AWS has too many moving parts for guesswork: IAM, S3 costs, EKS vs ECS, Lambda, EC2, monitoring, migrations. mkdev’s in-depth AWS audit helps you find what to improve and what to rethink. Check out the page and schedule a call: https://mkdev.me/b/audits/in-depth-aws-audit-and-assessment

A lot of AI discussions still jump too quickly to tools: which model, which vector database, which agent framework, which platform.

Those decisions matter, but they are not the starting point. The mkdev AI strategy guide makes a more practical point: before big platform choices, a company needs a management sponsor, the right AI talent, and a clear view of how hard the problem is from the business, data, and AI perspectives.

That is a very useful filter for 2026. The AI market is louder than ever, and every vendor has a story about productivity, automation, and agents. But if the problem is poorly scoped, if the data is not ready, or if the technical metric has no connection to a business KPI, the project can look impressive and still create very little value.

AI strategy should help companies decide what to build, what to buy, what to postpone, and what not to do at all.

That last part is underrated.

https://mkdev.me/posts/ai-strategy-guide-how-to-scale-ai-across-your-business

Cloud security often fails in the basics: unclear responsibility, excessive access, missing encryption, weak logging, and untested incident response.

This checklist breaks it down into 7 practical steps for reducing risk: https://mkdev.me/posts/cloud-security-checklist-7-essential-steps

Practical cloud work is always easier to explain through real projects. Our case studies show how mkdev helped teams with identity management, cloud infrastructure, AWS cost optimization, Kubernetes decisions and more.

Read them here: https://mkdev.me/b/cases

Many backend bugs are not caused by bad SQL syntax. They are caused by perfectly valid SQL running concurrently.

Two transactions can read the same row, calculate new values, and write back results that silently overwrite each other. Another transaction can re-run a query and see a different set of rows. A group of transactions can commit successfully, but still produce a result that could not happen if they had run one after another.

This is why transaction isolation levels matter. PostgreSQL gives you several tools here: the default Read Committed, stronger Repeatable Read, strict Serializable, and explicit locks when you need more control.

The catch is that stronger isolation may mean more retry logic and different performance characteristics. You do not need to use the strictest level everywhere, but you do need to understand what guarantees your code actually relies on.

We covered the main isolation levels and common anomalies in this article: https://mkdev.me/posts/transaction-isolation-levels-with-postgresql-as-an-example

From ETL pipelines and data lakes to warehouses, streaming, and cloud-scale infrastructure, mkdev helps teams make data available, organized, and ready for real business use. Check out the page and schedule a call: https://mkdev.me/b/consulting/data-engineering

A lot of GenAI discussions still focus on code generation. But for many data and AI teams, the bigger value is elsewhere.

The mkdev article points to a more practical pattern: GenAI helps teams search internal knowledge, draft requirements, create explanations for model recommendations, prototype faster and make expert knowledge easier to access. These are not always the most glamorous use cases, but they often remove real bottlenecks in how teams work.

That feels even more true in 2026. AI assistants can speed up parts of individual work, but faster output also creates new pressure on review, evaluation, governance and maintainability. The productivity question is no longer just “How much more can we generate?” It is “Can the organization absorb, verify and safely use what gets generated?”

For data and AI leaders, that is the real management challenge now.

https://mkdev.me/posts/how-genai-has-and-hasn-t-changed-the-way-allianz-thoughtworks-and-outbrain-lead-data-and-ai-teams

You can run data jobs in Jenkins. You can patch servers with Airflow. But should you?

This video breaks down how job execution systems work and how to think about choosing the right tool.

Watch it here: https://youtu.be/YjFbTNdXhQo?si=w8lcCQNg4N2XbltN

mkdev’s Helm Lightning Course is a practical introduction to Helm for people who already know the Kubernetes basics and want to understand how Helm actually helps in day-to-day deployments. It covers the core ideas behind Helm, including charts, releases, templates, values, upgrades, and rollbacks, using a simple pgAdmin example throughout the course.

It also goes beyond the basics with lessons on hooks, chart dependencies, ArtifactHub, and three useful Helm plugins: Helm Secrets, Helm Diff, and Helm Git. You can go through the course as articles here: https://mkdev.me/posts/why-do-you-even-need-helm, or watch the video version here: https://www.youtube.com/playlist?list=PLozcbFx8FoPHqL9Gm1IpboF45gekmgYjO. Use it for free and make your Kubernetes deployments easier to manage.

IAM policy generation is still one of the less glamorous parts of AWS work, but it matters a lot.

Too many applications run with permissions they don’t need because nobody wants to manually map every code path to every AWS API action. That’s understandable, but it’s also how small shortcuts become long-term risk.

IAM Access Analyzer gives you a native AWS path: analyze CloudTrail activity, generate a policy template, then review and customize it. ActionHero gives you another angle: observe the SDK calls your app makes and use that as input for a tighter policy. Both approaches have limitations, but they move the conversation from “what do we think this app needs?” to “what did this app actually try to use?”

That shift is still very relevant in 2026.

Read the article here: https://mkdev.me/posts/how-to-create-aws-iam-policies-with-actionhero-and-access-analyser

Not sure if your Google Cloud setup is still the right fit for your workloads?

mkdev’s GCP Audit reviews your infrastructure from security and cost to availability, automation and DevOps practices.

Check out the page and schedule a call: https://mkdev.me/b/audits/google-cloud-platform

One of our favorite AI projects wasn't flashy at all.

It was automatically generating alt text for more than a thousand images already published on our website. The goal wasn't to create new content—it was to improve accessibility, make images easier for search engines to understand, and eliminate hours of repetitive manual work.

Back then we relied on the first generation of GPT-4 Vision. In 2026, the implementation is even more straightforward. Current GPT multimodal models provide stronger image understanding, better OCR, more consistent descriptions, and generally require less prompt tuning while fitting into the same kind of automated processing pipelines.

The technology has improved, but the principle hasn't changed: the most valuable AI projects are often the ones users never notice. They simply make your systems—and your team's workflows—a little bit better every day.

If you're curious how we built the original solution and how the architecture works, check out the article below.

https://mkdev.me/posts/how-to-add-alt-text-to-1000-images-with-gpt-4-vision-ai

One Cloud Run setting can have a noticeable impact on your cloud bill: CPU allocation.

This article compares always-on vs on-demand CPU, explains why the cheaper-looking option isn't always the cheapest, and shows how workload patterns should drive your decision.

Read the full breakdown here: https://mkdev.me/posts/google-cloud-run-always-on-vs-on-demand-cpu-allocation