Developers Logs

I've been using WSL2 for most of my Windows dev work for a while now, and I finally sat down to understand exactly what's happening under the hood, mostly because I kept hitting the same disk space issue every few months and got tired of googling the fix each time. This post covers what WSL2 is, where it's actually useful, the trade-offs, picking a distro, the commands I use most, connecting it to Docker, and that disk space issue in detail.   What WSL2 Actually Is WSL1 worked by translating Linux system calls into Windows API calls on the fly. It was a clever approach but it had real limits: anything that needed actual kernel behavior, like Docker or proper namespaces, just didn't work. WSL2 does something different. It runs a real Linux kernel inside a lightweight Hyper-V virtual machine that Microsoft maintains. That's the part that matters. WSL2 isn't emulating Linux, it's running it. Each distro you install gets its own instance of this VM, backed by it...

Building an AI Research Assistant with LLM Tool Calling Large Language Models (LLMs) have revolutionized how we interact with artificial intelligence, but their true power is unlocked when they can use external tools. In this tutorial, we'll build a practical AI research assistant that helps students search and summarize academic papers from arXiv using Python and Ollama's LLM with tool-calling capabilities. 📁 Full Project Repository You can access the complete source code for this project on GitLab: AI Research Companion Repository What We're Building Our chatbot will serve as an intelligent research companion capable of searching arXiv—an open-access repository for scholarly preprints in fields like physics, mathematics, computer science, and economics. The assistant will not only find relevant papers but also extract and summarize detailed info...

Deploying Your Local RAG System with Chat Memory to Google Cloud Platform Deploying Your Local RAG System with Chat Memory to Google Cloud Platform This is the third installment in our comprehensive series on building and deploying RAG (Retrieval-Augmented Generation) systems. In Part 1 , we built a foundational RAG system using Ollama and Gemma. In Part 2 , we enhanced it with Redis-based chat memory functionality. Now, we’ll take the next crucial step: deploying our memory-enhanced RAG system to Google Cloud Platform (GCP) for production use. Moving from local development to cloud deployment opens up new possibilities for your RAG system. You’ll gain better accessibility, scalability, and the ability to serve multiple users simultaneously while maintaining the same powerful local AI capabilities we’ve built. Why Deploy to Google Cloud Platform? Before diving into the deployment process, let’s understand why GCP is an excellent choice for hosting your RAG ...

Building a Local RAG System with Chat Memory Building a Local RAG System with Chat Memory Using Redis This is a continuation of our comprehensive guide on building a local RAG system with Ollama and Gemma. If you haven’t read Part 1, we recommend starting there to understand the foundational concepts and basic implementation. In our previous article, we successfully built a functional RAG (Retrieval-Augmented Generation) system that could process documents and answer questions based on their content. However, our system had one significant limitation: it couldn’t remember previous conversations or maintain context across multiple interactions. Today, we’ll enhance our RAG system by adding chat memory functionality using Redis, enabling it to maintain conversation history and provide more contextual responses. This upgrade transforms our stateless question-answering system into an intelligent conversational AI that can reference previous exchanges. Why Add Ch...