Opinion I’m an eighth-gen­er­a­tion American, and let me tell you, I would­n’t trust my data, se­crets, or ser­vices to a US com­pany these days for love or money. Under our cur­rent gov­ern­ment, we’re sim­ply not trust­wor­thy.

In the Trump‑redux era of 2026, European en­ter­prises are fi­nally tak­ing data se­ri­ously, and that means pack­ing up from Redmond-by-Seattle and mov­ing their most sen­si­tive work­loads home. This is­n’t just com­pli­ance the­ater; it’s a straight‑up na­tional eco­nomic se­cu­rity play.

Europe’s dig­i­tal sov­er­eignty para­noia, long waved off as reg­u­la­tory chat­ter, is now feed­ing di­rectly into pro­cure­ment de­ci­sions. Gartner told The Reg last year that IT spend­ing in Europe is set to grow by 11 per­cent in 2026, hit­ting $1.4 tril­lion, with a big chunk rolling into “sovereign cloud” op­tions and on‑prem/​edge ar­chi­tec­tures.

The kicker? Fully 61 per­cent of European CIOs and tech lead­ers say they want to in­crease their use of lo­cal cloud providers. More than half say geopol­i­tics will pre­vent them from lean­ing fur­ther on US‑based hy­per­scalers.

The American hy­per­cloud ven­dors have fig­ured this out. AWS re­cently made its European Sovereign Cloud avail­able. This AWS cloud, Amazon claims, is “entirely lo­cated within the EU, and phys­i­cally and log­i­cally sep­a­rate from other AWS Regions.” On top of that, EU res­i­dents will “independently op­er­ate it” and “be backed by strong tech­ni­cal con­trols, sov­er­eign as­sur­ances, and le­gal pro­tec­tions de­signed to meet the needs of European gov­ern­ments and en­ter­prises for sen­si­tive data.”

Many EU-based com­pa­nies aren’t pleased with this Euro-washing of American hy­per­cloud ser­vices. The Cloud Infrastructure Service Providers in Europe (CISPE) trade as­so­ci­a­tion ac­cuses the EU Cloud Sovereignty Framework of be­ing set up to fa­vor the in­cum­bent (American) hy­per­cloud providers.

You don’t need a DEA war­rant or a Justice Department sub­poena to see the trend: Europe’s 90‑plus‑percent de­pen­dency on US cloud in­fra­struc­ture, as for­mer European Commission ad­vi­sor Cristina Caffarra put it, is a sin­gle‑shock‑event se­cu­rity night­mare wait­ing to rup­ture the EU’s dig­i­tal sta­bil­ity.

Seriously. What will you do if Washington de­cides to un­plug you? Say Trump gets up on the wrong side of the bed and de­cides to in­vade Greenland. There goes NATO, and in all the saber-rat­tling lead­ing up to the 10th Mountain Division be­ing shipped to Nuuk, he or­ders American com­pa­nies to cut their ser­vices to all EU coun­tries and the UK.

With the way things are go­ing, they’re not go­ing to say no. I mean, CEOs Tim Cook of Apple, Eric Yuan of Zoom, Lisa Su of AMD, and — pay at­ten­tion — Amazon’s Andy Jassy all went obe­di­ently to watch a fea­ture-length White House screen­ing of Melania, the uni­ver­sally-loathed, 104‑minute Amazon‑produced doc­u­men­tary about First Lady Melania Trump.

Sure, that’s a silly ex­am­ple, but for American com­pa­nies to do busi­ness to­day, they’re kow­tow­ing to Trump. Or, take a far more se­ri­ous ex­am­ple, when Minnesota com­pany CEOs called for “de-escalation” in the state, there was not one word about ICE or the gov­ern­men­t’s role in the blood­shed. It was the cor­po­rate equiv­a­lent of the mealy-mouthed “thoughts and prayers” American right-wingers al­ways say af­ter a US school shoot­ing.

Some com­pa­nies have al­ready fig­ured out which way the wind is blow­ing. Airbus, the European aero­space ti­tan, has put out a €50 mil­lion, decade‑long ten­der to mi­grate its mis­sion‑crit­i­cal ap­pli­ca­tions to a “sovereign European cloud.” Airbus wants its whole stack — data at rest, data in tran­sit, log­ging, IAM, and se­cu­rity‑mon­i­tor­ing in­fra­struc­ture — all rooted in EU law and over­seen by EU op­er­a­tors. As Catherine Jestin, Airbus’s ex­ec­u­tive vice pres­i­dent of dig­i­tal, told The Register: “We want to en­sure this in­for­ma­tion re­mains un­der European con­trol.”

Who can blame them? Thanks to the American CLOUD Act and re­lated US sur­veil­lance statutes, US‑headquartered providers must hand over European data re­gard­less of where the bytes sit. Exhibit A is that Microsoft has al­ready con­ceded that it can­not guar­an­tee data in­de­pen­dence from US law en­force­ment. Airbus is bet­ting that “data res­i­dency on pa­per” from AWS‑styled “EU sec­tions” is not enough. Real sov­er­eignty de­mands EU‑owned and run op­er­a­tions with full con­trac­tual and le­gal fire­walls. Sure, your data may live in Frankfurt, but your fate still rests in Seattle, Redmond, or Mountain View if an American com­pany owns your cloud provider.

Besides, do you re­ally want some Trump ap­pa­ratchik get­ting their hands on your data? I mean, this is a gov­ern­ment where Madhu Gottumukkala, the act­ing di­rec­tor of the US Cybersecurity and Infrastructure Security Agency, up­loaded sen­si­tive data into ChatGPT!

In re­sponse, Brussels is push­ing an open source‑led exit from hy­per­scaler lock‑in. Ministries are stan­dard­iz­ing on Nextcloud‑style col­lab­o­ra­tion stacks in­stead of Microsoft 365 to fund Euro‑native clouds via the European Cloud Alliance. Some coun­tries, like France, are al­ready shov­ing Zoom, Teams, and other US video­con­fer­enc­ing plat­forms out the door in fa­vor of a lo­cal ser­vice.

If you’re run­ning an EU‑based firm in 2026, the take­away is­n’t that AWS‑in‑Frankfurt is evil; it’s that for cer­tain work­loads, es­pe­cially na­tional se­cu­rity, in­dus­trial IP, or high‑pro­file con­sumer data fran­chises, EU‑native cloud and ser­vices are no longer a nice‑to‑have but a busi­ness con­ti­nu­ity plan re­quire­ment.

