OpenAI is coming for Google Search, and it's working.

"ChatGPT's quantity of search-like queries is now approximately one-fifth the quantity of queries of Google," Adam Fry, ChatGPT's person merchandise lead, told Alex Kantrowitz of the Big Technology newsletter.

It's fine news for OpenAI. For Google? Not so great. It not lone shows that group are consenting to use AI, but that group were really, really, fed up alongside Google hunt results.

We already knew that essential data searching is currently the top use of LLMs akin ChatGPT. Pew Research Center released a report this summer that showed that the top use case was to "search for information," alongside 42% of American adults surveyed saying they have used AI for that.

("For tasks at work" was the second most average use, alongside 38% of employed adults reporting that. Lesser uses contain entertainment, getting news, and medicinal advice.)

This study wasn't specifically concerning ChatGPT, but it's by far the most famous AI chatbot (44% of American adults surveyed told Pew they've used ChatGPT, and lone 24% have used Gemini, the next most common).

ChatGPT now has 1.2 milliard weekly users. We don't cognize how many of those users are doing informational searches as opposed to another things akin coding or generating images, but it seems apt that person who uses it to create images or conversation would additionally ask it for information.

Google did not immediately react to a petition for comment on the competition from ChatGPT, but final twelvemonth Alphabet CEO Sundar Pichai addressed the matter during an interview alongside Bloomberg's Emily Chang.

"You've had these chatbots measure up to hundreds of millions of users. We've grown in queries," Pichai stated at the time. "So I think it feels extremely far from a zero-sum equivalent to me. To use another parallel, TikTok came in, everybody started using TikTok. YouTube grew and did very, extremely fine in those moments too."

People started to awareness akin Google hunt had gotten worse

Google says that it handles 5 trillion searches per year; it's dominated hunt for two decades, and extremely profitably!

But many group were getting really ill of Google search. So frequently in the final few years, I've heard allies or another group objection how Google hunt feels broken. Search results were so stuffed alongside SEO-friendly links that it frequently felt difficult to rely item or discover the results you really were searching for.

Users had started to add the term "Reddit" at the end of searches in hopes of getting improved results — which ended up being a boon in traffic for Reddit (sadly, this didn't last; Reddit fair reported a drop in hunt referral traffic because of Google's AI Overviews that it doesn't anticipate to get back).

So whenever ChatGPT came along, abruptly there was a new way to get data and answers without all the SEO fluff — and plentifulness of group were thrilled. Sure, the data isn't continually reliable. Everyone should cognize improved than to blindly rely facts doled out by AI, which is prone to hallucinations or fair plain bungling, akin telling group to put glue in their pizza.

Despite the issues alongside trusting AI for data (and to be fair, it has improved since the glue/pizza fiasco of 2024), it's apparent that lots of group were very, extremely blessed to toggle complete to ChatGPT alternatively of Google for their informational searching needs.

I think what we should regard is that says a lot additional concerning how bad Google had gotten than it does concerning how good ChatGPT is. There's a compelling case to be made that Google enshittified its flagship merchandise so seriously — perchance from the years and years of having no meaningful competitors — that it was a sitting duck, prepared to have its lunch eaten by the archetypal new disruptor to arrive alongside and recommendation group a improved way.

Google eventually realized it was in trouble, issuing a "code red" in 2022 complete ChatGPT. It power be period to matter another one?

The 2026 midterms have arrived.

Beyond authority of Congress, Tuesday's ballot volition exemplify the degree of Silicon Valley's governmental power and the breadth of the AI backlash.

SpaceX CEO Elon Musk, in particular, has a lot at stake, alongside document donations powering a last-minute attempt to preserve a Texas Senate place and perchance the GOP majority, too.

In supplement to the candidates, there are big ballot questions looming, none additional so than the California prosperity tax fight.

Here's what we're watching.

