Meta's 'Dear Algo' Feature: How Threads Turned User Complaints Into Official Features [2025]

Introduction: When User Frustration Becomes Product Strategy

Since Threads launched in July 2023, one complaint has been consistent and loud: the algorithm sucks. Not the technical implementation—that's actually solid. The problem was simpler and more human. Users felt unheard. They'd watch their feed fill with content they didn't ask for, topics they didn't care about, and posts that seemed designed to frustrate rather than inform.

For months, frustrated Threads users started doing something unexpected. They began writing posts directly to the algorithm itself. "Dear algo, show me more design content." "Dear algo, stop showing me cryptocurrency posts." These weren't the typical complaint tweets. They were conversations with an invisible system, hoping that somewhere in Meta's infrastructure, someone was listening.

Turns out, they were.

Meta listened to those desperate pleas and did something remarkably unconventional for a major tech company. Instead of dismissing user feedback as noise, instead of building another recommendation algorithm in a lab, they turned those actual user expressions—the very "Dear algo" posts themselves—into an official feature. Now, when you write a "Dear algo" post on Threads, the platform takes you seriously. It actually changes what you see.

This is a rare moment in social media history. A platform acknowledged a fundamental problem and responded not with a press release promising improvements or a vague roadmap item, but with a real, usable feature that users literally invented themselves. It's smart product design, but it's also something more: it's Meta admitting that controlling what billions of people see is hard, and that maybe crowdsourcing the solution is the answer.

Here's what happened, why it matters, and what it reveals about the future of algorithmic feeds.

TL; DR

• The Feature: Threads users can write "Dear algo" posts to adjust their feed recommendations for three days
• User-Driven Innovation: The feature came directly from frustrated users who invented the practice organically
• How It Works: Posts starting with "Dear algo" trigger algorithmic adjustments; users can also ask to see fewer posts about specific topics
• Current Availability: Rolling out in the US, UK, Australia, and New Zealand with expansion planned
• Bigger Picture: This represents a shift in how platforms respond to algorithmic friction, turning complaints into features

The Problem: Threads' Algorithmic Frustration

Why Users Started Complaining Immediately

When Threads launched as Twitter's competitor in summer 2023, Meta had one massive advantage: billions of existing Instagram and Facebook users they could immediately onboard. But that advantage came with a hidden cost. Meta brought their recommendation algorithm with them—the same system that's been optimized for years to maximize engagement on other platforms.

The problem: that algorithm was designed for Instagram's visual-first ecosystem and Facebook's friend-and-family network. Threads is fundamentally different. It's text-first. It's more about discovering interesting voices and ideas than watching your aunt's vacation photos. Users wanted discovery, but Meta's algorithm kept showing them what would maximize engagement, which often meant controversy, arguments, and hot takes rather than quality content.

Within weeks, users realized they had almost no control. You couldn't tell Threads what you wanted to see. You couldn't demote topics you found exhausting. You couldn't ask for more of the content that actually made you happy. Unlike Twitter's reverse-chronological timeline (which you could toggle on), Threads was locked into its algorithm.

The frustration became a running joke. "Why am I seeing this?" became a common response to random posts in feeds. Users joked that they understood the algorithm less than the algorithm understood them. And then something organic happened.

The Accidental Birth of 'Dear Algo'

Someone—nobody's quite sure who—started writing "Dear algo" posts as a form of humorous protest. It was absurdist, really. Addressing an algorithm like it was a person, like it could understand natural language requests, like it cared about your preferences. The posts were funny because they were pointless. The algorithm doesn't read your posts and adjust. Of course it doesn't.

Except users kept doing it.

By early 2024, "Dear algo" posts weren't rare—they were everywhere. Users treated them like wishes spoken into the void. "Dear algo, show me more coffee content." "Dear algo, I don't care about celebrity gossip." "Dear algo, where are the interesting science posts?" Some users even got creative with their requests, turning them into mini-essays about what they actually wanted from their feed.

The posts weren't solving anything. They were cathartic complaints dressed up as algorithmic commands. But they revealed something important: users had clear preferences, but Threads gave them no way to express them. The "Dear algo" posts weren't a feature request in the traditional sense. They were users literally demonstrating what a feature would look like if it existed.

Meta's product team noticed. And instead of ignoring the meme, they did something unexpected: they built it.