It’s time to get se­ri­ous about dig­i­tal sov­er­eignty. The clock is tick­ing, and there’s no telling when Trump will go off. ®

TL;DR: Mandarin pro­nun­ci­a­tion has been hard for me, so I took ~300 hours of tran­scribed speech and trained a small CTC model to grade my pro­nun­ci­a­tion. You can try it here.

In my pre­vi­ous post about Langseed, I in­tro­duced a plat­form for defin­ing words us­ing only vo­cab­u­lary I had al­ready mas­tered. My vo­cab­u­lary has grown since then, but un­for­tu­nately, peo­ple still strug­gle to un­der­stand what I’m say­ing.

Part of the prob­lem is tones. They’re fairly for­eign to me, and I’m bad at hear­ing my own mis­takes, which is deeply frus­trat­ing when you don’t have a teacher.

My ini­tial plan was to build a pitch vi­su­aliser: split in­com­ing au­dio into small chunks, run an FFT, ex­tract the dom­i­nant pitch over time, and map it us­ing an en­ergy-based heuris­tic, loosely in­spired by Praat.

But this ap­proach quickly be­came brit­tle. There were end­less spe­cial cases: back­ground noise, coar­tic­u­la­tion, speaker vari­a­tion, voic­ing tran­si­tions, and so on.

And if there’s one thing we’ve learned over the last decade, it’s the bit­ter les­son: when you have enough data and com­pute, learned rep­re­sen­ta­tions usu­ally beat care­fully hand-tuned sys­tems.

So in­stead, I de­cided to build a deep learn­ing–based Computer-Assisted Pronunciation Training (CAPT) sys­tem that could run en­tirely on-de­vice. There are al­ready com­mer­cial APIs that do this, but hey, where’s the fun in that?

I treated this as a spe­cialised Automatic Speech Recognition (ASR) task. Instead of just tran­scrib­ing text, the model needs to be pedan­tic about how some­thing was said.

Speech is weird: you need to catch both lo­cal and global pat­terns:

Local in­ter­ac­tions

The dif­fer­ence be­tween a retroflex zh and an alve­o­lar z hap­pens in a split sec­ond. CNNs are ex­cel­lent at cap­tur­ing these short-range spec­tral fea­tures.

Global in­ter­ac­tions

Mandarin tones are rel­a­tive (a “high” pitch for me might be low for a child) and con­text-de­pen­dent (tone sandhi). Transformers ex­cel at mod­el­ing this longer-range con­text.

Conformers com­bine both: con­vo­lu­tion for lo­cal de­tail, at­ten­tion for global struc­ture.

Most mod­ern ASR mod­els (e.g. Whisper) are se­quence-to-se­quence: they turn au­dio into the most likely text. The down­side is they’ll hap­pily auto-cor­rect you.

That’s a fea­ture for tran­scrip­tion, but it’s a bug for lan­guage learn­ing. If my tone is wrong, I don’t want the model to guess what I meant. I want it to tell me what I ac­tu­ally said.

CTC works dif­fer­ently. It out­puts a prob­a­bil­ity dis­tri­b­u­tion for every frame of au­dio (roughly every 40 ms). To han­dle align­ment, it in­tro­duces a spe­cial to­ken.

If the au­dio is “hello”, the raw out­put might look like:

Collapsing re­peats and re­mov­ing blanks gives hello. This forces the model has to deal with what I ac­tu­ally said, frame by frame.

CTC tells us what was said, but not ex­actly when.

For a 3-second clip, the model might out­put a ma­trix with ~150 time steps (columns), each con­tain­ing prob­a­bil­i­ties over all to­kens (rows). Most of that ma­trix is just .

If the user reads “Nǐ hǎo” (ni3, hao3), we ex­pect two re­gions of high prob­a­bil­ity: one for ni3, one for hao3.

We need to find a sin­gle, op­ti­mal path through this ma­trix that:

This is ex­actly what the Viterbi al­go­rithm com­putes, us­ing dy­namic pro­gram­ming.

Most Mandarin ASR sys­tems out­put Hanzi. That hides pro­nun­ci­a­tion er­rors, be­cause the writ­ing sys­tem en­codes mean­ing rather than pro­nun­ci­a­tion.

Instead, I cre­ated a to­ken for every Pinyin syl­la­ble + tone:

If I say the wrong tone, the model ex­plic­itly pre­dicts the wrong to­ken ID.

I also nor­malised the neu­tral tone by forc­ing it to be tone 5 (ma5). This re­sulted in a vo­cab­u­lary of 1,254 to­kens, plus and .

I com­bined the AISHELL-1 and Primewords datasets (~300 hours to­tal), aug­mented by SpecAugment (time/frequency mask­ing). On 4× NVIDIA GeForce RTX 4090s, train­ing took about 8 hours. Instead of ob­sess­ing over loss, I mostly fo­cused on these met­rics:

Confusion Groups: er­rors be­tween dif­fi­cult ini­tial pairs like zh/​ch/​sh vs z/​c/​s.

I started with a “medium” model (~75M pa­ra­me­ters). It worked well, but I wanted some­thing that could run in a browser or on a phone with­out killing the bat­tery.

So I kept shrink­ing it, and I was hon­estly sur­prised by how lit­tle ac­cu­racy I lost:

The 9M-parameter model was barely worse. This strongly sug­gests the task is data-bound, not com­pute-bound.

The FP32 model was ~37 MB. After INT8 quan­ti­sa­tion, it shrank to ~11 MB with a neg­li­gi­ble ac­cu­racy drop (+0.0003 TER). Small enough to load in­stantly via on­nxrun­time-web.

To high­light mis­takes, we need forced align­ment. But I hit a nasty bug with lead­ing si­lence.

I recorded my­self say­ing “我喜欢…” and paused for a sec­ond be­fore speak­ing. The model con­fi­dently told me my first syl­la­ble was wrong. Confidence score: 0.0.

The align­ment as­signed the silent frames to wo3. When I av­er­aged prob­a­bil­i­ties over that span, the over­whelm­ing prob­a­bil­ity com­pletely drowned out wo3.

I de­cou­pled UI spans (what gets high­lighted) from scor­ing frames (what con­tributes to con­fi­dence).

