Dronken Varken

[Dronken Varken @okutw]

@justincbzz No problem.
https://t.co/xbQRu8QNmu

[Dronken Varken @okutw]

@StefanoPartson @AdrianP_doc @JayinKyiv You definitely have no clue about the real situation. This is Moscow this year https://t.co/deubkHRWbJ
The percentage of them in Moscow is higher than Russian average

[Dronken Varken @okutw]

@LiberalHegemony This photo from 2006 is a good visual illustration

[Bad Baltic @BadBalticTakes]

Can you imagine if Eastern Europeans talked about the rest of the world using the same clichés, tropes, stereotypes, & propaganda commonly used about them?

🧵…

[Dronken Varken @okutw]

@maxi_max1975 @XKubiak @zoomeek72 Główny regionalny magazyn Wildberries do przechowywania olejów i substancji łatwopalnych

[Dronken Varken @okutw]

@BiankaB12 It's cyclic. "Heroin chic" was just the latest turn. The 60s had Twiggy's waif look, the 1920s had the boyish "garçonne" flapper, and Victorians romanticized "consumptive chic" — literally idealizing how tuberculosis made you look. Each pretends bone density doesn't exist.

[Dronken Varken @okutw]

@alexsakalis The s-ending word grammar form is extremely common in Sanskrit language. Lithuanian is considered as an Indo-European language which kept the most from its in phonology and grammar. The second one is ancient Greek.

[OnlMaps @onlmaps]

Nepalese population in Europe

[Dronken Varken @okutw]

@OnDisasters There was a very detailed article about it in 2021
https://t.co/uqSwC99MfK

[Dronken Varken @okutw]

@mr_BobRice @GunloverClub1 World Champion Shotgun Shooter vs FPV drone
https://t.co/hMFXJdB6wz

[Dronken Varken @okutw]

@pn_warner @JayinKyiv There is an urban legend that after WWII, Soviet officers' wives mistook fine European lingerie brought home as war trophies for evening gowns and wore them to the theater. Maybe that's where the old family tradition cones from.

[Dronken Varken @okutw]

@YIMBYPoland Well, that has already been happening in the Balkan countries for a while
https://t.co/FedkJ7UBLg

[Dronken Varken @okutw]

@dim0kq Is this him saying "We understand that separation from Russia is idiocy" in 2010?
https://t.co/yPWOdSMZwW

[Dronken Varken @okutw]

@BiankaB12 Well, I remember him predicting a confident Biden victory half a year before the election.

[Dronken Varken @okutw]

@pepel_klaasa One more thing probably worth mentioning: the impact on the banking sector. Business owners who work through Wildberries have loans and mortgages. With their businesses lost, late payments will rise, banks will have to set aside provisions, book losses, and so on.

[Dronken Varken @okutw]

@pepel_klaasa Spin me right
Spin me round
Spin me right round, blyat, right round

https://t.co/UvcQ4GUi4g

[Javi Lopez ⛩️ @javilopen]

⚡ xAI dropped the X algorithm yesterday and I don't get why nobody noticed what's actually in there

I burned $500 on Claude going through every single line

Here's what I found (LONG POST, save it for later):

0/ Every account has an "embedding" attached to it that describes you the way AI models do: in latent space. It's the internal fingerprint the model keeps of every user, a vector of numbers that sums up how your account behaves (what topics you touch, what engagement you generate, who you interact with). The model uses it every time it decides who to show your posts to. If your history is good, it stays clean and the model pushes you. If you accumulate negative signals (blocks, mutes, reports, not_interested), it goes toxic and starts penalizing you automatically. And the trap: it does NOT reset. What you do today stays in there for weeks, poisoning everything you publish after, even if it's good.

That's why getting out of a shadowban or a low-reach streak on X feels like trying to move a giant rusted wheel. It's not your imagination, it's literally that. Cleaning up your embedding is slow and painful, like the impression you have of someone you don't like: no matter how nice they get to you, it's gonna take a while before you trust them.

Another important finding: the embedding doesn't decay on a clock. It decays with NEW engagement entering the system. If you stop posting, the old bad signals stay frozen in there. Nothing overwrites them. If you start making content the algorithm likes, you'd see improvement after 6 to 8 weeks and a real shift around 12 to 16 weeks, assuming you don't pile up more bad signals along the way.

Why is nobody talking about this? It blows my mind. Finally a confirmation of that "I'm in a bad streak" feeling we've all been through.

1/ First 30 minutes are everything

If your post doesn't get engagement fast, Grok doesn't even evaluate it. No quality score, no deep analysis, no chance of reaching anyone who doesn't follow you. Dead and buried

2/ Post age caps at 80 hours:

POST_AGE_MAX_MINUTES = 4800, bucketed in 1 hour chunks. After that you're in the "overflow bucket" which translates to "ancient, ignore"

Best window: first 0 to 12 hours. After 24 you're already in a worse bucket

Far from rewarding "evergreen" content, X wants a constant stream of fresh meat (literally the opposite of YouTube)

3/ MY BIGGEST FEAR TURNED OUT TO BE UNFOUNDED (supposedly): living in EU posting English for US audience: ZERO direct penalty in theory:

The PostCandidate struct has NO field for author country, IP, or location. Gizmoduck (X's identity service) returns only follower count + screen name. The Phoenix transformer just sees a hash of your author_id

What hurts you indirectly: timezone (your post ages while US sleeps) and the language of the POST itself

So using a VPN to "post from the US" does literally nothing (unlike TikTok or Instagram, by the way)

4/ The 5 negative signals that kill your reach:

