Michael Griffin

Arguing with the morning about the nature of light

@upholdreality Lansky ran kiddie brothels in and around Havana For the same evil blackmail purposes as Epstein's Little St. James Lolita Express NYC brownstone party mansion etc It's a tribal profession

A transnational Jewish-Italian mob network centered on Meyer Lansky and Santo Trafficante ran Cuba as a privatized extraction arm of US empire: gambling, laundering, narcotics, prostitution, political bribery. What the US could not openly run on its own border, the syndicate ran for it in Havana. Batista was on a $ 1.28-million-a-month retainer, delivered every Monday at noon. His development bank bankrolled half of every new mob casino. And this was not just gambling. Havana was a key node in the postwar heroin pipeline: Turkish opium, Marseille labs, Havana transshipment, New York distribution. By the late 1960s, that French Connection network supplied most of America’s heroin. Then Castro won. In January 1959 the casinos were smashed, the bosses fled or were detained, and Batista escaped with a fortune estimated around $300 million. The national lottery, once a graft channel, was converted into a housing fund. But the mob did not disappear. It was redeployed. Lansky’s lieutenant Doc Stacher later said Lansky offered to finance Castro’s assassination as early as 1959. By September 1960, the CIA was running the operation directly. The Agency hired Johnny Rosselli, Sam Giancana, and Santo Trafficante. The opening offer was $150,000. The weapon: poison pills from the CIA’s Technical Services Division. The 1975 Church Committee found concrete evidence of at least eight CIA plots against Castro between 1960 and 1965. The same Havana-Miami underworld that lost Cuba in 1959 became useful again as the deniable violence arm of US policy. Of the three mob figures the CIA hired, Giancana was murdered before he could testify to Congress. Rosselli was murdered after he did. Trafficante survived. The continuity is structural, not anecdotal: Tampa, Havana, Miami. Casinos became exile paramilitaries. Exile paramilitaries became lobby infrastructure. Jorge Mas Canosa, a Bay of Pigs veteran and CIA-radio figure, founded the Cuban American National Foundation in 1981 at the suggestion of Reagan's advisors. It was modeled on AIPAC and built to harden Cuba policy permanently. The lobby's most famous operative was Luis Posada Carriles: CIA-trained, Bay of Pigs veteran, Iran-Contra contractor under Oliver North, perpetrator of the 1976 mid-air bombing of Cubana Flight 455 (73 dead) and the 1997 Havana hotel bombings. By 1998, he had publicly named Mas Canosa as his financier. He died free in Miami in 2018. They did not just lobby. They wrote the laws. 1992: Cuban Democracy Act. 1996: Helms-Burton. 2019: Trump activates Title III, letting US claimants sue foreign firms using confiscated Cuban property. Today the legal afterlife of Batista’s Cuba runs through federal court: hotel chains, expropriation claims, embargo law, and Miami political power. Marco Rubio, now Secretary of State, is a product of that machine. When he says “freedom in Cuba,” hear the history underneath it: the old casino mobster class wants its island back

People whose lives are so empty whose souls are so dead they need to have an opinion on what you eat for dinner

In August 1994, a ranger named David Noble was exploring a remote canyon in Wollemi National Park, about 150 kilometers northwest of Sydney. He climbed down into a gorge and found a grove of trees he did not recognize. He collected samples. When he brought them back, botanists could not identify them either. Eventually the samples reached a paleobotanist who recognized the pollen structure: it was identical to fossils found in Australian geological formations dating back more than 90 million years. The tree was known to science only as a fossil. It had been extinct, on paper, since the Cretaceous. There were approximately 60 trees in that one canyon. All of them genetically identical. Not similar, identical: the wild population appears to have zero genetic variation between individuals, which suggests that what exists today may be the last survivors of a single ancient individual that has been reproducing vegetatively for millions of years. Scientists called it the botanical find of the century. The location was kept secret. It remains restricted. The trees have been propagated in cultivation worldwide. But the wild population is still 60 trees in one canyon in the Blue Mountains, in country that has been inhabited by Aboriginal Australians for at least 65,000 years, near a city of 5 million people, undiscovered until 30 years ago. #exotictrees #forestfacts #nature #science

A fairytale where the children get made into soup Where the monster eats your family one by one A story where the magic stone is hard to find The map is glass broken into a thousand pieces When you find it you must kill the servants of the evil king who guard it with their lives

The Princess Myth Hilary Mantel

chiapas 16.07.17

chiapas 22.08.17

Memorial Day has been set aside to pay homage to those who have been killed in action while serving in our Armed Services. We are an organization made up of survivors of the June 8, 1967, @IDF attack on the #USSLiberty. The attack killed 34 of our shipmates and wounded at least 174 others from a ship’s complement of 294. To date, the US government has not investigated the attack on our ship. That leaves the blood of our fallen shipmates available to be used by people and organizations whose agenda you claim to be abhorrent. It never ceases to amaze and outrage us at how casually you and your colleagues in Congress during and since the attack on our ship claim to find “anti-Semitism” so distasteful while refusing to do anything to remove the blood of our fallen shipmates from the “anti-Semitism” arsenal. We look forward to the day when members of Congress ensure that no country attacks a @USNavy ship or commits war crimes against the United States with impunity, as you have allowed @Israel to do. We hope that there are #USSLiberty survivors alive to see that day. Visit the New #USSLiberty Veterans Website--ussliberty.org @adl @PhilPhilTourney @TuckerCarlson @RepDonBacon @DanLamothe @AlexHortonTX @shaneharris @EricSchmittNYT @helenecooper @charlie_savage @nancyayoussef @glubold #RememberThe34

#Entérate | Hombres armados irrumpieron en Nueva Independencia, en la Sierra de Chiapas, y expulsaron a 45 familias cafetaleras de sus tierras. Su retorno sigue vetado por el miedo y la impunidad. ✍️ @AngelesMariscal @ChiapasParalelo piedepagina.mx/no-hay-condici…

@TerrinaMajnoona Guzheng - Chinese cousin of Japanese kōtō

@IsawInfo38 What is this instrument?

But this - ah Heaviest metalist - wow Learn from a master 古筝八级曲目《雪山春晓》完整版来啦～ @日照筝至尚古... xhslink.com/o/3NxNpGDg7si open rednote

@TerrinaMajnoona x.com/IsawInfo38/sta…

@IsawInfo38 Where is that?

This is beautiful

古代ローマではある時、奴隷であることがわかるように彼らに固有の色の服を着用するよう義務付けようという法案が元老院で審議された だが「彼らに自分たちがどれだけ数が多いかを思い起こさせるのはまずい」という理由で廃案になったという

It's not wasted It serves one purpose and it serves it very well It keeps the price artificially inflated The same behind the scenes power makes the money and tells the gov't what to do The line btwn the Adelson bice empire and the illegal drug industry is non-existent

《临安遗恨》～古风旋律里最伤感的一首！ @日照筝至尚... xhslink.com/o/5VHID9mnrhK open rednote

Pattern recog saw early days twitter where Mirren Chagall Kafka Serge Gainsbourg etc etc Jewish etc get hyped and posters acting all blankfaced - rigging the popularity algorithms all squirmy innocence all What? Evil teletubbies led straight to the bloodsoaked rubble of Gaza

𝘜𝘯𝘵𝘪𝘵𝘭𝘦𝘥 by Shaun Tan (1974-).

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.

Children - believing cats and dogs were domesticated bc they're cute and funny and it feels good to pet them so why not raccoons