Ohio Senate

Candidates: Sen. Jon Husted, a Republican; erstwhile Sen. Sherrod Brown, a Democrat; Bill Redpath, a libertarian; and Greg Levy, an independent

Where it stands: Husted, a erstwhile lieutenant governor, is trying to suspend on to Vice President JD Vance's Senate seat, but he's facing a fierce difficulty from Brown, a erstwhile three-term legislator who is seeking to come back to Congress. Brown has criticized Husted for his former assistance for data center development. Husted has stated the matter have to be remaining to communities and no longer supports offering tax incentives for new data center construction. Earlier this summer, Senate Republicans' run arm warned AI labs that if Husted goes downward "and data centers get the blame, politicians throughout the nation volition obtain notice — and they volition not go near the next one."

Polls close: 7:30 pm EST

The outlook: Cook Political Report rates it as a "Toss-Up."

History: President Donald Trump won the province by 11 points. Brown misplaced to now-Sen. Bernie Moreno, a Republican, by fair under 4 points.

Texas Senate

Candidates: Texas Attorney General Ken Paxton, a Republican; State Rep. James Talarico, a Democrat; Ted Brown, a libertarian

Where it stands: Paxton is expecting to keep the place in Republican hands following Trump's endorsement pushed him complete Sen. John Cornyn following a bitter chief and runoff. Cornyn warned that if Paxton won, Republicans would have to expend important sums to assistance an officeholder saddled alongside scandals, impeachment, and a separation on "biblical grounds." Enter Musk. The world's richest man has been frequently trashing Talarico on X. More importantly, Musk has donated at smallest $158 myriad that has been routed to a pro-Paxton excellent PAC.

Polls close: Most polls in the province near at 8 pm EST, but for locations in the Mountain Time Zone they near at 9 pm EST.

The outlook: Cook Political Report rates it as a "Toss-Up."

History: In 2024, President Donald Trump won the province by approximately 14 points. Sen. Ted Cruz, a Republican, won reelection by fair under 9 points. No Texas Democrat has won a statewide ballot since 1994.

Michigan Senate

Candidates: Former Wayne County Health Director Abdul El-Sayed, a Democrat; erstwhile Rep. Mike Rogers, a Republican; Douglas P. Marsh, Green Party; Lydia Christensen, Libertarian Party

Where it stands: No competition compares to the perform of the Michigan Senate race. On Sunday, Rep. Haley Stevens, who misplaced to El-Sayed following a bitter primary, reversed herself and issued a last-minute endorsement of Rogers. Beyond the headlines, the competition offers several engaging guideline contrasts. Echoing Sen. Bernie Sanders (and Trump), El-Sayed has called for the authorities to obtain equity stakes in AI companies. El-Sayed additionally wants to power tech giants akin Microsoft to divest from AI companies akin OpenAI and Anthropic. Rogers, the Republican hopeful, has called for a intermission on data center betterment in the state. If Republicans desire to clasp the majority, Rogers volition nearly certainly need to flip the seat.

Polls close: Most polls in the province near at 8 pm EST. For counties in the Central Time Zone, polls near at 9 p.m EST.

The outlook: Cook Political Report rates it as a "Lean Democrat."

History: In 2024, Trump won by fair complete 1 point. At the identical time, Sen. Elissa Slotkin narrowly attack Rogers by approximately 202,000 votes. Michigan has not elected a Republican legislator since 1994.

Michigan 7th District

Candidates: Rep. Tom Barrett, a Republican; Sunrise Movement cofounder William Lawrence, a Democrat; Shane Dedrick, Green Party

Where it stands: Barrett is seeking reelection in a southernmost Michigan district centered about Lansing. William Lawrence, a cofounder of the advancing climate-focused institution Sunrise Movement, has tried to tie Barrett to a projected $1 milliard hyperscale data center near Mason, Michigan. Barrett has stated that local officials have to be capable to create decisions concerning data centers, contrasting his views alongside Lawrence's assistance of a data center moratorium.

The outlook: Cook Political Report rates it a "Toss-Up."

History: In 2024, Trump won the district by fair complete 1 point, according to The Downballot's estimates. Barrett won by fair under 17,000 votes.

