A diagram showing how Twitter\u2019s recommendation pipeline works. Image Credits:<\/strong> Twitter<\/p>\n<\/div>\n

Twitter says it\u2019s 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 \u2014 there\u2019s no sign of them at the present.<\/p>\n

\u201cWe\u2019re going to look for suggestions, not just on bugs but also on how the algorithm should work,\u201d Musk said on the Spaces session. \u201cIt\u2019s going to be an evolving process. I wouldn\u2019t expect it to be a nonstop upward movement\u2026 but we\u2019re very open to what would improve the user experience.\u201d<\/p>\n

At first glance, the algorithm is fairly complex \u2014 but not necessarily surprising in any way from a technical standpoint. It\u2019s made up of multiple models, including a model for detecting \u201cnot safe for work\u201d or abusive content, determining the likelihood of a Twitter user interacting with another user and calculating a Twitter user\u2019s \u201creputation.\u201d (It\u2019s unclear what \u201creputation\u201d refers to, exactly; the high-level documentation isn\u2019t clear on that.) Several neural networks are responsible for ranking the tweets and recommending accounts to follow, while a filtering component hides tweets to \u2014 forgive the jargon \u2014 \u201csupport legal compliance, improve product quality, increase user trust, protect revenue through the use of hard-filtering, visible product treatments and coarse-grained downranking.\u201d<\/p>\n