24th April 2026

Chinese AI lab DeepSeek’s last model re­lease was V3.2 (and V3.2 Speciale) last December. They just dropped the first of their hotly an­tic­i­pated V4 se­ries in the shape of two pre­view mod­els, DeepSeek-V4-Pro and DeepSeek-V4-Flash.

Both mod­els are 1 mil­lion to­ken con­text Mixture of Experts. Pro is 1.6T to­tal pa­ra­me­ters, 49B ac­tive. Flash is 284B to­tal, 13B ac­tive. They’re us­ing the stan­dard MIT li­cense.

I think this makes DeepSeek-V4-Pro the new largest open weights model. It’s larger than Kimi K2.6 (1.1T) and GLM-5.1 (754B) and more than twice the size of DeepSeek V3.2 (685B).

Pro is 865GB on Hugging Face, Flash is 160GB. I’m hop­ing that a lightly quan­tized Flash will run on my 128GB M5 MacBook Pro. It’s pos­si­ble the Pro model may run on it if I can stream just the nec­es­sary ac­tive ex­perts from disk.

For the mo­ment I tried the mod­els out via OpenRouter, us­ing llm-open­router:

llm in­stall llm-open­router

llm open­router re­fresh

llm -m open­router/​deepseek/​deepseek-v4-pro ‘Generate an SVG of a pel­i­can rid­ing a bi­cy­cle’

Here’s the pel­i­can for DeepSeek-V4-Flash:

And for DeepSeek-V4-Pro:

For com­par­i­son, take a look at the pel­i­cans I got from DeepSeek V3.2 in December, V3.1 in August, and V3 – 0324 in March 2025.

So the pel­i­cans are pretty good, but what’s re­ally no­table here is the cost. DeepSeek V4 is a very, very in­ex­pen­sive model.

This is DeepSeek’s pric­ing page. They’re charg­ing $0.14/million to­kens in­put and $0.28/million to­kens out­put for Flash, and $1.74/million in­put and $3.48/million out­put for Pro.

Here’s a com­par­i­son table with the fron­tier mod­els from Gemini, OpenAI and Anthropic:

DeepSeek-V4-Flash is the cheap­est of the small mod­els, beat­ing even OpenAI’s GPT-5.4 Nano. DeepSeek-V4-Pro is the cheap­est of the larger fron­tier mod­els.

This note from the DeepSeek pa­per helps ex­plain why they can price these mod­els so low—they’ve fo­cused a great deal on ef­fi­ciency with this re­lease, es­pe­cially for longer con­text prompts:

In the sce­nario of 1M-token con­text, even DeepSeek-V4-Pro, which has a larger num­ber of ac­ti­vated pa­ra­me­ters, at­tains only 27% of the sin­gle-to­ken FLOPs (measured in equiv­a­lent FP8 FLOPs) and 10% of the KV cache size rel­a­tive to DeepSeek-V3.2. Furthermore, DeepSeek-V4-Flash, with its smaller num­ber of ac­ti­vated pa­ra­me­ters, pushes ef­fi­ciency even fur­ther: in the 1M-token con­text set­ting, it achieves only 10% of the sin­gle-to­ken FLOPs and 7% of the KV cache size com­pared with DeepSeek-V3.2.

In the sce­nario of 1M-token con­text, even DeepSeek-V4-Pro, which has a larger num­ber of ac­ti­vated pa­ra­me­ters, at­tains only 27% of the sin­gle-to­ken FLOPs (measured in equiv­a­lent FP8 FLOPs) and 10% of the KV cache size rel­a­tive to DeepSeek-V3.2. Furthermore, DeepSeek-V4-Flash, with its smaller num­ber of ac­ti­vated pa­ra­me­ters, pushes ef­fi­ciency even fur­ther: in the 1M-token con­text set­ting, it achieves only 10% of the sin­gle-to­ken FLOPs and 7% of the KV cache size com­pared with DeepSeek-V3.2.

DeepSeek’s self-re­ported bench­marks in their pa­per show their Pro model com­pet­i­tive with those other fron­tier mod­els, al­beit with this note:

Through the ex­pan­sion of rea­son­ing to­kens, DeepSeek-V4-Pro-Max demon­strates su­pe­rior per­for­mance rel­a­tive to GPT-5.2 and Gemini-3.0-Pro on stan­dard rea­son­ing bench­marks. Nevertheless, its per­for­mance falls mar­gin­ally short of GPT-5.4 and Gemini-3.1-Pro, sug­gest­ing a de­vel­op­men­tal tra­jec­tory that trails state-of-the-art fron­tier mod­els by ap­prox­i­mately 3 to 6 months.

