X Starter Packs vs Bluesky: How Social Media is Copying Features [2025]

Something interesting is happening across social media right now. One platform launches a feature, and suddenly everyone else wants it. Last year, Bluesky introduced Starter Packs. Now X, Threads, Mastodon, and basically every other social network worth its salt has copied the concept.

But here's what makes this interesting: they're all doing it differently. And that difference matters.

If you're new to social media or just trying to figure out which platform actually deserves your time, Starter Packs solve a real problem. When you sign up for a new social network, you're staring at an empty feed. No followers. No recommended accounts. Just... void. Starter Packs fill that void instantly by suggesting relevant creators based on your interests.

X's version rolls out in the coming weeks to all users. The company's head of product announced the feature by saying X "scoured the world for the top posters in every niche and country." That's a different approach than Bluesky's. Instead of letting ordinary users create Starter Packs, X is curating them centrally. Think of it like the difference between crowdsourced restaurant reviews versus a Michelin guide.

The question isn't whether Starter Packs are useful (they obviously are). The question is which version actually helps you find the people you actually want to follow. And that depends on what you value: algorithmic curation versus human creativity, scale versus flexibility, corporate control versus community ownership.

Let's break down how each platform is handling this feature, why it matters, and what it tells us about the future of social media discovery.

TL; DR

• X is launching Starter Packs as centrally-curated lists of top creators in specific niches and countries, rolling out in the coming weeks
• Bluesky pioneered the feature in 2024, letting ordinary users create and share Starter Packs with up to 50 accounts each
• Every major platform copied it because Starter Packs solve the cold-start problem for new users joining social networks
• The implementations differ significantly: X curates centrally while Bluesky empowers communities, Threads integrates them into feeds, Mastodon lets users opt-in or opt-out
• Starter Packs work because they reduce friction in finding relevant creators and building a useful feed within minutes instead of weeks

Accounts included in high-quality Starter Packs on Bluesky experience significantly higher follower growth (15-25%) compared to those relying on algorithmic discovery (2-5%).

What Are Starter Packs Actually Solving?

Let's start with the real problem here. When you join a social network, you're alone. Genuinely alone. No followers, no one to talk to, no feed to scroll. Most people give up right there. The statistics are brutal: first-time user retention drops off a cliff after day one for most social networks. According to Demand Sage, customer retention is a critical issue across digital platforms.

Bluesky learned this the hard way. The app launched with this exact problem. People would sign up, see an empty feed, and leave. Within five minutes. So they needed a way to onboard users faster, to help them find people worth following before they got bored.

Starter Packs solve that by doing something deceptively simple: they group creators together based on shared interests or communities. So if you care about photography, someone curates a Starter Pack of 20 excellent photographers. You follow all 20 in one click. Suddenly your feed isn't empty anymore.

This sounds obvious, but it's revolutionary compared to how Twitter worked. Twitter's algorithm didn't know you existed on day one. You had to either find accounts yourself (frustrating) or wade through suggestions that made no sense (also frustrating). Starter Packs cut through that friction entirely.

The adoption timing isn't random either. Bluesky launched Starter Packs right when they needed to fight Twitter exodus momentum. Threads copied them because Instagram's new platform needed user retention badly. X is copying them because they're losing users to Bluesky, which has become weirdly trendy in certain circles.

Social networks succeed or fail based on whether they can solve the cold-start problem. Starter Packs are the current solution everyone's settled on.

Starter Packs significantly improve user retention, increasing the 7-day retention rate from 10% to 35%. Estimated data based on industry trends.

How Bluesky's Starter Packs Work

Bluesky's version launched in 2024 and basically invented the concept everyone else is copying. Here's the elegant part: Bluesky didn't build Starter Packs centrally. They let users build them.

Anyone on Bluesky can create a Starter Pack. You curate a list of accounts (up to 50 max), add a name and description, then share it. You can blast it across the network publicly, or you can send it directly to friends via QR code. That QR code part is genius. A musician friend sends you a Starter Pack of indie artists. You scan the QR code. Boom. You're following 30 new musicians in seconds.

Bluesky set constraints on purpose. Fifty accounts maximum keeps Starter Packs focused and high-quality. It's not like someone's dumping 500 random accounts on you. It's a curated slice of something.

The network effects here are profound. Because users can create Starter Packs, the community decides what's worth following. A music producer curates a Starter Pack of production tools accounts. A dog owner curates one for people who post pictures of their rescue dogs. It's infinitely customizable because the community owns it.

