Engin Diri

OpenClaw is everywhere right now. The open-source AI assistant gained 9,000 GitHub stars in a single day, received public praise from former Tesla AI head Andrej Karpathy, and has sparked a global run on Mac Minis as developers scramble to give this “lobster assistant” a home. Users are calling it “Jarvis living in a hard drive” and “Claude with hands”—the personal AI assistant that Siri promised but never delivered.

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In my previous post about the Ralph Wiggum technique, Claude Code built a complete serverless URL shortener on AWS. The setup included Playwright MCP for end-to-end testing. It worked. But I kept wondering if there was something better for AI-driven browser automation. Then Vercel released agent-browser, and I had to try it.

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I was about to do something that felt either genius or completely reckless: hand over my AWS credentials to an AI and step away from my computer. The technique is called “Ralph Wiggum,” named after the Simpsons character who eats glue and says “I’m in danger” while everything burns around him. And honestly, that felt about right for what I was attempting.

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The upcoming retirement of ingress-nginx in early 2026 gives infrastructure teams both a deadline and an opportunity to rethink traffic management. Configuring the Ingress API often meant relying on controller-specific annotations that varied between implementations. The Gateway API offers a cleaner, standardized alternative. This post investigates the practical reality of this migration and explores why kgateway emerges as a robust solution for the future.

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Every developer building AI agents knows the gap between a working prototype and production deployment. Your fraud detection agent works perfectly on your laptop, but how do you deploy it with proper authentication, memory persistence, observability, and guardrails? This post walks through a complete journey from local development to production-ready AI agents using Amazon Bedrock AgentCore, the Strands SDK, and Pulumi.

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The IDE is dying, and so is tool calling. OpenAI is not going to win. And next year, you’re going to be shipping code that you’ve never reviewed before, even as an experienced engineer.

These are bold claims, but the way we use AI in 2026 for coding and agents is going to look completely different. In this post, I want to cover my predictions and why they matter right now for DevOps engineers. Some of these are definitely hot takes, but that’s what makes this conversation worth having.

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Ask a generic LLM to “fix my broken deployment,” and you’ll get generic advice. Ask Pulumi Neo the same question, and you’ll get a fix plan grounded in your actual infrastructure state.

The difference isn’t about better prompts or newer models. It’s about what the AI actually knows. Generic LLMs have been trained on the internet. Neo has been trained on your infrastructure.

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Welcome to the second post in our IDP Best Practices series. In this article, we explore how to create golden paths, pre-architected, reusable infrastructure patterns that help standardize and accelerate cloud development.

Modern cloud platforms offer endless options, over 200 AWS services, sprawling Azure catalogs, and countless DevOps tools. The result? Developers face decision fatigue and inconsistent implementations. Golden paths solve this by providing ready-to-use, production-grade infrastructure that encodes your organization’s best practices, security policies, and operational standards.

In this guide, you’ll learn how to build golden paths for your Internal Developer Platform using two core Pulumi constructs: Components, reusable infrastructure building blocks, and Templates, predefined, deployable patterns. You’ll see how to create infrastructure abstractions that are written once, shared across teams, and consumed in any language, turning weeks of setup into minutes of developer-ready infrastructure.

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Welcome to the first post in our IDP Best Practices series. In this guide, we’ll walk through the strategic foundations for designing an Internal Developer Platform that empowers developers without sacrificing governance, security, or operational control.

At Pulumi, we’ve worked with hundreds of teams facing the same core challenge: How do you give developers the infrastructure access they need, while maintaining the governance and security your organization requires?

That tension is at the heart of every IDP conversation. Teams want to move faster and innovate, but also need to stay compliant, control costs, and maintain operational stability.

The good news? You can do both, with a clear strategy and the right approach. This series shares proven best practices for designing, building, and scaling IDPs using Pulumi.

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Developers are losing days every month to infrastructure bottlenecks, compliance hurdles, and inconsistent environments. Platform engineering promised to fix that, yet too many platforms fail before they deliver real impact.

In this comparison of Backstage vs Pulumi IDP, we’ll explore why choosing the right architectural approach matters more than the tool itself.

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