Through the ex­pan­sion of rea­son­ing to­kens, DeepSeek-V4-Pro-Max demon­strates su­pe­rior per­for­mance rel­a­tive to GPT-5.2 and Gemini-3.0-Pro on stan­dard rea­son­ing bench­marks. Nevertheless, its per­for­mance falls mar­gin­ally short of GPT-5.4 and Gemini-3.1-Pro, sug­gest­ing a de­vel­op­men­tal tra­jec­tory that trails state-of-the-art fron­tier mod­els by ap­prox­i­mately 3 to 6 months.

I’m keep­ing an eye on hug­ging­face.co/​un­sloth/​mod­els as I ex­pect the Unsloth team will have a set of quan­tized ver­sions out pretty soon. It’s go­ing to be very in­ter­est­ing to see how well that Flash model runs on my own ma­chine.

Every great search must come to an end.

As IAC con­tin­ues to sharpen its fo­cus, we have made the de­ci­sion to dis­con­tinue our search busi­ness,

which in­cludes Ask.com. After 25 years of an­swer­ing the world’s ques­tions, Ask.com of­fi­cially closed on

May 1, 2026.

“To the mil­lions who asked…”

We are deeply grate­ful to the bril­liant en­gi­neers, de­sign­ers, and teams who built and sup­ported Ask over

the decades. And to you—the mil­lions of users who turned to us for an­swers in a rapidly chang­ing

world—thank you for your end­less cu­rios­ity, your loy­alty, and your trust.

Jeeves’ spirit en­dures.

Pre-order Now →

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Abstract:As ar­ti­fi­cial in­tel­li­gence (AI) tools be­come widely adopted, large lan­guage mod­els (LLMs) are in­creas­ingly in­volved on both sides of de­ci­sion-mak­ing processes, rang­ing from hir­ing to con­tent mod­er­a­tion. This dual adop­tion raises a crit­i­cal ques­tion: do LLMs sys­tem­at­i­cally fa­vor con­tent that re­sem­bles their own out­puts? Prior re­search in com­puter sci­ence has iden­ti­fied self-pref­er­ence bias — the ten­dency of LLMs to fa­vor their own gen­er­ated con­tent — but its real-world im­pli­ca­tions have not been em­pir­i­cally eval­u­ated. We fo­cus on the hir­ing con­text, where job ap­pli­cants of­ten rely on LLMs to re­fine re­sumes, while em­ploy­ers de­ploy them to screen those same re­sumes. Using a large-scale con­trolled re­sume cor­re­spon­dence ex­per­i­ment, we find that LLMs con­sis­tently pre­fer re­sumes gen­er­ated by them­selves over those writ­ten by hu­mans or pro­duced by al­ter­na­tive mod­els, even when con­tent qual­ity is con­trolled. The bias against hu­man-writ­ten re­sumes is par­tic­u­larly sub­stan­tial, with self-pref­er­ence bias rang­ing from 67% to 82% across ma­jor com­mer­cial and open-source mod­els. To as­sess la­bor mar­ket im­pact, we sim­u­late re­al­is­tic hir­ing pipelines across 24 oc­cu­pa­tions. These sim­u­la­tions show that can­di­dates us­ing the same LLM as the eval­u­a­tor are 23% to 60% more likely to be short­listed than equally qual­i­fied ap­pli­cants sub­mit­ting hu­man-writ­ten re­sumes, with the largest dis­ad­van­tages ob­served in busi­ness-re­lated fields such as sales and ac­count­ing. We fur­ther demon­strate that this bias can be re­duced by more than 50% through sim­ple in­ter­ven­tions tar­get­ing LLMs’ self-recog­ni­tion ca­pa­bil­i­ties. These find­ings high­light an emerg­ing but pre­vi­ously over­looked risk in AI-assisted de­ci­sion mak­ing and call for ex­panded frame­works of AI fair­ness that ad­dress not only de­mo­graphic-based dis­par­i­ties, but also bi­ases in AI-AI in­ter­ac­tions.