We sim­ply ig­nore frames where the model is con­fi­dent it’s see­ing si­lence:

This sin­gle change moved my con­fi­dence score for the first syl­la­ble from 0.0 → 0.99.

I can al­ready feel my pro­nun­ci­a­tion im­prov­ing while beta test­ing this. It’s strict and un­for­giv­ing, ex­actly what I needed.

Native speak­ers, in­ter­est­ingly, com­plained that they had to over-enun­ci­ate to get marked cor­rect. That’s likely a do­main-shift is­sue: AISHELL is mostly read speech, while ca­sual speech is faster and more slurred. Kids do poorly too: their pitch is higher, and they’re ba­si­cally ab­sent from the train­ing data. Adding con­ver­sa­tional datasets like Common Voice feels like the ob­vi­ous next step.

You can try the live demo here. It runs en­tirely in your browser. The down­load is ~13MB, still smaller than most web­sites to­day.

To see all avail­able qual­i­fiers, see our doc­u­men­ta­tion.

We read every piece of feed­back, and take your in­put very se­ri­ously.

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To see all avail­able qual­i­fiers, see our doc­u­men­ta­tion.

We read every piece of feed­back, and take your in­put very se­ri­ously.

Secure your code as you build

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PeerWeb is a rev­o­lu­tion­ary way to host and share web­sites us­ing WebTorrent tech­nol­ogy. Instead of re­ly­ing on cen­tral­ized servers, web­sites are dis­trib­uted across a peer-to-peer net­work, mak­ing them cen­sor­ship-re­sis­tant and al­ways avail­able. 🌍✨

Keep this PeerWeb tab open to host your site! As long as this tab re­mains open, your web­site will be avail­able to oth­ers through the peer-to-peer net­work.

💡 Alternative: Download our desk­top client for per­ma­nent host­ing with­out keep­ing browser tabs open:

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Nicole Sperling and Brooks Barnes, re­port­ing for The New York Times:

Amazon paid Ms. Trump’s pro­duc­tion com­pany $40 mil­lion for the rights to “Melania,” about $26 mil­lion more than the next clos­est bid­der, Disney. The fee in­cludes a re­lated do­cuseries that is sched­uled to air later this year. The bud­get for “Melania” is un­known, but doc­u­men­taries that fol­low a sub­ject for a lim­ited amount of time usu­ally cost less than $5 mil­lion to pro­duce. The $35 mil­lion for mar­ket­ing is 10 times what some other high-pro­file doc­u­men­taries have re­ceived.

All of which has a lot of Hollywood ques­tion­ing whether Amazon’s push is any­thing more than the com­pa­ny’s at­tempt to in­gra­ti­ate it­self with President Trump.

This is a good story, with mul­ti­ple in­dus­try sources with ex­pe­ri­ence mak­ing po­lit­i­cal doc­u­men­taries, but the Times’s own sub­head down­plays Amazon’s spend­ing on the film: “The tech gi­ant is spend­ing $35 mil­lion to pro­mote its film about the first lady, far more than is typ­i­cal for doc­u­men­taries.” They’re spend­ing $35 mil­lion now, to pro­mote it, but they al­ready paid $40 mil­lion for the rights to the film, $28 mil­lion of which is be­lieved to have gone to Melania Trump her­self. A $35 mil­lion to­tal spend would be a lot com­pared to other high-pro­file doc­u­men­taries, but it’s a $75 mil­lion to­tal spend. This is not just a lit­tle fishy — it’s a ver­i­ta­ble open air seafood mar­ket.

To grasp just how un­cus­tom­ary Amazon’s mar­ket­ing push for “Melania” is, con­sider how Magnolia Pictures han­dled “RBG,” a por­trait of Ruth Bader Ginsburg dur­ing her 25th year as a Supreme Court jus­tice, in 2018.

CNN Films pro­duced “RBG” for around $1 mil­lion. The pro­mo­tional bud­get, in­clud­ing an awards cam­paign that helped it land two Oscar nom­i­na­tions, to­taled about $3 mil­lion. The film de­buted in 34 the­aters and ex­panded into 432 lo­ca­tions over sev­eral weeks. It ul­ti­mately col­lected $14 mil­lion, enough to rank as the year’s No. 1 po­lit­i­cal doc­u­men­tary.

On Friday, “Melania” will also be re­leased in 1,600 the­aters over­seas, where FilmNation, a New York com­pany, is han­dling dis­tri­b­u­tion in more than 20 coun­tries. International ticket sales are ex­pected to be weak, ac­cord­ing to box of­fice an­a­lysts.

Gold and sil­ver prices plunged Friday, as President Donald Trump’s nom­i­na­tion for the next chair of the Federal Reserve, Kevin Warsh, ap­peared to re­lieve con­cerns about the cen­tral bank’s in­de­pen­dence and sent the dol­lar soar­ing. Spot sil­ver was down 28% at $83.45 an ounce, trad­ing near its lows of the day. Silver fu­tures plum­meted 31.4% to set­tle at $78.53, mark­ing its worst day since March 1980.

The sharp moves down were ini­tially trig­gered by re­ports of Warsh’s nom­i­na­tion. However, they gained steam in af­ter­noon U. S. trad­ing as in­vestors who piled into the met­als raced to book prof­its. Metals were also un­der pres­sure as the dol­lar spiked higher, mak­ing it more ex­pen­sive for for­eign in­vestors to buy gold and sil­ver and spoil­ing the the­ory that met­als would re­place the green­back as the globe’s re­serve cur­rency. The dol­lar in­dex last traded around 0.8% higher. “This is get­ting crazy,” said Matt Maley, eq­uity strate­gist at Miller Tabak. “Most of this is prob­a­bly ‘forced sell­ing.’ This has been the hottest as­set for day traders and other short-term traders re­cently. So, there has been some lever­age built up in sil­ver. With the huge de­cline to­day, the mar­gin calls went out.”