How 'Dear Algo' Actually Works

The Technical Implementation

What makes this feature clever is that it's simpler than it sounds. When you write a post that starts with "Dear algo," Threads recognizes the pattern and triggers a specific set of algorithmic adjustments. You don't need to use exact syntax. You don't need to be precise. The system is designed to be forgiving because it's matching real human language, not code.

Here's what actually happens under the hood: Threads parses your "Dear algo" post to extract the topics you mentioned. If you write "Dear algo, show me more posts about sourdough baking," the system identifies "sourdough baking" as a topic preference. It then weights your algorithmic feed to surface more content related to baking, fermentation, breadmaking, and related topics. The effect isn't permanent—it lasts approximately three days.

Why three days? That's a deliberate design choice. Three days is long enough to actually see a difference in your feed—long enough for recommendations to shift noticeably. But it's short enough that you're not permanently locked into a preference. If you change your mind, or if the topic gets boring, the algorithm reverts. It's a middle ground between full control and algorithmic autonomy.

The negative version works similarly. Write "Dear algo, stop showing me posts about cryptocurrency," and the system demotes crypto-related content. Again, for three days. Again, it reverts afterward. This is actually harder to implement than the positive version because the system has to distinguish between "I'm not interested in this topic" and "I hate this topic and never want to see anything related to it again." Meta chose the middle path: temporary suppression rather than permanent blocking.

What You Can Actually Request

Users can request pretty much any topic. Sports, politics, cooking, technology, design, philosophy, fitness—if it's a topic that exists on Threads, you can ask for more of it. You can also ask the algorithm to stop showing you content about specific topics.

The feature also includes a sneaky social component. You can retweet (or "repost" in Threads terminology) another user's "Dear algo" post, and those topic preferences get reflected in your own feed. This is brilliant psychology. If your friend writes "Dear algo, show me more indie game development," and you repost it, you'll start seeing more indie game development posts too. It encourages users to discover topics from their network, and it spreads topic preferences virally.

There's one limitation worth noting: the feature doesn't work retroactively on your entire feed. It doesn't rerank your existing posts. It only affects what you see going forward. So if you write "Dear algo, show me more about typography," your next three days of feed browsing will include more typography content, but your previously-loaded feed stays the same. You have to scroll or refresh to see the changes.

The Broader Strategic Significance

Why Meta Made This Move

On the surface, this looks like a simple product feature. But it's actually Meta signaling something important about how they think about algorithmic control and user agency. By making "Dear algo" official, Meta is essentially saying: "We know our algorithm isn't perfect. We know you have preferences. Instead of us trying to guess what you want, you tell us."

This is a significant shift from the typical big tech approach, which is to position the algorithm as an invisible authority. "Trust us, we know what you like." But Threads users had made clear they didn't trust that claim. The "Dear algo" meme was a vote of no confidence in algorithmic authority.

Meta's response was smart because it co-opts that frustration rather than fighting it. Instead of defending their algorithm, Meta offers users a workaround. "You don't like what we're showing you? Tell us what you want. We'll adjust." It's a psychological win that costs almost nothing technically.

There's also a data collection angle. Every time someone writes a "Dear algo" post, Meta learns what they actually want. The system gets clearer, more specific preference data than it could extract from engagement metrics alone. Users are essentially training the algorithm by telling it directly what they care about.

How This Compares to Other Platforms

Twitter (now X) has always offered users the ability to switch between algorithmic and chronological feeds. It's a binary choice, not a nuanced adjustment. Bluesky, the decentralized social platform, goes further by letting users choose from different algorithmic feeds built by different creators. But both approaches assume algorithmic feeds are optional or replaceable.

Threads is saying something different: your algorithm doesn't need to be replaced. It just needs to listen to you better. The "Dear algo" feature is a form of algorithmic customization that doesn't require technical skill. You don't need to understand how recommendation systems work. You just need to know what you want to see.

Instagram and Facebook already have preference settings buried in menus, but nobody uses them because they're not visible, not intuitive, and not connected to the social experience the way "Dear algo" posts are. "Dear algo" is social feedback. It's a performance. It's something you can show your friends. That changes the whole dynamic.

