(ノಠ益ಠ)ノ彡 ɐʇɐᗡ

@iruletheworldmo bro you are one of the least intelligent people on the internet and it shows

just turn on your camera spread false negative ai sentiment take home the money. the new algo is simple

I'm done. I'm f***ing done.

okay ......... im about to lose my marbles to cuteness

横須賀ヴェルニー公園の夜景3D Gaussian Splatting 3DGS of Yokosuka Verny Park at night. Testing Postshot V1.1 with Photometric Compensation. It's clean with very little artifacts.

@AniC_dev docker and an old laptop is free

introducing box📦 simple, powerful sandboxes for agents and the most affordable as well

all this remote networking and VM stuff i've been doing made me realize i can remote into my xbox from anywhere on the planet i haven't played it in years, but i *could* if i wanted

@boneGPT @RnaudBertrand TIL third worlders smell

@RnaudBertrand if you don't post for the love of the game, the algo can smell it

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.

@alightinastorm o7

@DataDeLaurier i do my homework

Ok, fine, let's see: More people are searching daily for "counter strike" than "play games online", the search volume for web games is tiny and you aren't even close to disrupting miniclip, poki, king There are dozens of platforms over decades old, some of them are heavily curated while others have game portfolios of hundreds of thousands of games If you go to the ones with visible metrics, you'll see games which are about 5-10 years old reach 100k+ ratings on the very large platforms Now let's see, going on your app, all games are indeed fast slop but somehow every game has thousands of comments and likes? If you actually open the replies it's full of arabic slop and mainly other creators spamming every other game to play theirs instead. So, why? Is it maybe that your daily organic traffic is, in fact, tiny and you are trying to keep the platform alive by running ads and dangling carrots over creators heads? You set the bar for payouts to 100k plays in 30 days So you're running probably a retarded amount of ads on meta to attract people to play the slop games Let's assume you get some amazing CPC on your campaigns of $0.05 of which every second user becomes a "significant play". That means the cost to monetize one creator artificially is at least $5-20 k, which you'll absolutely try to avoid and spread over multiple creators instead so you don't have to pay additional payouts on top of ads You can run massive UGC campaigns, SEO, whatever you want, but your CAC is ass, your CLTV is probably ass and you have to pump thousands into creators WITHOUT paying them just to onboard new creators all day These are just 5 minute estimates but your platform is toast and @sequoia probably knows or whoever in their team approved this coal was doing you a favor Oh and your X launch video was botted and paid to go viral, your platform got zero signal on the largest vibe coding gametech space, X You can't buy your way in to this, we are not that dumb

@Teknium @supaborg the diff is nous is comprised of highly intelligent humans 99.99% of "startups" think AI is all you need yeah, hermes builds it's self now, but that would be impossible if yall were retarded slop shills. remember openclaw? lolllllll

@supaborg What does it actually mean to run the business btw Hermes builds all the things at Nous now

I still refuse to believe they gave $30M to this unproven idea that is just extracting money. FUuuk!!!!ng crazy. I’ll personally pay $5,000 to anyone who can show me 1) a REAL business built with Polsia (not just a landing page) 2) that this business has customers. Drop it below. If it’s legit, I’ll wire you the $5K.

@lalopenguin im very experienced at auto restoration dm me anytime you have questions

this was my dads, it ran when i was younger, and then sat in the garage for as long as i can remember.. then my sister got it... it sat in her garage for 9 years, and now my brother just took it off her hands, and it's on its way to san diego we know nothing about the condition, or how to fix cars at all, but i guarantee w AI .. we can get this running. to be continued

@meowbooksj and they all have insomnia from monitoring the situation while their us moots sleep

@DataDeLaurier probably on us time for their cs jobs

BTW i have 19k followers

@meowbooksj my indians are still sleeping

@DataDeLaurier ironically they’re actually awake

@meowbooksj so you just have a bunch of indian scam accounts following you. got it.

@DataDeLaurier everyone sleeping

@Xplo8E @earthtojake stop using ai to write reply's ffs

@earthtojake Text-to-CAD gets interesting only when the print actually closes the loop. The Bambu step is the proof, not the prompt.

this part was generated AND printed with one prompt using codex + text-to-cad: “Make me a thick Möbius strip, and use computer use to print on my Bambu Labs A1 Mini” went to the gym, came back 1.5 hours later and it was sitting on the build plate

@sadeghian_ali the average mind is too retarded to create OC. the average mind attempting to create a game? lol, no.

The average mind fails to comprehend the unlock when 1000x more people can create interactive content

@1776Diva @nikitabier @MarioNawfal yt-dlp is programmatic and i bet its what the video thieves use at scale removing the 'download' option does nothing.

@nikitabier @MarioNawfal So why is the feature even available? Remove the "download video" option if you're penalizing people for it. 🤷🏻‍♀️

🚨 BREAKING: 🇺🇸 Footage from ABC News has captured the moment shots were fired outside the White House. Key details: -ABC News footage captured audio of the gunfire in real time -Roughly 20 to 30 rounds were reported fired outside the White House -The Secret Service rushed press from the North Lawn into the briefing room The situation remains active and developing No confirmation yet on injuries, a suspect, or motive President Trump's status not yet confirmed Source: ABC News

i'll never understand why some people watch every one of my steps, privately fork and modify my projects and sell it as their own which is all fine, i explicitly release it as MIT but why are you not following bro?