Контент от mkdev

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AI Strategy Guide: How to Scale AI Across Your Business

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Evolution of Capacity Management: From Bare Metal to Kubernetes

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Processing Background Jobs on AWS: Lambda vs ECS vs ECS Fargate

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The Ultimate Guide to Cloud Cost Optimization

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The carrot and stick of the EU AI Act

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What is the EU AI Act?

AI Literacy, Humans In The Loop and Machine Learning Evaluation

Error Budget in Cognitive Tasks

Helidon: простой и быстрый Java фреймворк

Для разработки веб-приложений на Java часто используется фреймворк Spring, но в последнее время становятся популярны и другие решения. В своей новой статье Ринат Мухамедгалиев рассказывает про набирающий популярность фреймворк Helidon. Мы будем использовать его модификацию Helidon SE и для примера напишем простенькое приложение.

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Announcing the Open Source Terraform Provider for OpenAI

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Tired of managing OpenAI configs by hand? mkdev just open-sourced a Terraform provider that lets you automate everything—from API keys to generative AI workflows—all as code. Read the article to see how it turns infrastructure and AI into one seamless experience.

Dockerless

Re-explore containers from open standards perspective: learn all about Open Container Initiative (OCI) and how to see containers for what they really are.

[mkdev dispatch #78] We Live In the Future (Except When Europe Bans It)

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In the 78th mkdev dispatch Leo talks about his frustrations with European data protection laws. Also inside: understanding and remediating cold starts, HTTP/2, the Dockerless Course conclusion and more!

DevOps Accents, episode 64: Chat GPT is making you dumber (not a clickbait, there is a research for that)

Chat GPT affects yout ability to think critically! But does it actually make you “dumber,” or does it free up mental bandwidth for more meaningful work? Together with data scientist Paul Larsen, we unpack recent studies from Microsoft and MIT, real cases from software development and education, and translate it into practical rules: when it’s safe to delegate to AI and when you should deliberately “work the mental muscles” yourself (code review, testing, architectural decisions). We also touch on the org-level effect: what happens to a team’s skills if engineers become AI babysitters, and how to build proper model monitoring and evaluation instead of endless human-in-the-loop, and what companies can do to minimize risks.