Currently learning
Prompt engineering, AI-assisted debugging, and knowing when not to use AI at all.
Every project is a chance to learn where these tools genuinely help and where a human touch still matters most. I'm building in the open — this page tracks the toolbox, the milestones, and what I'm learning next.
The same four steps, every time.
"If I cannot explain the change, it does not go live."
A rotating toolbox of AI assistants I use for writing, coding, and problem-solving.
What it is
Anthropic's AI assistant, built for careful reasoning, writing, and code — with a large context window for working across many files at once.
What I use it for
My main pair-programmer. Architecture decisions, writing and refactoring code, and most of the actual building on this site.
What it is
OpenAI's general-purpose AI assistant — one of the most widely used chatbots for writing, research, coding help, and everyday questions.
What I use it for
Everyday writing, brainstorming, and quick research — usually the first place I go with a loosely-formed idea.
What it is
xAI's AI assistant, built into X (Twitter), known for a more direct, less filtered tone and awareness of real-time trends and conversations.
What I use it for
A second opinion when I want a blunter, less hedged take — or to cross-check how a different model reasons about the same question.
What it is
An AI-native code editor (a fork of VS Code) with chat and autocomplete built directly into the development workflow.
What I use it for
In-editor pair programming — fast iteration on layout, CSS, and JavaScript without leaving the code.
What it is
Google's multimodal AI model, integrated across Google's products, capable of working with text, images, and code together.
What I use it for
Cross-checking Claude's reasoning on trickier problems and getting a genuinely different perspective before committing to an approach.
What it is
Microsoft's AI assistant, built on OpenAI's models and woven into Windows, Edge, and Microsoft 365.
What I use it for
Comparing suggestions against more traditional, Microsoft-centric tooling — useful when a project touches that ecosystem.
What it is
An open-source runtime for running large language models entirely on your own hardware — no cloud, no API calls.
What I use it for
Running Gemma 4 (12B) locally for anything involving private or sensitive data, and as a fast daily driver that never leaves my machine.
Mac Mini · Ollama · Gemma 4 (12B) — sized to run comfortably on the hardware in front of me, not in someone else's data center. Client files never leave this desk.
Claude, ChatGPT, Grok, Cursor, Gemini, Microsoft Copilot, Ollama, and their respective logos and trademarks are the property of their owners — Anthropic, OpenAI, xAI, Anysphere, Google, Microsoft, and Ollama, Inc. They're shown here purely to identify the tools I personally use; this does not imply any affiliation, sponsorship, or endorsement by those companies. Links open each provider's own site in a new tab.
A few milestones along the way.
This is xAI's agent platform, not the Grok chatbot — worth keeping straight. Instead of asking it a question and reading the answer, I hand it an actual task and walk away. It runs on its own persistent cloud desktop, logging into the same sites and tools I'd use myself, and only checks back in when it needs my sign-off. I've started running a few in parallel and showing one a workflow once so it can repeat it — genuinely useful for the boring recurring stuff like inbox triage, expenses, and reproducing bugs. Still early and in beta, bundled into the paid SuperGrok and Cursor plans, but it's shifted how I think about what an "AI assistant" even is.
Picked up an Apple Mac Mini and started experimenting with local AI models — no cloud required. Ollama running Gemma 4 (12B) has become a genuinely fast, capable daily driver, running entirely on my own hardware. Just as important: it opens up a way to work with private or sensitive data entirely within my own, fully controlled environment, instead of sending it off to a cloud AI service.
Now focused on getting more deliberate about how I work with AI — better prompts, better judgment about when to trust the output, and figuring out where it fits into a real product workflow.
Brought AI into my actual workflow — using Cursor and Claude to speed up front-end development and explore ideas faster. This very site was built with AI assistance.
Started comparing how different models reason through the same problem, learning to spot where each one's strengths and blind spots show up.
Started experimenting with AI for everyday writing, brainstorming, and researching — mostly out of curiosity about what it could actually do.
The trust rules behind every project, no exceptions.
An illustrative example, not a real transcript — three draft directions, then the human last mile.
Prompt: "Write a 12-word eyebrow for a locksmith in Bad Kreuznach."
"Trusted metalwork craftsmanship for homes and businesses across Bad Kreuznach."
"Precision locksmithing and steelwork, proudly serving the Bad Kreuznach region."
"Railings, staircases, steel — local locksmith craft built to last generations."
"Balcony railings, staircases, and stainless steel — built in Bad Kreuznach, for the long run."
Prompt engineering, AI-assisted debugging, and knowing when not to use AI at all.
Claude and Cursor, for how naturally they fit into an actual coding workflow.
Faster front-end iteration, drafting copy, and exploring ideas before committing to one.
Updated whenever a tool, model, or habit changes.