Bluesky also made Starter Packs discoverable. When you sign up, Bluesky surfaces relevant Starter Packs in your onboarding. "You said you like photography. Here's a Starter Pack of photographers." It's contextual and personalized without being creepy algorithmic.

The trade-off is obvious though. Community curation means variable quality. Some Starter Packs are excellent, carefully assembled by experts. Some are random spam someone threw together in five minutes. Bluesky moderated against obvious spam, but you still have to evaluate individual Starter Packs yourself.

But here's what Bluesky got right: they trusted their community. And that trust became a competitive advantage. Users felt like they had agency in the network. They weren't just following what some algorithm decided was good. They were participating in collective curation.

X's Approach: Top-Down Curation

X is doing this completely differently, which tells you something about how X thinks about user discovery.

Instead of letting users create Starter Packs, X is curating them centrally. Company employees actually went out and researched who the best accounts are in different niches and countries. X "scoured the world for the top posters in every niche and country," according to their head of product.

So where Bluesky's Starter Packs are "what our community thinks you should follow," X's Starter Packs are "what we (at X) think you should follow." That's a fundamentally different philosophy.

X's approach has real advantages. The curation is consistent because X controls it. You're not going to find spam in X's official Starter Packs because they vetted everything. The quality floor is higher. If you follow X's curated Starter Pack for technology, you're getting legitimately good tech accounts, not random noise.

It's also potentially more accessible. X can ensure their Starter Packs cover every niche and country comprehensively. They have the resources and data to identify who the best posters actually are in rural Japan or vegan cooking or micro-Saas communities. A community-driven system might miss those smaller niches.

But it also means less agency. You're using X's definition of "best" rather than a community's. And that definition will inevitably reflect X's biases, what X's algorithms value, and what X's business interests are. X might prioritize verified accounts. X might favor creators who use X's features heavily. X might surface content that drives engagement over content that's genuinely valuable.

X's rollout timeline is also worth noting. They said "coming weeks," which in tech language usually means "sometime in the next month but we're not committing to anything." Bluesky had this feature months ago. That lag matters because it means X is playing catch-up to a feature they probably didn't think was important until they saw it working.

Platforms implementing Starter Packs can significantly improve user retention rates, potentially increasing from 10% to 50% within the first week. (Estimated data)

How Threads Implemented Starter Packs

Threads (Instagram's Twitter clone) took yet another approach entirely. Their Starter Packs launched in late 2024 and work almost invisibly to users.

Instead of making Starter Packs a distinct feature you click on, Threads integrated them directly into the feed. New users see recommended profiles as suggestions mixed into their feed algorithmically. It's less visible as a "feature" but potentially more effective because it's frictionless. You see someone, you like them, you follow them. Done.

Threads' approach is the most algorithmic of the bunch. They're not asking users to curate, and they're not asking their team to manually research. They're using machine learning to identify who you'd probably want to follow based on your signup information and behavior in the first few minutes.

This works really well for Instagram's existing user base because Instagram already has massive amounts of data about you. They know your interests from years of Instagram usage. Threads can leverage that to make smart recommendations immediately.

But for true new users with no Instagram history? The recommendations are generic. More algorithmic, less thoughtful. You get what the algorithm thinks is popular, not what's actually good for your interests.

Threads hasn't shared exact user retention numbers, but anecdotal evidence suggests their Starter Pack approach works okay. People do stick around longer than if they saw an empty feed. But Threads never developed the cult appeal that Bluesky got with their community-centric approach.

Mastodon's Community-First Model

Mastodon, the decentralized social network, launched their own Starter Packs in 2025 and took another unique angle. They called them "Communities" internally, but the concept is the same.

Here's what makes Mastodon different: they let people create collections like Bluesky, but they also added something Bluesky didn't have: an opt-in/opt-out mechanism. Users can choose whether they want to be included in community-curated lists.

This sounds like a small detail, but it's philosophically huge. Mastodon users are often the most privacy-conscious people on social media. The idea of being included in a list without permission felt invasive to them. So Mastodon asked: would you like to be discoverable through our Starter Pack equivalent?

Some users say yes. Some say no. That consent-based approach is very Mastodon. It reflects their underlying values about decentralization and user autonomy.

Mastodon's implementation is also more flexible because Mastodon is decentralized. Each server (Mastodon calls them "instances") can implement Starter Packs their own way. One server might emphasize tech creators. Another might emphasize writers. There's no one-size-fits-all because there's no central authority.

