Rengoku

Set Your Heart Ablaze.

[Dave Portnoy @stoolpresidente]

This gotta be the funniest tweet of the tournament.

[MisterChip (Alexis) @2010MisterChip]

TUCHEL, CAGÓN. Te vas a Miami y despues a tu casa.

[Piers Morgan @piersmorgan]

What a disgusting, repellent, racist pig. We all stand with you @KMbappe against this shameful woman who I sincerely hope is hounded out of her job asap. How the hell is she an elected senator in Paraguay?

[Polymarket Sports @PolymarketSport]

🚨Oliver Kahn:

“If we're rewriting football history now, I have a small suggestion: I'd like FIFA to rescind the yellow card shown to Michael Ballack in the 2002 World Cup semifinal, the one that ruled him out of the final. And while we're at it, we might as well replay the final against Brazil."

[Kylian Mbappé @KMbappe]

Madame Celeste Amarilla,
Vous êtes une femme méprisable et indigne de sa fonction.
Vous ne représentez pas le Paraguay, ce pays qui a transpiré la passion et l’honneur tout au long de la compétition. Par votre inconscience et votre racisme décomplexé, le monde entier a déjà oublié le parcours et l’effort historique que vos joueurs ont réalisés durant cette coupe du monde pour laisser place à une dame incompétente donnant la pire image possible de son pays.
Je ne laisserai jamais aux gens comme elle, la liberté de laisser propager leur haine et leur racisme à travers le monde.

[Lfc Kev @Kev1Lfc]

I hope the USA get absolutely destroyed by Belgium. Pathetic cheats.

[A Morte ☠️ @diabodacharneca]

@WhiteHouse Same vibe, bruh.

[Bosnian Football @BosniaNTBall]

Just end the torunament now. This is disgraceful! #FIFAWorldCup

[Piers Morgan @piersmorgan]

This will be the biggest story, and potential scandal, of the World Cup…

[Hennez @Hennez_hof]

@stoolpresidente This is the most absolute brain dead take I’ve seen all day.

You casual football fans truly believe this will have no consequence? The real losers in this situation are your own USMNT.

Any success at this World Cup will be tainted, because it is straight corruption 🤣

[Gunther Eagleman™ @GuntherEagleman]

LOL!!! “Nah bro I have the Trump card, I win.”

Trump card always wins!

[Mario Nawfal @MarioNawfal]

🚨🇻🇪 UPDATE: More shocking footage out of Venezuela from last week's devastating earthquakes...

At least 1,450 people are dead, 3,150 are injured, and nearly 50,000 are missing.

It was the worst earthquake to hit the country in a century, back-to-back 7.2- and 7.5-magnitude seismic events, occurring just 39 seconds apart.

The epicenters were centered near San Felipe, Yaracuy, along the boundary of the Caribbean and South American tectonic plates.

The tremors sent powerful shockwaves that were felt as far away as Colombia and Aruba.

More than 774 buildings have totally or partially collapsed, and up to 58,000 structures suffered varying degrees of damage.

Writer: Michael

[Geiger Capital @Geiger_Capital]

This is true diversity.

Unique nations and peoples with their own shared values, principles, traditions and history. Flooding every country with the same mass migration is erasing distinct cultures.

[Arnaud Bertrand @RnaudBertrand]

So I spent some time studying the new Twitter/X algorithm today since the latest version was published about a week ago on Github (https://t.co/3jzdav3Ywp).

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 (https://t.co/YtGomG9RGz).

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.

[Everything Price Sufferer (but especially eggs) @agraybee]

I didn't know Helen of Troy could generate so much conflict.

[Peter McCormack 🏴‍☠️🇬🇧🇮🇪 @PeterMcCormack]

Whoever replaces Starmer will fail.
Just as the Conservatives failed.

You can’t build a country on low growth, low productivity, high tax, high welfare, high inflation and heavy regulations.

It’s basic math - you materially make the lives of the majority worse.

Reform, Greens, whoever else wants the gig - you will face the same.

Until someone defeats the managerial state, nothing will improve and we’ll have more PMs than Tottenham has managers.

[Bernie Sanders @BernieSanders]

It is time for the U.S. to end military aid to Israel.

But we're not going to wait 10 years to do it.

The time to stop arming Netanyahu and hold him accountable for his crimes against humanity is NOW.

[X Freeze @XFreeze]

Elon Musk: "If you punish people too much for failure, then they will respond accordingly, and the innovation you will get will be very incrementalist

Nobody's gonna try anything bold for fear of getting fired or being punished in some way. So risk-reward must be balanced and favor taking bold moves, otherwise it will not happen"

[Anthony Pompliano 🌪 @APompliano]

It feels wrong that US military members are punished for betting on themselves to successfully carry out military missions, while politicians can bet on themselves through insider trading of companies they govern.

Either ban both of them or allow both of them.

[Dale Stark @DaleStarkA10]

Soldiers should know better. You’re only allowed to do this if you’re a member of congress.