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Home News Apps and Distribution

Threads Dear Algo Feature Uses Public Posts To Manually Adjust Feed Algorithms

March 4, 2026
in Apps and Distribution
Reading Time: 3 mins read
Threads Dear Algo Feature Uses Public Posts To Manually Adjust Feed Algorithms
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We usually treat social media algorithms like the weather. We complain about them, but we generally accept that we cannot control them. Meta is trying to change that dynamic on Threads by letting you talk directly to the machine. But there is a specific social cost attached to this new power. You have to issue your commands where everyone else can hear you.

Key Takeaways

  • Threads launched “Dear Algo,” an AI feature for personalizing feeds through public posts.
  • Feed adjustments requested through “Dear Algo” remain active for three days after submission.
  • Threads reached 141.5 million daily active mobile users as of January 7, 2026.

Threads has introduced a feature called “Dear Algo.” It allows users to explicitly tell the app what they want to see more or less of in their feeds. This is a departure from the standard “Not Interested” buttons found on rival platforms like X or Bluesky.

The timing is notable. Threads has been gaining ground on X, specifically in mobile usage. Data shows Threads hit 141.5 million daily active mobile users in early January 2026, compared to 125 million for X. This new feature seems designed to keep those users engaged by making the feed feel more responsive to real-time interests.

The big deal

Most social media feeds are black boxes. You tap a heart or pause on a video, and the system guesses what that means. It is an imperfect game of charades. “Dear Algo” attempts to replace that guessing game with direct instructions. It acknowledges that your interests change based on context. You might love football on Sunday but find it annoying on Tuesday.

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This also addresses the “spoiler” problem. If you have not seen the latest episode of a popular show, standard algorithms are dangerous. They see everyone talking about the show and assume you want to see it too. This tool gives you a manual brake to pull for specific topics.

How it works

You activate the feature by writing a public post that starts with the phrase “Dear Algo.”

Think of it like requesting a song from a wedding DJ. You walk up and shout your request so he can hear you over the noise. He changes the music for a few songs to match your vibe, but eventually, he goes back to his setlist. The change is immediate but it does not last all night.

On Threads, you type “Dear Algo, show me more posts about podcasts” or “less about politics.” The system processes this text command and adjusts your ranking signals. These adjustments last for three days. After that period, your feed reverts to its standard behavior.

The catch

The main trade-off is privacy. You cannot send these requests secretly. You must post them to your public profile. This means your followers can see exactly what you are trying to filter out. It might be awkward to publicly announce you want to see less of a topic that your friends post about frequently.

Meta frames this as a community feature. Other users can see your request and repost it to apply the same filter to their own feeds. However, this public nature might stop people from using it honestly.

The feature is also geographically limited. It is currently available only in the U.S., the U.K., Australia, and New Zealand. The article does not specify when other regions will get access.

What now?

If you live in a supported region, you can test this immediately. It is particularly useful for temporary events like sports games or award shows where you want to flood your feed with updates or block them entirely.

Meta plans to expand this to more countries eventually. The real test will be whether users keep doing this after the novelty wears off. Typing out a command is more friction than clicking a button. Watch to see if “Dear Algo” posts become a regular part of the feed or just a short-lived trend.

Tags: chatbotsfine-tuningGooglemake.comquantizationretrievalvector databaseswatermarkingworkflow automation
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