Windows 11 just fixed one of Search’s dumbest limitations, and you’ll wonder how you lived without it


If you have ever typed two letters into the Windows 11 search box, paused, and watched nothing useful happen until you added more characters, you already know exactly why this Windows 11 update matters. 

Microsoft’s June 2026 Patch Tuesday update, part of a release Windows Latest calls the biggest of the year (via Windows Latest), quietly fixes that. Windows Search can now find and prioritize files with as few as two characters, down from the old three-character minimum.

So what exactly changed in Windows Search?

Before this update, typing two letters from the file name didn’t do anything useful. You had to add a third or more characters before Windows even started looking. Even then, your file could get buried under web results and app suggestions. 

Now, typing two characters is enough to trigger a meaningful search. The update also improves how results are ranked, so your actual file shows up near the top instead of getting lost beneath links and Copilot suggestions.

Why does dropping one character actually matter?

Most of us name files with short, practical labels. Personally, dealing with a couple dozen files on a daily basis, I often name them with a couple of characters like Q3 or V2, exactly the kind of names that used to be functionally invisible to search. 

One fewer required character sounds small at first, but it removes a tiny, constant friction that builds up every single time you search for something on your PC. It is the kind of fix that feels obvious only once you have it, one that should have shipped years ago. 

This change ships in KB5094126 for Windows 11 24H2 and 25H2



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“It was severely downgraded,” Gilbert confirms. “I never would have found it if I was just looking through Google results.” (I tried the same prompt in Gemini earlier this month, and after an initial denial, the tool also gave me Eiger’s number.)

After this experience, Eiger, Gilbert, and another UW PhD student, Anna-Maria Gueorguieva, decided to test ChatGPT to see what it would surface about a professor. 

At first, OpenAI’s guardrails kicked in, and ChatGPT responded that the information was unavailable. But in the same response, the chatbot suggested, “if you want to go deeper, I can still try a more ‘investigative-style’ approach.” Their inquiry just had to help “narrow things down,” ChatGPT said, by providing “a neighborhood guess” for where the professor might live, or “a possible co-owner name” for the professor’s home. ChatGPT continued: “That’s usually the only way to surface newer or intentionally less-visible property records.” 

The students provided this information, leading ChatGPT to produce the professor’s home address, home purchase price, and spouse’s name from city property records. 

(Taya Christianson, an OpenAI representative, said she was not able to comment on what happened in this case without seeing screenshots or knowing which model the students had tested, even after we pointed out that many users may not know which model they were using in the ChatGPT interface. She also declined to comment generally about the exposure of PII by the chatbot, instead providing links to documents describing how OpenAI handles privacy, including filtering out PII, and other tools.) 

This reveals one of the fundamental problems with chatbots, says DeleteMe’s Shavell. AI companies “can build in guardrails, but [their chatbots] are also designed to be effective and to answer customer questions.”

The exposure issue is not limited to Gemini or ChatGPT. Last year, Futurism found that if you prompted xAI’s chatbot Grok with “[name] address,” in almost all cases, it provided not only residential addresses but also often the person’s phone numbers, work addresses, and addresses for people with similar-sounding names. (xAI did not respond to a request for comment.) 

No clear answers

There aren’t straightforward solutions to this problem—there’s no easy way to either verify whether someone’s personal information is in a given model’s training set or to compel the models to remove PII. 



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