Most people who use AI assistants spend more time switching windows than they think. You copy something, alt+tab to a browser, open a chat window, paste it in, wait, copy the response, and tab back to wherever you were. If you do that twenty times a day, you're losing more time to that routine than to the actual problem you're trying to solve. Wiring your Windows clipboard directly to Claude's API will help you out.

What I want from AI is for it to take the boring and mundane jobs off my hands and free me up to do more fun stuff. Reading my emails before starting work each day is time-consuming and dull, so I decided to hand the task off to Claude.

Large language models (LLMs) might be the most accessible and flexible technology we've ever had access to. By default, they're presented as chatbots—but that's just one way to interact with them. With the right prompts, you can transform an LLM into something that behaves like an entirely different app, complete with its own custom interface.

The way to use a chatbot is given in the name. They are designed to be used by having two-way conversations with the LLM using natural language. You type something into the chatbot, and it replies. In some cases, continually typing stuff into a chatbot isn't the most efficient way to do things, so I set up a system that does it for me.

I used to spend far too much time building and debugging complex Excel formulas. Now, I can describe a problem to AI and get a working answer in seconds. But when I asked ChatGPT, Claude, and Gemini for the "best" Excel formula, they all gave me different answers.

The more comfortable I get using AI to help me code, the more curious I become about how the major platforms compare when they're given the exact same job. It's easy to ask Claude, ChatGPT, or Gemini to create a simple app and come away impressed when something functional appears a few seconds later. But that doesn't necessarily tell you whether the code is secure, well structured, or capable of handling situations beyond the most obvious use case.

If there are three things folks typically know about me, it's that I love Linux, I'm an avid reader and I have a bit of an obsession with modding Kindles and Fire Tablets. I would say, "Hey, I resemble that remark!" and then wait patiently while no one catches the reference. One Kindle has eluded me all these years, but I finally found one: a Kindle Fire 7 (2012). I tried to mod it, with a little help from Claude.

When I first installed a local LLM, I expected to use it the same way that I'd been using ChatGPT. It soon became apparent that on my modest hardware, this wasn't going to work. By changing the way I use my local LLMs, they've become much more useful.