arXiv-is­sued DOI via DataCite

Submission his­tory

From: Jiannan Xu [view email] [v1]

Sat, 30 Aug 2025 11:40:11 UTC (3,032 KB)

[v2]

Thu, 11 Sep 2025 16:59:36 UTC (3,032 KB)

[v3]

Mon, 9 Feb 2026 13:24:26 UTC (5,723 KB)

The NetHack DevTeam is an­nounc­ing the re­lease of NetHack 5.0.0 on

May 2, 2026

NetHack 5.0 is an en­hance­ment to the dun­geon ex­plo­ration game NetHack,

which is a dis­tant de­scen­dent of Rogue and Hack, and a di­rect de­scen­dent

of NetHack 3.6.

NetHack 5.0.0 is a re­lease of NetHack. As a .0 ver­sion, there may be some

bugs en­coun­tered. Constructive sug­ges­tions, GitHub pull re­quests, and bug

re­ports are all wel­come and en­cour­aged.

Along with the game im­prove­ments and bug fixes, NetHack 5.0 strives to make

some gen­eral ar­chi­tec­tural im­prove­ments to the game or to its build­ing

process. Among them, 5.0:

Has its source code com­pli­ant with the C99 stan­dard.

Removes bar­ri­ers to build­ing NetHack on one plat­form and op­er­at­ing sys­tem,

for later ex­e­cu­tion on an­other (possibly quite dif­fer­ent) plat­form and/​or

op­er­at­ing sys­tem. That ca­pa­bil­ity is gen­er­ally known as “cross-compiling.”

See the file “Cross-compiling” in the top-level folder for more in­for­ma­tion

on that.

The build-time “yacc and lex”-based level com­piler, the

“yacc and lex”-based dun­geon com­piler, and the quest text file pro­cess­ing

pre­vi­ously done by NetHack’s “makedefs” util­ity, have been re­placed with

Lua text al­ter­na­tives that are loaded and processed by the game dur­ing play.

A list of over 3100 fixes and changes can be found in the game’s sources

in the file doc/​fix­es5 – 0-0.txt. The text in there was writ­ten for the

de­vel­op­ment team’s own use and is pro­vided “as is”. Some en­tries might be

con­sid­ered “spoilers”, par­tic­u­larly in the “new fea­tures” sec­tion.

Existing saved games and bones files will not work with NetHack 5.0.0.

Checksums (sha256) of bi­na­ries that you have down­loaded from nethack.org

can be ver­i­fied on Windows plat­forms us­ing:

  certUtil -hashfile nethack-500-win-x64.zip SHA256

or

  certUtil -hashfile nethack-500-win-ar­m64.zip SHA256

The fol­low­ing com­mand can be used on most plat­forms to help con­firm the lo­ca­tion of

var­i­ous files that NetHack may use:

  nethack –showpaths

As with all re­leases of the game, we ap­pre­ci­ate your feed­back. Please sub­mit any

bugs us­ing the prob­lem re­port form. Also, please check the “known bugs” list

be­fore you log a prob­lem - some­body else may have al­ready found it.

Happy NetHacking!

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Pull re­quest overview

This PR changes the Git ex­ten­sion’s git.ad­dAICoAu­thor set­ting so that AI co-au­thor trail­ers are en­abled by de­fault, mak­ing the de­fault be­hav­ior au­to­mat­i­cally add a Co-authored-by trailer when AI-generated code con­tri­bu­tions are de­tected.

Changes:

Updates git.ad­dAICoAu­thor con­fig­u­ra­tion de­fault from “off” to “all”.

Copilot’s find­ings

Files re­viewed: 1/1 changed files

Comments gen­er­ated: 1

Screenshot Changes

Base: 3c1b53dd Current: eec3f9cf

Changed (3)

blocks-ci screen­shots changed

Replace the con­tents of test/​com­po­nent­Fix­tures/​blocks-ci-screen­shots.md with:

<!– auto-gen­er­ated by CI — do not edit man­u­ally –>

#### ed­i­tor/​codeEd­i­tor/​CodeEd­i­tor/​Dark


#### ed­i­tor/​codeEd­i­tor/​CodeEd­i­tor/​Light


NoiceBroice

ref­er­enced

this pull re­quest

in ThomasSnowden37/Harmoniq-Charts

Co-authored-by: Copilot <copi­lot@github.com>

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***Please take out a mem­ber­ship to sup­port the light of truth.***

