SShortSingh.
Back to feed

How One Developer Uses Traefik and Docker to Automate Reverse Proxy in a Homelab

0
·2 views

A developer shared their homelab setup using Traefik as a reverse proxy to manage multiple Docker-based services such as Pi-hole, Vaultwarden, and custom applications. Rather than manually editing Nginx configs and juggling port numbers, Traefik automatically discovers Docker containers and routes traffic using container labels. The setup handles HTTPS certificates automatically via Let's Encrypt, eliminating the need for manual SSL configuration. Traefik is configured as the sole internet-facing service, while all other containers communicate internally through a dedicated Docker network. A key design choice is disabling automatic container exposure by default, ensuring only explicitly labeled services are made publicly accessible.

Read the full story at DEV Community

This is an AI-generated summary. ShortSingh links to the original source for the complete article.

Discussion (0)

Log in to join the discussion and vote.

Log in

Related stories

0
ProgrammingDEV Community ·

AI Incident-Response Agent Cuts Resolution Time from 47 to 2 Minutes Using Memory

A developer experiment tested an AI incident-response agent across three identical payment gateway timeout failures, varying only the agent's access to historical memory. Without prior context, the first incident took 47 minutes to resolve; by the third run, with two confirmed resolutions stored in memory, the same fix was applied in 2 minutes. The agent identified the repeated pattern — restarting the payment service and verifying the gateway connection — as a confirmed, multi-incident solution rather than a one-off suggestion. Human engineers verified each resolution before it was saved as reusable memory, preventing unconfirmed fixes from becoming institutional knowledge. The team cautioned that the results reflect a controlled demo scenario and that real-world outcomes will depend on incident volume and the accuracy of recorded resolutions.

0
ProgrammingDEV Community ·

Developer Builds AI Incident Response Agent Using Groq and Hindsight Memory Tool

A developer has created an AI-powered Incident Response Agent that helps engineers investigate production issues by combining Groq's reasoning capabilities with Hindsight's persistent incident memory. When an engineer submits an incident description, the system retrieves relevant past incidents from Hindsight and feeds that context to Groq, which then generates a structured investigation report. The report includes possible root causes, step-by-step investigation guidance, recommended resolutions, and prevention suggestions. In a demonstration, the agent successfully diagnosed a database connection pool exhaustion issue and surfaced a past incident where the pool size was raised from 20 to 50 as a resolution. The project, built with Python, is currently a working prototype and its source code has been published on GitHub.

0
ProgrammingDEV Community ·

How to Use AI Coding Tools Heavily Without Letting Your Skills Decay

A developer writing for DEV Community argues that while AI tools dramatically boost coding speed, they risk eroding core skills by removing the thinking and struggle that build lasting mental models. Unlike traditional tools such as linters or debuggers, which eliminate mechanical work, AI can bypass the reasoning process entirely. The author illustrates this with a debugging scenario where solving a problem manually takes longer but produces durable understanding, while an instant AI fix may result in near-zero learning. To counter this, the author recommends deliberate AI use — such as forming a hypothesis before prompting, or asking AI to explain a bug rather than fix it outright. The key takeaway is not to use AI less, but to consciously preserve the friction that drives genuine comprehension.

0
ProgrammingDEV Community ·

ContractRift: Open-Source Tool Detects Silent API Contract Drift Before It Breaks Production

Silent API changes—such as removed fields, altered response types, or new required parameters—can break production systems even when uptime monitors show no outage. A developer built ContractRift, an open-source tool (AGPL-3.0), to address this gap after encountering repeated real-world failures caused by undocumented provider-side changes. The tool works by sending scheduled, authenticated requests to APIs, learning a structural baseline from early responses, and then flagging deviations such as missing fields or type changes as breaking, warning, or informational. It also tracks specific values like LLM model aliases and MCP tool parameters, deduplicates change alerts by fingerprinting, and can gate deployments on unreviewed breaking drift. ContractRift is self-hostable via Docker and includes a live demo, with the project inviting community feedback on false positives across different APIs.