Docker

You can unify access to Ollama, vLLM, cloud providers like OpenAI, Anthropic, and Google, plus custom model servers behind one OpenAI-compatible endpoint using LiteLLM Proxy . LiteLLM is a reverse proxy. It maps the standard /v1/chat/completions request to each provider’s native API. From one YAML file it handles auth, model routing, load balancing, fallbacks, rate limits, and spend tracking. Your app calls one endpoint with one key, and LiteLLM picks the right backend. You can swap models, add providers, or run A/B tests without touching app code.

You can expose a local dev server to webhooks from GitHub, Stripe, or Twilio. Run cloudflared next to a FastAPI app. This drops port forwarding, public IPs, and paid ngrok plans. Cloudflare Tunnels open an outbound-only encrypted link from your machine to Cloudflare’s edge. The edge then proxies webhook requests back to your local FastAPI endpoint with full TLS, auto reconnect, and no firewall changes.

The trick works because cloudflared opens QUIC connections outward from your machine. No inbound ports ever open on your router. Cloudflare’s edge gets the webhook POST from GitHub or Stripe. It routes that POST through your tunnel and hands it to localhost:8000, where FastAPI handles it. You get a stable, public URL like webhooks.yourdomain.com that survives reboots.

Pair Meilisearch v1.12’s fast REST API with HTMX 2.0’s hx-get and hx-trigger attributes, and you get a real-time, typo-tolerant search box that returns results in under 50ms. You write no custom JavaScript and pull in no React or Vue. The server renders HTML fragments that HTMX swaps into the DOM, so the whole search box stays under 15 KB of total JS. This post covers the full setup, from Docker Compose to a working search UI with faceted filtering.

You can build a fully local, sandboxed code interpreter agent. You pair Ollama (running a reasoning model such as Scout, the smallest Llama 4 variant , or DeepSeek R1) with a Docker container that runs the generated Python code. The agent sends a prompt to the local LLM, which writes Python. That code goes into a locked-down container with no network and strict limits. The output feeds back to the LLM so it can fix and retry. The whole loop runs on your machine with zero cloud calls.

You have sent the message “it works on my machine” at least once in your career. Maybe you have been on the receiving end of it. Either way, the problem is always the same. Two machines that should be identical are not, and no one can say why. One has Python 3.11, the other has 3.12. One has a system OpenSSL that some C extension links against, and the other does not. One engineer installed a package six months ago and forgot.

Gatus is a single-binary monitoring tool that probes your services and shows a public status page at a URL you control. You define every check in one YAML file. So your whole setup can live in Git next to the rest of your stack. There is no need for a database, no web UI to click through, and no per-monitor pricing. If you self-host a blog, a Gitea instance , a Home Assistant server, or a mail relay, Gatus gives you a simple way to know when something breaks.