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Strong and Unique Passwords Really Matter? Denmark's Case

Thu, 15 Oct 2026 00:00:00 +0000

Why Every Password Needs to Be Unique and Complex (Denmark Just Proved It)

Last Friday I saw a headline that made me spit out my coffee. Records of 8.8 million people in Denmark’s national register were exposed, reportedly because a tiny company used the password 123456.

The company, Pays ApS in Odense, had just two employees and legal access to the CPR register, Denmark’s national ID system. The attacker reportedly used a former employee’s password from an older leak, and it worked on three accounts, including admin. Around 14 million searches later, someone noticed. Not because of security, but because of an unusually large bill.

I laughed. Then I felt uncomfortable, because the lesson isn’t “Denmark was careless”. That password was weak and it was reused, and most of us are guilty of at least one of those.

Two different mistakes

People mix these up, so let me separate them.

Complex means how hard a password is to guess. 123456 fails instantly. It’s one of the first things any attacker tries.

Unique means you use it on one account only. A password can be brilliant and still fail this test.

Denmark failed both. And you need to pass both, because each protects you from a different kind of attack.

Why complex matters

Guessing attacks are automated. Software tries the most common passwords first, then common patterns, then everything else, very fast. Short or predictable ones like 123456, password1 or yourname2026 fall almost immediately.

What helps most is length, not clever symbols. A long passphrase is harder to crack than a short one stuffed with symbols, and much easier to remember. Avoid famous quotes or anything a stranger could guess from your social media.

Why unique matters even more

Here is where most people stop. They make one strong password, feel safe, and use it everywhere.

Let’s say MyD0g$IsCute#2026!. Very strong. But you used it on ten sites, including some random wallpaper site you joined in 2011 that stores passwords badly. Some kid hacks it for fun. Now he has your strong password, and your bank too.

I learned this the hard way. In 2013, Adobe leaked 153 million accounts, and 1.9 million of them used 123456. I remember thinking: who does that? Then I checked myself and found I was using basically myname123 on Yahoo Messenger, Gmail, old forums and a throwaway email. A friend warned me to change my passwords. I changed one, the forum one, and left Gmail alone because it felt safe.

One week later I got a security alert: someone had tried to log into my Gmail from another city. I spent the whole night on recovery forms. Another friend, who works in IT, looked at the mess and said: one leak is all it takes.

That’s not just my story. In 2019, the Collection #1 to #5 dumps put billions of emails and passwords from hundreds of small breaches into circulation. Lists like that get fed into login pages on autopilot. Microsoft later said that when it scanned its own accounts in early 2019 against billions of leaked credentials, it found 44 million accounts using already-leaked passwords.

Denmark was the same story, just compressed. Nobody cracked anything. Someone took a password that had already leaked and typed it into a new door. In security this is called credential stuffing, and a script does it while the attacker sleeps.

Think of keys. You can have an expensive deadbolt on your front door. But if you copy that same key for your gym locker and your bike lock, your house is only as safe as the gym locker.

So what do I do now?

Reuse isn’t laziness. Most of us have dozens of accounts, and the brain takes the shortcut. So the answer can’t be “try harder to remember”. It has to be “stop remembering”.

A password manager. I use Bitwarden, which is free. Your browser’s built-in one is also fine, as long as it isn’t autofilling 123456. I remember exactly one password, a long sentence-style one: first-pc-was-beige-and-loud-as-a-jet. That old computer really did sound like it was taking off. Everything else is random, like xT9$kL..., generated and filled in for me. That gives me complex and unique without any effort.

2FA on what matters. Gmail, bank, Instagram. With 2FA, a leaked password alone is much harder to use, because the attacker needs a second factor too. An authenticator app is best, but even SMS is better than nothing.

Check yourself. Go to https://haveibeenpwned.com and enter your old email. Mine showed up in six breaches. If yours shows up too, don’t panic. Start with email, because it can reset everything else, then your bank, then anything with a saved card. The rest can wait a week.

One key for one lock, and make the key long. That’s it.

