👋 Hello! I’m Özkan Pakdil, a software engineer with professional experience since 2004.

• I specialize in Java development and C# but consider myself a full-stack developer with expertise in backend, database, server-side development, UI, and UX.

• My technical journey spans various technologies including Spring Boot, Kubernetes, React, and many more. I’m a Linux enthusiast since 1999 and enjoy optimizing system performance.

• This blog contains articles about technologies I use and my experiences in software development. Feel free to explore my projects on GitHub.

After setting up CUDA on my other laptop, I moved to a different(older) machine that doesn’t have an NVIDIA GPU. This one is an everyday laptop with integrated Intel graphics, but that doesn’t mean we have to settle for slow CPU-only performance. On this machine, I switched to the Vulkan backend for llama.cpp and the results were even more dramatic than I expected. Machine Hardware Info This laptop is running Debian 13 (Trixie/Sid) with the following specs: ...

Setting up NVIDIA CUDA on Debian 13 (Trixie/Sid) to run Large Language Models (LLMs) can be a bit of a journey, especially if you’re transitioning from the default open-source drivers to the proprietary stack required for GPGPU workloads. Over the last few days, I’ve been working on getting llama.cpp to run with CUDA on my laptop to see how much of a difference it makes compared to pure CPU execution. ...

Note: These are suggested optimizations I have not personally tried yet. I’m blogging them as I’m planning to test them throughout this week. OrbStack is highly optimized for macOS, using a proprietary, high-performance networking stack and a custom VirtioFS implementation with aggressive caching. Podman, while being open-source and standard-compliant, can be tuned to significantly bridge the performance gap. The following plan outlines key areas where Podman’s performance can be improved on macOS: ...

I have been using Atlassian MCP with internal Confluence and Jira, and it has been wonderful. Finding internal information is often challenging and time-consuming. To be honest, searching through Jira or Confluence and locating the right information can be really difficult. Create Jira and Confluence API tokens from your internal site profile page. For example: https://internalconfluence.company.com/profile/personal for Confluence and https://jira.company.com/secure/admin/CreateAPIToken!default.jspa for Jira. These URLs may vary depending on your setup. Create an mcp.json file in the .vscode folder for Visual Studio Code, or place this MCP configuration in the appropriate folder for your IDE of choice: { "mcpServers": { "mcp-atlassian": { "command": "uvx", "args": ["mcp-atlassian"], "env": { "JIRA_URL": "https://jira.company.com", "JIRA_USERNAME": "your.email@company.com", "JIRA_API_TOKEN": "your_api_token", "CONFLUENCE_URL": "https://internalconfluence.company.com/wiki", "CONFLUENCE_USERNAME": "your.email@company.com", "CONFLUENCE_API_TOKEN": "your_api_token" } } } } Remember to run podman-desktop or docker desktop. Because this MCP works as a docker container. ...

Have you ever wondered exactly which files are being hammered by your Linux system in real-time? While tools like iotop or lsof are great, sometimes you want something more visual, custom, and lightweight. In this post, I’ll walk you through how I built a Real-time File I/O Heatmap using the power of eBPF for data collection and Java 25 for a modern Terminal UI (TUI). What is eBPF and Why Use It? eBPF (Extended Berkeley Packet Filter) is a revolutionary technology that allows you to run sandboxed programs in the Linux kernel without changing kernel source code or loading kernel modules. ...