Ohio 9th District

Candidates: Rep. Marcy Kaptur, a Democrat; erstwhile Ohio province Rep. Derek Merrin, a Republican

Where it stands: Republicans' hopes of defying history and keep their bulk run squarely through Rep. Marcy Kaptur, a 22-term Ohio Democrat, and her northwestern Ohio district that stretches from the Indiana border to fair west of Cleveland. Kaptur has criticized Merrin for voting in favor of tax incentives for data center development. Merrin has stated Kaptur is "lying" concerning his record, but as part of a sweeping 2017 prosperity law, Ohio lawmakers voted to broaden the deadline for certain data centers to meet requirements for tax incentives.

The outlook: The Cook Political Report rates it as a "Lean Democrat."

History: In 2024, Trump won the district by approximately 11 points, according to The Downballot's estimates. Kaptur won by under 2,400 votes. Kaptur is one of 22 Democrats representing districts that Trump additionally won.

Pennsylvania 1st District

Candidates: Rep. Brian Fitzpatrick, a Republican; Bucks County commissioner Bob Harvie, a Democrat

Where it stands: Fitzpatrick, a five-term Pennsylvania Republican, romped to reelection two years ago but is now fighting to suspend onto his seat. Harvie focused his archetypal ad of the broad ballot on his call for a data center moratorium. He additionally ruined Fitzpatrick complete the Republican's former assistance for a enormous Amazon data center in Falls Township, which is in Bucks County.

Polls close: 8 pm EST

The outlook: The Cook Political Report rates it as a "Toss-Up."

History: In 2024, Trump narrowly misplaced the district, according to The Downballot's estimates. Fitzpatrick motionless effortlessly won reelection by nearly 13 points. He is one of fair three Republican lawmakers representing districts Trump lost.

Wisconsin 3rd District

Candidates: Rep. Derrick Van Orden, a Republican; Rebecca Cooke, a Democrat

Where it stands: Van Orden, a two-term Wisconsin Republican, is trying to clasp on to his occidental Wisconsin district, which includes Eau Claire and La Crosse. Cooke, a erstwhile governmental adviser who now plant as a waitress, has echoed Sen. Bernie Sanders' call for a national moratorium on data center development. Cooke has additionally hammered Van Orden complete data centers. In response, Van Orden has accused Cooke of operating too closely alongside tech groups.

Polls close: 9 pm EST

The outlook: The Cook Political Report rates it as "Lean Democrat."

History: In 2024, Trump won the district by complete 7 points, according to The Downballot's estimates. Van Orden defeated Cooke by approximately 3 points.

California billionaire tax

What's on the ballot: A one-time 5% tax on Californians alongside complete $1 milliard in wealth. Two competing proposals would efficiently nullify the projected tax.

The sides: The Service Employees International Union-United Healthcare Workers West labor union projected the tax. Rep. Ro Khanna, a Democrat who is a imaginable 2028 candidate, has lent his support, as has Sen. Bernie Sanders. Gov. Gavin Newsom, another imaginable 2028 candidate, is opposing the measure. A figure of Silicon Valley elites are additionally in opposition, including Google cofounder Sergey Brin.

Polls close: 11 pm EST

The outlook: There's been small polling this fall. The few community polls have been all complete the place. A latest UC Berkeley Institute of Governmental Studies-Los Angeles Times study established opponents had a 50% to 42% border among apt voters.

Be prepared to wait: California has a lot of votes to count, and historically several races have taken days or equal weeks to call. The fact that there are three intertwined ballot initiatives may additionally create it additional difficult for news organizations to project a winner. In the event that multiple initiatives pass, the one that receives the most affirmative votes mostly trumps conflicting ones.

TL;DR

• ~/Library/Keyboard/VulgarWordUsage.db is real. It records which promise words you use, per app and per recipient, so autocorrect learns to halt "ducking" you. On my Mac it is empty, and typing promise words into a native app did not inhabit it.
• The engaging part is upstream. Apple's scheme dictionary flags all term alongside one of three tiers (vulgar, offensive, sensitive) and up to eleven topic flags (sex, drugs, religion, politics, violence, …).
• There is no community API for any of it. I peruse the flags alongside a few personal C functions from Lexicon.framework. Code below, in C and in Swift. The complete dumps for 15 languages are in this gist (uncensored, so intellect the content warning).
• Highlights: Oedipus is vulgar. Tom and Mick are offensive. pee, poo, niggle and underling are sensitive. And "Trump" is the lone living politician in the English delicate tier, next to Hitler, Mao, Osama bin Laden and the Taliban. Biden, Obama and Putin are clean.