The Roll-Out and Current Availability

Where You Can Use It Right Now

Meta released the feature initially in the United States, United Kingdom, Australia, and New Zealand. The company announced that expansion to additional countries would follow, though they haven't specified a timeline. As with most major Meta features, international roll-out is slower than US launch.

The feature is available to all Threads users in the supported regions regardless of account age or follower count. You don't need a special account status. You don't need to opt-in. The first time you write a "Dear algo" post, the system recognizes it and activates the algorithmic adjustment.

One interesting note: the feature works whether you write the full post or just start it with "Dear algo." You could write "Dear algo, I'm so tired of algorithm posts, show me more actual content about things I care about," and it would work. The system doesn't require formal syntax.

What's Coming Next

Meta hasn't announced specific enhancements to the feature, but several logical expansions seem likely. The three-day window could eventually become customizable—maybe you could request a one-week adjustment or a permanent preference. The system might eventually support more granular controls, like "show me posts from professional designers" versus "show me posts from design enthusiasts" for the same topic.

Longer term, this could evolve into something closer to algorithmic composability, where users can mix and match different preference signals. Imagine writing "Dear algo, 70% technology, 20% design, 10% philosophy," and the system allocates your feed accordingly. That's not confirmed, but it's the logical endpoint of this philosophy.

User Adoption and Real-World Impact

How Threads Users Are Actually Using It

Within the first week of launch, "Dear algo" posts spiked across the platform. But the interesting part wasn't that users were excited about algorithmic control—it's what they actually asked for. The most common requests fell into a few patterns.

First, discovery requests. "Dear algo, show me more posts from journalists and writers." "Dear algo, show me more about machine learning." These were users trying to break free from echo chambers and find more niche content. Second, suppression requests. "Dear algo, stop showing me AI discourse." "Dear algo, I don't want to see politics." These were users trying to maintain their sanity by excluding topics they found exhausting.

Third, and most interesting, recommendation requests. "Dear algo, show me what people I follow are reading." "Dear algo, surface the posts my followers seem to be engaging with." These requests revealed that users wanted their networks' preferences to influence their feeds.

Metrics from Meta suggest adoption has been solid but not explosive. That makes sense. This isn't a feature that changes overnight—it's a steady improvement. Users aren't posting "Dear algo" requests every day. They post them when they're frustrated or when they notice their feed is missing something they care about.

The most telling metric: repost rates for "Dear algo" posts are high relative to other feature announcements. Users are actively sharing each other's preferences, which means the social aspect is working. When your friend reposts "Dear algo, show me more about gardening," it signals to their network that gardening is worth paying attention to.

What Data Is Being Collected

Every "Dear algo" post is data. Meta now has millions of explicit preference statements from users. "I like this, I don't like that." That's training data for future algorithm improvements. Traditional engagement metrics are indirect signals. "Dear algo" posts are direct signals.

This could make Threads' algorithmic recommendations significantly better over time. Not through some magical AI breakthrough, but through simple preference aggregation. If 10,000 users write "Dear algo, show me more science posts," Meta knows that science content resonates with a specific demographic. They can incorporate that signal into their models.

There's a privacy dimension too. Preference statements aren't as sensitive as browsing history, but they're more explicit. You're publicly declaring what you care about. Meta is collecting that data (it's part of your account profile), and that could theoretically be used for everything from ad targeting to future product development.

The Psychological Genius Behind the Feature

Humans Want to Be Heard

The psychological insight underlying "Dear algo" is simple but powerful: people want to feel listened to, especially by systems that govern their experience. Traditional algorithmic feeds are one-directional. The algorithm feeds you content. You consume it. There's no conversation.

"Dear algo" creates the illusion of conversation. You speak, the algorithm responds. It's anthropomorphization—treating a system like it's a person who can understand you. That's not necessarily wrong. It's actually quite clever from a UX perspective. Users feel like they have agency because they do.

Compare this to the traditional approach where users complain about algorithms but have no way to talk back. They write angry posts. They switch platforms. They disengage. The "Dear algo" feature channels that frustration into a productive action. You're not just complaining—you're configuring your experience.

The Social Signal Advantage

Here's where Meta's strategy gets sophisticated. By making "Dear algo" posts public and shareable, Meta turned individual preference into social currency. A user isn't just adjusting their algorithm for themselves—they're publicly declaring their taste. That creates social incentives to write thoughtful, specific requests.

