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There’s a moment that kind of sneaks up on you when you’ve been writing for a while, especially if you’ve started using AI tools regularly. You stop asking whether AI was used at all, and instead start wondering how much it actually shaped what you’re reading. That shift is subtle, but once you notice it, you can’t really unsee it.
Local LLMs have rapidly evolved beyond text and are now capable of producing high-quality images directly on-device. For users running Apple Silicon machines—especially M-series Mac Studios and MacBook Pros—this represents a major shift in what’s possible without relying on cloud services. Just a few years ago, image generation required powerful remote GPUs, subscriptions, and long processing times. Today, thanks to optimized models and Apple’s Metal acceleration, you can generate and edit images locally with impressive speed and quality. The result is a workflow that is faster, private, and entirely under...
Recently, after some bad experiences with OpenAI's ChatGPT and CODEX, I decided to look into and learn more about running local AI models. On its face it was intimidating, but I had seen a lot of people in the MacAdmins community posting examples of macOS setups, which really helped lower the bar for me both in terms of approachability and just making me more aware of the local AI community that exists out there today.
I really feel like in this era of AI it's essential to write about and share experiences for others who are leveraging AI, especially now that AI usage seems almost ubiquitous. Specifically, when it comes to AI in development and the rapid growth of AI-driven automations in the IT landscape, I believe there's a need for open discussion and exploration.
So there I was, a typically day, a typical weekend. As a ChatGPT customer, I had heard good things about Codex and had not yet tried the platform. To date my experience with agentic coding was simply snippit based support with ChatGPT and Gemeni where I would ask questions, get explanations and support with squashing bugs in a few apps that I work on, for fun, on the side. There were a few core features in one of the apps I built that I wanted to try implementing but the...
Managing and maintaining a growing collection of scripts in a GitHub repository can quickly become cumbersome without automation. Whether you're writing bash scripts for JAMF deployments, maintenance tasks, or DevOps workflows, it's critical to keep things well-documented, consistently versioned, and easy to track over time. This includes ensuring that changes are properly recorded, dependencies are up-to-date, and the overall structure remains organized.