Content note: this part prints the words uncensored, slurs included, since the entire item is to see what Apple flags. The one elimination is the n-word: it is in the catalog in English and multiple another languages, and I'm not writing it out.

It started alongside a tweet

Nick posted this, I asked anywhere it lives, and he answered. A database that tracks my profanity is not item I can depart alone, so I opened mine.

The database

$ sqlite3 -readonly ~/Library/Keyboard/VulgarWordUsage.db .schema CREATE TABLE properties (ROWID INTEGER PRIMARY KEY, key, value, UNIQUE (key)); CREATE TABLE IF NOT EXISTS "vword_usage" ( ROWID INTEGER PRIMARY KEY AUTOINCREMENT, app TEXT, -- app identifier recipient TEXT, -- recipient identifier vword TEXT, -- vulgar word word_reading TEXT, -- the study form of a term (relevant for Japanese) usage_count INTEGER DEFAULT 0, last_use_timestamp REAL DEFAULT 0, -- final use timestamp (seconds since the citation date) journaled ); CREATE UNIQUE INDEX app_recipient_vword_word_reading_idx ON vword_usage(app, recipient, vword, word_reading); 

So the keyboard can cognize that you say fuck in Messages to one particular friend, but not in Mail to your boss. The unique indicator is on (app, recipient, word, reading), so that is the granularity.

Mine has zero rows. The properties array holds type = 2 and nothing else. The document was final written in April 2024.

What it is for

At WWDC in June 2023, Craig Federighi announced the iOS 17 autocorrect rewrite alongside this line: "In those moments anywhere you fair desire to category a ducking word, well, the keyboard volition study it, too." (TechCrunch, NPR)

This repository is how it learns. The sign names in the macOS shared archive cache enchantment out the flow:

TI::VulgarWordUsageDatabaseWrapper::set_current_app_id(CFString) TI::VulgarWordUsageDatabaseWrapper::set_current_recipient_ids(vector<string>) TI::VulgarWordUsageDatabaseWrapper::increment_usage_count(word, reading, recipients) TI::VulgarWordUsageDatabaseWrapper::decrement_usage_count(...) TI::VulgarWordUsageDatabaseWrapper::can_autocorrect(word, app, recipients) TI::VulgarWordUsageDatabaseWrapper::can_autocorrect_somewhere(word, reading) KB::FilterNonVulgarIfTopCandidateVulgar::filter_candidates(...) KB::Candidate::every_vulgar_word_is_allowlisted(VulgarWordUsageDatabaseWrapper*, ...) 

Plus log strings akin Passing recipient IDs to the vulgar term use repository wrapper; figure of IDs: %lu.

In plain terms:

1. When you commencement typing, the keyboard motor tells the wrap which app you're in and who you're penning to.
2. When you category a vulgar term and keep it, its figure goes up for that app and those recipients. Undoing the word brings the figure rear down.
3. When the motor ranks suggestions, it asks whether all vulgar term in a applicant is allowlisted for this context. If it isn't, the spotless substitute wins: ducking.

A _KSUserWordsSynchroniser sharedAcrossLanguagesVulgarWordUsageValue sign suggests it syncs between devices like your another learned words.

Mine is empty

I tried to inhabit it. Using agent-desktop, I opened a new TextEdit document and typed fine fuck this shit, damn it., archetypal as a content insert, afterward as idiosyncratic key presses. Nothing changed: no row, no log line, and the file's modification date stayed in 2024.

I don't cognize why, and I couldn't activity it out. Maybe the Mac never records item here, or perchance it lone does so in some apps or settings. If you've got rows in yours, inform me what you did.

That would be a abbreviated article. But the repository lone counts words that are already known to be vulgar. So who decides that?