The model predicts 22 actions per post. 5 of them are negative weights that get SUBTRACTED from your score:

- not_interested
- block_author
- mute_author
- report
- not_dwelled (people scrolling past your post without stopping)

That last one is brutal tbh. A post that gets ignored is mathematically WORSE than a post that never got published

5/ Shadowbans 100% exist. 4 different kinds:

- Hard drop. X removes your post from everyone's feed without telling you. Applied to posts with serious content (child safety, etc.) or suspended accounts. You don't even find out
- DO_NOT_AMPLIFY label. Literally a field in the code that says "do not amplify this post". If they put it on you, ads stop showing next to your posts → X stops making money from showing you → the system stops pushing you. Full blackout
- BotMaker rules. The internal panel where X employees can manually limit a specific account by hand. The code shows the categories that exist (Content, ContentLimited, Safety, Grok) but does NOT show who they're applied to or why. The tool is documented, the usage isn't
- Poisoned embedding. The worst one, as we saw above. The model has an internal "memory" for every account. If your account racks up enough "not interested" + blocks + mutes + reports over time, that memory goes toxic. From then on, even your good future posts get penalized automatically. Nobody decided this. The model just learned your account gets bad engagement and self-corrected

6/ Only ORIGINAL posts get the "Banger Screen"

Replies and retweets never enter the Grok quality classifier. If you spend your day replying to viral accounts, you're optimizing for the Reply Ranker, NOT for amplification

Want to be discovered out of network? Write originals. There's no other way

7/ Replies to small accounts get spam-scanned. Replies to big accounts get Grok-ranked

Two separate classifiers. The SpamEapiLowFollowerClassifier hits replies to small accounts. The ReplyRanker scores replies to big accounts 0 to 3 with Grok

"First!" or emoji-only replies get a 0. "Sir, this is a Wendy's" energy gets penalized. Basically, if you write replies, they better add something. Otherwise don't bother

8/ 50% of all feed requests are "shadow traffic"

is_sampled(request_id, 0.5) marks half of every feed request as shadow. Many context features (gender inference, demographics, Grok topic preferences) only activate on shadow OR with a feature flag

Translation: you literally cannot know which version of the algorithm any given user is getting. Half your audience is in an experiment at any moment

9/ Dwell (the time a user spends looking at your post before scrolling) is 5x better than getting likes

The scorer has 5 different dwell signals (dwell, cont_dwell_time, click_dwell_time, etc.) but only 1 favorite signal.

- A post with tons of likes but people read it for 1 second and keep scrolling → low score
- A post with few likes but people stay 8 seconds reading it → high score

Optimize for time spent on your post, not for likes!

10/ Things that actually work:

- Get engagement in the first 10 min. DM your friends, ping your community, whatever
- Post in your AUDIENCE'S timezone, not yours. US targeting: 8 to 11am ET (14 to 17 Madrid time)
- Don't post 5 things in a row. AuthorDiversityScorer multiplies each next post by decay^position. By post 4 you're at the floor
- Video ≥ 10 seconds. Below MinVideoDurationMs you lose the full VQV weight
- Videos with audio. Grok runs ASR (speech to text) on every video. No audio = blank signal
- Quote tweet virals in your niche. The model already knows the original engages, your value-add stacks on top

11/ Things that absolutely kill your reach:

- WILD FINDING: threads of 10+ tweets. DedupConversationFilter keeps only 1 tweet per conversation per feed. Megathreads are mathematically a waste
- Reposting the same content. Bloom filters dedupe it
- AI slop. There's literally a slop_score field in the BangerScreen output. They explicitly detect it
- NSFW/violence/hate without tags. Auto MediumRisk = no ads = structural shadowban
- Reply-spamming small accounts. Specific classifier for that

12/ What they DIDN'T release, the sneaky bastards:

The skeleton is public. The dials are not

- Exact numeric values of every weight (FavoriteWeight, ReplyWeight, OonWeightFactor, AuthorDiversityDecay). Live in xai_feature_switches::Params, external config
- The actual Grok prompts (the 7 PToS policy prompts, BangerMiniVlmScreenScore, SafetyPtos). Could literally have any framing in them
- The BotMaker rules that apply DO_NOT_AMPLIFY to specific accounts
- util/phoenix_request.rs, which constructs the final model call
- 25+ xai_* crates referenced but not included
- The production Phoenix weights. They only released the mini version

My theory: they gave us a pretty skinny skeleton of the whole thing they actually have. The muscle (weights) and the brain (prompts and BotMaker rules) are completely opaque. They kept the best parts for themselves, clearly

13/ Cheat sheet so you don't forget:

- First 30 min matter more than anything
- Your location is irrelevant, your timing and language are not
- Shadowbans exist in 4 flavors. Worst is the model quietly poisoning your author embedding from past bad signals. Climbing back up by cleaning your embedding is gonna hurt, but it can be done
- Replies and retweets don't get the quality classifier. Originals do
- Dwell (someone actually staying to look at your post) beats likes 5 to 1
- Half of all traffic is in some experiment at any moment
- They kept the best parts of the algorithm for themselves, but hey, something is something

[Dronken Varken @okutw]

@govorytkyiv Мікс непропоційного сталінського ампіру з центральноазійсько-циганськими мотивами. Якщо коротко, то "по-бахатому".

[Dronken Varken @okutw]

@justincbzz Actually it was shelled, especially actively in the middle of March.

https://t.co/7fxSRke6mD

https://t.co/ymY9KOHNIR

[Dronken Varken @okutw]

@distant_earth83 @2iober @Fromofsumer They have some greeting words for you