About
Lifelong learner. AI enthusiast. Semi-vibe coder. Tech advocate.
Curious about interesting things, how systems fit together, and all things tech.
I study how AI fits into real work, then build, test, and document what actually helps.
Why Jabez AI exists
There is no shortage of AI news, tools, prompts, and opinions. If anything, the bigger problem is bombardment: too many tools, too many claims, and too much pressure to keep up.
The harder question is knowing what is actually useful, where it fits in a workflow, where it adds real value, and what should probably stay manual.
I’m interested in the space between hype and implementation—the point where you have to decide what to use, what not to use, when to use it, and how to make the whole system work better.
How I think and work
Learn → Build → Test → Document → Publish → Improve
That loop is how I make sense of new technology. I try to understand the system first, identify the real constraint, and test the smallest useful intervention. I use AI where judgment adds value, use deterministic automation where the rules are already known, and avoid adding complexity just because a new tool exists.
Sometimes the right answer is an AI assistant. Sometimes it is automation. Sometimes it is a better process. Sometimes it is doing nothing new at all.
What you can expect
I do not want Jabez AI to become another AI-news feed or tool directory. I want it to help people make better technology decisions.
The goal is not to convince you to use more AI. It is to help you make better decisions about where AI belongs.
- Real AI experiments, including what did not work
- Practical tutorials and build logs
- Decision frameworks and workflow thinking
- Careful tool evaluations
- Reusable resources that help you try the method yourself
When I test something, I aim to show the setup, the evidence, the limitations, and what I would change next. The useful lesson is not always “use this tool.” Often, it is knowing where the tool belongs—and where it does not.
About me
Jabez ClarkeAI PractitionerAutomation BuilderCX Leader
I’m Jabez Clarke. I’m interested in systems, technology, learning, and the connections between them.
My background in technical support and customer experience leadership shapes how I approach AI: understanding real problems, building practical systems, and testing whether they actually work.
I tend to learn by building things, testing ideas, and documenting what changes my mind. That naturally pulls me toward systems thinking: understanding how the pieces connect, finding the real constraint, and choosing an intervention that is useful without making the whole system more complicated.
FOLLOW THE WORK
I share new Jabez AI experiments, builds, articles, and observations on LinkedIn between publications.
How AI is involved
Jabez AI is custom built with the help of AI tools and agentic development workflows.
I use AI as a collaborator in research, prototyping, testing, writing, and implementation, but I review, test, and take responsibility for what gets published. The goal is not to automate judgment away. It is to use the right kind of help at the right stage of the work.
Start here
If you are new to Jabez AI, choose the path that matches what you need:
- Need to decide where AI fits?
AI Tool Fit Finder → - Want to see an AI system built and audited?
I Built an AI Business Intelligence Tool. Here’s What Grounding Alone Didn’t Solve. → - Want to understand how ChatGPT Skills work?
ChatGPT Skills Explained: I Built One and Tested It → - Want to see a controlled AI experiment?
Skills Lab: Building and Testing a ChatGPT Systems Strategist Skill →
Start small. Keep the review step. Let the evidence decide what deserves to become a bigger system.