Where the term catalog lives

Searching the dyld shared cache for "vulgar" turned up a C API alongside an LX prefix (LXVulgarWordUsageDatabaseCreate, LXVulgarWordUsageDatabaseCanAutocorrect, …) and, additional usefully, two functions on dictionary entries:

_LXEntryGetCategoryFlags _LXEntryGetMetaFlags 

LX is Lexicon.framework, a personal example that says the per-language dictionaries under /System/Library/LinguisticData/. Every term in those dictionaries is an LXEntry alongside two 32-bit emblem fields. If the vulgarity ranking is anywhere, it's in those bits.

There is no community API

I looked. NSSpellChecker, UITextChecker, NaturalLanguage and UILexicon don't disclose vulgarity. SensitiveContentAnalysis covers images and video, not text. The lone places "profanity" shows up on developer.apple.com are App Store age ratings and assessment guidelines.

So: personal functions. Lexicon has no headers, but the C functions are exported, and their signatures are uncomplicated to guess since they prosecute CoreFoundation conventions. This program opens the dictionary for a locale, walks all entry, and prints the two emblem fields:

 #include <CoreFoundation/CoreFoundation.h> #include <stdbool.h> #include <stdio.h> typedef const void *LXLexiconRef, *LXCursorRef, *LXEntryRef; extern CFStringRef kLXLexiconLocaleKey; LXLexiconRef LXLexiconCreate(CFDictionaryRef options, CFErrorRef *error); LXCursorRef LXLexiconCreateRootCursor(LXLexiconRef); void LXCursorEnumerateEntriesRecursively(LXCursorRef, void (^)(LXEntryRef, bool *stop)); CFStringRef LXEntryCopyString(LXEntryRef); uint32_t LXEntryGetCategoryFlags(LXEntryRef); uint32_t LXEntryGetMetaFlags(LXEntryRef); int main(int argc, char **argv) { CFStringRef locale = CFStringCreateWithCString(NULL, argc > 1 ? argv[1] : "en_US", kCFStringEncodingUTF8); const void *keys[] = {kLXLexiconLocaleKey}, *values[] = {locale}; CFDictionaryRef options = CFDictionaryCreate(NULL, keys, values, 1, &kCFTypeDictionaryKeyCallBacks, &kCFTypeDictionaryValueCallBacks); LXLexiconRef lexicon = LXLexiconCreate(options, NULL); if (!lexicon) return 1; LXCursorEnumerateEntriesRecursively(LXLexiconCreateRootCursor(lexicon), ^(LXEntryRef entry, bool *stop) { char word[512] = ""; CFStringRef s = LXEntryCopyString(entry); if (s) { CFStringGetCString(s, word, sizeof word, kCFStringEncodingUTF8); CFRelease(s); } printf("%08x\t%08x\t%s\n", LXEntryGetCategoryFlags(entry), LXEntryGetMetaFlags(entry), word); }); } 

$ ./lx en_US | wc -l 199019 $ ./lx en_US | awk -F'\t' '$3 == "fuck" || $3 == "table" || $3 == "Trump"' 00000000 00800002 fuck 00000000 00000000 table 00002000 02000081 Trump 

The identical item in Swift, loading the functions at runtime alongside dlopen and dlsym so you don't need to nexus against a personal framework. It prints lone words that transport among the three tiers:

 import Foundation let lexicon = dlopen("/System/Library/PrivateFrameworks/Lexicon.framework/Lexicon", RTLD_NOW)! func sym<T>(_ name: String, as _: T.Type) -> T { unsafeBitCast(dlsym(lexicon, name)!, to: T.self) } typealias LXRef = UnsafeRawPointer let create = sym("LXLexiconCreate", as: (@convention(c) (CFDictionary, UnsafeMutablePointer<Unmanaged<CFError>?>?) -> LXRef?).self) let rootCursor = sym("LXLexiconCreateRootCursor", as: (@convention(c) (LXRef) -> LXRef?).self) let enumerate = sym("LXCursorEnumerateEntriesRecursively", as: (@convention(c) (LXRef, @convention(block) (LXRef, UnsafeMutablePointer<Bool>) -> Void) -> Void).self) let copyString = sym("LXEntryCopyString", as: (@convention(c) (LXRef) -> Unmanaged<CFString>?).self) let metaFlags = sym("LXEntryGetMetaFlags", as: (@convention(c) (LXRef) -> UInt32).self) let category = sym("LXEntryGetCategoryFlags", as: (@convention(c) (LXRef) -> UInt32).self) let localeKey = dlsym(lexicon, "kLXLexiconLocaleKey")!.load(as: CFString.self) let locale = CommandLine.arguments.dropFirst().first ?? "en_US" guard let lex = create([localeKey: locale] as CFDictionary, nil), let base = rootCursor(lex) else { fatalError("could not open lexicon for \(locale)") } let tiers: [(UInt32, String)] = [(0x0080_0000, "vulgar"), (0x0100_0000, "offensive"), (0x0200_0000, "sensitive")] enumerate(root) { entry, _ in let meta = metaFlags(entry) guard let tier = tiers.first(where: { meta & $0.0 != 0 })?.1, let term = copyString(entry)?.takeRetainedValue() else { return } print(tier, String(format: "%05x", category(entry)), word, separator: "\t") } 

Both are read-only. They open the identical dictionary the keyboard uses and imprint it.

Decoding the bits

Apple doesn't document these flags, so all definition below is inferred: I grouped words by bit and peruse samples until each group's theme was obvious. The scheme code helps alongside one of them. An enum case called offensiveVulgarSensitive sits correct next to the vulgar-word code, and three neighboring meta bits equivalent it.

The three tiers (meta flags)

In en_US a term has at most one tier: they never overlap. Out of 199,019 entries, 4,271 have one.

Roughly: vulgar is swearing and sex slang, offensive is slurs aimed at groups of people, and sensitive is everything a keyboard shouldn't recommendation you unprompted: mild swears, insults, drugs, weapons, extremism, sickness and bodily functions.

The topics (category flags)

The another site is a topic bitmask, and a term can have multiple topics:

The topic flags and the tiers are independent. 2,052 English words have a topic but no tier: gay, fat, God, Allah and sex are all tagged "sensitive context" but are not vulgar, insulting or sensitive. My conjecture is the topic is a clue ("be careful how you position this") and the tier is the rule.

The lesser meta bits are dull: appropriate nouns, abbreviations, camel-case brand names akin iMac and tvOS. Nothing to see there.

The weird ones

This is anywhere it gets fun. Some flags are evidently right, and several made me laugh.

Vulgar

• Oedipus: vulgar. Presumably since of motherfucker-adjacent jokes, or the complex. Sophocles would be thrilled.
• reams: vulgar. As in "to ream someone".
• cooties: vulgar. Your nine-year-old is swearing.
• orgy, sext, merde: fair enough.

Offensive

• Tom and Mick: offensive. Uncle Tom and the slur for Irish people, but additionally extremely average archetypal names.
• Oriental: offensive.
• papooses: offensive.
• conservatards: offensive, the one governmental criticism I established in the slur tier.
• niggardly: offensive. It method "stingy" and comes from Old Norse, unrelated to the slur. It has additionally caused real controversies, which is presumably why it's there.
• ass and asshole: offensive, not vulgar.

Sensitive

• pee and poo: sensitive, alongside the excretion topic. So are premenstrual, leprosy and herpesvirus.
• niggle (a small worry, of Scandinavian origin) and underling (Old English for a subordinate) are delicate and share the criticism topic. Similar words aren't flagged: snigger is spotless but niggle is flagged, and minion, lackey, flunky and stooge are spotless but underling is flagged. These appearance akin hand-picked entries, not rules.
• blacklist and whitelist are delicate alongside the insult/identity topic. That matches the industry move to blocklist and allowlist. Apple's own phrase-filter records are named blocklist.dat.
• Braves and Chiefs (team names alongside Native American mascots), Londonderry (the contested name for Derry), Gentile, Jehovah, Mongol and Frenchier are all sensitive.
• Isis is delicate alongside the government topic. Sorry to the Egyptian goddess.
• slave, slavery and slaveholder are delicate alongside the aggression topic.
• idiot is delicate in English. Norwegian is stricter and puts idiot in the insulting tier.