If you write "Dear algo, show me more design content," you're not just getting more design posts. You're also signaling to your network that you care about design. That matters. It influences how people perceive you. This is why repost rates are high. Your friends aren't just reposting because they want the same algorithmic benefits—they're reposting because they want to affiliate with those preferences.

It's the same mechanism that makes music taste important on social media. You don't tell people what music you like just to listen to it yourself. You tell them because it's part of your identity. Meta understood that. They made "Dear algo" requests something you could own socially.

The Limitations and Honest Assessment

What This Feature Doesn't Solve

Let's be direct: "Dear algo" is a workaround, not a solution. It addresses user frustration with algorithmic control, but it doesn't fix the underlying problem that recommendation algorithms have inherent biases and blindspots.

Three days is temporary. If you ask for more design content, you'll get it for 72 hours, then revert to baseline. This works for exploring new interests, but it doesn't help if you want sustained algorithmic change. If you're trying to permanently shift your feed away from politics and toward technology, you'll need to write "Dear algo" requests constantly.

The feature also doesn't address algorithmic opacity. You can tell the algorithm what you want, but you still don't know exactly how it works or why certain posts appear ahead of others. You're trading algorithmic autocracy for algorithmic responsiveness, which is better but not full transparency.

The Potential for Manipulation

There's also a darker possibility. What if bad actors use "Dear algo" posts to game the recommendation system? What if they write posts designed to trigger specific algorithmic responses, then delete them? Or what if coordinated groups write identical "Dear algo" posts to artificially boost visibility of certain topics?

Meta probably has safeguards for this. They can likely detect coordinated inauthentic behavior and weight preference signals accordingly. But it's worth noting that any system that responds to user requests can be gamed. The algorithmic responsiveness that makes "Dear algo" valuable also makes it a potential attack surface.

What Users Actually Want

The "Dear algo" feature addresses a real frustration, but anecdotal feedback suggests users want more. They want the ability to set permanent preferences. They want more granular control. They want to understand why the algorithm makes the decisions it does. Some want the ability to switch to a chronological timeline (which Threads still doesn't offer natively).

Meta likely won't grant all these requests. A fully transparent, user-controlled algorithm is harder to optimize. It's harder to monetize. It's harder to keep users engaged for hours per day. But "Dear algo" shows that users have clear preferences, and Meta is willing to listen when those preferences are expressed publicly enough to become a trend.

The Bigger Picture: Algorithmic Accountability

A Shift in Platform Philosophy

What's really significant about "Dear algo" isn't the feature itself. It's what it represents: a platform implicitly acknowledging that algorithmic control matters and that users have a right to some level of say in how algorithms affect them. That's not radical, but it's notable coming from Meta, a company that has historically defended opaque algorithms as necessary for personalization.

Threads is young. It's hungry for users and differentiation from Twitter/X. That gave Meta an incentive to listen to user complaints that they might have ignored on more mature platforms. If Threads had 500 million users instead of 100 million, would they have built "Dear algo"? Probably not as quickly.

But here's the strategic insight: Meta is learning that users value platforms that let them have a voice, even in abstract systems like algorithms. As other platforms become aware of this, they'll face pressure to implement similar features. This could spark a broader shift toward more responsive, less opaque algorithmic feeds.

What Regulators Might Say

The EU's Digital Services Act and similar regulations increasingly require platforms to provide "meaningful control" over algorithmic recommendations. "Dear algo" is Meta's answer to what "meaningful control" looks like. It's not full transparency. It's not the ability to completely bypass algorithmic feeds. But it's directional control, and that could satisfy regulatory requirements for user agency.

Other platforms are watching. Regulators are watching. When Threads' "Dear algo" feature works well—when users adopt it, when they feel heard, when engagement remains strong—it becomes harder for other platforms to claim they can't implement similar features.

Technical Deep Dive: How Recommendation Algorithms Respond to Preferences

The Mathematics Behind Adjustment

When you write a "Dear algo" post, you're triggering a mathematical modification to your recommendation function. Let's call your recommendation algorithm R. Normally, R takes inputs (your history, current trends, engagement data) and outputs a ranked list of posts.

What "Dear algo" does is modify the weight vector within R. If you ask for more design content, Meta increases the weight assigned to design-related features in the input vector. Mathematically, it might look like:

Where w is a weight that decays over three days. The adjustment might be a 1.5x or 2x boost to design content probability, while simultaneously reducing weight on topics you asked to suppress.