Super Intelligence or Artificial intelligence? Why the label changes nothing

Thu, 15 Oct 2026 00:00:00 +0000

Super CEO? Come on

I keep seeing this phrase everywhere now — Super Intelligence. SI. As if someone in Washington decided to just rebrand AI overnight and we all have to play along.

Which, apparently, actually happened. On September 29th, Trump signed that order — “Inaugurating the Era of Super Intelligence” — and told every federal agency to stop using the word Artificial Intelligence. No more AI. Now it’s SI. ai.gov is becoming si.gov. The official reasoning was that “artificial” sounds fake, like it’s counterfeit, and this stuff is real and powerful, so we should call it super.

I read that and just thought — this is exactly how a bad company promotes people. Like, textbook bad management.

Think about it. You hire this kid. Brilliant kid. Memory like a sponge, works 24 hours straight, never complains, can chew through a 100-page spreadsheet before you finish your coffee. He never takes a sick day. Absolute beast of an employee, everyone loves him. Do you make him the CEO? Do you hand him the keys to the building and say “you’re the super boss now, we’re all reporting to you”?

No. You don’t. You say thank you, great job, now do the next task. You keep him in his role, because he is still working inside YOUR office. Using YOUR computers. Following YOUR budget and YOUR guidelines you wrote three years ago. He’s maximizing your system. He didn’t invent a new company out of thin air.

That’s what we’re doing with this SI thing. We’re taking our own tool and crowning it as our superior. And it’s weird.

I work with these models almost every day for work, and let me tell you what they actually are when you strip away the marketing. They don’t wake up with ideas. They don’t sit and stare out the window and go “hmm, what if E=mc3?” They don’t get bored and decide to invent a new field of physics for fun. They are trained on… well, us. Billions of pages of us. Our books, our art, our code, our homework, our dumb arguments on Reddit from 2011, our Wikipedia edits. When it writes you a beautiful essay, you’re not seeing a super genius at work. You’re seeing a really, really polished, incredibly fast mirror of us. It’s predicting the next most likely word based on patterns that we created over centuries of arguing and writing and building.

So in that company analogy that I keep coming back to — we are still the founders. The humans. We set the goal, we provide the data, we built the hardware in some freezing data center in Arizona, and we maintain the ultimate authority to pull the plug when it starts saying nonsense. The machine is the employee. A stupidly fast, incredibly useful employee who never sleeps, but an employee nonetheless.

My calculator can do math a million times faster than me. I never once thought my calculator was smarter than me, or that I should work for it. It’s a tool. It needs me to tell it what to calculate. Same logic.

Now, the pushback I always get here — and I get it, I really do — is the intelligence explosion thing. The Bostrom crowd, the whole Silicon Valley futurist circle. The theory goes like this: once AI gets as smart as a pretty good human computer scientist, it will be given the task of improving its own code. And because it doesn’t need sleep or coffee breaks, it could theoretically redesign itself over and over, thousands of generations of improvement in a matter of days or weeks. Each version smarter than the last. Boom, real superintelligence that leaves us in the dust.

And they point to stuff we see now that is a little spooky. Like when a model solves a coding bug using a pathway that its own creators can’t fully explain. The famous black box problem. The developers look at each other and go “we don’t really know how it did that.” So they say, see? It’s already beyond us.

Honestly? Even if that happens, even if it rewrites itself a thousand times in a week, it doesn’t solve the core paradox. Because even if it rewrites itself a thousand times, where did it start? In our sandbox. Using our math. Our logic. Our data structures. Our Python libraries. If it discovers a new law of physics tomorrow, great, amazing, but it discovered it using instruments and concepts that we discovered first and fed into its memory banks. It have access to telescopes we built, data we collected.

It’s finding connections between dots at a scale I can’t even comprehend, sure. I will give it that. But we drew the dots in the first place. We drew them and numbered them.

