John

So I spent some time studying the new Twitter/X algorithm today since the latest version was published about a week ago on Github (#updates--may-15th-2026" target="_blank" rel="nofollow noopener">github.com/xai-org/x-algo…). My goal was to answer why so many people have seemingly seen such a dramatic drop in their posts' reach. The first answer, which is actually somewhat unrelated to the ranking algorithm on Github, is the auto-translate feature, rolled out worldwide on April 7, 2026 (x.com/nikitabier/sta…). Before that date, if you wrote in English about, say, the Trump-Xi Beijing summit, you were competing for attention with maybe 5,000 other English-language accounts writing on geopolitics. After that date, your post is competing for attention with other posts on the same topic IN EVERY LANGUAGE ON EARTH. For some topics that do command global attention like geopolitics, that's a very brutal multiplier: you used to be one of 5,000, you're suddenly one of 50,000 (something of that order): MUCH more difficult to stand out. Secondly, the number of followers you have matters far less than it used to: each post now has to earn its audience reader by reader, on the predicted engagement of the post, and how its topic matches what each reader has recently been engaging with. Here is how the algorithm works, in simple terms: when you, as a reader, open your feed, the algorithm doesn't load "posts from accounts you follow." Instead it runs a 2-stage prediction of what posts you're likely to engage with in that very moment. The first stage is the retrieval stage. The system narrows billions of posts on X/Twitter that day down to roughly 1,500 candidates by matching the semantic content of each post - what it's about - against what you as a reader have recently engaged with. Some candidate posts come from accounts you follow; others are pulled from across the platform by pure topic similarity to your recent interests. You can test this retrieval stage easily: start disproportionally engaging with - say - Brad Pitt videos and you'll bit by bit see your timeline flooded with Brad Pitt content, most of it from accounts you've never followed and never heard of. Then there's the ranking stage. Each of these candidate posts for your feed is fed through a Grok-based model that tries to understand if you'll engage with the post. It looks at 15 engagement metrics: 1) P(favorite) — the reader likes the post 2) P(reply) — the reader replies to it 3) P(repost) — the reader reposts it 4) P(quote) — the reader quote-tweets it 5) P(click) — the reader clicks a link in it 6) P(profile_click) — the reader taps through to your profile 7) P(video_view) — the reader watches the video 8) P(photo_expand) — the reader expands an image 9) P(share) — the reader shares it (DM, off-platform, etc.) 10) P(dwell) — the reader stops scrolling and lingers on the post 11) P(follow_author) — the reader follows you after seeing it 12) P(not_interested) — the reader marks "not interested" 13) P(block_author) — the reader blocks you 14) P(mute_author) — the reader mutes you 15) P(report) — the reader reports the post Fifteen predicted actions, each multiplied by a weight, summed: that sum is the score that determines in which priority a post will be seen among other candidates. Please note that posting something with a video or an image can give your post an advantage as 2 actions are specifically for these: video_view and photo_expand. No video or photo and you don't get a score for these. Also, naturally, having a video maximizes the chance that a user will "dwell" on your post to watch it. Also note that 4 of these actions carry negative weights (not_interested, block_author, mute_author and report): meaning that if the model expects a post to generate a lot of negativity, it'll get de-boosted quite dramatically. But note, first and foremost, what's NOT in there: none of the things that, naively, one might think a serious information platform would weigh. There is no P(this post is true and well-sourced). No P(the author actually knows what they're talking about). No P(this person has spent a decade building a body of work that has held up). No P(this account has earned the right to be taken seriously on this topic). No P(the author has a large following from credible people). The model does not seem to care - at all - about any of that. Every post starts from zero. You could have ten years of rigorous, well-sourced analysis behind you - or you could be just an uneducated rando who registered yesterday. To this algorithm, you're both just a bag of engagement probabilities. Now, sure, to be fair, there is a "brand" effect that's not covered by the algorithm: someone who has in fact built a brand will naturally have better engagement metrics because people recognize their account. But that's an indirect, second-order effect. And crucially, it's legacy: those "brands" were built under earlier versions of the algorithm that gave followers and reputation more weight. Lastly, several other features of the new algorithm compound the dilution, none of them visible from outside but all consequential. The May 15 update added an "impression bloom filter," tightening the rule that once a reader has been served a post, the system won't serve it to them again. Before, a strong post could marinate in someone's feed across multiple refreshes and accumulate engagement on the second or third pass. Now it basically gets one shot. Also, your own posts compete with each other. An "Author Diversity Scorer" inside the ranking stage attenuates the score of every subsequent post of yours that ends up in a reader's candidate pool. In plain terms: if multiple of your posts land in a reader's candidate pool, the system shows one at full strength and dampens the others. So don't post several times consecutively on the same topic. And, last but not least, another huge impact on reach is that, in the old algorithm, when someone reposted or quote-tweeted you, your post was broadcast to their followers' timelines - a repost from an account with 100,000 followers was a huge boost. In the new algorithm, that mechanism is vastly demoted: reposts - like every post - need to go through the retrieval and ranking stage mentioned above, so a repost from a big account is a long way from the boost it used to be. This is especially brutal for low-effort quote tweets, which used to function as cheap amplification: now they often can't even clear the retrieval stage - they simply don't contain enough novel semantic content for the system to match them to anyone's interests. So, putting it all together, the reach collapse comes from many forces stacking at once: - Auto-translate makes your posts compete for attention against an order of magnitude more content - The retrieval stage matches posts by topic, not by who follows you - The ranking stage scores purely on predicted engagement with no weight for credibility, expertise, or track record - The bloom filter narrows every post's window to one strong shot - The diversity scorer penalizes prolific posting - Reposts no longer carry much distribution power Each of these alone would dent your reach. Combined, they amount to a complete reset: your audience that you built painstakingly over years basically doesn't matter much anymore, and it's much - much - harder to stand out even if you're a big account. People structurally rewarded by this algorithm are folks who: - Post visually (videos/images) - Post on globally popular topics because they clear the retrieval stage easily - Provoke strong emotional reactions - likes, replies, reposts - Don't care about accuracy or seriousness because the algorithm doesn't measure it - Don't care about their existing audience because every post is judged in isolation anyway In short this new algorithm, like so many on social media, is all about maximizing whether people will engage with something - not about whether they should.