This isn't complicated compared to modern recommendation systems. The complex part is extracting the topic from your natural language request (that's NLP), and ensuring the modification doesn't break the algorithm or create perverse incentives.

Why Three Days?

The three-day decay is deliberate. Algorithmic systems need to balance responsiveness with stability. If preferences lasted forever, users would need to constantly override them. If they lasted one hour, the feature would feel pointless. Three days is approximately the cycle time users think about their preferences.

Meta probably tested different decay windows. Three days likely emerged as the sweet spot where users see real change (enough for multiple browsing sessions) but the system reverts before people get bored with the adjusted recommendations.

Comparing Algorithmic Control Across Platforms

Threads vs. The Competition

Threads' approach is unique because it combines explicit topic preference with social visibility. You're not hiding your preferences in a settings menu. You're expressing them publicly, which amplifies their importance both algorithmically and socially.

The Future of User-Responsive Algorithms

What Could Come Next

If "Dear algo" succeeds—and early indicators suggest it will—Meta will likely expand the concept. Possibilities include:

Persistent Preferences: Move away from three-day expiration to longer windows or permanent preference setting, but require users to actively confirm they want the change to persist.

Algorithmic Mixing: Let users specify topic ratios. "70% technology, 20% design, 10% philosophy." This gives them full compositional control without requiring them to constantly override.

Source Preferences: "Show me more from verified journalists," "more from my close friends," "more from accounts I follow but don't engage with." This shifts preference from topic to source.

Transparency Layers: Pair algorithmic responsiveness with transparency. When the algorithm ranks posts, show why. "You're seeing this because you asked for more design content." This maintains responsiveness while increasing understanding.

Cross-Platform Learning: Eventually, preferences might transfer between Threads, Instagram, and Facebook. You ask for more design content on Threads, and your Instagram feed adjusts accordingly. That requires significant backend work but offers obvious user value.

The Regulatory Angle

Regulators globally are pushing for user control over algorithmic systems. The EU's Digital Services Act requires platforms to offer meaningful choice over how content is ranked. The UK Online Safety Bill has similar requirements. "Dear algo" is Meta's answer to that regulatory pressure.

But it's also a strategic play. By implementing user-responsive algorithms voluntarily, Meta makes it harder for regulators to demand more restrictive changes. "We're already giving users control," they can argue. "What more do you want?" The feature is both a genuine improvement and a regulatory defense.

Case Studies: How Different User Groups Use 'Dear Algo'

The Professional Use Case

Designers and technologists have embraced "Dear algo" specifically to filter out noise. A UX designer might write: "Dear algo, show me more posts from designers, design systems, and UX research. Stop showing me politics and celebrity news." By combining positive and negative requests, they optimize their feed for professional development.

These users often write "Dear algo" posts multiple times per week, creating a sustained signal that overrides the baseline algorithm. They're not just using the feature for discovery—they're using it to maintain a professional learning environment within a social platform.

The Casual User

Casual users typically write one or two "Dear algo" posts, usually when they notice their feed is missing something they like. "Dear algo, show me more about gardening," or "stop showing me crypto posts." They use it as a corrective tool rather than an ongoing preference system. This suggests that most users see the feature as occasional adjustment rather than core functionality.

The Curious Explorer

A subset of users is explicitly using "Dear algo" to discover new topics. They'll write a post asking for something they've never engaged with before: "Dear algo, show me posts about ancient history," or "show me woodworking projects." They treat the three-day window as a guided tour of new topics, evaluating whether sustained interest exists.

This group proves the feature's discovery value. Traditional algorithms trap users in their existing interest patterns. "Dear algo" lets them break free temporarily to explore.

The Role of Natural Language Processing

How Threads Understands Your Requests

For "Dear algo" to work, Threads needs to extract meaningful information from natural language text. When you write "Dear algo, show me more posts about sourdough baking," the system needs to identify "sourdough baking" as the requested topic.

This is harder than it sounds. What if you write "Dear algo, I love sourdough but hate gluten-free bread"? The system needs to parse both positive and negative signals. What if you write "Dear algo, more posts from the design community, especially typography"? The system needs to recognize both the general category and the specific subcategory.

