Self-hosting provides a hands-on way to learn modern infrastructure, covering essential skills such as deployment, networking, storage, monitoring, and system reliability.
1. **Awesome Selfhosted**: A curated list of open-source applications across various service categories.
2. **Coolify**: An open-source PaaS for deploying apps, databases, and services on your own servers.
3. **n8n**: A visual workflow automation platform for connecting APIs and services.
4. **Uptime Kuma**: A monitoring system for tracking service uptime, status dashboards, and alerts.
5. **Nextcloud Server**: A private cloud platform for file synchronization, storage, and collaboration.
6. **Immich**: A self-hosted photo and video management and backup platform.
7. **Memos**: A lightweight Markdown note-taking tool with a timeline interface.
8. **Proxmox VE Helper Scripts**: Community scripts for managing LXC containers and VMs on Proxmox VE.
9. **Awesome Tunneling**: A curated list of tools for secure remote access to local services via tunneling.
10. **Self-Hosting Guide**: A comprehensive reference guide covering hardware, software, and infrastructure concepts.
A single developer built a powerful search and monitoring tool for the web using a simple SQLite database and a clever bot, highlighting the potential of individual creators to tackle complex problems.
A Raspberry Pi 5 can transform a home network into a faster, safer, and easier-to-manage system by consolidating DNS, VPN, and monitoring tools into a single, low-power machine.The author implemented Pi-hole (ad blocking & DNS), Unbound (recursive DNS resolver for privacy & speed), and WireGuard (secure VPN for remote access). For monitoring, tools Uptime Kuma, Netdata, or Prometheus with Grafana provide real-time insights into network performance and security.
TraceRoot.AI is an AI-native observability platform that helps developers fix production bugs faster by analyzing structured logs and traces. It offers SDK integration, AI agents for root cause analysis, and a platform for comprehensive visualizations.
• Continuous Integration (CI) and Continuous Deployment (CD) pipelines for Machine Learning (ML) applications
• Importance of CI/CD in ML lifecycle
• Designing CI/CD pipelines for ML models
• Automating model training, deployment, and monitoring
• Overview of tools and platforms used for CI/CD in ML