National Economic Council Director Kevin Hassett had been the fa­vorite to re­place Powell for some time, but Warsh be­came the front-run­ner in pre­dic­tion mar­kets in re­cent days. In a note on Friday morn­ing, Evercore ISI’s Krishna Guha said the mar­ket was “trading Warsh hawk­ish.“”The Warsh pick should help sta­bi­lize the dol­lar some and re­duce (though not elim­i­nate) the asym­met­ric risk of deep ex­tended dol­lar weak­ness by chal­leng­ing de­base­ment trades — which is also why gold and sil­ver are sharply lower,” the fir­m’s vice chair­man said.“But, we ad­vise against over­do­ing the Warsh hawk­ish trade across as­set mar­kets — and even see some risk of a whip­saw. We see Warsh as a prag­ma­tist not an ide­o­log­i­cal hawk in the tra­di­tion of the in­de­pen­dent con­ser­v­a­tive cen­tral banker.“Clau­dio Wewel, FX strate­gist at J. Safra Sarasin Sustainable Asset Management, told CNBC’s “Squawk Box Europe” on Friday that a “perfect storm” of geopo­lit­i­cal ten­sions had helped pre­cious met­als move higher this year, point­ing to the U. S. cap­ture of Venezuelan President Nicolás Maduro and Washington’s threats to use mil­i­tary force in Greenland and Iran.More re­cently, he said, spec­u­la­tion over who would be nom­i­nated as the next Fed chair had been in­flu­enc­ing met­als mar­kets. “The mar­ket has clearly been pric­ing the risk of a much more dovish con­tender, that’s been largely help­ing the gold price along with other pre­cious metal prices. Over the last 24 hours, the news flow has changed a lit­tle bit,” Wewel said, prior to Trump’s an­nounce­ment.

Gold and sil­ver both en­joyed record-smash­ing ral­lies in 2025, surg­ing 66% and 135%, re­spec­tively, over the course of the year. Coeur Mining lost 17%. Silver ETFs were dragged into the ac­tion, with the ProShares Ultra Silver fund last seen more than 62% lower. The iShares Silver Trust ETF lost 31%. Both funds were headed for their worst days on record. Precious met­als have been on a stel­lar rally over the past 12 months, amid broader mar­ket volatil­ity, the de­cline of the U.S. dol­lar, bub­bling geopo­lit­i­cal ten­sions and con­cerns about the in­de­pen­dence of the Federal Reserve. Katy Stoves, in­vest­ment man­ager at British wealth man­age­ment firm Mattioli Woods, told CNBC on Friday morn­ing that the moves were likely “a mar­ket-wide re­assess­ment of con­cen­tra­tion risk.” “Just as tech stocks — par­tic­u­larly AI-related names — have dom­i­nated mar­ket at­ten­tion and cap­i­tal flows, gold has sim­i­larly seen in­tense po­si­tion­ing and crowd­ing,” she said. “When every­one is lean­ing the same way, even good as­sets can sell off as po­si­tions get un­wound. The par­al­lel is­n’t ac­ci­den­tal: both rep­re­sent ar­eas where cap­i­tal has flooded in based on pow­er­ful nar­ra­tives, and con­cen­trated po­si­tions even­tu­ally face their day of reck­on­ing.“Mean­while, Toni Meadows, head of in­vest­ment at BRI Wealth Management, con­tended that gold’s run to the $5,000 mark had hap­pened “too eas­ily.” He noted that the un­wind­ing of the green­back had sup­ported gold prices, but that the dol­lar had ap­peared to sta­bi­lize. “Central bank buy­ing has dri­ven the longer-term rally but this has tailed off in re­cent months,” he said. “The case for fur­ther re­serve di­ver­si­fi­ca­tion is still there though as Trump’s trade poli­cies and in­ter­ven­tion in for­eign af­fairs will make a lot of coun­tries ner­vous about hold­ing U.S. as­sets, es­pe­cially those coun­tries in the emerg­ing mar­kets or aligned to China or Russia. Silver will mir­ror the di­rec­tion of gold, so it is not sur­pris­ing to see falls there.”

Visibility and con­text on the threats that mat­ter most. Contact Us & Get a Demo

This week Google and part­ners took ac­tion to dis­rupt what we be­lieve is one of the largest res­i­den­tial proxy net­works in the world, the IPIDEA proxy net­work. IPIDEA’s proxy in­fra­struc­ture is a lit­tle-known com­po­nent of the dig­i­tal ecosys­tem lever­aged by a wide ar­ray of bad ac­tors.

This dis­rup­tion, led by Google Threat Intelligence Group (GTIG) in part­ner­ship with other teams, in­cluded three main ac­tions:

Took le­gal ac­tion to take down do­mains used to con­trol de­vices and proxy traf­fic through them.

Shared tech­ni­cal in­tel­li­gence on dis­cov­ered IPIDEA soft­ware de­vel­op­ment kits (SDKs) and proxy soft­ware with plat­form providers, law en­force­ment, and re­search firms to help drive ecosys­tem-wide aware­ness and en­force­ment. These SDKs, which are of­fered to de­vel­op­ers across mul­ti­ple mo­bile and desk­top plat­forms, sur­rep­ti­tiously en­roll user de­vices into the IPIDEA net­work. Driving col­lec­tive en­force­ment against these SDKs helps pro­tect users across the dig­i­tal ecosys­tem and re­stricts the net­work’s abil­ity to ex­pand.

These ef­forts to help keep the broader dig­i­tal ecosys­tem safe sup­ple­ment the pro­tec­tions we have to safe­guard Android users on cer­ti­fied de­vices. We en­sured Google Play Protect, Android’s built-in se­cu­rity pro­tec­tion, au­to­mat­i­cally warns users and re­moves ap­pli­ca­tions known to in­cor­po­rate IPIDEA SDKs, and blocks any fu­ture in­stall at­tempts.

We be­lieve our ac­tions have caused sig­nif­i­cant degra­da­tion of IPIDEA’s proxy net­work and busi­ness op­er­a­tions, re­duc­ing the avail­able pool of de­vices for the proxy op­er­a­tors by mil­lions. Because proxy op­er­a­tors share pools of de­vices us­ing re­seller agree­ments, we be­lieve these ac­tions may have down­stream im­pact across af­fil­i­ated en­ti­ties.

In con­trast to other types of prox­ies, res­i­den­tial proxy net­works sell the abil­ity to route traf­fic through IP ad­dresses owned by in­ter­net ser­vice providers (ISPs) and used to pro­vide ser­vice to res­i­den­tial or small busi­ness cus­tomers. By rout­ing traf­fic through an ar­ray of con­sumer de­vices all over the world, at­tack­ers can mask their ma­li­cious ac­tiv­ity by hi­jack­ing these IP ad­dresses. This gen­er­ates sig­nif­i­cant chal­lenges for net­work de­fend­ers to de­tect and block ma­li­cious ac­tiv­i­ties.

