How Starter Packs’ Human Recommendations Fuel Blueky’s Growth

The impressive growth of BlueSky during November has seen the student become the master. The former Twitter project, set up to research open and decentralised standards, has become the most likely replacement in the microblogging space. Bluesky was in the right place at the right time, and Silicon Valley and the VC industry will study the rapid rise in its user base for years to come.

One of those factors will surely be the introduction and use of Starter Packs.

Rather than relying on an algorithm to suggest new accounts for a user to follow, they can find a Starter Pack on a specific topic and either follow everyone in the pack with one click or scroll through and choose individuals from the list. Given individual users put together these Starter Packs, it feels far more community-based than anything seen in Twitter. A new user joins the service, possibly finds a few people they followed on Twitter, and then decides to follow people in a space; that could be Eurovision, Scottish Politics, or their favourite podcast. I’m sure there’s a wide range of topics you can find.

(Mind you, Starter Pack discovery needs a bit of work).

Nevertheless, using a Starter Pack hands new users something that could have taken weeks, and that’s a sense of community. What could be a chaotic timeline that overwhelms a new user to the point of not moving forward, they get an immediate community with discussions, debate, and involvement.

There’s another subtle influence that Starter Packs offer. They’re human. It’s not a great big AI mess of predictions and weightings; it’s smart people who know a subject highlighting other smart people who know the subject. 

Personal recommendations are powerful, and BlueSky has used them to harness growth and influence. The question now is if the team can use this power responsibly.

You can find me on Bluesky: at ewanspence.bsky.social.