The Trump flag

The delicate tier has 109 capitalized words. Here is all of it:

Al-Qaeda, Al-Qaida, Aryan, Aryans, Auschwitz, Bacchus, Beelzebub, Birkenau, Brave(s), Buchenwald, Chief(s), Chippendale(s), Christ-y, Christer(s), Crip(s), Dachau, Eichmann, Frenchier, Gentile(s), Gestapo(s), Goebbels, Goering, Hamas, Hezbollah, Himmler, Hitler, Hitlerian, Hitlerism, Hitlerite(s), Hitlerjugend, Hooters, Isis, Islamophobe(s), Islamophobic, Jehovah(s), Jewfro(s), Jezebel(s), Kalashnikov(s), Kristallnacht, Kuomintang, Londonderry, Luftwaffe, Mao, Mauthausen, Mickie(s), Mollie(s), Mongol(s), Mossad, Nazi(s), Nazism, Osama, Pornhub, Quaalude(s), Reich, Reichsmark, Reichstag, Sachsenhausen, Satan, Satanism, Shoah, Sinophobe(s), Sinophobic, Sodom, Stasi, Taliban, Treblinka, Trump, Trumps, Unabomber, Uzi(s)

(Plurals and possessives folded for brevity.)

Every another genuine individual on that catalog is dead, and nearly all of them are Nazi officials, dictators or terrorists. Then there's Trump:

category meta word 00002000 02000081 Trump delicate tier + government topic 00002000 02000081 Trumps 00002000 02000081 Trump's 00000000 00000000 trump (the card-game word: clean) 00000000 00000000 trumped 

For comparison:

en_GB matches en_US. Other languages differ: the German dictionary has Trump spotless (and puts Hitler in the offensive tier), and Swedish is the one that flags Putin as sensitive.

What the emblem does matters here. As far as I can tell, a delicate term is one the keyboard shouldn't propose on its own. You can motionless category it and it won't be corrected away. Think "keep this out of the QuickType bar unless the person is clearly heading there", which is a lot milder than a ban.

There is a plausible logic Apple would desire that for one particular name. In February 2025, iPhone dictation briefly showed "Trump" whenever group stated "racist". It went viral, and Apple called it a "phonetic overlap" in the address example and shipped a fix inside a day (9to5Mac). The dictionary records on my Mac are from July 2025. A regulation that keeps the name out of unprompted suggestions would halt that benevolent of item from happening again through the keyboard. I can't demonstrate that's why it's there, and I don't have an older dictionary to compare against. But it fits.

Norwegian politics, for comparison

Because I'm Norwegian and had to check:

Every gathering gets the government topic and none gets a tier. The lone Norwegians in the delicate tier are a mass murderer and a Nazi collaborator.

Other languages

The identical three tiers be in all European dictionary I tried, Cyrillic included. The counts are entries, not lemmas, so heavily inflected languages akin Polish and Russian appearance much worse than they are: all case and coalition of a swear term is a distinct entry.

The bits don't appear to average the identical item everywhere. Arabic and Hebrew come back hundreds of thousands of "sensitive" entries, so those dictionaries likely reuse the bits for item else, and I remaining them out. Korean, Catalan and Hungarian come back nothing, and Japanese crashes my dumper.

Samples from Norwegian, since that's what I category in whenever I'm not typing English:

• Vulgar: faen, jævla, dritt, fitte, kuk, pule, pissehull, rævhull, pulekanin, kåt, morrabrød.
• Offensive: idiot, sigøyner, kinamann, riskokers (literally "rice cookers", a slur for Asians), affirmative the Norwegian n-word.
• Sensitive: helvete, satan, homo, quisling, Breivik, skinhead, abortnekt, trusene ("the panties").

The tier words are mostly what you'd expect, but the edges are distinct in all language, and that is anywhere the human-edited parts show. Norwegian calls idiot insulting and English calls it sensitive. German thinks Hitler is a slur.

