Open Source

This is were I will blog about Open Source technology.

Exploring AI, Kubernetes, and Multicloud Cost Management: My Latest Pluralsight Articles

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As cloud-native infrastructure, Kubernetes, AI, and multicloud strategies continue to evolve, I recently had the opportunity to publish several new articles on the Pluralsight Blog focused on some of the biggest operational and architectural trends happening right now.

These articles explore the growing role of AI in Kubernetes operations, the realities of multicloud cost management, and the rise of agentic tooling for cloud platforms.

New Blog Posts on Pluralsight.com

Agentic CLI for AKS: FAQs and how to use it

In this article, I break down Microsoft’s emerging Agentic CLI for AKS experience and explain what it actually is, how it works, and where it fits into Kubernetes operations. The post explores how the tool uses AI to assist with troubleshooting and diagnostics for Azure Kubernetes Service environments while still keeping humans in control of operational decisions.

You can read it here:

Agentic CLI for AKS: FAQs and how to use it

Best multicloud cost management tools and methods

Multicloud environments can create massive flexibility, but they also create significant operational and financial complexity. In this article, I explore practical multicloud cost management strategies, tooling approaches, and methods organizations can use to improve visibility and optimize spend across AWS, Azure, and GCP.

Read the article here:

Best multicloud cost management tools and methods

Understanding AI agents for Kubernetes

AI agents are quickly becoming one of the most interesting emerging areas in cloud-native operations. This article explores what AI agents for Kubernetes actually are, the problems they aim to solve, and some of the current tools and approaches appearing in the ecosystem. I also discuss where these systems may realistically help platform teams and where caution is still needed.

Check it out here:

Understanding AI agents for Kubernetes: Tools, use cases, and more

These topics sit at the intersection of AI, cloud engineering, Kubernetes operations, platform engineering, and FinOps, and they represent some of the biggest conversations happening across the industry right now. If you’re working in cloud-native infrastructure, platform engineering, DevOps, or AI-enabled operations, I hope these articles provide useful insight and practical perspective.

Be sure to follow my profile on Pluralsight so you will be notified as I release new courses! 

Here is the link to my Pluralsight profile to follow me:

https://www.pluralsight.com/authors/steve-buchanan

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Course 31 “Applying Terraform in Google Cloud Environments” Published!

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Ready to Level Up Your GCP Skills? My New Terraform Course Is Live. I’m excited to share that my latest Pluralsight course, Google Cloud Environments: Applying Terraform, is now live!

Infrastructure as Code (IaC) has become a foundational skill for modern cloud engineering teams, and Terraform continues to be one of the most widely adopted tools for managing cloud infrastructure consistently and at scale. In this course, I focus on how to apply Terraform effectively within Google Cloud environments to help engineers move beyond basic concepts and into practical, real-world implementation patterns.

Whether you’re a cloud engineer, DevOps engineer, platform engineer, or someone expanding into Google Cloud, this course is designed to help you build confidence working with Terraform in GCP environments.

What You’ll Learn

In this course, we walk through how Terraform can be used to deploy and manage infrastructure in Google Cloud using Infrastructure as Code principles. Topics include:

  • Understanding Terraform workflows in Google Cloud
  • Configuring providers and authentication
  • Managing infrastructure declaratively
  • Working with state management
  • Deploying and updating cloud resources
  • Using reusable Terraform configurations and modules
  • Applying Terraform concepts to real Google Cloud scenarios

The course is designed to help bridge the gap between learning Terraform syntax and actually applying it in cloud engineering environments.

Why Terraform Matters

Terraform enables teams to define infrastructure in code, making deployments more repeatable, scalable, and reliable. Instead of manually configuring cloud resources through portals and scripts, teams can standardize infrastructure deployments and treat infrastructure similarly to application code.

As organizations continue adopting cloud-native and multi-cloud strategies, Infrastructure as Code skills are becoming increasingly valuable across engineering and operations teams.