A ro­bust res­i­den­tial proxy net­work re­quires the con­trol of mil­lions of res­i­den­tial IP ad­dresses to sell to cus­tomers for use. IP ad­dresses in coun­tries such as the US, Canada, and Europe are con­sid­ered es­pe­cially de­sir­able. To do this, res­i­den­tial proxy net­work op­er­a­tors need code run­ning on con­sumer de­vices to en­roll them into the net­work as exit nodes. These de­vices are ei­ther pre-loaded with proxy soft­ware or are joined to the proxy net­work when users un­know­ingly down­load tro­janized ap­pli­ca­tions with em­bed­ded proxy code. Some users may know­ingly in­stall this soft­ware on their de­vices, lured by the promise of “monetizing” their spare band­width. When the de­vice is joined to the proxy net­work, the proxy provider sells ac­cess to the in­fected de­vice’s net­work band­width (and use of its IP ad­dress) to their cus­tomers.

While op­er­a­tors of res­i­den­tial prox­ies of­ten ex­tol the pri­vacy and free­dom of ex­pres­sion ben­e­fits of res­i­den­tial prox­ies, Google Threat Intelligence Group’s (GTIG) re­search shows that these prox­ies are over­whelm­ingly mis­used by bad ac­tors. IPIDEA has be­come no­to­ri­ous for its role in fa­cil­i­tat­ing sev­eral bot­nets: its soft­ware de­vel­op­ment kits played a key role in adding de­vices to the bot­nets, and its proxy soft­ware was then used by bad ac­tors to con­trol them. This in­cludes the BadBox2.0 bot­net we took le­gal ac­tion against last year, and the Aisuru and Kimwolf bot­nets more re­cently. We also ob­serve IPIDEA be­ing lever­aged by a vast ar­ray of es­pi­onage, crime, and in­for­ma­tion op­er­a­tions threat ac­tors. In a sin­gle seven day pe­riod in January 2026, GTIG ob­served over 550 in­di­vid­ual threat groups that we track uti­liz­ing IP ad­dresses tracked as IPIDEA exit nodes to ob­fus­cate their ac­tiv­i­ties, in­clud­ing groups from China, DPRK, Iran and Russia. The ac­tiv­i­ties in­cluded ac­cess to vic­tim SaaS en­vi­ron­ments, on-premises in­fra­struc­ture, and pass­word spray at­tacks. Our re­search has found sig­nif­i­cant over­laps be­tween res­i­den­tial proxy net­work exit nodes, likely be­cause of re­seller and part­ner­ship agree­ments, mak­ing de­fin­i­tive quan­tifi­ca­tion and at­tri­bu­tion chal­leng­ing.

In ad­di­tion, res­i­den­tial prox­ies pose a risk to the con­sumers whose de­vices are joined to the proxy net­work as exit nodes. These users know­ingly or un­know­ingly pro­vide their IP ad­dress and de­vice as a launch­pad for hack­ing and other unau­tho­rized ac­tiv­i­ties, po­ten­tially caus­ing them to be flagged as sus­pi­cious or blocked by providers. Proxy ap­pli­ca­tions also in­tro­duce se­cu­rity vul­ner­a­bil­i­ties to con­sumers’ de­vices and home net­works. When a user’s de­vice be­comes an exit node, net­work traf­fic that they do not con­trol will pass through their de­vice. This means bad ac­tors can ac­cess a user’s pri­vate de­vices on the same net­work, ef­fec­tively ex­pos­ing se­cu­rity vul­ner­a­bil­i­ties to the in­ter­net. GTIG’s analy­sis of these ap­pli­ca­tions con­firmed that IPIDEA proxy did not solely route traf­fic through the exit node de­vice, they also sent traf­fic to the de­vice, in or­der to com­pro­mise it. While proxy providers may claim ig­no­rance or close these se­cu­rity gaps when no­ti­fied, en­force­ment and ver­i­fi­ca­tion is chal­leng­ing given in­ten­tion­ally murky own­er­ship struc­tures, re­seller agree­ments, and di­ver­sity of ap­pli­ca­tions.

Our analy­sis of res­i­den­tial proxy net­works found that many well-known res­i­den­tial proxy brands are not only re­lated but are con­trolled by the ac­tors be­hind IPIDEA. This in­cludes the fol­low­ing os­ten­si­bly in­de­pen­dent proxy and VPN brands:

The same ac­tors that con­trol these brands also con­trol sev­eral do­mains re­lated to Software Development Kits (SDKs) for res­i­den­tial prox­ies. These SDKs are not meant to be in­stalled or ex­e­cuted as stand­alone ap­pli­ca­tions, rather they are meant to be em­bed­ded into ex­ist­ing ap­pli­ca­tions. The op­er­a­tors mar­ket these kits as ways for de­vel­op­ers to mon­e­tize their ap­pli­ca­tions, and of­fer Android, Windows, iOS, and WebOS com­pat­i­bil­ity. Once de­vel­op­ers in­cor­po­rate these SDKs into their app, they are then paid by IPIDEA usu­ally on a per-down­load ba­sis.Fig­ure 1: Advertising from PacketSDK, part of the IPIDEA proxy net­workOnce the SDK is em­bed­ded into an ap­pli­ca­tion, it will turn the de­vice it is run­ning on into an exit node for the proxy net­work in ad­di­tion to pro­vid­ing what­ever the pri­mary func­tion­al­ity of the ap­pli­ca­tion was. These SDKs are the key to any res­i­den­tial proxy net­work—the soft­ware they get em­bed­ded into pro­vides the net­work op­er­a­tors with the mil­lions of de­vices they need to main­tain a healthy res­i­den­tial proxy net­work.

While many res­i­den­tial proxy providers state that they source their IP ad­dresses eth­i­cally, our analy­sis shows these claims are of­ten in­cor­rect or over­stated. Many of the ma­li­cious ap­pli­ca­tions we an­a­lyzed in our in­ves­ti­ga­tion did not dis­close that they en­rolled de­vices into the IPIDEA proxy net­work. Researchers have pre­vi­ously found un­cer­ti­fied and off-brand Android Open Source Project de­vices, such as tele­vi­sion set top boxes, with hid­den res­i­den­tial proxy pay­loads.

