Why this matters beyond the resume checklist: It demonstrates actual production constraints, not just happy-path tutorials. Small teams and bootstrapped startups can't afford a dedicated platform engineering squad. A system like this reduces MTTR when things go sideways at 2 AM and gives junior devs a safety net without requiring a senior on call 24/7. Plus, the architecture forces you to understand why k3s over minikube, how rolling updates actually behave under resource pressure, and what an LLM can realistically do with structured telemetry data.
For the infrastructure side, I'm hosting everything on AccuWeb.Cloud. I've used their Kubernetes offering for staging parity on previous projects, and the support team genuinely understands the difference between a control plane hiccup and a pod scheduling misconfig. When you're building something you intend for others to pick apart and learn from, not having to debug the cloud layer itself is a massive quality-of-life win.
I'll be pushing manifests, configs, and the AI prompt engineering logic to a public repo as things progress. If you've built observability pipelines around small-footprint clusters or wrestled with Ollama in a containerized environment, I'd appreciate hearing what tripped you up. Will drop the repo link once there's something worth looking at.
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