Meta's NLP systems are sophisticated enough to handle this, but there are limits. Nuance gets lost. If you write "Dear algo, I'm interested in philosophy but not academic philosophy," the system might miss that crucial distinction.

Over time, Meta will improve the system by analyzing which "Dear algo" requests actually lead to satisfied users (i.e., sustained engagement) and which don't. If users keep asking for the same preference repeatedly, that's a signal the algorithm didn't extract it correctly the first time.

Privacy and Data Collection Implications

What Meta Learns From Your Preferences

Every "Dear algo" post is a preference statement that Meta logs, analyzes, and incorporates into their models. This is valuable data. Traditional engagement metrics are indirect. "Dear algo" posts are explicit preferences.

Meta will use this data for algorithmic improvement, but also for advertising. If you publicly declare "I care about design," advertisers will pay more to reach you. If you say "I don't want to see politics," that's also useful—it tells advertisers when not to show you certain content.

The privacy concern is moderate because you're voluntarily making public statements. You're not revealing anything the algorithm couldn't eventually infer from your behavior. But the directness is worth noting. You're giving Meta clearer signals than you might intend.

Retention and Analysis

Threads will retain your "Dear algo" posts and preference signals indefinitely (barring account deletion). That's standard social media practice. Those signals inform your user profile, which feeds into algorithmic and advertising systems.

One safeguard: preferences are individual and local to Threads. Your "Dear algo" preferences don't automatically sync to Instagram or Facebook, which maintains some separation between platforms. That could change in the future as Meta integrates its systems more closely.

Implementation Challenges Meta Likely Faced

Topic Extraction at Scale

Extracting topics from millions of "Dear algo" posts is computationally intensive. Meta needs to normalize topics (so "design," "visual design," and "graphic design" all map to the same underlying category), handle ambiguity, and process in real-time.

This is solvable but non-trivial. Meta probably uses a combination of pre-trained NLP models and custom classifiers trained on Threads' specific vocabulary.

Preventing Spam and Gaming

If the system responds to explicit requests, spammers and bad actors will try to game it. Write enough "Dear algo, show me cryptocurrency," posts and pump crypto content visibility. Coordinate a group of bot accounts to request the same topic and artificially amplify it.

Meta has to detect these patterns. They likely use engagement-based weighting (real user satisfaction matters more than request volume) and behavioral analysis to flag suspicious patterns.

Maintaining Algorithm Performance

Adding a new preference signal to an already complex algorithm is risky. It can degrade performance for users who don't write "Dear algo" posts. Meta needs to ensure the feature improves the feed for participating users while not breaking it for everyone else.

This probably required A/B testing and careful rollout. Meta likely started with a small percentage of users, validated performance gains, then expanded.

The Elon Musk Sidetrack: Other Tech Stories That Week

The Moon Catapult Plan

While Meta was rolling out algorithmic features, Elon Musk was reportedly planning something far more ambitious: a satellite catapult on the Moon. According to audio obtained by The New York Times, Musk told x AI employees that the company needed to establish an AI satellite factory on the lunar surface and launch satellites using an electromagnetic catapult.

The physics is fascinating if impractical. The Moon has one-sixth of Earth's gravity, which reduces launch energy requirements. But minimum escape velocity is still approximately 3,800 miles per hour—five times the speed of sound. Satellites would need to withstand incredible forces, and electromagnetic railguns powerful enough to accelerate payloads to those speeds don't exist at scale yet.

But that's the point. Musk wasn't proposing something feasible in the next five years. He was articulating a vision: to build computational infrastructure off-planet to achieve AI capabilities that Earth-based systems can't. It's audacious, possibly delusional, but also characteristic of Musk's thinking.

Apple's Siri Problem

Meanwhile, Apple was reportedly struggling with its Siri relaunch. The company had been working on a significantly upgraded voice assistant, but the project was falling behind schedule. Siri's problem has always been that it works well for simple commands ("Set a timer," "Call Mom") but struggles with complex requests or context understanding.

Rumors suggested Apple wanted Siri to be smarter, more conversational, and more integrated with Apple's ecosystem. But integrating AI improvements with privacy preservation is technically difficult, and Apple's commitment to on-device processing limits how sophisticated their AI can be without more processing power.