You may be wondering 2 things. 1) What happened to Trump? Who is he listening to? 2) Why does it feel like the internet isn’t real and engagement is fluctuating based on “particular topics” lately? I think I can help answer both questions. There is a company called Vine & Fig Tree (VFT). VFT is a pro-Israel organization with ties to the administration. Earlier this year, VFT was at the White House meeting with Sebastian Gorka. Shortly after that White House meeting, I was contacted through a third party and asked to script-write for VFT. The individual who contacted me is publicly very Christian and widely perceived as America First. I was told the script would be used to create an AI-generated video on behalf of the White House, specifically for NSC and Sebastian Gorka. They told me: “Yeah we have to do this on behalf of them [the administration] because they don’t want it to look like it’s actually coming from the WH.. you know what I mean? I mean, it worked out for them and Nick Shirley.” I was then given a Dropbox link containing research, polling data, internal comments, and strategy material compiled by VFT and the third party involved. Inside the Dropbox were 7 folders. Through those documents, I learned more about what this organization actually does. Their reports monitor major conservative and "dissident-right" accounts and frequently frame those accounts as vulnerable to, or participating in, foreign influence operations. The reports include information regarding @NickFuentes, @hodgetwins, @RealCandaceO, @TuckerCarlson, @jacksonhinklle, @IanCarrollShow, and @MarioNawfal just to name a few. They also collected polling and response data surrounding @joekent16jan19’s resignation from the administration. In another report, they argue that distrust surrounding Charlie Kirk’s assassination was mostly due to Americans falling for Russian, Iranian, and Pakistani propaganda networks. In that same Charlie Kirk report they state, "This represents an urgent national security threat... and demands a whole-of-government response on par with cyberattacks or terrorism." The internal comments attached to these reports are what stood out most. They talk about "going after" Fuentes, stating "undermining his Christian identity is probably a good Idea." They contemplate "getting" @MattWalshBlog or @michaeljknowles to publish on behalf of VFT. They suggest collaborating with NCRI, founded by Joel Finkelstein - a multi-million dollar organization that tracks "hate speech" on social media. Another internal comment weighs in on how they will advise politicians based on their data which also compiles info surrounding JD Vance's 2028 run: “There is definitely a way to use this in our favor: tell politicians that there are two wings of the party, they don’t overlap, the majority lies here, and this is where you should be if you want to get re-elected..." The documents also discuss: Burner profiles, burner ad accounts, AI-generated interview-style videos, audience personas, “troll content briefs”, engagement testing, and ideological audience segmentation. If you're wondering whether the White House is actually listening to VFT... It's worth reviewing the White House's latest 16-page Counterterrorism Strategy touted by Gorka. More to follow.

.@RCMPgrcPolice liken China police to FBI as "partner" in public safety but won't disclose Beijing agreement signed by @MarkJCarney "without their permission."  #cdnpoli" target="_blank" rel="nofollow noopener">blacklocks.ca/calls-china-po… @MichaelCooperMP @CommrRCMPGRC

@FaytuksNetwork I don’t understand why Europeans are so reluctant to just send 2 fucking ships to the strait and keep Trump happy. Because they are risking the US leaving NATO and we all know Europe alone against Russia isn’t looking good. I am saying this as someone that lives in Europe.