The fol­low­ing SDKs are con­trolled by the same ac­tors that con­trol the IPIDEA proxy net­work:

We per­formed sta­tic and dy­namic analy­sis on soft­ware that had SDK code em­bed­ded in it as well as stand­alone SDK files to iden­tify the com­mand-and-con­trol (C2) in­fra­struc­ture used to man­age proxy exit nodes and route traf­fic through them. From the analy­sis we ob­served that EarnSDK, PacketSDK, CastarSDK, and HexSDK have sig­nif­i­cant over­laps in their C2 in­fra­struc­ture as well as code struc­ture.

Tier One: Upon startup, the de­vice will choose from a set of do­mains to con­nect to. The de­vice sends some di­ag­nos­tic in­for­ma­tion to the Tier One server and re­ceives back a data pay­load that in­cludes a set of Tier Two nodes to con­nect to.

Tier Two: The ap­pli­ca­tion will com­mu­ni­cate di­rectly with an IP ad­dress to pe­ri­od­i­cally poll for proxy tasks. When it re­ceives a proxy task it will es­tab­lish a new ded­i­cated con­nec­tion to the Tier Two IP ad­dress and be­gin prox­y­ing the pay­loads it re­ceives.

The de­vice di­ag­nos­tic in­for­ma­tion can be sent as HTTP GET query string pa­ra­me­ters or in the HTTP POST body, de­pend­ing on the do­main and SDK. The pay­load sent in­cludes a key pa­ra­me­ter, which may be a cus­tomer iden­ti­fier used to de­ter­mine who gets paid for the de­vice en­roll­ment.The re­sponse from the Tier One server in­cludes some tim­ing in­for­ma­tion as well as the IP ad­dresses of the Tier Two servers that this de­vice should pe­ri­od­i­cally poll for task­ing.{“code”:200,“data”:{“sched­ule”:24,“thread”:150,“heart­beat”:20,“ip”:[redacted],“info”:“US”,“node”:[{“net_­type”:“t”,“con­nect”:“49.51.68.143:1000”,“proxy”:“49.51.68.143:2000”},{“net_­type”:“t”,“con­nect”:“45.78.214.188:800”,“proxy”:“45.78.214.188:799”}]}

Figure 4: Sample re­sponse re­ceived from the Tier One Server

The Tier Two servers are sent as con­nect and proxy pairs. In all analy­ses the pairs have been IP ad­dresses, not do­mains. In our analy­sis, the pairs are the same IP ad­dress but dif­fer­ent ports. The con­nect port is used to pe­ri­od­i­cally poll for new proxy task­ing. This is per­formed by send­ing TCP pack­ets with en­coded JSON pay­loads.{“name”: “0c855f87a7574b28df383eca5084fcdc”, “o”: “eDwSokuyOuMHcF10″, “os”: “windows”}

Figure 5: Sample en­coded JSON sent to Tier Two con­nect port­When the Tier Two server has traf­fic to route to the de­vice, it will re­spond back with the FQDN to proxy traf­fic to as well as a con­nec­tion ID.www.google.com:443&c8e­b024c053f82831f2738b­d48afc256

Figure 6: Sample proxy task­ing from the Tier Two server­The de­vice will then es­tab­lish a con­nec­tion to the proxy port of the same Tier Two server and send the con­nec­tion ID, in­di­cat­ing that it is ready to re­ceive data pay­loads.8a9b­d7e7a806b2c­c606b7a1d8f495662|ok

Figure 7: Sample data sent from de­vice to the Tier Two proxy port­The Tier Two server will then im­me­di­ately send data pay­loads to be prox­ied. The de­vice will ex­tract the TCP data pay­load, es­tab­lish a socket con­nec­tion to the spec­i­fied FQDN and send the pay­load, un­mod­i­fied, to the des­ti­na­tion.

The SDKs each have their own set of Tier One do­mains. This comes pri­mar­ily from analy­sis of stand­alone SDK files.

Download re­quests to files from the Hex SDK web­site redi­rect to cas­tarsdk\.com. The SDKs are ex­actly the same.

The EarnSDK JAR pack­age for Android has strong over­laps with the other SDK brands an­a­lyzed. Earlier pub­lished sam­ples con­tained the Tier One C2 do­mains:

Of note, these do­mains were ob­served as part of the BadBox2.0 bot­net and were sink­holed in our ear­lier lit­i­ga­tion. Pivoting off these do­mains and other sig­na­tures, we iden­ti­fied some ad­di­tional do­mains used as Tier One C2 do­mains:

Our analy­sis of var­i­ous mal­ware sam­ples and the SDKs found a sin­gle shared pool of Tier Two servers. As of this writ­ing there were ap­prox­i­mately 7,400 Tier Two servers. The num­ber of Tier Two nodes changes on a daily ba­sis, con­sis­tent with a de­mand-based scal­ing sys­tem. They are hosted in lo­ca­tions around the globe, in­clud­ing the US. This in­di­cates that de­spite dif­fer­ent brand names and Tier One do­mains, the dif­fer­ent SDKs in fact man­age de­vices and proxy traf­fic through the same in­fra­struc­ture.

The IPIDEA ac­tors also con­trol do­mains that of­fer free Virtual Private Network ser­vices. While the ap­pli­ca­tions do seem to pro­vide VPN func­tion­al­ity, they also join the de­vice to the IPIDEA proxy net­work as an exit node by in­cor­po­rat­ing Hex or Packet SDK. This is done with­out clear dis­clo­sures to the end user, nor is it the pri­mary func­tion of the ap­pli­ca­tion.

We iden­ti­fied a to­tal of 3,075 unique Windows PE file hashes where dy­namic analy­sis recorded a DNS re­quest to at least one Tier One do­main. A num­ber of these hashes were for the mon­e­tized proxy exit node soft­ware, PacketShare. Our analy­sis also un­cov­ered ap­pli­ca­tions mas­querad­ing as OneDriveSync and Windows Update. These tro­janized Windows ap­pli­ca­tions were not dis­trib­uted di­rectly by the IPIDEA ac­tors.

We iden­ti­fied over 600 ap­pli­ca­tions across mul­ti­ple down­load sources with code con­nect­ing to Tier One C2 do­mains. These apps were largely be­nign in func­tion (e.g., util­i­ties, games, and con­tent) but uti­lized mon­e­ti­za­tion SDKs that en­abled IPIDEA proxy be­hav­ior.