As AI chat­bots con­tinue to ad­vance, Russia is in­fect­ing them with Kremlin-manipulated con­tent tai­lored to in­flu­ence the global in­ter­net, dis­tort­ing the pub­lic’s un­der­stand­ing of facts and abil­ity to make well-in­formed de­ci­sions.—Ex­pos­ing Pravda: How pro-Krem­lin forces are poi­son­ing AI mod­els and rewrit­ing Wikipedia, Atlantic Council

Yesterday, I read a Wikipedia page for a book I’m about to re­view. I am still un­set­tled.

The page was stripped of re­al­ity, and in its place was a san­i­tized fairy­tale where Putin is good and the book — a bru­tal and damn­ing his­toric ac­count of Soviet abuses — is sub­tly and not so sub­tly un­der­mined from every di­rec­tion.

Once I got over the shock of what I had just read — it was like be­ing forced into an al­ter­nate re­al­ity — I be­gan in­ves­ti­gat­ing Russia’s re­la­tion­ship to Wikipedia. Perhaps not sur­pris­ingly, the Russian state has been steadily dis­tort­ing truth, ex­ploit­ing the plat­for­m’s crowd-sourc­ing ar­chi­tec­ture to in­flu­ence pub­lic knowl­edge.

Malign Activity

In a re­port by the Institute for Strategic Dialogue ti­tled Identifying Sock Puppets on Wikipedia, its au­thors used a ‘semantic clus­ter­ing’ ap­proach to fo­cus on the “English-language Wikipedia en­try for the Russo-Ukrainian war, and 48 other pages about Ukraine that link di­rectly to it.”

The au­thors wrote:

Malign ac­tiv­ity has tar­geted a num­ber of in­for­ma­tion en­vi­ron­ments, in­clud­ing every ma­jor so­cial me­dia plat­form: Twitter, Facebook, YouTube, Instagram, TikTok, stand­alone web­sites and many oth­ers. This pa­per, how­ever, is ded­i­cated to pos­si­ble plat­form ma­nip­u­la­tion on a venue that tends to be much less re­searched than main­stream so­cial me­dia: Wikipedia.This re­port pre­sents work that set out to cre­ate, trial and eval­u­ate a method to try to de­tect covert and or­gan­ised ma­nip­u­la­tion of Wikipedia at scale.

Malign ac­tiv­ity has tar­geted a num­ber of in­for­ma­tion en­vi­ron­ments, in­clud­ing every ma­jor so­cial me­dia plat­form: Twitter, Facebook, YouTube, Instagram, TikTok, stand­alone web­sites and many oth­ers. This pa­per, how­ever, is ded­i­cated to pos­si­ble plat­form ma­nip­u­la­tion on a venue that tends to be much less re­searched than main­stream so­cial me­dia: Wikipedia.

This re­port pre­sents work that set out to cre­ate, trial and eval­u­ate a method to try to de­tect covert and or­gan­ised ma­nip­u­la­tion of Wikipedia at scale.

As I quickly learned, mul­ti­ple re­ports have ex­plored “organized ma­nip­u­la­tion” on Wikipedia’s en­tries on Russia’s in­va­sion of Ukraine.

Portal Kombat

Between September and December 2023, the French de­fense agency Vigilance and Protection Service against Foreign Digital Interference (VIGINUM) an­a­lyzed “information por­tals” dis­sem­i­nat­ing pro-Russ­ian con­tent and tar­get­ing sev­eral west­ern coun­tries, in­clud­ing France.

In the VIGINUM re­port, PORTAL KOMBAT: A struc­tured and co­or­di­nated pro-Russ­ian pro­pa­ganda net­work, re­searchers in­ves­ti­gated a net­work of 193 sites that ini­tially cov­ered news from “Russian and Ukrainian lo­cal­i­ties.”

According to the re­search, the cov­er­age changed the day af­ter Russia in­vaded Ukraine and be­gan to tar­get oc­cu­pied Ukrainian ter­ri­to­ries and west­ern coun­tries sup­port­ing Ukraine and its pop­u­la­tion.