What it doesn’t have — and this is the part people skip when they get excited about SI — is three things. First, intent. It doesn’t want anything. It’s programmed to reduce an error number to zero. It doesn’t wake up and think “today I want to cure cancer.” It is told to. Second, consciousness. It doesn’t feel the joy of solving something after struggling for three days. It doesn’t feel the weight of making a wrong call. And third, creativity out of nothing. Human breakthroughs are messy. They come from irrational leaps, from heartbreak, from a mistake in the lab, from seeing something random on the train. A machine operates strictly within its mathematical parameters, no matter how wide we make them.

So until a piece of software wakes up on its own, decides it wants to start its own company, feels something like embarrassment or pride, and creates something that has absolutely zero connection to any human data — which, let’s be real, has not happened and is nowhere close to happening — it is not the boss. It can’t be. It can’t outrank the hierarchy that created it.

You can change the title on a government website all you want. You can call it Super Intelligence, Ultra Intelligence, Whatever-Intelligence. You can make all the executive orders you want. That doesn’t change who built who.

We built it. It’s the best, most transformative employee we’ve ever had in human history. It’s going to change everything about how we work.

But we are still the ones who need to stay in the corner office. And we should probably remember that before we hand over the keys.

From 'Boot Disk Not Found' to the Green Hills of XP: Growing Up in India's Assembled PCs and Early 2000s Life (2000-2005)

Mon, 12 Oct 2026 00:00:00 +0000

From ‘Boot Disk Not Found’ to the Green Hills of XP: Growing Up in India’s Assembled PCs and Early 2000s Life (2000-2005)

If you walked into an Indian school computer lab between 2000 and 2005, the dates in foreign computer magazines made no sense.

Globally Windows XP was already out. Big launch, new design.

Here, nobody cared. We were happily one step behind. And honestly, it was better that way.

The grey box that ruled everything

Nobody bought branded PC. IBM, Compaq — too costly. Only big offices had it.

We all had “assembled” PC. You went to Nehru Place in Delhi, or Lamington Road in Mumbai, or SP Road in Bangalore. Small shop, dust, open boxes everywhere.

You say “Bhaiya 25k ka PC banao”. He picks parts.

Pentium 2, maybe 3 if you are rich. 32MB RAM. 64MB if your father agrees. 20GB hard disk that makes sound like small tractor. And that heavy CRT monitor. Everyone covered it with plastic cover with flowers on it when not in use.

These PCs were weak. So new Windows would not run. That’s why old Windows stayed so long. It was not backwardness. It was practical. We used what worked.

2000 to 2002: Windows 95 and NT just wouldn’t die

Globally Windows 95 was dead by 2000. In India? It was king.

My school typing class — all PCs on Windows 95. Light, rarely crashed. Teacher would switch on and that old startup sound would come.

And in offices, colleges — Windows NT. No colors. Just plain grey. Boring to look. But it could run 4 days non-stop with data entry. So they loved it. I saw NT in my uncle’s govt office in 2002 and I thought this is what serious computers look like.

And we fully skipped Windows ME. ME came in 2000 and was a total disaster. Blue screen daily. Because we were late, we never installed it. Being one step behind saved us.

Then 2002 changed everything.

2002 to 2004: Cyber cafes and Windows 98 SE — the real early 2000s life started

Every gully had a cyber cafe suddenly. One board outside: “Internet - 20 Rs per hour”. Inside 3-4 PCs with curtains between them.

And all ran Windows 98 Second Edition.

Why 98 SE? Perfect middle. It could handle USB pen drive. That was miracle. Before that only floppy — 1.44 MB and always corrupt. Suddenly 64MB pen drive came and we thought we are in future.

And games. If you went to cafe in 2003, you were not browsing. You were playing.

The starting list:

Dangerous Dave. Small guy jumping over fire. Road Rash. Hit other biker with chain. Virtua Cop 2. Two friends, one mouse, full shouting.

But then two kings that ate all our pocket money.

First — FIFA. We played all mixed — FIFA 98: Road to World Cup, FIFA 99 and FIFA 2000. FIFA 99 was most famous in India. Indoor stadium. You could push goalkeeper. And that intro song — Fatboy Slim — “right about now, funk soul brother”. That meant FIFA is starting.

