As repeatedly promised by Twitter CEO Elon Musk, Twitter has opened a portion of its source code to public inspection, including the algorithm it uses to recommend tweets in users’ timelines.

On GitHub, Twitter published two repositories containing code for many parts that make the social network tick, including the mechanism Twitter uses to control the tweets users see on the For You timeline. In a blog post, Twitter characterized the move as a “first step to be[ing] more transparent” while at the same time “[preventing] risk” to Twitter itself and people on the platform.

On a Twitter Spaces session today, Musk clarified:

“Our initial release of the so-called algorithm is going to be quite embarrassing, and people are gonna find a lot of mistakes, but we’re going to fix them very quickly,” Musk said. “The analog, here, that we’re aspiring to is the great example of Linux as an open source operating system … One can, in theory, discover many exploits for Linux. In reality, what happens is the community identifies and fixes those exploits.”

On that second point in the blog post about preventing risk, the open source releases don’t include the code that powers Twitter’s ad recommendations or the data used to train Twitter’s recommendation algorithm. Moreover, they include few instructions on how to inspect or actually use the code — reinforcing the idea that the releases are strictly developer-focused.

“[We excluded] any code that would compromise user safety and privacy or the ability to protect our platform from bad actors, including undermining our efforts at combating child sexual exploitation and manipulation,” Twitter wrote. “We [also took] steps to ensure that user safety and privacy would be protected.”

Twitter says it’s working on tools to manage code suggestions from the community and sync changes to its internal repository. Presumably, those will be made available at a future date — there’s no sign of them at the present.

At first glance, algorithm is fairly complex — but not necessarily surprising in any way from a technical standpoint. It’s made up of multiple models, including a model for detecting “not safe for work” or abusive content, the likelihood of a Twitter user interacting with another user and calculating a Twitter user’s “reputation.” (It’s unclear what “reputation” refers to, exactly; the high-level documentation isn’t clear.) Several neural networks are responsible for ranking the tweets and recommending accounts to follow, while a filtering component hides tweets to — forgive the jargon — “support legal compliance, improve product quality, increase user trust, protect revenue through the use of hard-filtering, visible product treatments and coarse-grained downranking.”

In an engineering blog post, Twitter reveals more about the recommendation pipeline, which it claims runs approximately five billion times per day:

“We attempt to extract the best 1,500 tweets from a pool of hundreds of millions … Today, the For You timeline consists of 50% [tweets from people you don’t follow] and 50% [tweets from people you follow] on average, though this may vary from user to user,” Twitter wrote. “Ranking [tweets] is achieved with a ~48-million-parameter neural network that is continuously trained on tweet interactions to optimize for positive engagement (e.g. likes, retweets and replies).”

The release of the source code comes after controversies involving tweaks to Twitter’s recommendation algorithm. According to Platformer, in February, Elon Musk called on Twitter’s engineers to reconfigure the algorithm so his tweets would be more widely viewed. (Twitter later walked back this change — somewhat.) A previous change

Twitter reveals some of its source code, including its recommendation algorithm by Kyle Wiggers originally published on TechCrunch