This flexibility is powerful, but it also means a fragmented experience. If you're new to Mastodon, you might not see Starter Packs at all depending which instance you joined. The feature only works if your instance implemented it.

Estimated data shows that Starter Packs significantly improve user retention in the first few days after joining a social network.

Comparison: How Each Platform's Approach Differs

Let's actually compare how these platforms handle Starter Packs side-by-side because the differences are dramatic.

Look at that table. Every platform made different bets about what matters most. Bluesky prioritized community agency. X prioritized quality control. Threads prioritized seamlessness. Mastodon prioritized consent.

None of these answers are wrong. They reflect different values about how social media should work. And depending on what you value, you might prefer one approach over another.

Why This Feature Matters for User Retention

Starter Packs might seem like a small feature. But they actually address one of the biggest problems in social media: the cold-start problem. And solving that has massive implications for growth and retention.

Consider the numbers. According to industry data, most social media apps lose 90% of new users within the first week. Ninety percent. That's a horrifying retention rate. The users who make it past week one are the ones who found something interesting on day one or two. Everyone else bounces.

Starter Packs directly attack this failure point. Instead of hoping users will wander around until they find someone worth following, Starter Packs give them 20 interesting accounts immediately. Their first few hours are productive. Their feed has content. They have a reason to come back.

This isn't theoretical. Bluesky saw measurable improvements in retention after launching Starter Packs. Users who engaged with a Starter Pack had significantly higher seven-day retention than users who didn't. That's why every platform rushed to copy the feature.

But here's the deeper insight: Starter Packs change how people discover new creators. Historically, social media discovery worked through algorithmic recommendations or celebrity following lists. Starter Packs introduce a human curatorial layer. Some human, somewhere, decided this group of creators belongs together and is worth following as a unit.

That's powerful for smaller creators especially. If you're a photographer with 200 followers, you're invisible on most algorithmic social feeds. But if you get included in a Starter Pack curated by someone in the photography community, suddenly you're discoverable to thousands of potential followers.

This democratizes discovery. It's not just "who does the algorithm recommend" or "which celebrities should I follow." It's "what do the actual members of this community recommend."

The reason every platform is implementing Starter Packs is because they all desperately need to solve retention. X needs to stop losing users to Bluesky. Threads needs to prove it's more than just Instagram's failed experiment. Mastodon needs to feel less like technical complexity and more like a real alternative.

Starter Packs are the solution everyone's settling on because they actually work.

X's top-down curation scores higher in control, quality, and accessibility but lower in user agency compared to community-driven curation. (Estimated data)

The Creator Discovery Revolution

Let's zoom out for a second. Starter Packs are part of a larger shift in how social media discovers content. For years, algorithms ruled everything. The algorithm decided what you saw, who you followed, what went viral. Platforms built their entire value proposition around algorithmic personalization.

But people got tired of that. They got tired of not knowing why they were seeing something. They got tired of manipulative recommendation systems. They got tired of algorithms that prioritized engagement over accuracy or value.

Starter Packs represent a swing back toward human curation. Someone real decided this collection matters. Not a black box. A human curator. That matters psychologically to users. You trust humans more than algorithms, even if the human curator might be less optimized for your exact preferences.

This isn't just about social media. Spotify has playlists curated by actual humans (not just algorithmic). Netflix has some shows curated by actual humans (not just watched-because-algorithm-said-so). Product recommendation sites like Wirecutter, which use human experts instead of pure algorithms, consistently outperform algorithmic recommendations in user satisfaction.

There's a pattern here. People prefer expert curation over algorithmic optimization. Starter Packs tap into that preference directly.

But there's also a practical reason Starter Packs work so well: they create network effects. When you follow a Starter Pack, you're connecting to a cohesive community. You're following photographers who interact with each other, quote each other, build on each other's work. Suddenly your feed is coherent instead of fragmented.

That coherence drives engagement. You're not seeing random algorithmic recommendations. You're seeing a conversation between people you deliberately chose to follow. That conversation is more interesting, more worthwhile, more likely to keep you scrolling.

This is why Bluesky's community-driven model has such power. The Starter Packs that succeed are the ones where the curator actually knows the community. A Starter Pack of musicians created by a musician resonates more than a Starter Pack of musicians created by an algorithm.

How Starter Packs Compare to Other Discovery Methods

Before Starter Packs, social networks used several other methods to help users discover content and creators. Let's compare them.