Controls same — arrow keys, D for shoot, S for pass. In cafe we played 1 vs 1, 5 rupees per match. Whole cafe watching behind you.

Second — Need For Speed 2 SE. NFS 2 Special Edition. First real 3D racing we saw on assembled PC. Ferrari, McLaren. Last track with dinosaur and jungle. We didn’t race properly. We just pressed C to change camera view and crashed for fun. Two players on one keyboard — one with arrows, one with A, W, S, D. Keyboard would shake.

If Road Rash was for 10 minutes, FIFA and NFS 2 were for 2 hours. You went for “one hour internet” and played FIFA for 3 hours and forgot to check email.

The sound of that time — first Windows 98 chime, then dial-up modem. Krrr… trrrrr… pishhhh… You prayed phone line doesn’t cut. When it connected, whole cafe cheered. One image took 10 minutes to load. And we were happy.

After the modem connected — the supporting memories

Okay modem done. What did we do? We didn’t open Google. We opened chat.

Yahoo! Messenger was king. Yellow icon. And BUZZ button. You press BUZZ and whole window shakes. In silent cafe everyone looked.

Then MSN Messenger. For cool kids. MSN had NUDGE. Louder than buzz. And winks — big smiley bouncing.

If you were slightly techy, you had ICQ. Uh-oh sound. Flower logo. Random ID — 34289112. People showed off low-number ID like car number plate.

And real hackers were on mIRC. Black screen, green text. Just #delhi #india #teenchat. You typed /join #chat. We went there and just said “asl pls?” — age, sex, location. That was full starter in 2003.

Around 2004 Google became homepage. Before we used Yahoo search.

And then late 2004 — Orkut came. And it killed everything. Scraps, testimonials, communities like “I Love India” with 500k members. You waited full day for someone to write “hi thx for add, scrap back”. And you begged friends “Pls write testimonial yaar”. That was blue tick in 2005.

Winamp — the light show

Every PC had Winamp. Brown player with green display. Nobody just listened. We watched it.

You play Atif Aslam or Linkin Park and press Visualization. Full screen dancing lights. We called it “lighting”. We changed skins like clothes — Alien one day, Metallica next day. And .m3u playlist — “My Fav 50 Songs” — saved in pen drive. That was our Spotify.

And that voice — “Winamp, it really whips the llama’s ass”. We waited for it every time.

123greetings.com — 2 hours for one card

Today you send WhatsApp sticker in 2 seconds. Back then you sent e-card. And it took 2 hours.

Site full of glitter. Every card had music that hanged PC. You click Birthday -> Friend -> Animated. 100 cards. Each preview took 1 minute with Flash.

Then you type: “Happy B’day dear frnd, stay blessed, have a gr8 year ahead”. Choose music — “Baar Baar Din Ye Aaye”. Enter friend’s email. Click send. Watch ad. Total 2 hours gone for one card. And we were proud.

These were supporting memories. Main memory was Windows itself.

How we installed Windows — the main story

Today you click Update. Done.

Back then? Installing Windows was a full-day job. A surgery.

Windows 95: The floppy and CD dance

Most old motherboards could not boot from CD. So you needed two things. One boot floppy. One Windows CD.

Put floppy in. Loud click-whirrr. Black screen. Only A: blinking. No mouse. You typed commands.

Floppy loads CD drivers. Otherwise PC didn’t know CD drive exists.

Once it shows D: drive, you remove floppy, put heavy Windows 95 jewel-case CD, type D:\SETUP and pray.

Worst case — no CD drive — Windows 95 came on 13 to 26 floppies. You sat 3 hours. Insert Disk 1… wait. Disk 2… wait. On Disk 18 it says “Read Error”. All waste. Start again. Many people cried here.

Windows 98: Slightly better but still headache

By 2002, new boards could boot from CD directly. Set in BIOS. Big relief.

But most Indian PCs still had old parts, second-hand parts. So that boot floppy — you still kept it in drawer. Label written by marker: “98 Boot Disk - Don’t Touch”.

