DevOps Accents

In this episode of DevOps Accents, Kirill previews for Leo his speech for DevOps Pro Europe 2026. He examines the rise of Kubernetes as a near-requirement in tech—for both organizations and professionals—and why its dominance has surprisingly little to do with scaling, microservices, or capacity concerns. Also in this conversation:How to become a public speaker?When not to use Kubernetes?Do we need to talk about GitOps?Check out our website to learn more about the services mkdev offers for companies.Subscribe to our newsletter.Buy mkdev merchandise here.Show Notes:DevOps Pro Europe 2026.Kirill’s speech on May 20.mkdev’s tips for passing CKA exam.Preparing for CKA certification with a real example.all mkdev videos on Kubernetes and containers.CNCF report Leo mentions.Podcast editing: Mila Jones / [email protected]

In this episode of DevOps Accents Kirill is back from Bits & Pretzels 2025 in Munich and is ready to discuss the conference experience with Leo and Pablo. We also talk about networking and tech hiring in 2025 and anywhere ops. In this episode:Kirill’s impressions of Bits & Pretzels 2025;The pitch contest on the conference;The networking role of conferences;Changes in traditional recruitment and work environment.Check out our website to learn more about the services mkdev offers for companies.Subscribe to our newsletter.Buy mkdev merchandise here.Show Notes:Bits & Pretzels 2025 (Munich) recap;Izar Aerospace, European rocket startup mentioned by Kirill;Scottish Four-Day Week Public-Sector Trial;Facebook Jobs Relaunch (2025) - is it an indicator that social hiring is back?Starlink Direct-to-Cell as a vital part of anywhere ops.

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.Also in this episode:Cognitive offloading vs. genuine cognitive-load savings; why “less mental pain” ≠ “less intelligence.”An “error budget” for tasks: where the cost of a mistake is high vs. where AI can operate under monitoring.Human-in-the-loop vs. production ML practices: logs, quality metrics, drift, and periodic re-evaluation.How the “charming politeness” of chat models lowers skepticism and how to counter it.“Dosed” use of assistants: what to do yourself (planning, hypothesis framing) vs. what to hand over to AI.Graph-based models in the enterprise: why large companies pick standard, lower-risk use cases.Data contracts, CI/CD for data, and “AI for data → better data for AI.”Advice for listeners: avoid flattening teams into “assembly & polishing” roles and preserve core competencies.Show notes:• Survey of knowledge workers: higher trust in GenAI correlates with less critical-thinking engagement. Microsoft Research• LLM-assisted writing reduced recall and neural engagement; 83% in the LLM group couldn’t accurately quote their own text. MIT Media Lab• After habituation to AI support, endoscopists’ without-AI accuracy dropped (−6 percentage points ADR). The Lancet Gastroenterology & Hepatology• A high-schooler’s first-person account of AI disrupting classroom learning. The Atlantic• Relational/graph foundation model for enterprise predictive apps; background + papers. Kumo.ai · Research• GenAI cyber-risk in practice: threat model and developer guidance by Paul Larsen. Article 1 · Article 2Imkdev is a consultancy for Public Cloud (AWS/GCP), Data/ML, and Platform Engineering. We help companies accelerate delivery, reduce cloud costs, and deploy GenAI safely (while not making you dumber along the way) - from audits and architecture to productionizing models, monitoring, and team training: https://mkdev.me/Visit mkdev.me and subscribe to MKDEV Dispatch for new episodes and articles: https://mkdev.me/dispatchBuy mkdev merchandise here: https://store.mkdev.me

Data Science 2.0 is the shift from dashboards to decision workflows. Suzanne El-Moursi shows how teams can go beyond “reports nobody trusts” to agentic, governed analytics, using a pragmatic stack (infrastructure → composable tools → governance) so insight can flow from ingestion through modeling, validation, and visualization without the usual stalls. We focus on how the data scientist role evolves from dashboard factory to orchestration and stewardship so the right decision happens at the right time. Also in this episode:Problems we face in data in different eras of tech;The role of a data scientist in the age of AI;What parts of the classic data scientist job fade?Who does the data evaluation?3 things to learn right now;How to make your project AI agent ready?Check out our website to learn more about the services mkdev offers for companies.Subscribe to our newsletter.Buy mkdev merchandise here.Show Notes:About Suzanne El-Moursi: Co-founder & CEO of Brighthive, a Chicago-based platform building agentic AI for the data lifecycle (BrightAgent) with governance built in; earlier leadership stints at IBM and GE Healthcare; and a board member at Uniting Voices Chicago.Brighthive Newsletter on LinkedIn where they share their expertise and observation on the Data Science transformation.Follow Suzanne on LinkedIn for more insights.Check out Brighthive, Suzanne's company, an AI-powered enterprise data platform that uses coordinated agents to automate the full data lifecycle inside a data warehouse.

Kubernetes alone isn’t enough! This is what we discuss with Mark Fussell, co-creator of Dapr and Diagrid co-founder. How Dapr’s sidecar gives developers clean APIs (pub/sub, service invocation, secrets, state) with tiny overhead? And how durable workflows and “agentic” apps with Dapr Agents actually function? Also in this epsiode:A short history of architecture models;Differences between service meshes and Dapr;Agentic systems and conductors: what is what?The future of agentic applications;Will the sidecar model go away?Check out our website to learn more about the services mkdev offers for companies.Subscribe to our newsletter.Buy mkdev merchandise here.Show Notes:Follow Mark on X.Follow Mark on Linkedin.Regular posts and insights on Dapr and Diagrid.State of Dapr Report by CNCF that we discussed during the episode.