Hands-On Cloud Engineering Skills

One thing I always try to emphasize in technical training is practical application. The goal is not just to understand Terraform conceptually, but to understand how engineers actually use it in day-to-day cloud operations and platform engineering work.

Google Cloud and Terraform together create a powerful combination for:

  • Automated infrastructure deployments
  • Consistent environment management
  • Scalable cloud operations
  • DevOps and platform engineering workflows
  • Repeatable infrastructure provisioning

Pluralsight also provides broader Terraform and cloud engineering learning paths that complement these skills with additional hands-on labs and cloud-focused training.

Who This Course Is For

This course is a great fit for:

  • Cloud engineers
  • DevOps engineers
  • Platform engineers
  • SREs
  • IT professionals transitioning into cloud engineering
  • Anyone looking to strengthen their Terraform and Google Cloud skills

If you already have some familiarity with cloud concepts and want to deepen your Infrastructure as Code knowledge in GCP, this course should provide a solid next step.

Check It Out

You can view the course here: Google Cloud Environments: Applying Terraform on Pluralsight

I hope this course serves as a valuable resource in your IaC journey. Thank you for your continued support, and Be sure to follow my profile on Pluralsight so you will be notified as I release new courses! 

Here is the link to my Pluralsight profile to follow me:

https://www.pluralsight.com/authors/steve-buchanan

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Bridging the Clouds: Back on RunAs Radio

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It’s hard to believe, but it’s been a couple of years since I last sat down with Richard Campbell on RunAs Radio. Technology moves fast, but the cloud landscape has matured in ways that were only just beginning during my last visit.

I recently joined Richard for my third appearance on the show (Episode #1025) to talk about a challenge that is becoming the “new normal” for major SaaS providers: Expanding a Cloud-Native stack across multiple clouds.

From Single-Cloud Roots to Multi-Cloud Reality

At Jamf, we’ve built a powerful reputation for managing Apple devices at scale. Historically, our SaaS product was rooted in AWS. However, as our customer base grows, now serving over 70k+ customers worldwide the demand for flexibility grows with it.

In this episode, we discuss the journey of bringing those SaaS workloads to Azure and AKS. It isn’t just about “moving” code; it’s about architecting for consistency without losing the unique benefits of each cloud provider.

Kubernetes: The Common Ground (But Not the Whole Story)

One of the key takeaways from our chat is that while Kubernetes (AKS, EKS, GKE) provides the common operating system for the modern cloud, it isn’t a “magic wand” for multi-cloud.

To achieve true consistency, you have to look past the orchestrator and focus on the surrounding ecosystem. We dove into the complexities of:

  • IaC & Deployment: Why tools like OpenTofu are becoming essential for maintaining cloud-agnostic deployments.
  • Observability: Using Prometheus and Grafana to ensure that your SRE teams see the same data regardless of whether the backend is Azure or AWS.
  • Identity: Navigating the friction between different identity providers to ensure a seamless experience for the end user and how platforms like Okta support this.

The Docker & AI Connection

We couldn’t have a conversation in 2026 without touching on the elephant in the room: AI. As a Microsoft MVP focused on AKS and a Docker Captain, I’ve been watching closely how the Kubernetes and container ecosystem is evolving to support AI/ML workloads. Richard and I spent some time discussing how Docker, Inc. is positioning itself in this space and how developers can leverage these tools to build AI-ready applications without getting locked into a single vendor’s proprietary stack.

Reflections on a Maturing Landscape

Coming back to RunAs Radio for a third time allowed me to reflect on just how much our industry has shifted. We’ve moved past the “is the cloud safe?” phase and into the “how do we optimize for a multi-cloud world?” phase.

Whether you are a platform engineer, a developer, or a technical leader, the lessons I’ve learned at Accenture, Microsoft, helping startups, and now at Jamf while scaling across multple clouds are applicable to almost any modern enterprise.

