This comprehensive guide walks you through integrating Kong, a powerful unified API platform, into your home lab environment. Starting with Docker installation and a custom Kong image, it covers PostgreSQL deployment, MetalLB setup for load balancing, and Kong service and route configuration. The post concludes with troubleshooting tips and instructions for deploying your customized Kong image in a K3s cluster. This step-by-step tutorial empowers you to efficiently manage APIs in your home lab using Kong.

Explore AI integration in a home lab with LocalAI, Chroma, and Langchain4j. Begin by creating a custom LocalAI image, deploying it alongside Chroma, and configuring the Kubernetes environment. The post details deploying and exposing services, ensuring seamless communication between applications. Learn to modify endpoints in the Langchain4j application for smooth integration with the Home Lab setup. With a focus on simplicity, this guide empowers users to harness the capabilities of these AI tools within a controlled home environment, fostering experimentation and development.

Explore my journey of enhancing my tech blog by adopting Hugo, a powerful static site generator. Discover the streamlined setup process using Chocolately, theme selection, and local site preview. Dive into seamless integration with Giscus for robust GitHub Discussions-powered comments. Enhance user experience with Pagefind, a fully static search library, for efficient content discovery. Follow this step-by-step guide to leverage these tools and elevate your tech blog's functionality and engagement. Uncover insights into Hugo, Giscus, and Pagefind, making your blogging experience more dynamic and user-friendly.

Discover a streamlined approach to GitLab installation, repository migration, and CI/CD optimization. This comprehensive guide covers setting up GitLab in your home lab, upgrading to the latest version, configuring external URLs, and migrating repositories. Learn how to install GitLab Runner, Docker, and seamlessly integrate them into your development workflow. The guide concludes with optional steps for registering GitLab Runner as a service. Elevate your version control and continuous integration experience with this concise and practical tutorial.

In this post, I expand my Home Lab by adding a perpetual LLAMA model for on-demand inferencing. The steps involve crafting a Dockerfile, packaging Microsoft's Phi2 model, and deploying it with K3S. This ensures a continuously accessible LLAMA server for seamless integration into various inferencing projects.

In this blog, I explored Autogen's Agent AutoBuild and experimented with the Mixtral 8x7B model. I configured Autogen, envisioning a software academy project for coding novices. Through code snippets, I showcased AutoBuild's multi-agent system creation and tailored a task that wrote a General Paper article on art and courage. The Mixtral 8x7B model in LM Studio brought excitement but posed challenges with duplicate content. Check out the blog for a firsthand look at the dynamic interplay between Autogen and cutting-edge AI, complete with code snippets and images.

In this post, I explored enhancing Autogen's capabilities by enabling seamless Java code execution. Drawing inspiration from 0xlws' fork supporting JavaScript, I embarked on modifying Autogen to robustly support Java. I detailed the setup process, including installing Java on Windows Subsystem for Linux (WSL) and modifying key files. The post includes code snippets showcasing the changes, recompilation steps, and instructions for generating Java code. I extended functionality to additional test cases, seamlessly switching between Java and Python code execution. Docker integration for Java code execution was also optimized, showcasing Autogen's versatility and robust development experience.

In my recent blog, I demonstrated setting up a multi-agent conservation using Autogen. Employing agents "for_motion" and "against_motion," each engaged in a dynamic debate, facilitated by a neutral party. The debate evolved through multiple rounds, with each agent providing substantiated arguments, exceeding 300 words per response. The facilitator ensured debate guidelines were adhered to. The messages were then passed to an assistant tasked with synthesizing a comprehensive article. Utilizing Autogen's versatility, I showcased the system in action, debating different questions and generating diverse articles. The blog includes a full script in app.py and snapshots of the entire debate session.

I explored AutoGen, an innovative framework on GitHub, enabling the development of Large Language Model (LLM) applications. Collaborating with LM Studio, I set up a local LLM application, showcasing the step-by-step process. Installing LM Studio involved configuring context length, enabling GPU acceleration, and setting CPU threads. The integration process showcased a seamless environment for running local LLMs. Additionally, I explored the AutoGen setup, including installing Anaconda and creating a virtual environment. With the provided guidelines, I executed the app.py script, generating a stock price comparison chart through AutoGen's dynamic conversation.