Worthy Trying is a practical publication about what happens when AI is used on real work—not just what the technology promises it can do.
Our articles grow out of projects: writing and SEO systems, website debugging, coding, image generation, construction instructions, research, workflow design, business comparisons, and the mistakes that appear while trying to make those things work reliably.
Why Worthy Trying Exists
AI makes it easy to generate an answer. That does not automatically make the answer useful, correct, repeatable, or worth publishing.
We became interested in the gap between what AI appears able to do and what happens when the result has to survive a real task. Sometimes the tool saves substantial time. Sometimes it creates a new problem. Sometimes a failed approach teaches more than a successful first attempt.
Worthy Trying documents those lessons so another reader can begin with more evidence than we had.
What We Publish
The site is organized around seven kinds of work: using AI, reliability, long-running workflows, writing and SEO, images and design, coding, and practical real-world projects.
We do not create a separate page simply because another keyword variation exists. A topic should have a distinct reader job and enough legitimate value to stand on its own.
Failures Belong Here Too
A failed workflow is not automatically wasted work. If it exposes a repeatable limitation, a hidden dependency, a bad assumption, or a better way to structure the task, it can become useful evidence.
That is why Worthy Trying includes mixed and unsuccessful results alongside the things AI did well. The goal is not to prove that AI is always good or always bad. The goal is to understand where it actually helps, where it needs stronger controls, and when a human should remain firmly in charge.
How We Handle Evidence
We distinguish firsthand project observations from broader factual claims. When a statement depends on outside facts, current product behavior, search guidance, security recommendations, or another external source, we check the evidence appropriate to that claim.
Where a direct test exists, we prefer it: run the code, inspect the page, reconcile the dimensions, compare the image with the specification, or measure the result.
You can read the full publication method on How We Test AI at Worthy Trying.
What We Want a Reader to Leave With
Every Worthy Trying article should answer a real question, explain what the work taught us, and make the next decision easier. We would rather publish a smaller collection of connected, useful pages than manufacture a large inventory of nearly identical content.
If you are new here, browse all experiments or start with the big questions on the homepage.