Before install, 98 ran ScanDisk. Blue screen, checking 8GB or 20GB disk block by block. So slow. You go make chai, come back, still 40%.

And product key — 25 characters. Everyone wrote it directly on CD with permanent marker. Lose paper, CD useless.

Windows XP: Finally the green hills

When XP came to normal people around 2004-2005, install changed completely.

No more floppies. Single CD, bootable.

You boot it, solid blue screen with grey text and yellow bar at bottom. Looked scary but automatic. It asked to format. We saw NTFS first time here. Before we used FAT32.

Then after text part, colorful install. It said “39 minutes remaining”. That was lie. Always lie. Took 1.5 to 2 hours. But good thing — you could walk away. No disk swapping. You could go play outside.

While installing it showed slides: “Windows XP is faster… more secure…” We read it because nothing else to do.

And when it finished first time — that Bliss wallpaper. Green hill, blue sky. On same CRT monitor where we saw grey Windows 98 for 3 years, suddenly this hill came. It felt like future finally arrived in India, even if world had it 3 years ago.

Why this made us engineers

Because installing was so hard, every colony had one 17-year-old expert. PC broke, you call him. He knew BIOS, fdisk, partitioning.

He came with small bag — 2-3 floppies, one CD wallet, one screwdriver. That was his toolkit.

Living through that — from ‘Boot Disk Not Found’ error to the green hills of XP, from juggling 26 floppies to watching XP install by itself — that was our early 2000s life.

The FIFA shouts, the NFS 2 crashes, the Yahoo BUZZ, the Orkut scrap, the Winamp lighting, the 123greetings glitter — they were just supporting actors. Main actor was Windows.

Now my laptop updates at night while I sleep. I don’t even notice. Sometimes I miss that krrr-trrr-pishhh sound and tension of Disk 18 failing. It was annoying then.

But now I know — from Boot Disk to Bliss — that struggle taught a whole generation in India to actually understand machines, not just click them.

How to Remember Numbers: The Relative-Math Method

Sun, 11 Oct 2026 00:00:00 +0000

Stop Memorizing, Start Decoding

Someone reads out a six-digit code and you nod, repeat it once, maybe twice, then look away to type it and it’s just gone. So you ask again. Most of us have done this more times than we’d admit.

Curious about this? We also covered “Why the Image in Your Eye Is Upside Down (And How Your Brain Fixes It)” here: https://wjhnow.com/p/why-the-image-in-your-eye-is-upside-down/

Here’s the odd part, though. Years ago, while travelling, I saw the number 304818 for less than five seconds. I never wrote it down, never tried to remember it, and it’s still sitting in my head. A code you repeated ten times a minute ago is already lost, and this one stayed for years. The only difference I can find is that the brain didn’t store six digits. It caught a relation in a glance, 30, then 48 and 18 both ending in 8, bigger one first, and a relation is what stays. That’s the whole idea behind what I call the Relative-Math Method: stop trying to hold digits, and look for the small rule already hiding inside the number.

Why long numbers slip away

It isn’t really a flaw in you. In 1956 the psychologist George Miller published “The Magical Number Seven, Plus or Minus Two,” and his point was that working memory holds about seven items, give or take two. Newer research says it may be closer to four. Either way, a six-digit code is too much if every digit counts as its own item, which is exactly how we treat it when we repeat it like a parrot. Computers store raw strings, we don’t. We remember through stories, pictures and relationships, and a number with no relationship just gets thrown away. So the fix is to give the number one, and hold a starting anchor plus one simple rule instead of six digits.

One thing before the examples, because it trips people up: the order of the pieces isn’t fixed. Sometimes the bigger chunk first is easier to hold, sometimes the smaller, and you decide that per number. Then you put the choice inside your phrase so it travels with the number.

Finding the relation

Take 304818 again. Reading it as one long string is where people lose the order, so split it into pairs: 30 - 48 - 18. The last two pairs both end in 8 (48 and 18), so all you need is 30 and the “rule of 8s.” Then pick the order. 48 is bigger than 18, so the bigger one goes first, and that stops you flipping it into 30-18-48. In your head it’s just: starts with 30, ends in 8s, biggest first.

