Technology & Development


All posts by date

In Memory of Tteck (1969-2024)

This page is dedicated to the memory of Tteck, whose Proxmox Helper Scripts have made home labs and self-hosting accessible to countless enthusiasts. His contributions to the community have been invaluable, and his work continues to live on through the community. If you've benefited from his work, please consider supporting his family through Ko-fi.

The scripts are now community-maintained at community-scripts.github.io/ProxmoxVE

Key Areas of Exploration

Home Infrastructure & Self-Hosting

Currently exploring and documenting:

  • Building resilient home lab infrastructure
  • Proxmox virtualization and container management
  • Network architecture and security
  • Self-hosted service management

Recommended Resources:

Local AI & Machine Learning

Focus areas:

  • Running large language models locally
  • AI infrastructure optimization
  • Model fine-tuning and quantization
  • Privacy-focused AI implementations

Essential Projects:

  • LocalAI - Self-hosted, community-driven AI solution
  • PrivateGPT - Private document QA using LLMs
  • Ollama - Get up and running with large language models locally

Development & Infrastructure

Experience with:

  • Ghost CMS deployments
  • Infrastructure as Code
  • Container orchestration
  • CI/CD implementation

My Contributions:

Community Resources

Home Lab Essentials

Documentation:

GitHub Repositories:

Local AI Implementation

Projects & Tools:

Learning Resources:

Books Worth Reading

Technical Foundations

  • "Designing Data-Intensive Applications" by Martin Kleppmann
    Essential reading for understanding distributed systems
  • "Site Reliability Engineering" by Betsy Beyer et al.
    Google's approach to managing large-scale systems
  • "Infrastructure as Code" by Kief Morris
    Fundamental patterns for managing services

AI & Machine Learning

  • "Deep Learning" by Ian Goodfellow et al.
    Comprehensive overview of deep learning fundamentals
  • "Machine Learning Engineering" by Andriy Burkov
    Practical guide to deploying ML systems

Future Explorations

Areas I'm currently researching and will be writing about:

  • Optimizing home lab power consumption
  • Local LLM deployment strategies
  • Network segregation for AI workloads
  • Automated backup solutions

Questions to Consider

  • How do we balance system complexity with maintainability?
  • What are the practical limits of home lab infrastructure?
  • How can we make AI more accessible while maintaining privacy?
  • Where is the sweet spot between automation and control?

Connect & Learn


This hub is evolving as I explore and document new areas. Last updated: 17/02/2025