This week we took a num­ber of steps de­signed to com­pre­hen­sively dis­man­tle as much of IPIDEA’s in­fra­struc­ture as pos­si­ble.

We took le­gal ac­tion to take down the C2 do­mains used by bad ac­tors to con­trol de­vices and proxy traf­fic. This pro­tects con­sumer de­vices and home net­works by dis­rupt­ing the in­fra­struc­ture at the source.

To safe­guard the Android ecosys­tem, we en­forced our plat­form poli­cies against tro­janiz­ing soft­ware, en­sur­ing Google Play Protect on cer­ti­fied Android de­vices with Google Play ser­vices au­to­mat­i­cally warns users and re­moves ap­pli­ca­tions known to in­cor­po­rate IPIDEA soft­ware de­vel­op­ment kits (SDKs), and blocks any fu­ture in­stall at­tempts.

We took le­gal ac­tion to take down the do­mains used to mar­ket IPIDEA’s prod­ucts, in­clud­ing proxy soft­ware and soft­ware de­vel­op­ment kits, across their var­i­ous brands.

We’ve shared our find­ings with in­dus­try part­ners to en­able them to take ac­tion as well. We’ve worked closely with other firms, in­clud­ing Spur and Lumen’s Black Lotus Labs to un­der­stand the scope and ex­tent of res­i­den­tial proxy net­works and the bad be­hav­ior they of­ten en­able. We part­nered with Cloudflare to dis­rupt IPIDEA’s do­main res­o­lu­tion, im­pact­ing their abil­ity to com­mand and con­trol in­fected de­vices and mar­ket their prod­ucts.

While we be­lieve our ac­tions have se­ri­ously im­pacted one of the largest res­i­den­tial proxy providers, this in­dus­try ap­pears to be rapidly ex­pand­ing, and there are sig­nif­i­cant over­laps across providers. As our in­ves­ti­ga­tion shows, the res­i­den­tial proxy mar­ket has be­come a “gray mar­ket” that thrives on de­cep­tion—hi­jack­ing con­sumer band­width to pro­vide cover for global es­pi­onage and cy­ber­crime. More must be done to ad­dress the risks of these tech­nolo­gies.

Residential prox­ies are an un­der­stud­ied area of risk for con­sumers, and more can be done to raise aware­ness. Consumers should be ex­tremely wary of ap­pli­ca­tions that of­fer pay­ment in ex­change for “unused band­width” or “sharing your in­ter­net.” These ap­pli­ca­tions are pri­mary ways for il­licit proxy net­works to grow, and could open se­cu­rity vul­ner­a­bil­i­ties on the de­vice’s home net­work. We urge users to stick to of­fi­cial app stores, re­view per­mis­sions for third-party VPNs and prox­ies, and en­sure built-in se­cu­rity pro­tec­tions like Google Play Protect are ac­tive.

Consumers should be care­ful when pur­chas­ing con­nected de­vices, such as set top boxes, to make sure they are from rep­utable man­u­fac­tur­ers. For ex­am­ple, to help you con­firm whether or not a de­vice is built with the of­fi­cial Android TV OS and Play Protect cer­ti­fied, our Android TV web­site pro­vides the most up-to-date list of part­ners. You can also take these steps to check if your Android de­vice is Play Protect cer­ti­fied.

Residential proxy providers have been able to flour­ish un­der the guise of le­git­i­mate busi­nesses. While some providers may in­deed be­have eth­i­cally and only en­roll de­vices with the clear con­sent of con­sumers, any claims of “ethical sourc­ing” must be backed by trans­par­ent, au­ditable proof of user con­sent. Similarly, app de­vel­op­ers have a re­spon­si­bil­ity to vet the mon­e­ti­za­tion SDKs they in­te­grate.

We en­cour­age mo­bile plat­forms, ISPs, and other tech plat­forms to con­tinue shar­ing in­tel­li­gence and im­ple­ment­ing best prac­tices to iden­tify il­licit proxy net­works and limit their harms.

To as­sist the wider com­mu­nity in hunt­ing and iden­ti­fy­ing ac­tiv­ity out­lined in this blog post, we have in­cluded a com­pre­hen­sive list of in­di­ca­tors of com­pro­mise (IOCs) in a GTI Collection for reg­is­tered users.

Vishing for Access: Tracking the Expansion of ShinyHunters-Branded SaaS Data TheftGuidance from the Frontlines: Proactive Defense Against ShinyHunters-Branded Data Theft Targeting SaaSClosing the Door on Net-NTLMv1: Releasing Rainbow Tables to Accelerate Protocol Deprecation

ICE is us­ing a smart­phone app called “Mobile Fortify” to scan faces and cap­ture con­tact­less fin­ger­prints, in­stantly pulling back names and bi­o­graph­i­cal data — and court fil­ings say the same en­coun­ters are be­ing fol­lowed by re­vo­ca­tions of Global Entry and TSA PreCheck.

That turns “trusted trav­eler” into chill­ing of speech. DHS runs both the sur­veil­lance and the pro­gram, and be­ing “under in­ves­ti­ga­tion” can be enough to lose your sta­tus even if protest­ing it­self can­not legally be a dis­qual­i­fier.

The Department of Homeland Security and Immigration and Customs Enforcement is ex­pand­ing use of iden­ti­fi­ca­tio­nand track­ing — not just im­mi­gra­tion tar­gets but also on U. S. cit­i­zens who are doc­u­ment­ing, protest­ing, and ob­serv­ing en­force­ment op­er­a­tions. And par­tic­i­pat­ing in these events is get­ting Global Entry yanked.

The gov­ern­ment is us­ing a smart­phone app called “Mobile Fortify” that lets agents scan a face and even cap­ture ‘contactless’ fin­ger­prints and run them through bio­met­ric match­ing sys­tems to re­turn names and bi­o­graph­i­cal data. Reportedly the agency has used Mobile Fortify over 100,000 times. They use BI2 Technologies for smart­phone iris scan­ning against a large law en­force­ment iris data­base. The agency de­fends this all as “lawful.”

In ad­di­tion, ICE uses li­cense plate reader data, com­mer­cial phone lo­ca­tion data, drones, and other tools to mon­i­tor protests by U. S. cit­i­zens.

