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

Upgrade your wardrobe, desk or everyday carry with unique cloud and DevOps swag from the mkdev Store: https://store.mkdev.me/#!/

Can AWS Lambda MicroVMs replace GitHub-hosted runners? This article compares costs, isolation, scaling and runtime limits, then shows how to deploy serverless GitHub Actions self-hosted runners with an open-source Terraform module: https://mkdev.me/posts/using-lambda-microvms-as-github-actions-runners

Move beyond isolated AI prototypes. mkdev helps teams align AI with business goals, build the right technical foundation and scale successful initiatives across the organization. Check out the page and schedule a call: https://mkdev.me/b/consulting/ai

AI models have become substantially more capable since the EU AI Act entered into force in August 2024. OpenAI’s current GPT-5.6 models can support complex professional work, use tools and complete multi-step tasks with much less human guidance than earlier GPT generations.

Greater capability does not remove the need for governance. It makes it more important to understand where AI is operating, which decisions it influences and how humans supervise its output.

Under the EU AI Act, a spam filter, an AI recruitment system and a general-purpose model are not treated as the same kind of risk. The obligations depend on the use case, the system’s role and its potential effect on health, safety and fundamental rights. Businesses may also face transparency requirements when people could reasonably mistake AI-generated output for human-created content.

By mid-2026, AI literacy and prohibited-practice requirements are already applicable, as are obligations for general-purpose AI model providers. Further transparency rules apply from 2 August 2026, while major high-risk-system requirements follow later.

Read the article for a practical overview of the timeline and its implications for your business.

https://mkdev.me/posts/when-does-the-eu-ai-act-come-into-force-and-what-does-this-mean-for-your-business

Without open standards, every container tool could have its own incompatible image format, runtime and registry protocol. This video explains how OCI prevents that fragmentation. Watch it here: https://www.youtube.com/watch?v=oUlD_dOdL04

Discover the foundations of OpenShift 4 cluster management, starting with its reimagined installation experience and continuing through its most important features. Free mkdev course —

Articles: https://mkdev.me/posts/production-openshift-cluster-in-35-minutes-first-look-at-okd-4-and-the-new-openshift-installer
Videos: https://www.youtube.com/playlist?list=PLozcbFx8FoPGM-Dk1jVBu58pwUGP9WGIX

A request passing through a CDN can arrive at your backend with much more context than it had when it left the user’s browser.

Depending on the provider and configuration, that context can include an approximate country, region, city, timezone, postal code and coordinates. Cloudflare and Amazon CloudFront can generate this information from the visitor’s IP address and forward it as HTTP headers.

This is fast, inexpensive and requires very little application code. It is not precise enough for navigation or other location-sensitive features, but it is often sufficient for analytics, localisation and broad geographic restrictions.

We explain the setup and trade-offs in this article: https://mkdev.me/posts/free-and-lightning-fast-ip-geolocation-from-a-cdn-provider

Kubernetes complexity can hide security gaps, wasted capacity and operational friction. mkdev audits your clusters and applications, then delivers practical recommendations and concrete backlog items. Check out the page and schedule a call: https://mkdev.me/b/audits/kubernetes-audit-assessment

“We only use ChatGPT as a copilot, so the EU AI Act does not really apply to us.”

That is an understandable assumption, but it is incorrect. Article 4’s AI literacy requirement applies to organisations whose employees use tools such as ChatGPT for professional purposes, including relatively ordinary tasks such as drafting marketing copy or translating text.

A human reviewing the answer does not remove the requirement. The person providing that oversight needs enough knowledge to recognise unreliable information, inappropriate recommendations, disclosure of confidential data and situations in which AI should not be used at all.

This has become more relevant with GPT-5.5 and GPT-5.6. Current models can perform deeper analysis, interact with tools and complete multi-step work that earlier chatbots could not reliably handle. Treating them as slightly more convenient search engines is no longer an adequate approach to governance.

The AI Act does not prescribe one identical course for everyone. It expects organisations to take a role-specific and risk-based approach. Our article looks at why ChatGPT falls within the requirement and where companies should begin.

https://mkdev.me/posts/my-company-is-using-chatgpt-does-the-ai-act-literacy-requirement-apply-to-us

Struggling to decide what matters when learning a new technology? Start with the concepts, explore the details when a real project demands them, and don’t confuse theoretical knowledge with experience. Learn more through the Prometheus example in this article.

https://mkdev.me/posts/how-to-learn-new-technologies-prometheus-example

In the 95th mkdev dispatch Kirill talks about cloud providers inventing new execution layers for AI Agents to operate in. Also inside: trusting Claude Code a bit too much, tracking Amazon Bedrock Costs, spinel and more!

https://mkdev.me/posts/do-ai-agents-need-new-containers-95

Should you define every GKE resource yourself or use a Terraform module?

Building a basic cluster from individual resources is a good way to understand what Terraform is actually creating. A maintained module becomes useful once you need repeatable configurations, multiple environments and more advanced GKE features without duplicating large amounts of code.

This article demonstrates both approaches, including VPC-native networking, node pools, service accounts and the complete Terraform deployment lifecycle.

A practical introduction for anyone starting to automate GKE: https://mkdev.me/posts/deploying-gke-clusters-with-terraform

Your delivery pipeline should help you move faster without sacrificing stability. mkdev designs CI/CD systems around your team, product and business, from organizational challenges to technical implementation. Check out the page and schedule a call: https://mkdev.me/b/consulting/majestic-pipeline