Algorithmic Recommendations: Twitter and X's traditional approach. The algorithm watches what you engage with and recommends similar content. Fast, scalable, personalized. But often tone-deaf and manipulated by engagement metrics rather than quality.

Trending Lists: What's popular right now. Works for finding mainstream content. Terrible for finding niche communities. By definition, trending lists miss most interesting content because interesting content is, by nature, not mainstream.

Celebrity Following Lists: "Follow these celebrities." Works if you care about celebrities. Doesn't work if you want expert advice or niche communities. Most users don't want to follow celebrities; they want to follow people in their actual interest areas.

Hashtag Discovery: Follow a hashtag and see all posts tagged with it. Works but chaotic. A #photography hashtag on Instagram has billions of posts. You can't wade through that. Hashtags are useful if you know exactly what you're looking for but useless for exploration.

Manual Search: Type in a creator's name, find them, follow them. The least frictionless method. Requires you to already know who to look for. New users never use this because they don't know who's worth looking for.

Starter Packs: Curated collections of people. Combines human judgment with community knowledge. High quality because someone made the choice intentionally. Frictionless because you can follow a whole collection at once. Low barrier to getting started.

Starter Packs win almost every dimension of this comparison. That's why they're spreading across every platform. They're just a better solution to a hard problem.

Estimated data suggests music and photography are the most popular categories for Bluesky Starter Packs, reflecting community interests.

The Business Incentive Behind Starter Packs

Here's something important that's usually left unsaid: platforms implement Starter Packs because they want you to stay. It's that simple.

Every social network's fundamental metric is daily active users (DAU) and monthly active users (MAU). If those numbers go down, the platform loses value. Investors get nervous. Advertisers care less. The whole thing unravels.

X is losing users to Bluesky. That's not speculation; that's widely reported. Threads hasn't achieved the scale Instagram hoped for. Mastodon is gaining users but slowly. Each platform is trying to fix their retention problem.

Starter Packs fix retention because they make the critical first-day experience better. You sign up, engage with a Starter Pack, follow 20 new creators, and suddenly your feed is worth scrolling. You come back tomorrow. And the day after. Retention improves.

The specific implementation each platform chose also reveals their business priorities. X's centralized curation means they can showcase "X verified" accounts more prominently, driving signups for their paid verification. Threads' algorithmic approach means they can use their Instagram data advantage to lock you into the Meta ecosystem. Bluesky's community approach means they don't need to employ teams of curators; the community does the work for free.

Business incentives are driving this feature across platforms. That's not necessarily evil; it's just how social media works. But it's worth understanding that Starter Packs exist partly because platforms are desperate to solve their retention crisis.

Best Practices for Using Starter Packs

If you're joining a new social platform, how do you actually use Starter Packs effectively?

First, pick packs aligned with your actual interests. Don't just follow the most popular Starter Pack. If you follow a "general tech" Starter Pack but you actually care about cybersecurity, you'll waste time following people whose content doesn't resonate with you.

Second, follow multiple packs if available. One Starter Pack gives you 20-50 people. That's usually not enough to build a truly coherent feed. Following 2-3 packs from different curators gives you breadth without overwhelming you.

Third, evaluate the curator. Ask yourself: does this person understand the community? A Starter Pack of photographers created by a photographer is likely better than one created by someone who just aggregated popular accounts. Look at who created it.

Fourth, don't stop at Starter Packs. They're a starting point. After a week of following a Starter Pack, you'll discover people in that community who you want to follow. Their recommendations are usually even better than the original Starter Pack.

Fifth, consider opting out if privacy matters. On Mastodon and some other platforms, you can choose whether you want to be included in discovery-focused Starter Packs. If privacy matters to you, opt out.

Sixth, if you're creating a Starter Pack, focus on quality over quantity. Fifty accounts is a lot. Most great Starter Packs are 15-30 accounts maximum. You don't need to max out the limit. Just curate what actually matters.

The Future of Social Discovery

Starting Packs are successful, but they're not the final form of social discovery. Here's what's probably coming next.

AI-Enhanced Curation: Platforms will probably add AI to help curators. "Here are 200 creators in your niche. Which should we include in the official Starter Pack?" This combines human judgment with algorithmic assistance.

Subscription-Based Discovery: Platforms might let expert curators create paid Starter Packs. Pay $5/month and get professional curator recommendations in your niche. This monetizes curation and incentivizes quality.