The sites in this net­work pro­duce “no orig­i­nal con­tent but mas­sively re­lay pub­li­ca­tions from sources that are pri­mar­ily three types: so­cial me­dia ac­counts of Russian or pro-Russ­ian ac­tors, Russian news agen­cies, and of­fi­cial web­sites of lo­cal in­sti­tu­tions or ac­tors.”

“The main ob­jec­tive seems to be to cover the Russo-Ukrainian con­flict by pre­sent­ing pos­i­tively ‘the spe­cial mil­i­tary op­er­a­tion’ and den­i­grat­ing Ukraine and its lead­ers. Very ide­o­log­i­cally ori­ented, this con­tent re­peat­edly pre­sents in­ac­cu­rate or mis­lead­ing nar­ra­tives. As for the por­tal tar­get­ing France, pravda-fr[.]com, it di­rectly con­tributes to po­lar­ize the Francophone dig­i­tal pub­lic de­bate.”

“The main ob­jec­tive seems to be to cover the Russo-Ukrainian con­flict by pre­sent­ing pos­i­tively ‘the spe­cial mil­i­tary op­er­a­tion’ and den­i­grat­ing Ukraine and its lead­ers. Very ide­o­log­i­cally ori­ented, this con­tent re­peat­edly pre­sents in­ac­cu­rate or mis­lead­ing nar­ra­tives. As for the por­tal tar­get­ing France, pravda-fr[.]com, it di­rectly con­tributes to po­lar­ize the Francophone dig­i­tal pub­lic de­bate.”

VIGINUM caught an in­ser­tion of the site pravda-fr[.]com be­ing used as a source for a Wikipedia ar­ti­cle about a “geopolitical sit­u­a­tion” in the Red Sea.

In a foot­note, they wrote: “The Wikipedia ar­ti­cle ti­tled ‘Operation Guardian of Prosperity’ cre­ated on December 22, 2023, was edited the next day by user ‘@ Lataupefr,’ who in­serted two ar­ti­cles from pravda-fr[.]com with sources be­ing Russian-pro Telegram chan­nels ‘@ BrainlessChanel’ and ‘@ kom­pro­mat­media.’”

See mod­i­fi­ca­tions: https://​fr.wikipedia.org/​w/​in­dex.php?ti­tle=Opéra­tion_­Gar­di­en_de_la_prospérité&diff=prev&ol­did=210810683

Foreign Digital Interference

The pre­cise se­lec­tion of these pro-Russ­ian sources…proves there’s a real tar­get­ing ef­fort to dis­sem­i­nate the strate­gic nar­ra­tives… Given its tech­ni­cal char­ac­ter­is­tics, the processes im­ple­mented and the pur­sued pur­pose, this net­work con­sti­tutes for­eign dig­i­tal in­ter­fer­ence.—VIG­INUM

Those words — for­eign dig­i­tal in­ter­fer­ence — are very im­por­tant.

The West has ne­glected to fight on the bat­tle­field that has been right in front of them the en­tire time — the in­ter­net.

This week, we learned JD Vance and Marjorie Taylor Greene pro­moted a fake story by the Russian dis­in­for­ma­tion net­work — Storm-1516 — which is linked to the GRU and be­lieved to em­ploy work­ers from the Internet Research Agency, the St. Petersburg op­er­a­tion that at­tacked American minds to help in­stall Donald Trump in 2016 and whose out­put was pro­moted by mem­bers of Trump’s 2016 cam­paign, which I re­cap in this se­ries:

The story Vance and Greene pro­moted was an ob­vi­ous fake — a lie about yachts be­ing pur­chased with mil­i­tary aid to Ukraine. It’s im­por­tant to never for­get that Vance is Peter Thiel’s repli­cant and to­gether, they backed Rumble, which is a full-throated Russian pro­pa­ganda net­work.

A decade af­ter the 2016 US elec­tion, we are watch­ing the es­ca­la­tion of in­for­ma­tion war­fare as new tools are weaponized.

