How to design an agentic harness: components, 13 harnesses compared, a data engineering reference architecture, risk controls, and a phased roadmap. Free PDF.
Data Migration Without the Drama: A 2026 Guide for CEOs and CIOs
Most data migrations run over time or budget. Here are the root causes, a gated phase-by-phase plan, AI guardrails, and a checklist for CEOs and CIOs.
The Model Is the Easy Part: Why the Agentic Harness Decides What Ships
The same AI model can cost 5x more depending on the harness around it. Here’s what an agentic harness is, what we’d build first, and the controls we won’t skip.
The Shift Toward Local AI: A Developer’s Perspective
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.
Customizing Devcontainers Without Affecting Your Team
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.