You can listen to the full episode here: RunAs Radio #1025: SaaS on Multiple Clouds with Steve Buchanan

I’d love to hear your thoughts. Is your organization looking at multi-cloud for SaaS, or are you doubling down on a single provider?

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Speaking at Open Source North 2025 on Multi-Cloud

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I am excited to share that I will be speaking at this year’s Open Source North conference on May 29, 2025, at the University of St. Thomas in St. Paul.

This year, I’m teaming up with my fellow Jamf, Levi McCormick (Director of Engineering at Jamf), for a session that is very close to our daily reality: Multi-Cloud Without the Marketing or Designing for Multi-Cloud Without Losing Your Mind.

Why this talk? In the cloud industry, “Multi-Cloud”, “Cloud Native”, and “Iac via Terraform” are often sold as magic pills for redundancy, cost savings, unifaction and more across clouds. But for the people actually building and maintaining these systems, it can often feel like a recipe for complexity and technical debt.

At Jamf, Levi and I work on our infrastructure efforts across AWS, Azure, and GCP. We’ve learned—sometimes the hard way—what works, what doesn’t, and where the “hype” version of cloud differs from the “production” version. We wanted to build a session that focuses on the practical:

  • How to design for portability without over-engineering.
  • Managing identity, networking, and security across different providers.
  • Avoiding the “lowest common denominator” trap.
  • Keeping your sanity while managing three different clouds.

Open Source North is a great local event to the MN Tech scene because of the high-caliber community and the focus on real-world engineering. Whether you are a cloud veteran or just starting to look at a second provider, we’d love to see you there.

The Details:

If you’re attending, please connect on LinkedIn or find us after the session. We’d love to hear how your team is tackling these same challenges!

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State of App Dev Report by Docker

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As devs, platform engineers, and DevOps practitioners, we all feel it: the pace of change is relentless. New tools, new architectures, new expectations, and AI. It can be hard to separate where to invest our time from hype.

That’s exactly why I want decided to write this post about the 2025 Docker State of Application Development Report from Docker.

This report is not marketing fluff. It’s based on insights from over 4,500 developers and engineering professionals and offers a grounded snapshot of how application development is actually evolving today.

Although published in 2025, this report covers long-running trends that continue to shape modern application development. Areas like containerized workflows, cloud-based development environments, AI-assisted tooling, and shared responsibility for security evolve over time rather than changing overnight.

Referencing the 2025 report ahead of the 2026 release provides valuable context. It establishes a baseline for understanding where the industry is coming from, which patterns are proving durable, and which challenges continue to persist. I’ll be looking out for the 2026 report. If you havent checked it out the 2025 report yet you should.

As a Docker Captain, I strongly encourage you to read the full report. But first, here are some of the key takeaways that stood out to me:

Remote-First Development Is Becoming the New Normal

One of the biggest shifts in 2025 is how developers are working:

  • 64% of developers now use non-local development environments as their primary setup
  • Only 36% rely primarily on local machines

That’s a significant change from previous years, and it speaks to the reality that cloud-based workflows, remote dev environments, and tools that unify development environments are now mainstream. This shift isn’t just a trend — it’s redefining how teams collaborate and deliver software efficiently.

Developer Productivity Still Faces Friction Points

The report highlights that, despite improvements in tooling and culture, many teams still experience bottlenecks in everyday work:

  • Pull requests stuck in review
  • Tasks without clear estimates
  • Slowdowns in the “inner development loop”

Even with great culture and tooling, friction still exists, especially around planning and execution. Knowing where dev productivity stalls helps us focus improvements where they matter most.

Learning Is Shifting to Self-Guided, Online Resources

Developers are reinventing how they learn:

  • 85% of respondents use online courses or certifications
  • Traditional sources like books or on-the-job training are less dominant

This highlights a bigger trend in continuous learning and self-driven skill development — especially important as the pace of change in languages, platforms, and architectures continues to accelerate.

