mkdev

We’ve been using Infrastructure as Code for over a decade. The tools have changed, but the principle hasn’t: infrastructure should be understandable, version controlled, tested and automated. Learn how we work and start a conversation: https://mkdev.me/b/consulting/iac

AWS gives you plenty of ways to analyze cloud spend, but the analysis is only as useful as the metadata behind it. For smaller AWS setups, you don’t necessarily need an elaborate tagging framework. A simple baseline of environment, workload, and name can already make it much easier to understand which application or environment is responsible for a particular part of the bill. Terraform’s default_tags can help apply that baseline consistently across supported resources. The other important step is easy to miss: tags need to be activated as Cost Allocation Tags before you can properly use them for cost analysis. A relatively small amount of tagging discipline can make Cost Explorer considerably more useful. Our article walks through a simple approach to getting started with AWS cost allocation tags: https://mkdev.me/posts/control-aws-costs-with-these-3-cost-allocation-tags

In the 98th mkdev dispatch, Pablo talks about how AI is creating a new digital divide where access to powerful tools depends not on infrastructure or skills, but on geography, provider restrictions, and permission to participate in the AI economy. Also inside: scaling Terraform across many Teams and more! https://mkdev.me/posts/the-ai-divide-is-no-longer-about-access-to-the-internet-98

Kirill Shirinkin has spent decades across software development, infrastructure and DevOps. For nearly a decade, his own development setup barely changed. Then AI arrived. Now he shares what he’s learned from rebuilding his workflow around AI: how he works with coding agents, manages context, makes architecture decisions, organizes parallel work, automates reviews and deployments, and decides what should still remain firmly in the engineer’s hands. He also looks at the less glamorous side of working at AI speed: avoiding bad technical decisions, keeping projects under control, and staying sane when the amount of work you could do suddenly feels almost limitless. https://mkdev.me/posts/the-agentic-engineering-myth-1-year-of-coding-with-ai

There are two GenAI security concepts that are easy to mix up: jailbreaks and prompt injection. A jailbreak generally tries to make an AI system ignore its restrictions and produce something it shouldn’t. Prompt injection can go further. The goal may be to manipulate an AI system into taking actions or accessing systems and data that the attacker should never be able to reach. As GenAI gets connected to more tools, APIs, and business systems, that distinction becomes increasingly important. The potential consequences move from “the chatbot said something bad” to data leakage, compromised systems, and operational disruption. Our guide explains these risks from a product manager’s perspective, alongside another major issue: how employees and users handle sensitive data with GenAI tools. Read the full article on mkdev: https://mkdev.me/posts/genai-security-risks-for-product-managers-dd73bdc2-4f2e-4227-93b3-375da081d906

Using Google Cloud doesn't automatically mean you're using the right services in the right way. Our GCP Audit helps uncover unnecessary costs, security gaps, reliability issues and opportunities to simplify your infrastructure. Take a look at what we cover, and schedule a conversation with our team: https://mkdev.me/b/audits/google-cloud-platform

For a while, microservices felt like the inevitable destination of every serious application. Then teams discovered the other side of the equation: more services also mean more network calls, more APIs, more deployments, more dependencies, and more opportunities for things to fail in ways that are difficult to reproduce locally. Google’s Service Weaver experimented with an interesting alternative. Developers could structure a Go application as a set of components without immediately committing every component to a separate service. Deployment topology could be decided later. Service Weaver didn’t become the future of application development, and the project has since been archived. But the problem it was trying to solve hasn’t disappeared. The interesting part of Service Weaver in 2026 isn’t the framework itself. It’s the question it leaves behind: should our code architecture really be so tightly coupled to our deployment architecture? https://mkdev.me/posts/service-weaver-monolithic-or-microservice

Want to understand containers beyond Docker? Our free Dockerless course takes you through OCI, open container standards, and the fundamentals that make modern containers work. Article series: https://mkdev.me/posts/what-s-wrong-with-docker-introduction-to-the-dockerless-course Video: https://www.youtube.com/playlist?list=PLozcbFx8FoPH30kYPbPuPsvxASWoLo9XB

A useful Cloud Run distinction: Services → request-driven applications Jobs → run-to-completion workloads And when the workload can be divided, Cloud Run Jobs can execute multiple tasks in parallel. We break down the idea with a practical example here: https://www.youtube.com/watch?v=n8GyTp-kP_M

The interesting part about modern image models might not be image quality anymore. It’s iteration. Generating one impressive image is easy. Generating 50 or 100 versions, remembering what you disliked about previous attempts, making targeted corrections and gradually converging on something useful is a different problem. That’s why combining Claude Code with image-generation models turned out to be more interesting than simply using another image-generation UI. Claude can maintain the context of the task and use Nano Banana Pro or GPT Image 2 as tools. It can look at the output, notice that an object is positioned strangely or that the result doesn’t quite satisfy the request, and take another shot. That turns image generation from a sequence of isolated prompts into something closer to an iterative creative workflow. We explored the approach in this article and made the skills public: https://mkdev.me/posts/unlimited-image-generation-with-nano-banana-pro-gpt-image-2-and-claude-code-skills