At some point last year I had a folder in my browser called "AI tools to try" with 94 bookmarks in it. I know because I counted. I had been adding things for about eight months — every time a new tool got mentioned in a newsletter, or showed up in a thread I was reading, I would save it and think I'd come back to it later.
I never came back to most of them. And the ones I did check eventually, maybe a third were either gone, had changed completely, or turned out to not be what I thought they were from the original description.
The bookmarks folder approach just doesn't work for this space. Tools move too fast.
The thing nobody tells you about AI tool research
When you're trying to find the right tool for something specific — let's say you need to generate short-form video content from existing footage — you don't just have a discovery problem. You have a recency problem and a comparison problem stacked on top of it.
The recency problem: a tool that wasn't good enough six months ago might be genuinely useful now. The comparison problem: you can't evaluate a tool in isolation. You need to know what else exists in that specific category and what the real differences are, not just the marketing differences.
Search engines mostly surface the same articles over and over. The results for "best AI video tool" are dominated by posts written when the tools were in very early stages, and they rank well because of age and backlinks, not because they reflect the current state of anything.
What actually changed when I started using a directory
I started using AIDirHub a few months ago, partly out of frustration with the bookmarks situation. It covers 325+ tools across 27 categories, and the category structure was immediately more useful than anything I had tried before.
The difference isn't just having a list. It's having a list organized around what you're actually trying to do. There's a real difference between "AI writing tools" as a category and breaking that out into long-form content, short-form copy, translation, summarization, and so on. The latter actually helps you figure out whether a tool fits your situation.
What I use it for most is the comparison layer. When I find something interesting, I want to see what's next to it in the same category — what's the obvious alternative, what are the main functional differences, what does pricing look like across options. That's hard to reconstruct from bookmarks and searches. It's straightforward when the tools are already grouped by function.
The bookmarks folder still exists
I'll be honest: I still save individual links sometimes. Old habits. But the folder has 11 things in it now instead of 94, and most of them are tools I've already tested and am actively considering.
The shift was less about finding a better tool and more about changing how I approach the research phase. Starting from a directory category instead of a search query means you're already operating at the right level of specificity. You're comparing things that are actually comparable, not just things that happened to rank for the same keyword.
For anyone who works in a field where AI tooling is actually relevant to what you do day to day — and that's a lot of fields at this point — having a structured way to navigate what exists is genuinely worth the small amount of time it takes to orient yourself. The discovery problem isn't going away. The space is still expanding. If anything, having good habits around how you track it matters more than it did a year ago.