AI Adoption Is Real, But Not Uniform

AI continues to influence how software is built, but adoption is still uneven:

  • Some teams are deeply integrating AI tools
  • Others are more cautious or selective

The report frames AI as an enabler, not a magic bullet. Developers are using AI to assist with documentation, research, and repetitive tasks, but real productivity gains depend on meaningful integration into workflows and data quality.

Security Is a True Team Effort

Security is no longer siloed:

  • Teams of all sizes report that developers, leads, and operations are involved in security
  • Only a small fraction of organizations outsource security entirely

The idea that “security is someone else’s job” is gone — fixing vulnerabilities and embedding security thinking into the development lifecycle is now a collective responsibility.

What This All Means for Developers

Taken together, these findings show a software landscape that’s:

  • More distributed and cloud-native
  • More self-taught and adaptable
  • More collaborative around security
  • Still facing persistent productivity barriers

These trends have real implications for how we build teams, invest in tooling, and think about developer experience.

Go Read the Full Report

The 2025 Docker State of Application Development Report is packed with additional insights, data, and analysis. Whether you’re a developer curious about AI adoption, a manager thinking about remote workflows, or a team lead prioritizing security practices, there’s something in this report for you.

Check out the full report on Docker’s blog:
https://www.docker.com/blog/2025-docker-state-of-app-dev

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Azure Hub-and-Spoke Architecture Explained and Automated with OpenTofu

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This is my first blog of the new year (2026)! Since being re-awarded as a Microsoft MVP, Microsoft provided me with a fresh set of Azure credits. One of the first things I wanted to do was rebuild my Azure lab environment. This time, I wanted to do it the right way. I wanted it to mirror how I would design and deploy a real enterprise environment, including running fully on private endpoints and following a proper hub-and-spoke network model.

Just as importantly, I wanted everything defined in Infrastructure as Code (IaC) so I could spin environments up and down whenever I needed. That also aligns perfectly with what my team at Jamf is working on right now. We are making some changes to our underlying Azure architecture, including deeper network isolation, security controls, intergration with Jamf security cloud security products, and a shift from Bicep to OpenTofu. We will also be using AI agents to do a lot of the heavy lifting in that refactor. I will be sharing more about that in future blogs and talks as much as I am able to publicly.

Because OpenTofu is at the center of that work, I decided to build my entire Azure lab using OpenTofu and a full hub-and-spoke architecture. This gives my team a real, working reference base implementation that we can build on for production designs. I also want to share this with the larger tech community.

If you are note familiar with OpenTofu it is an open source infrastructure-as-code engine based on Terraform that lets you define, deploy, and manage cloud infrastructure using declarative configuration files, and you can learn more at https://opentofu.org.

You can access the GitHub Repository of my “OpenTofu Azure Hub and Spoke” solution here: https://github.com/Buchatech/OpenTofu-Azure-HubSpoke-public

Lets break down whats in the solution I built.

Solution Architecture

The solution deploys a production-style Azure network and platform foundation that includes:

  • Hub VNet with Azure Firewall, VPN Gateway, and DNS Private Resolver
  • Spoke VNet with peering and default routes through the firewall
  • Key Vault and Azure Container Registry using private endpoints
  • Optional Jumpbox VM for secure management access
  • GitHub Actions CI/CD pipeline using OIDC authentication

How the Automation Works

This is a multi-part solution built around a bootstrap Bash script (bootstrap.sh) and a fully generated OpenTofu repository.

The bootstrap script creates everything you need to get started:

  1. It creates an Azure Storage Account to store your OpenTofu remote state.
  2. It generates a complete OpenTofu project, including modules, variables, and environment structure.
  3. It configures the backend so OpenTofu uses Azure Storage for state.
  4. It creates a ready-to-use GitHub Actions pipeline for CI/CD.

Once the repository is generated, you can deploy your Azure environment by running OpenTofu locally or by pushing the repo to GitHub and letting the pipeline handle deployments for you. Within minutes, you can have a fully functional Azure hub-and-spoke environment up and running, and you can customize the generated modules to fit your own requirements.

