Raphael Wieland · WielandTech
I build software and keep it running
Kubernetes platforms, Django apps, data pipelines and LLM integrations. I write the code, run it on a cluster in my closet, and I'm the one who gets the alert when it breaks. Everything below is live.
- Live from the homelab
- The cluster this site runs on, right now.
- Availability · 30 days
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- Latency · p99
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- Requests / sec
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- Errors · 5xx
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- Saturation · CPU
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- Deployments healthy
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- Pods running
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- Nodes ready
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- Postgres data
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Loading live stats…How it's built →
Work
01 / Shipped and running
WARN Index
US layoff notices from 47 states and DC in one searchable place. Scraped daily, enriched by an LLM, mapped and charted.
48jurisdictions, daily
Live
- Python
- Playwright
- PostgreSQL
Flock camera map
A map of 336,000 Flock Safety surveillance devices and license plate readers that doesn't track the people looking at it.
0third-party requests
Live
- MapLibre
- PMTiles
- tippecanoe
r12d
Privacy-first QR stickers, short links, a photo inventory and lost-and-found for households and small businesses.
0visitor trackers
Live
- Django
- PostgreSQL
- S3
K3s homelab platform
A small production platform in a closet. GitOps with Flux, a review app for every pull request, managed Postgres, and full observability.
100+review-app deploys
Live
- Kubernetes
- K3s
- Flux
Restroom QR ad network
Venues put QR stickers in their restrooms, patrons scan them, and ad revenue is split across venues by traffic.
0raw IPs stored
Live
- Django
- PostgreSQL
- ntfy
One Django app, two domains
The personal site behind raphaelwieland.com. One image served two domains, with CI that refuses to ship a broken deploy.
2domains, one image
Live
- Django
- PostgreSQL
- Redis
Services
02 / What I can build for you
01
Kubernetes & GitOps platforms
Every change goes through Git, every pull request gets its own copy of the app, and the pipeline catches mistakes before production does. It is the setup I run at home for 12 apps.
02
Django & Python web apps
Django apps with real tests, migrations checked in CI, and privacy and multi-tenancy designed in from the first model.
03
Data pipelines & scraping
Scrapers that assume the source will change. Scheduled jobs, validation, a saved snapshot of every failure, and clean data in Postgres or your warehouse.
04
AI & LLM integration
LLMs where they actually help: enrichment and extraction with a confidence score on every answer, agents that hand a human a pull request to review, and costs you can predict.
Something to build, or something broken
A few lines about what it is and where it runs is plenty.
Get in touch