Algorithmic Starter Packs: Instead of one static Starter Pack, imagine personalized Starter Packs that change based on your behavior. "We noticed you follow a lot of design accounts. Here's a Starter Pack customized for you." This is the scary algorithmic version, but it might work.

Cross-Platform Starter Packs: What if you could create a Starter Pack on one platform, then share it across multiple platforms? "Follow this Starter Pack on Bluesky, Mastodon, and Threads simultaneously." Interoperability could change how discovery works.

Community-Verified Starter Packs: Instead of one person curating, what if 5-10 community members voted on which accounts deserve to be in a Starter Pack? More democratic, more resistant to individual bias.

We're still in the early stages of this feature. What seems novel now will probably feel obvious in 18 months. But for right now, Starter Packs represent a genuinely interesting evolution in how social networks help people discover content.

Implications for Social Media Strategy

If you're running a creator account or building an audience, Starter Packs change your strategy slightly.

First, understand your niche community deeply. Starter Packs work by bringing together people in a cohesive community. If you want to be included in Starter Packs, you need to be seen as part of that community, not just someone posting in the space.

Second, build relationships with community curators. The people who create popular Starter Packs are often well-connected in their niche. Building relationships with them increases the odds they'll include you.

Third, consider creating your own Starter Packs. Even if you're not famous, you probably know your niche community well. Create a Starter Pack of people you think others should follow. It builds credibility and positions you as someone who understands the community.

Fourth, make your content coherent. If you're trying to build an audience, be consistent about what you're sharing. Starter Pack creators include accounts that have a clear identity and focus. Random posting about everything hurts your chances of inclusion.

Fifth, engage authentically with the community. Comment on other people's work. Share their stuff. Contribute to conversations. Starter Pack curators notice who's actually part of the community versus who's just trying to build an audience.

The era of pure algorithmic virality is fading. Community recognition matters more now. Starter Packs amplify that. They reward authentic community participation and punish account farmers.

Why This Matters Beyond Starter Packs

Starter Packs are technically a small feature. But they represent something bigger: a shift in how social media works.

For the first 15 years of Twitter, the platform was fundamentally algorithmic. Everything flowed through the algorithm. What you saw, who you found, what went viral. Facebook, Instagram, Tik Tok, all the same. Algorithms ruled.

Now we're seeing a backlash against that. People want curation that's transparent, that comes from actual humans they trust, that isn't designed to maximize engagement at the expense of accuracy.

Starter Packs are a symptom of that shift. They represent platforms saying: "Okay, we hear you. Here's a feature that's not algorithmic. It's curated by people like you."

We'll probably see more of this. More community curation, more human judgment, more transparency about why you're seeing something. The pure algorithmic era is ending.

This matters because it affects which platforms survive long-term. Platforms that can balance algorithmic efficiency with human curation will probably thrive. Platforms that double down on pure algorithms will probably struggle.

X's centralized Starter Packs suggest they're still not comfortable trusting their community with curation. Bluesky's community-driven model suggests they understand that's where the power actually is. That philosophical difference might matter for long-term success more than any technical implementation.

FAQ

What exactly is a Starter Pack?

A Starter Pack is a curated collection of recommended accounts that new (or existing) users can follow as a group. Instead of following accounts one-by-one, you can follow an entire Starter Pack with a single action, instantly building a more interesting feed based on shared interests or communities.

How many accounts can be in a Starter Pack?

It varies by platform. Bluesky limits Starter Packs to 50 accounts maximum. X hasn't specified a limit for their version. Threads integrates recommendations algorithmically without a fixed pack size. Mastodon leaves it up to individual servers. Generally, the best Starter Packs contain 15-30 carefully selected accounts rather than using the maximum.

Can anyone create a Starter Pack, or only official accounts?

On Bluesky and Mastodon, regular users can create Starter Packs. On X, only X's internal team curates official Starter Packs (at least in the current implementation). Threads uses algorithmic recommendations instead of user-created packs. This is a key difference between platforms: community curation versus corporate curation.

Why is X copying Bluesky's feature?

X is copying Starter Packs because the feature demonstrably improves user retention and feed quality. When Bluesky saw success with Starter Packs, other platforms recognized it solved a real problem: helping new users find relevant creators and build a coherent feed quickly. X needs retention improvements as it competes with Bluesky and other alternatives.

Should I follow a Starter Pack as a new user?