Deployment Modes

The bootstrap bash script supports two deployment modes depending on how advanced and locked-down you want the environment to be.

FULL Mode (Default)
This is the enterprise-grade option.

  • Hub VNet with Azure Firewall, VPN Gateway, and DNS Private Resolver
  • Spoke VNet with peering and default route through the firewall
  • Private endpoints for Key Vault and Azure Container Registry
  • Optional Jumpbox VM for secure management
  • GitHub Actions CI/CD pipeline with OIDC authentication

BASIC Mode
This is a simpler version for learning or labs.

  • Hub VNet with Azure Firewall only
  • Spoke VNet with peering and default route through the firewall
  • Public access for Key Vault and Azure Container Registry
  • No Jumpbox, VPN Gateway, or DNS Private Resolver
  • GitHub Actions CI/CD pipeline with OIDC authentication

What the bootstrap.sh Script Does

When you run the bootstrap script, it will:

  1. Prompt you to select FULL or BASIC deployment mode
  2. Create an Azure Storage Account for OpenTofu remote state in rg-tfstate
  3. Generate the full OpenTofu repository structure based on your choice
  4. Configure the OpenTofu backend to use the storage account
  5. Create GitHub Actions workflow files for CI/CD
  6. Output the storage account details and the GitHub secrets you need to configure

From there, you are ready to deploy and customize the script and OpenTofu based on your Azure hub-and-spoke environment entirely through code.

Here is the Readme from the repo. It goes even more in depth into my “OpenTofu Azure Hub and Spoke” solution. I hope you find it useful!

********************************************************************************

Azure Hub-Spoke with OpenTofu

Azure base network architecture solution

This repository contains a production-ready, modular OpenTofu configuration that deploys Azure hub-spoke network topology with two deployment modes (private or public) to match your requirements and budget.

Architecture Overview

This solution deploys a hub-and-spoke network architecture (visual shows full-private deployment):

Enterprise-grade Azure network architecture lab environment with Site-to-Site VPN, Azure Firewall, DNS Private Resolver, and core services

This repository contains a production-ready, modular OpenTofu (Terraform) configuration that deploys a complete Azure hub-spoke network topology designed for hybrid cloud scenarios, connecting your on-premises network (e.g., UniFi network) to Azure.

Architecture Overview

This lab deploys a hub-and-spoke network architecture following Azure best practices (visual shows full private deployment):

┌──────────────────────────────────────────────────────────────────────┐
│                            AZURE CLOUD                                │
│                                                                        │
│  ┌─── HUB VNet (rg-lab-hub-network) ────────────────────────┐        │
│  │ 10.10.0.0/16                                              │        │
│  │                                                            │        │
│  │  ┌──────────┐  ┌───────────┐  ┌────────────┐  ┌───────┐ │        │
│  │  │  Azure   │  │    VPN    │  │    DNS     │  │Jumpbox│ │        │
│  │  │ Firewall │  │  Gateway  │  │  Private   │  │  VM   │ │        │
│  │  │(10.10.1.0│  │(10.10.2.0)│  │  Resolver  │  │(Mgmt) │ │        │
│  │  │)+ DNAT   │  │           │  │(10.10.4-5.0│  │subnet │ │        │
│  │  │SSH:2222  │  │           │  │)           │  │       │ │        │
│  │  └─────┬────┘  └─────┬─────┘  └────────────┘  └───────┘ │        │
│  │        │             │                                     │        │
│  │        │             │  Site-to-Site VPN                  │        │
│  └────────┼─────────────┼─────────────────────────────────────┘        │
│           │             │                                               │
│           │  VNet Peering + Gateway Transit                            │
│           │             │                                               │
│  ┌────────▼─ SPOKE VNet (rg-lab-spoke1-network) ──────┐               │
│  │ 10.20.0.0/16                                        │               │
│  │                                                      │               │
│  │  ┌──────────┐  ┌──────────┐  ┌──────────────────┐ │               │
│  │  │   Apps   │  │   APIs   │  │   Data/Services  │ │               │
│  │  │ Subnet   │  │ Subnet   │  │     Subnet       │ │               │
│  │  │          │  │          │  │  - ACR (Private) │ │               │
│  │  │          │  │          │  │  - Key Vault     │ │               │
│  │  └──────────┘  └──────────┘  └──────────────────┘ │               │
│  │                                                      │               │
│  │  Traffic routed through Azure Firewall ─────────────┘               │
│  └──────────────────────────────────────────────────────               │
│                                                                         │
│  ┌─── Management RG (rg-lab-management) ────────────┐                 │
│  │  - Azure Container Registry (ACR)                 │                 │
│  │  - Azure Key Vault                                 │                 │
│  │  - Private Endpoints in Spoke Data subnet         │                 │
│  └────────────────────────────────────────────────────┘                 │
│                                                                         │
└─────────────────────────────┬───────────────────────────────────────────┘
                              │
                      S2S VPN Tunnel (IPsec)
                              │
              ┌───────────────▼──────────────┐
              │   ON-PREMISES NETWORK        │
              │   (e.g., UniFi Router)       │
              │   192.168.1.0/24             │
              │                              │
              │   SSH → Azure Firewall:2222  │
              │   → DNAT → Jumpbox:22        │
              └──────────────────────────────┘

