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.

Are you deeply invested in the Google ecosystem? Do you actively use Google Keep, Tasks, Calendar, and Drive in your day-to-day life? Individually, each of these is an excellent tool—minimal by design but solid in functionality. The only problem is that these apps are mostly isolated from one another. But if you add Gemini as an orchestration layer, all of these tools can start talking to each other. That's exactly what I did, effectively turning Gemini into my personal project management system.

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.

Windows has a way of breaking in ways that feel almost personal. What makes these issues so draining isn't that the problems are complicated; it's that the built-in tools Microsoft gives you to fix them either don't work or don't even come close to addressing what's actually wrong. I started handing these problems to Claude instead, to see if it can do more than just free up space on your Windows PC. I was surprised at how good it was at finding a reasonable solution.

AI promises to make tedious work easier, but I wanted to know whether it could deliver in real Excel projects. Rather than asking Claude for formulas or snippets of code, I tested whether it could handle three types of automation: creating a workbook from scratch, building a reusable reporting system, and developing a tool that analyzes existing spreadsheets. The goal was to see how much of the work Claude could handle and where I would still need to step in.

Popular AI chatbots such as Claude, Gemini, and ChatGPT can analyze data from spreadsheets and other uploaded files. You can upload spreadsheets and ask them to create summaries, find patterns, and more. I wanted to use ChatGPT to analyze my spending to see if it could find ways to save money, but I didn't want to upload my personal information to the cloud. Using a PII-detection tool and a local LLM, I was able to strip away the personal information before I uploaded my files.