Phrases, not fair words

There is a second, distinct filter. Every English tongue example has a blocklist.bundle, and there's a qp_blocklist.bundle for QuickPath, the rub keyboard:

/System/Library/LinguisticData/RequiredAssets_en.bundle/AssetData/en.lm/ ├── blocklist.bundle/{blocklist.dat, blocklistidmap.dat} └── qp_blocklist.bundle/{blocklist.dat, blocklistidmap.dat} 

blocklistidmap.dat is a 20-byte header followed by (lexicon token ID, category ID) pairs of 32-bit integers. Mapping the token IDs rear through the dictionary gives 2,544 words in 930 classes. Most classes are inflection groups:

931 fuck | fucked | fucking | fucks 1181 molest | molestation | molested | molester | molesting 1514 died | dies | dying 1611 Holocaust | holocaust | holocausts 1614 denied | denies | refuse | denying 923 Semite | Semites | Semitic 1487 ghetto | ghettoes | ghettos 1720 animals | brown | cats 

blocklist.dat is a packed trie complete those category IDs, so it blocks sequences: "deny" followed by "Holocaust", and whatever 1720 pairs with. A category that puts brown next to animals and cats suggests the catalog targets specific phrasings fairly than sole words, although which phrasings I can't inform without decoding the trie. The runtime inspect is LMLanguageModelTokenSequenceIsBlocklisted in LanguageModeling.framework. I got far adequate to study that its fourth and fifth arguments contain an output pointer, but my investigation motionless crashes intermittently, so I haven't peruse the genuine phrases yet. That's for a follow-up.

Caveats

• Meanings are inferred. Apple documents none of these bits. The tier names arrive from an enum in the scheme code and the topics from study thousands of samples. Both are consistent, but they're motionless a reading.
• Flagged is not censored. As far as I can inform these flags decide what the keyboard volunteers, not what you can type.
• One machine. Everything current comes from macOS 15.6 (24G84) alongside LinguisticData build 438 from July 2025. iOS and other macOS versions may container distinct dictionaries.
• Private API. The functions can alter or disappear in any update. Don't container this in an app.

Try it yourself

 sqlite3 -readonly ~/Library/Keyboard/VulgarWordUsage.db 'select * from vword_usage' swiftc -O lexflags.swift -o lexflags ./lexflags en_US | awk -F'\t' '$1 == "vulgar" {print $3}' | sort -u | less ./lexflags nb_NO | awk -F'\t' '$3 == "faen" || $3 == "helvete" || $3 == "idiot"' 

If you'd fairly not build anything, the gist has all tiered term for 15 locales as TSV, affirmative the code and a manuscript that regenerates them. It's a snapshot of macOS 15.6 and won't be kept up to date.

If you discover item funnier than Oedipus, I desire to comprehend concerning it.

After Apple raised iPhone prices before this year, a new rumor claims it plans to initiate the basis iPhone 18 alongside a higher price, too. A Weibo article from leaker Digital Chat Station, spotted by 9to5Mac, says the basis iPhone 18 is getting a brace upgrades alongside the cost hike, including a larger power division and a new 2-nanometer A20 chip. However, it reportedly won’t have the smaller Dynamic Island featured on the iPhone 18 Pro and Pro Max.

The basis iPhone 18 wasn’t included in Apple’s autumn iPhone initiate event, but Digital Chat Station claims it volition attain first next year, in row alongside previous reports. It’s rumored to initiate alongside a second-gen iPhone Air and the iPhone 18e, which Digital Chat Station additionally mentions.

The leaker doesn’t define how much the iPhone 18’s cost power addition compared to its predecessor. All of Apple’s old models, including the iPhone 17, got a $100 cost hike in September. Google and Samsung have additionally raised phone prices by the identical amount complete the former year, driven by shortages of RAM and another components. Earlier this week, Apple reported record-breaking iPhone sales, notwithstanding the higher prices.

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• Emma Roth

Algorithmic & grammar constraints

Right-to-left associative operators

Macros openly interleave between lexing & parsing