The delay underscores a fundamental challenge for Apple: Open AI's Chat GPT and Anthropic's Claude have set user expectations for conversational AI. Users expect intelligence and naturalness. Apple's traditional approach of building everything in-house is slower than acquisition or partnership strategies competitors use.

Gaming Updates

Remedy Entertainment released the first gameplay trailer for Control Resonant, the sequel to the acclaimed Control. The trailer showed a surreal, gravity-defying New York City—exactly what fans expected given the original game's reality-bending mechanics.

Nintendo also announced Pokémon Pokopia, a cozy, low-stakes Pokémon game where you play as a Ditto and explore a pastoral landscape inspired by Stardew Valley and Animal Crossing. The game explicitly rejected the competitive, battle-focused formula that dominates the franchise, signaling that Pokémon's audience has diversified beyond the core competitive community.

Sony's Headphone Arms Race

Sony launched the WF-1000XM6 earbuds, the latest iteration of what's arguably the best wireless earbuds on the market. The XM series has dominated the category through relentless iteration: improved noise cancellation, refined audio quality, better comfort.

But competition is fierce. Bose's Quiet Comfort Ultra Earbuds now match Sony on noise cancellation. Sennheiser's Momentum True Wireless 4 offers superior sound quality at a comparable price. Technics has emerged as a dark horse with the AZ100, offering audiophile-quality sound in a compact form.

The earbud market has matured. No single brand dominates on all dimensions. Sony's advantage is brand trust and consistent innovation, but that's no longer enough against specialists.

What This All Means: The Intersection of Algorithms, User Agency, and Platform Competition

The week that Meta shipped "Dear algo" was busy across tech. Elon Musk was dreaming of Moon factories. Apple was struggling with AI. Nintendo was redefining what Pokémon could be. Sony was refining incremental excellence.

But Meta's quiet feature release might have been the most significant. It acknowledged something major tech companies rarely admit: algorithms aren't infallible authorities. They're tools that should respond to users. By turning user complaints into an official feature, Meta showed that listening to your users—even when they're joking—can lead to better products.

"Dear algo" won't replace the push for algorithmic transparency or regulation. It won't make recommendation systems perfect. But it's a step toward platforms that see users as active participants in algorithmic systems rather than passive consumers of feeds.

That shift matters. It changes how users think about social media. Instead of "the algorithm controls me," it becomes "I have input into what the algorithm does for me." That's not freedom, but it's not powerlessness either. It's a form of agency within a system designed by someone else.

As regulation tightens and user expectations rise, more platforms will follow. They'll have to. Once users experience even limited algorithmic control, going back to opaque systems becomes harder to justify.

FAQ

What exactly is the 'Dear algo' feature on Threads?

The "Dear algo" feature is a way for Threads users to directly request algorithmic adjustments by writing posts that begin with "Dear algo." When you write a request like "Dear algo, show me more design content," the algorithm temporarily prioritizes that topic in your feed for approximately three days. You can also request fewer posts about topics you want to avoid. The feature recognizes natural language, so you don't need to follow specific syntax—just express your preference conversationally.

How long do 'Dear algo' preference adjustments last?

Algorithmic adjustments from "Dear algo" posts persist for approximately three days. After that period, your feed reverts to the baseline algorithm. This time window was chosen deliberately to give users enough time to see meaningful changes in their feed (typically 2-3 browsing sessions) while ensuring the system doesn't permanently lock you into preferences you might later want to change. If you want preferences to last longer, you can write additional "Dear algo" posts to reinforce them.

Can I permanently change my Threads feed preferences without writing 'Dear algo' posts?

Not currently through "Dear algo" alone, but you can combine the feature with traditional preference settings if Threads has added them. The "Dear algo" feature is explicitly temporary, which is a design choice that keeps the feature discoverable and prevents users from getting stuck in too-narrow algorithmic lanes. For permanent control, you'd need to rely on any built-in preference settings in Threads' settings menu, though the platform doesn't currently offer the level of persistent algorithmic control that some competing platforms provide.

What happens if I repost someone else's 'Dear algo' post?

When you repost another user's "Dear algo" post, their topic preferences are reflected in your own algorithmic feed adjustments. This means if your friend writes "Dear algo, show me more about woodworking," and you repost it, you'll also start seeing more woodworking content in your feed for three days. This creates a social discovery mechanism where you can inherit preferences from people whose taste you trust, essentially letting their algorithmic requests influence your own recommendations.