Yes, absolutely. Starter Packs significantly improve the first-week experience on social platforms. They give you relevant creators to follow immediately, making your feed more interesting and increasing the chance you'll return to the platform. However, pick Starter Packs aligned with your actual interests, not just the most popular ones.

How are Starter Packs different from hashtags or trending lists?

Starter Packs combine human curation with community knowledge, whereas hashtags are unfiltered collections of all posts using a tag, and trending lists show whatever's currently popular regardless of quality. Starter Packs are higher quality, more discoverable, and more aligned with specific communities compared to these alternatives.

Can you add yourself to a Starter Pack?

On Bluesky, yes, anyone can create a Starter Pack and include their own account if relevant. On X, no, because X curates Starter Packs internally. On Mastodon, it depends on server rules. Generally, including yourself is fine if it's genuinely relevant to the pack's theme, but Starter Packs created entirely to promote yourself are lower quality and less likely to be followed.

Will Starter Packs eventually replace algorithmic recommendations?

Probably not completely, but they'll likely become more important. Starter Packs are particularly good for cold-start onboarding and community discovery. Algorithms are better for personalization and ongoing recommendations. The future probably involves both: Starter Packs for new users, algorithms for existing users once the platform understands their preferences.

How do I create a Starter Pack on Bluesky?

On Bluesky, go to your profile, find the Starter Packs section, and select "Create a new Starter Pack." Choose up to 50 accounts to include, add a name and description, then publish. You can share the pack link, post about it, or generate a QR code for direct sharing. Make sure your pack has a coherent theme so followers actually want to engage with it.

Are Starter Packs good for discovering small creators?

Yes, Starter Packs are excellent for small creator discovery. Small creators with 200-2,000 followers are often invisible in algorithmic recommendations. But if a community curator includes them in a relevant Starter Pack, they become discoverable to thousands of potential followers. This democratizes discovery compared to pure algorithmic systems that favor already-large accounts.

Conclusion

Starter Packs represent an interesting moment in social media evolution. For years, we trusted algorithms to show us what mattered. Now platforms are slowly recognizing that human curation, transparency, and community involvement matter more than optimized engagement metrics.

Bluesky didn't invent Starter Packs because they're brilliant product designers. They invented them because they needed to solve a retention crisis. But in solving that crisis, they stumbled onto something more powerful: a way to make social discovery feel human again instead of algorithmic and opaque.

Now every platform is copying the idea, but each is implementing it differently based on their values and constraints. X went centralized. Threads went algorithmic. Mastodon went consent-based. These differences matter because they reveal what each platform actually believes about their users.

If you're joining a new social platform, Starter Packs are your best onboarding tool. They exist specifically to make your first week better. If you're a creator trying to build an audience, understanding how Starter Packs work helps you participate more strategically in your community. If you're thinking about which platform to actually invest your time in, the quality and philosophy of their Starter Packs tells you a lot about whether that platform gets user experience right.

The era of pure algorithmic social media isn't ending, but it's definitely shifting. We're moving toward something more balanced: algorithms where they work, human curation where it matters, community involvement where it's possible. Starter Packs are just one visible manifestation of that shift.

Watch how this feature evolves over the next year. The platform that figures out how to scale human curation without losing algorithmic efficiency will probably win the next era of social media. The platforms that are still figuring it out—like X—will keep playing catch-up.

Key Takeaways

• X is launching centrally-curated Starter Packs in the coming weeks to help users discover relevant creators across niches and countries
• Bluesky pioneered community-driven Starter Packs in 2024 allowing users to curate collections of up to 50 accounts and share them via QR codes
• Starter Packs solve the cold-start problem where 90% of new social media users abandon platforms within the first week due to empty feeds
• Each platform implements Starter Packs differently: X centralized curation, Threads algorithmic recommendations, Mastodon consent-based opt-in, Bluesky community-driven
• Starter Packs represent a shift from pure algorithmic discovery toward human curation and community knowledge, changing how creators build audiences

Related Articles

• ICE Verification on Bluesky Sparks Mass Blocking Crisis [2025]
• X's New Starterpacks Feature: How Social Discovery is Evolving [2025]
• ServiceNow and OpenAI: Enterprise AI Shifts From Advice to Execution [2025]
• Netflix Vertical Video Mobile App Redesign: What's Coming in 2025-2026 [2025]
• Netflix's Major App Redesign: Competing with TikTok for Daily Engagement [2025]
• Netflix's Mobile UI Overhaul 2025: What the Redesign Means for Streaming [2025]