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Docker Hardened Images Are Now Free: What This Means for Developers and Platform Teams

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Last week Docker made a big move for the container ecosystem. Docker Hardened Images (DHI) are now free and open source, making secure container foundations accessible to everyone.

If you build, deploy, or operate containerized workloads, this is one of those changes that quietly but meaningfully improves day to day security and reliability.

Let’s break down what Docker Hardened Images are, why they matter, and how you can start using them today.

What Are Docker Hardened Images?

Docker Hardened Images are base container images that come pre-hardened for security and transparency. Instead of starting from a generic base image and layering on your own security practices, DHI gives you a safer starting point out of the box.

They are designed to reduce common container risks without adding operational overhead or complexity.

In practical terms, this means Docker has already done the work many teams struggle to keep up with.

What You Get Out of the Box

When you use Docker Hardened Images, your base images now:

  • Include automated security metadata
  • Are minimalist and optimized for faster builds and startup times
  • Contain significantly fewer known vulnerabilities (CVEs) from the start
  • Are fully free and open source

This shifts container security left, right to the foundation of your application images.

There still is a paid version of Docker Hardened Images for those that have enterprise needs. Here is a breakdown of what you get with the Free Docker Hardened Images and the Paid version.

Why This Is a Big Deal

Most container vulnerabilities originate from base images. Teams often inherit outdated packages, unused libraries, or poorly maintained dependencies without realizing it.

Docker Hardened Images help address that by:

  • Reducing the attack surface before you write any application code
  • Improving transparency into what is inside your images
  • Lowering the burden on platform and security teams
  • Making secure defaults accessible even to small teams and solo developers

Security becomes the baseline rather than an afterthought.

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My First Docker Captain Summit Experience

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As many of you know, I was honored to be named a Docker Captain earlier this year (2025). This week, I had the incredible opportunity to attend my very first Docker Captain Summit, and what an experience it was.

The event reminded me a bit of the Microsoft MVP Summit, but with even closer access to the Docker product teams across multiple areas. Every year, the Captain Summit takes place in a different location, bringing together Docker staff from product groups, community management, marketing, and DevRel, along with fellow Docker Captains from around the world.

At the summit, we got an inside look at Docker’s roadmap and were among the first to learn about upcoming products and initiatives. We also had the opportunity to provide direct feedback to the product teams, helping shape the future of Docker from the community’s perspective.

This year’s summit was held in Istanbul, and it was a fantastic few days of connecting with so many brilliant people. I finally met in person several Docker staff members and Captains I’ve been collaborating with online. It was also a chance to reunite with friends from Microsoft and the MVP community.