Is 'Dear algo' available worldwide?

"Dear algo" initially launched in the United States, United Kingdom, Australia, and New Zealand. Meta announced plans to expand to additional countries, but hasn't specified a timeline or which regions will receive the feature next. Geographic rollout tends to follow Meta's standard pattern of testing in English-speaking markets first, then expanding to other regions based on performance data and localization requirements.

Can the algorithm learn my preferences without me writing 'Dear algo' posts?

Yes. Threads algorithms continuously learn from your engagement patterns—what you like, share, comment on, and how long you look at different posts. "Dear algo" is a supplement to that implicit learning, not a replacement. The key difference is that "Dear algo" lets you explicitly override what the algorithm infers from your behavior, which is useful if your actual preferences differ from your engagement patterns or if the algorithm has made incorrect assumptions about your interests.

What topics can I request in 'Dear algo' posts?

You can request virtually any topic that exists on Threads—technology, design, cooking, philosophy, sports, fitness, arts, science, news, entertainment, and more. The system's natural language processing extracts topics from your request, so you have significant flexibility in how you phrase your preferences. You can be specific ("typography" instead of just "design") or broad ("technology"). You can also nest preferences ("design but specifically UX/UI not graphic design"), though very specific nuance may not be extracted perfectly.

Does writing 'Dear algo' posts affect my other Threads metrics like engagement or follower count?

No. "Dear algo" posts are algorithmic configuration—they don't directly affect your engagement metrics or visibility to other users. They're purely about what you see, not about how visible your content is to others. However, if your "Dear algo" posts get reposted frequently, that engagement counts toward your normal metrics. The algorithmic effect is unidirectional: your requests change your feed, not how others see your posts.

Can I turn off 'Dear algo' or exclude certain posts from triggering preferences?

Not explicitly. There's no reported option to disable the feature for your account. However, you can control your own preferences by not writing "Dear algo" posts. If you prefer the baseline algorithm without manual adjustments, simply don't use the feature. The three-day expiration also means any preferences you set will naturally revert, so there's no permanent lock-in effect even if you experiment with the feature.

Conclusion: The Evolution of User-Platform Relationships

Meta's "Dear algo" feature represents something subtle but significant: a recognition that algorithmic systems need human feedback to work well, and that users deserve some voice in shaping their digital environment.

For years, social media platforms have hidden behind algorithms as though they were laws of physics—inevitable, immutable, beyond questioning. But algorithms are code, and code can be changed. More importantly, code can listen.

By turning user complaints into an official feature, Meta demonstrated that sometimes the best product insights come from users themselves. Nobody at Meta's headquarters invented "Dear algo." Users did. Meta just formalized what users had already created.

That's worth paying attention to because it signals a broader shift. As regulation tightens, as users become more aware of how algorithms affect them, and as competition for attention intensifies, platforms will need to offer users more control. Not full transparency—that's probably not coming. Not the ability to completely opt out of algorithmic feeds—that would hurt engagement too much. But meaningful, visible, social forms of control.

"Dear algo" is one answer to that challenge. It's not perfect. It's temporary. It doesn't address algorithmic opacity or the fundamental incentives that make platforms optimize for engagement over quality. But it's real, it's usable, and it works.

For Threads, it's a competitive advantage. For the broader platform ecosystem, it's proof that user-responsive algorithms are possible at scale. For Meta, it's a strategic move that satisfies regulatory pressure while maintaining algorithmic optimization.

For you, it's the ability to tell your algorithm what you actually want, instead of letting it guess.

That might not sound revolutionary, but it is. Because for the first time, the algorithm has to listen.

Key Takeaways

• Meta officially launched 'Dear algo' feature that lets Threads users adjust algorithmic recommendations by writing posts with natural language requests
• The feature emerged organically from users making joke posts to the algorithm, which Meta recognized as genuine product feedback and made into reality
• Preferences from 'Dear algo' posts last approximately three days before reverting to baseline, balancing user control with algorithmic stability
• This represents a strategic shift in how platforms respond to algorithmic complaints, turning frustration into user empowerment instead of defensive arguments
• Comparable platforms offer different algorithmic control methods, but Threads' approach uniquely combines explicit topic control with social visibility and temporary preferences

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