AI Models, Rewriting Wikipedia, and Laundering Content

As Atlantic Council re­ports in Exposing Pravda: How pro-Krem­lin forces are poi­son­ing AI mod­els and rewrit­ing Wikipedia:

“Russia has ex­panded, de­vel­oped, and tai­lored an in­flu­ence cam­paign tar­get­ing much of the world, spread­ing its con­tent in Wikipedia ar­ti­cles and in pop­u­lar ar­ti­fi­cial in­tel­li­gence (AI) tools. As elec­tion cam­paigns in Romania and Moldova took place, or as po­lit­i­cal dis­cus­sions be­tween US President Donald Trump and Ukrainian President Volodymyr Zelenskyy un­folded, a net­work of in­au­then­tic pro-Russ­ian por­tals ramped up its ac­tiv­ity, laun­der­ing con­tent from sanc­tioned news out­lets and align­ing global in­for­ma­tion sources with the Kremlin nar­ra­tive ma­chine.”

“Russia has ex­panded, de­vel­oped, and tai­lored an in­flu­ence cam­paign tar­get­ing much of the world, spread­ing its con­tent in Wikipedia ar­ti­cles and in pop­u­lar ar­ti­fi­cial in­tel­li­gence (AI) tools. As elec­tion cam­paigns in Romania and Moldova took place, or as po­lit­i­cal dis­cus­sions be­tween US President Donald Trump and Ukrainian President Volodymyr Zelenskyy un­folded, a net­work of in­au­then­tic pro-Russ­ian por­tals ramped up its ac­tiv­ity, laun­der­ing con­tent from sanc­tioned news out­lets and align­ing global in­for­ma­tion sources with the Kremlin nar­ra­tive ma­chine.”

The Atlantic Council ref­er­enc­ing the French re­port notes that much of the fakes are com­ing from the Pravda net­work, which it calls a “collection of fraud­u­lent news por­tals tar­get­ing more than eighty coun­tries and re­gions through­out the world, launched by Russia in 2014. In 2024, the French dis­in­for­ma­tion watch­dog Viginum re­ported on the op­er­a­tion, iden­ti­fy­ing the ma­li­cious ac­tiv­ity of a Crimea-based IT busi­ness, find­ings that the Atlantic Council’s Digital Forensic Research Lab (DFRLab) later con­firmed, which showed di­rect Russian in­volve­ment with the net­work.”

The Pravda net­work acts as an in­for­ma­tion laun­dro­mat, am­pli­fy­ing and sat­u­rat­ing the news cy­cle with tropes em­a­nat­ing from Russian news out­lets and Kremlin-aligned Telegram chan­nels. During the 2024 “super-election year,” the net­work cre­ated web­sites specif­i­cally tar­get­ing NATO, as well as Trump, French President Emmanuel Macron, and other world lead­ers and politi­cians.—Ex­pos­ing Pravda

The Atlantic Council re­port iden­ti­fies this or­ga­nized ma­nip­u­la­tion as global — “a Russian on­line in­flu­ence op­er­a­tion that has taken root across the global in­ter­net.”

Think of this in terms of transna­tional or­ga­nized crime, ex­cept in­stead of drugs, or hu­man traf­fick­ing, or arms traf­fick­ing, we’re al­low­ing un­friendly for­eign pow­ers to ma­nip­u­late our col­lec­tive re­al­ity — his­tory, cul­ture, our shared nar­ra­tive.

The Atlantic Council also notes that Russia’s strat­egy, “in a likely at­tempt to evade global sanc­tions on Russian news out­lets, is now poi­son­ing AI tools and Wikipedia. By pos­ing as au­thor­i­ta­tive sources on Wikipedia and re­li­able news out­lets cited by pop­u­lar large lan­guage mod­els (LLMs), Russian tropes are rewrit­ing the story of Russia’s war in Ukraine. The di­rect con­se­quence is the ex­po­sure of Western au­di­ences to con­tent con­tain­ing pro-Krem­lin, anti-Ukrain­ian, and anti-West­ern mes­sag­ing when us­ing AI chat­bots that rely on LLMs trained on ma­te­r­ial such as Wikipedia.

“As AI chat­bots con­tinue to ad­vance, Russia is in­fect­ing them with Kremlin-manipulated con­tent tai­lored to in­flu­ence the global in­ter­net, dis­tort­ing the pub­lic’s un­der­stand­ing of facts and abil­ity to make well-in­formed de­ci­sions. This op­er­a­tion opens the door to ques­tions re­gard­ing the trans­parency of the train­ing of AI mod­els and the mod­er­a­tion of con­tent em­a­nat­ing from known Russian-manipulated sources that have per­sis­tently di­vided the West on its sup­port for Ukraine.”