Of course, not everything we discussed can be shared publicly because of NDAs, but I can tell you that we all walked away with some exciting insights and some awesome Docker swag.

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Im Speaking at BITCON 2025 – Easiest Way to Run LLMs Locally: Meet Docker Model Runner

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🎤 I’m excited to share that I’ll be returning to BITCON in a week! I will be speaking at BITCON 2025, a gathering focused on Black voices in technology, innovation, and community. You can check out the full speaker lineup here: BITCON 2025 Speakers. The conference this year is virtual and its free. You can check out the site here: https://bitcon.blacksintechnology.net

The conference has a ton of great speakers lined up from some of the largest tech companies such as Google, Microsoft, and more. And to top it off the keynote this year is Kelsey Hightower! You dont want to miss this one.

My Session: “The Easiest Way to Run LLMs Locally: Meet Docker Model Runner”
Docker Captain: Steve Buchanan DMR session

At BITCON, I’ll be presenting “The Easiest Way to Run LLMs Locally: Meet Docker Model Runner”. In this session, I’ll look at:

  • Why run LLMs locally? The benefits in terms of cost, privacy, latency, and control
  • How Docker Model Runner simplifies things — containerizing large models, managing dependencies, and lowering friction
  • Demo and walkthrough — showing you step by step how to get a model up and running on your own machine or server
  • Best practices, pitfalls, and tips — what I’ve learned building and deploying these systems
  • Q&A / hands-on help — to get you started with your own setup

My goal is that attendees leave with a concrete, reproducible process they can apply right away.

Why It Matters

Large language models (LLMs) are powerful, but running them locally has often felt out of reach for smaller teams, indie devs, or people in resource-constrained environments. With the right tooling (like Docker Model Runner), we can lower that barrier—unlocking more experimentation, more privacy, and more control over where and how inference happens.

I believe this aligns well with the mission of BITCON: elevating voices, demystifying advanced tech, and making it accessible. I hope this talk helps bridge a gap for folks who want to explore AI locally without getting lost in infrastructure.

I am excited to be speaking at BITCON again. To learn more about my session check it out here:

BITCon Session: The Easiest Way to Run LLMs Locally: Meet Docker Model Runner

BITCON is free! Be sure to register today: HERE

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Recent Blog Posts: MCP Servers, Dev, Multi-cloud Mastery, and Cloud Engineer Resumes

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This is a shorter post, but I wanted to take a moment to share what I’ve been working on lately. Over the past few months I’ve been publishing a steady stream of blog posts on Pluralsight, covering topics across cloud, AI, JavaScript, and beyond. There’s a lot happening in tech right now, and I’ve been fortunate to collaborate with the Pluralsight team to dive into some of these exciting areas:

Check out an overview the blog posts and use the the following links to read more:

Behind the Buzzword: What is MCP (MCP Server)?
A breakdown of MCP servers and why they matter in the evolving landscape of AI.
👉 Read the post

How to Run an LLM Locally on Your Desktop
Exploring why and how you might want to run a large language model on your own machine, with a closer look at Docker Model Runner.
👉 Read the post

What to Emphasize on Your Resume as a Cloud Engineer
Tips on showcasing the skills that make cloud engineers stand out in today’s job market.
👉 Read the post

Multicloud Mastery: How to Train Teams in AWS, Azure, and GCP
Practical advice on enabling engineering teams to work across multiple clouds with confidence.
👉 Read the post

6 Cloud Cost Optimization Strategies and Tools for AWS, Azure, and GCP
A set of proven strategies and tools to help control and reduce cloud spend.
👉 Read the post

How to Add User Authentication to Your JavaScript App
A straightforward guide to securing your JavaScript applications with simple authentication techniques.
👉 Read the post

I’ll be continuing to publish more content in the months ahead, so stay tuned for future posts on cloud-native engineering, AI, and practical developer skills. If you found these articles useful, I’d love for you to check them out and share them with your network.

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