What does it actually look like to work with a personal AI agent day-to-day? Real workflows combining MCP servers, OpenClaw skills, and 4 weeks of production usage patterns. The good, the unexpected, and the genuinely useful.
After building both OpenClaw skills and MCP servers, what did I learn about the right abstraction layer? And where are personal AI agents heading in the next 5 years? A reflection on tooling, standards, and the shift from AI-as-service to AI-as-infrastructure.
How do you build an AI agent with genuine agency that remains safe? Defense-in-depth with network isolation, identity-driven boundaries, permission documentation, and audit trails. Production lessons from 4 weeks of real usage.
How to build Model Context Protocol servers that actually work in production. Deep-dive in auto-generation from API routes, permission models, and why 13 small tools beat one smart tool.
Why I moved my AI agent stack from Kubernetes to a bare metal laptop, what broke along the way, and the 98% success rate that followed. A technical journey through OpenClaw, Ollama, and production AI infrastructure.
Deep dive into creating a production-ready research agent with LangGraph, streaming support, and OpenWebUI integration featuring collapsed reasoning sections
How to run the Azure DevOps MCP Server in Kubernetes using the ToolHive Operator for integration with Open WebUI and other AI tools.
On November 11, 2025, the Kubernetes community announced the end of Ingress NGINX. The project that had been the de facto standard for routing traffic to Kubernetes workloads for years is retiring in March 2026. No new releases, no bugfixes, no security patches. For me, this meant one thing: time to migrate. Why Ingress NGINX is Shutting Down The story behind the retirement is actually quite sad. Despite its enormous popularity, the project was maintained for years by just one or two volunteers working in their spare time, on evenings and weekends....
How I built an AI-powered system that deploys real Azure infrastructure through natural conversation, running 100% locally with open-source models - no API costs!
Prerequisites Windows with WSL2 installed NVIDIA RTX 5070 GPU NVIDIA drivers installed on Windows host Issue Overview RTX 50-series GPUs use CUDA compute capability sm_120, which requires PyTorch with CUDA 12.8 support. Standard PyTorch installations only support up to sm_90, causing runtime errors. Installation Steps 1. Setup Conda Environment # Install Miniconda (if not already installed) wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh bash Miniconda3-latest-Linux-x86_64.sh source ~/.bashrc # Create new environment conda create -n f5-tts python=3....