It al­ways comes back to Ukraine.

But it does­n’t stop with Ukraine.

Russia won’t stop un­til Russia is stopped.

Through these as­saults, they are dis­arm­ing what should be the only sub­stan­tive re­sis­tance to their re­build­ing the for­mer Soviet bloc.

They have no right to dic­tate our will, and it’s pa­thetic that we’re let­ting them.

The Sum of All Human Knowledge

In a re­port ti­tled Characterizing Knowledge Manipulation in a Russian Wikipedia Fork, the au­thors used a dataset of “1.9 mil­lion Russian Wikipedia ar­ti­cles and its fork,” which they call “an or­ga­nized ef­fort to ma­nip­u­late knowl­edge.”

As the world’s largest en­cy­clo­pe­dia and the ninth most vis­ited web­site glob­ally. Wikipedia holds an in­flu­en­tial po­si­tion within the web ecosys­tem… main­tained through a col­lab­o­ra­tive com­mu­nity ef­fort to be­come the ‘sum of all hu­man knowl­edge’ (Sutcliffe 2016).—Characterizing Knowledge Manipulation in a Russian Wikipedia Fork

Its au­thors note that “knowledge on Wikipedia has a ma­jor so­ci­etal im­pact” and iden­tity mul­ti­ple au­thor­i­tar­ian coun­tries such as China and Turkey which sim­ply block the plat­form al­to­gether.

In a sec­tion of the re­port ti­tled “Relevance,” re­searchers ex­plain how “national iden­tity and pub­lic opin­ion can be in­flu­enced by the in­for­ma­tion cit­i­zens are find­ing on­line about their his­tory… Wikipedia was ranked the 6th most im­por­tant in­for­ma­tion about his­tory, pass­ing mu­seum vis­its, col­lege courses, and so­cial me­dia (Burkholder and Schaffer 2021). Therefore, at­tempts to ma­nip­u­late Wikipedia con­tent, even if they hap­pen in other plat­forms, could have a sig­nif­i­cant so­ci­etal im­pact.”

They warn that Wikipedia con­tent is fre­quently used for train­ing Large Language Models (LLMs) and that “manipulated ver­sions of Wikipedia used as train­ing data for LLMs can en­cour­age AI-powered sys­tems that pro­mote ideas with spe­cific bi­ases.”

Immediately af­ter its de­but, Elon Musk’s Grokipedia was ex­posed for push­ing ex­trem­ist ide­ol­ogy and pub­lish­ing Russian pro­pa­ganda.

Last year, Musk called for a boy­cott of Wikipedia and con­tin­ues to call it Wokepedia, spread­ing his own pro­pa­ganda. Trump’s regime has threat­ened to re­voke the tax-ex­empt sta­tus of the non-profit, which turned 25-years-old this year.

Trump, whose al­ter­nate re­al­ity lie fac­tory, Truth Social, is a fun-house mir­ror of the name Pravda, which means ‘truth’ and ‘justice’ in Russian, and was the name of the of­fi­cial news­pa­per of the Central Committee of the Communist Party of the Soviet Union.

While Trump helps Putin re­build the Soviet em­pire, I’ll be over here pub­lish­ing a re­port on the book that took it down.

****

2016 Election Attack — The Book!

American Monsters — The Book — Buy Here!

Donations Welcome

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Bette Dangerous is a reader-funded mag­a­zine. Thank you to all monthly, an­nual, and found­ing mem­bers.

I ex­pose the cor­rup­tion of bil­lion­aire fas­cists, while re­ly­ing on mem­ber­ships for sup­port.

Thank you in ad­vance for con­sid­er­ing the fol­low­ing:

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More info about Bette Dangerous - This mag­a­zine is writ­ten by Heidi Siegmund Cuda, an Emmy-award win­ning in­ves­tiga­tive re­porter/​pro­ducer, au­thor, and vet­eran mu­sic and nightlife colum­nist. She is the co­host of RADICALIZED Truth Survives, an in­ves­tiga­tive show about dis­in­for­ma­tion and is part of the Byline Media team. Thank you for your sup­port of in­de­pen­dent in­ves­tiga­tive jour­nal­ism.

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Begin each day with a grate­ful heart.

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