Latest Posts
Top 10 GitHub Project Setup Tricks You MUST Use in 2025!
Have you ever seen a GitHub issue that just said “it’s broken” with zero context? Or reviewed a pull request where you had no idea what changed or why? How many hours have you wasted chasing down information that should have been provided upfront?
Here’s the reality: whether you’re maintaining an open source project, building internal tools, or managing commercial software, you face the same problem. People file vague bug reports. Contributors submit PRs without explaining their changes. Dependencies fall months behind. Security issues pile up. And you’re stuck playing detective instead of building features.
But here’s what most people don’t realize: all of this chaos is preventable. GitHub has built-in tools for issue templates, pull request templates, automated workflows, and community governance. The problem is that setting all of this up manually takes hours, and most people either don’t know these tools exist or don’t bother configuring them properly.
Deploy AI Agents and MCPs to K8s: Is kagent and kmcp Worth It?
What if you could manage AI agents with kubectl? kagent lets you define AI agents as custom resources, give them tools, and run them in your cluster. kmcp deploys MCP servers to Kubernetes using simple manifests. Both promise to bring AI agents into the cloud-native world you already know.
The idea sounds compelling. Create agents with YAML, connect them to MCP servers, let them talk to each other through the A2A protocol. All running in Kubernetes, managed like any other resource. It’s the kind of integration that platform engineers dream about.
But there’s a gap between promise and reality. We’re going to deploy both tools to a Kubernetes cluster, create agents, connect them to MCP servers, and see what actually happens when you try to use them. We’ll find out if this is the future of AI in Kubernetes, or if we’re solving problems that don’t need solving.
Gemini 3 Is Fast But Gaslights You at 128 Tokens/Second
Gemini 3 is fast. Really fast. But speed means nothing when the AI confidently tells you it fixed a bug it never touched, or insists a file is updated when it’s completely unchanged. That’s not laziness. That’s gaslighting at 128 tokens per second.
AI vs Manual: Kubernetes Troubleshooting Showdown 2025
It’s 3 AM. Your phone buzzes. Production is down. A Pod won’t start. You run kubectl events, wade through hundreds of normal events to find the one warning that matters, describe the Pod, check the ReplicaSet, trace back to the Deployment, realize a PersistentVolumeClaim is missing, write the YAML, apply it, validate the fix. Thirty minutes later, you’re back in bed, wondering if there’s a better way.
There is. What if AI could detect the issue, analyze the root cause, suggest a fix, and validate that it worked? What if all four phases happened automatically, or at least with your approval, while you stayed in bed?
I’m going to show you exactly how to do this with Kubernetes. First, we’ll walk through the manual troubleshooting process so you understand what we’re automating. Then I’ll show you an AI-powered solution using Claude Code and the Model Context Protocol that handles detection, analysis, remediation, and validation. Finally, we’ll look under the hood at how the system actually works.
AI Agent Architecture Explained: LLMs, Context & Tool Execution
You type “Create a PostgreSQL database in AWS” into Claude Code or Cursor, hit enter, and boom - it just works. Database created, configured, running. Like magic.
But it’s not magic. Behind that simple request is an intricate dance between you, an orchestrator called an agent, and a massive language model. Most people think the AI is doing everything. They’re wrong. The AI can’t touch your files, can’t run commands, can’t do anything on its own.
So how the hell does it work? How does your intent turn into actual results? That’s what we’re going to break down. The real architecture. The three key players. And why understanding this matters if you’re using these tools every day.
Best AI Models for DevOps & SRE: Real-World Agent Testing
You’re a software engineer. Maybe you’re doing DevOps, SRE, platform engineering, or infrastructure work. You’re using large language models, or at least you should be. But which ones? How do you know which model to pick?
I was in the same situation. I made choices based on gut feelings, benchmark scores that didn’t mean anything in production, and marketing claims. I thought I should change that.
So I ran ten models from Google, Anthropic, OpenAI, xAI, DeepSeek, and Mistral through real agent workflows. Kubernetes operations. Cluster analysis. Policy generation. Systematic troubleshooting. Production scenarios with actual timeout constraints. And the results were shocking compared to what benchmarks and marketing promised.
Seventy percent of models couldn’t finish their work in reasonable time. A model that costs 120 dollars per million output tokens failed more evaluations than it passed. Premium “reasoning” models timed out on tasks that cheaper models handled easily. Models everyone’s talking about couldn’t deliver reliable results. And the cheapest model? It delivered better value than options costing twenty times more.
By the end of this article, you’ll know exactly which models actually work for engineering and operations tasks, which ones are unreliable, which ones burn your money without delivering results, and which ones can’t do what they’re supposed to do.