The Shift Toward Local AI: A Developer’s Perspective

John Hoestje Articles, Artificial Intelligence, Automation, Machine Learning, Programming, Tutorial Leave a Comment

AI has moved fast, faster than most of us expected, and the cost of keeping up is starting to show. What began as inexpensive, wide-open access to powerful cloud models has shifted into a landscape of rising token prices, stricter quotas, and unpredictable availability. More teams are starting to ask a question that would have sounded unrealistic a year ago: should we start hosting our AI models locally?

In this post, I’ll walk through why token costs and model access are becoming harder to rely on, why open models and local hardware are closing the gap, and what actually happened when I ran a full agentic workflow on my own machine, starting with the cost problem that kicked this off.

Devcontainer.json customization guide blog header — gitignore template, git skip-worktree, and custom devcontainer feature methods, by Jake Everhart, Keyhole Software

Customizing Devcontainers Without Affecting Your Team

Jake Everhart Agentic AI & AI-Accelerated Development, Articles, Development Technologies & Tools, DevOps, Infrastructure As Code (IaC) Leave a Comment

There’s no single “correct” way to customize a devcontainer, just like there’s no one right way to use most tools in software development. Devcontainers can be great for solo projects, to yield an isolated development workspace with no risks of polluting (or being polluted by) the external environment. They can also give teams a consistent, baseline environment for bootstrapping engineers, with integrations like GitHub Codespaces exposing that environment in a variety of ways. But the gap between those workflows is large, and managing it sometimes requires weighing a team’s needs vs. individual preferences.

It’s a balancing act, and getting it wrong can make your containers brittle, difficult to maintain, and cause enough overall frustration to outshine the benefits.

Yet there are options available for a more-granular approach. We don’t have to decide just between bare-minimum or include-everything – we can curate containers that are focused on the core tools used across the entire team, while still providing room for per-user expansions.

Today, we’ll explore a few approaches for customizing a shared devcontainer setup across different team scenarios, without having to abandon shared devcontainers altogether.

Header graphic for Todd Horn's article on AI in SAFe, showing a code editor with an AI-flagged change and a human-approved sign-off.

Beyond Coding: Engineering in an AI-Native SAFe World

Todd Horn Agentic AI & AI-Accelerated Development, Agile, All Industries, Articles, Artificial Intelligence, Project Management Leave a Comment

Artificial Intelligence is changing where engineering value is created: less in producing artifacts by hand, and more in making better technical decisions. In SAFe, that change extends well beyond code generation. It affects how Agile Release Trains prepare for PI Planning, how architects manage the Architectural Runway, how teams validate quality, and how organizations learn from delivery data.

In my earlier three-part series on the Foundations of SAFe, I covered its values, core principles, benefits, drawbacks, and practical application. As SAFe evolves, the next question is how AI will change the way those principles are practiced in real engineering organizations.

This article looks at what AI practices mean for engineering work inside SAFe. Rather than just faster coding, the real gains are in better planning, stronger architecture, faster feedback, and more accountable engineering decisions across the Scaled Agile Framework.