Global Entry is ad­min­is­tered by the Department of Homeland Security, its data was used to train Mobile Fortify, and cit­i­zens in the pro­gram are sub­ject to hav­ing the sta­tus re­voked if they’re be­ing in­ves­ti­gated. DHS in­ves­ti­gates peo­ple protest­ing DHS.

Customs and Border Protection can deem you in­el­i­gi­ble “at its sole dis­cre­tion” if you pre­sent a po­ten­tial risk for ter­ror­ism, crim­i­nal­ity, or are oth­er­wise no longer con­sid­ered low risk. It bases risk de­ter­mi­na­tions partly on demon­strated com­pli­ance.

They can kick yo uout for ar­rests or be­ing the sub­ject of an in­ves­ti­ga­tion by any law en­force­ment agency, or sus­pect con­duct that is ‘terrorism-related’.

Protesting is­n’t a listed or ‘valid’ rea­son for hav­ing Global Entry re­voked, but be­ing ar­rested at a protest is. Impeding or in­ter­fer­ing with the agency is. And be­ing in­ves­ti­gated is.

In a court fil­ing, Nicole Cleland says she ob­serv­ing ICE ac­tiv­i­ties in her neigh­bor­hood when an agent ap­proached her ve­hi­cle, ad­dressed her by name, said they had “facial recog­ni­tion” and warned she was “impeding” and could be ar­rested if it hap­pened again. Three days later, she re­ceived email no­tice her Global Entry and TSA PreCheck sta­tus was re­voked.

Homeland Security does con­tin­u­ous checks on Global Entry mem­bers, and may un­cover a past con­vic­tion that was­n’t dis­closed dur­ing the ap­pli­ca­tion (generally mi­nor of­fenses over 10 years old, such as a DUI, are fine if you dis­close them) or a new con­vic­tion. Breaking pro­gram rules or rules in the im­mi­gra­tion hall such as fail­ing to de­clare items or bring­ing in­el­i­gi­ble fam­ily mem­bers with you into the Global Entry queues can get you kicked out if the cus­toms of­fi­cer de­cides to make an is­sue of it.

You can lose Global Entry for com­plain­ing about a cus­toms of­fi­cer. Putting an ap­ple from your flight in your bag, and then not de­clar­ing it can cost you your Global Entry. So can at­tempt­ing a coup against the United States.

So, too, now it seems just for protest­ing against gov­ern­ment pol­icy. And that has a huge chill­ing ef­fect on pub­lic dis­sent. If you’re pun­ished for ex­press­ing views con­trary to those in power, you’ll be less likely to ex­press those views. It’ll then ap­pear to oth­ers that there’s a con­sen­sus sup­port­ing those in power, mak­ing it harder for still oth­ers to dis­sent. That’s what you get in au­thor­i­tar­ian regimes — pref­er­ence fal­si­fi­ca­tion, where every­one pub­licly tows the dom­i­nant line and is un­will­ing to re­veal their true be­liefs.

While more peo­ple are be­ing kicked out of Global Entry than ever be­fore, 39% of peo­ple who ap­peal the re­vo­ca­tion win. Plus, DHS de­ci­sions on Global Entry are sub­ject to ju­di­cial re­view, at least in the ninth cir­cuit.

More From View from the Wing

In my YouTube chan­nel, for some time now I started to re­fer to the process of writ­ing soft­ware us­ing AI as­sis­tance (soon to be­come just “the process of writ­ing soft­ware”, I be­lieve) with the term “Automatic Programming”.

In case you did­n’t no­tice, au­to­matic pro­gram­ming pro­duces vastly dif­fer­ent re­sults with the same LLMs de­pend­ing on the hu­man that is guid­ing the process with their in­tu­ition, de­sign, con­tin­u­ous steer­ing and idea of soft­ware.

Please, stop say­ing “Claude vibe coded this soft­ware for me”. Vibe cod­ing is the process of gen­er­at­ing soft­ware us­ing AI with­out be­ing part of the process at all. You de­scribe what you want in very gen­eral terms, and the LLM will pro­duce what­ever hap­pens to be the first idea/​de­sign/​code it would spon­ta­neously, given the train­ing, the spe­cific sam­pling that hap­pened to dom­i­nate in that run, and so forth. The vibe coder will, at most, re­port things not work­ing or not in line with what they ex­pected.

When the process is ac­tual soft­ware pro­duc­tion where you know what is go­ing on, re­mem­ber: it is the soft­ware *you* are pro­duc­ing. Moreover re­mem­ber that the pre-train­ing data, while not the only part where the LLM learns (RL has its big weight) was pro­duced by hu­mans, so we are not ap­pro­pri­at­ing some­thing else. We can pre­tend AI gen­er­ated code is “ours”, we have the right to do so. Pre-training is, ac­tu­ally, our col­lec­tive gift that al­lows many in­di­vid­u­als to do things they could oth­er­wise never do, like if we are now linked in a col­lec­tive mind, in a cer­tain way.

That said, if vibe cod­ing is the process of pro­duc­ing soft­ware with­out much un­der­stand­ing of what is go­ing on (which has a place, and de­moc­ra­tizes soft­ware pro­duc­tion, so it is to­tally ok with me), au­to­matic pro­gram­ming is the process of pro­duc­ing soft­ware that at­tempts to be high qual­ity and strictly fol­low­ing the pro­duc­er’s vi­sion of the soft­ware (this vi­sion is multi-level: can go from how to do, ex­actly, cer­tain things, at a higher level, to step­ping in and tell the AI how to write a cer­tain func­tion), with the help of AI as­sis­tance. Also a fun­da­men­tal part of the process is, of course, *what* to do.

I’m a pro­gram­mer, and I use au­to­matic pro­gram­ming. The code I gen­er­ate in this way is mine. My code, my out­put, my pro­duc­tion. I, and you, can be proud.

If you are not com­pletely con­vinced, think to Redis. In Redis there is not much tech­ni­cal nov­elty, es­pe­cially at its start it was just a sum of ba­sic data struc­tures and net­work­ing code that every com­pe­tent sys­tem pro­gram­mer could write. So, why it be­came a very use­ful piece of soft­ware? Because of the ideas and vi­sions it con­tained.

Programming is now au­to­matic, vi­sion is not (yet).

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