As you might imagine, I do a lot of web search in the course of my day. Often these queries are quick reference lookups; I need to find a company’s social media, for example, or I want to see how to spell someone’s name.

Unfortunately these kinds of searches on Google are being met more and more with AI-generated results. I have tried all the little tricks to avoid them, but the tricks are getting less and less useful as Google appears determined to ram AI down our collective throats. Every time I see the AI response being generated for a basic search, I wonder how much energy and water it cost and I feel bad that I set it off.

After using a number of my own tools to try and get information without doing an open search on Google, I have combined some of the elements of MegaGladys, SearchTweaks, and RSSGizmos to make MiniGladys, a Wikipedia-based set of four tools that both surfaces reference information from Wikipedia and uses Wikipedia data to create more focused Google searches should they need to be done. MiniGladys is free and has no ads. Let me show you how it works, using Kendrick Lamar as an example.

Tab 1: Reference/Overview

To get going with MiniGladys, start typing in a Wikipedia article title on the left side of the page. That form will autocomplete so don’t worry about getting it exactly right. You’ll get a dropdown list of possible matches as you type. When you choose one the Reference/Overview tab will update with information from your chosen Wikipedia article.

Notice in this example that this page has populated with external reference links to Kendrick Lamar information as well as a wealth of social media links so you don’t have to winkle them out of Google. There’s also the text of the entire Wikipedia article and an link to the original if you’d prefer that.

This first tab provides a basic overview of a Wikipedia topic. If you want to see what that topic is doing in the news, you need MiniGladys’ second tab: Gossip Machine.

Tab 2: Gossip Machine

Gossip Machine analyzes Wikipedia page views and identifies days which have unusual spikes in views, and turns that information into date-bounded Google News searches because public attention usually means something happened. You have several predetermined time spans to choose from: views from the last week, month, year, two years, and five years. Dates are listed by viewing spike — in the example above, Kendrick Lamar’s Wikipedia page had over 1.7 million views on February 10, the day after his Super Bowl performance. Click on the “Google News search for this date” link, and you’ll get a Google News search result for Kendrick Lamar date-bounded to February 10. Because it’s a single day, the results are hyperfocused on Kendrick Lamar’s performance.

Perhaps after reading all this you’re interested in Kendrick Lamar and you want to keep up with what he’s doing. That’s when you need the Custom RSS tab.

Tab 3: Custom RSS

The Custom RSS tab generates keyword-based RSS feeds for 11 sources, including Reddit, Google News, Bing, and WordPress. The query is editable if you want to add more keywords to the feed. Simply tick the checkbox for the source you want and the keyword-based feed appears.

The last tab is for more exploring. Do you want to do some deeper searching around Kendrick Lamar, but don’t know enough about him to add query words? Use the power of Wikipedia to find related topics and bundle them into either Google or Google News searches.

Tab 4: Related Topics

Related Topics searches for your chosen topic across Wikipedia and sorts the resulting pages by the number of times your topic was mentioned. You can filter for a minimum number of mentions. Each related topic has a Google and Google News search that searches for your topic and the related topic together. Sometimes the Google searches generate an AI result, but I find if that’s the case I limit my searches to the Google News links.

Fast Facts and an Opportunity for More Informed Searching

I’m very pleased with how this turned out. The quick facts in the first tab, the very focused news searches from preset date spans in Gossip Machine, the keyword-based RSS feeds that are super-easy to generate, and the related topics that make more complicated queries possible without subject foreknowledge. I’ve already got MiniGladys pinned in my browser and expect I’ll be using it many times a day. Perhaps you’ll find it useful as well.