Longer numbers feel scarier but sometimes collapse faster. 5025750 is seven digits, and it starts with 50, which is clean and easy to hold. Half of 50 is 25. Multiply that 25 by 3 and you get 75. Then a 0 at the end, matching the 0 at the start. Start at 50, half, times 3, ends in 0.

Some numbers hand you a frame for free. 401224 has a 4 at the front and a 4 at the back, so you’ve got 4 [ ] 4 before doing anything. Inside, a clean sequence starts, 0, 1, 2, and to get from that 2 to the closing 4 you just double it, 2 times 2, so the 2 repeats. You end up with 4-0122-4. Front and back are 4, 0-1-2 inside, double the 2.

3471 is a different kind of number, because I’d look at the big picture first. Three of the digits (3, 7 and 1) are odd, which sets the expectation before any math. Then 3 and 4 are neighbours in counting, so 34 is the anchor, and adding them (3 + 4 = 7) gives you the third digit. To match the odd theme, finish with the very first odd number there is, 1. Three odds, start 3-4, add to get 7, finish with 1.

With 631892 the dominoes fall one after the other. Start with 63 and multiply, 6 × 3, and you get 18. Add the digits of that 18, 8 + 1, and you get 9. Then the lowest remaining even digit, 2, finishes it off.

24816 starts with what the eye spots first, the even-number pattern 2, 4, 6, 8, except the order is shifted. The 8 jumps ahead of the 6, so the baseline is 2-4-8, and the 6 isn’t alone, it’s the result of doubling the 8, which turns the ending into 16. So: 2-4-8, then double the 8 to get 16.

And some numbers are easier from the back. With 725350 I’d scan the end first, where 50 is a clean anchor and most of the digits are odd (7, 5, 3, 5). Just before the 50 sits 53, which you can lock by noting it’s the 2nd odd number after 50 (skipping 51). Now the back end is locked, [ ][ ][ ] 5 3 5 0. Moving backwards, subtract the digits of 53 (5 - 3) and you get 2, leaving one open slot at the front, [ ] 2 5 3 5 0. Add that 2 to the next digit (5) and you get the very first digit, 7 (2 + 5 = 7). It looks long on paper, but in your head it takes about two seconds: lock 50, put 53 before it, subtract to get 2, add 2 and 5 for the leading 7.

When the number fights back

Not every number cooperates, of course. An online challenge once threw 591638 at this method, a number built on purpose to break it. No runs, no rhythm, no landmark numbers like 50 or 25 or 22, three odd digits (5, 9, 1) against three even ones (6, 3, 8) so no Majority Rule either, and every obvious equation hits a wall. 5 + 9 is 14, not 1. 1 × 6 is 6, but then 6 × 3 doesn’t give 8. The fallback would be memorising it rigidly as 59 - 16 - 38, which is exactly what we’re trying to avoid.

What worked, eventually, was ignoring the middle and keeping only the first digit and the last, 5 and 8. That leaves four digits inside, 9-1 and 6-3. 9 + 1 is 10, and half of 6 is 3. So the number is 5, then the ten-pair, then the half-pair, then 8: 5 - 91 - 63 - 8.

Whether that counts as a win is arguable, honestly. With six digits there are a lot of possible relations, and you can usually find one after the fact. What matters more is that the phrase carries the order too, because without it 519638 and 563918 are close enough to fool a tired brain. So the phrase is 5, ten-pair big first, half-pair, 8. If one rule fails, try the pairs, try the ends, try a different order. A number with nothing at all is very rare, and the more you focus, the more expert you become. It’s like training your brain.

Why bother slowing down

All of this takes an extra second or two, and I think that’s the right trade. Slowing down for long-term memory is much better than a faster approach that only works for short-term memory. Parrot repetition is quick and it’s gone in half a minute. Finding the rule takes a moment, but recall afterwards is almost instant, and you don’t doubt yourself because the logic proves the sequence is right. You’re storing meaning instead of sound, so it sticks.

The famous memory-champion systems, the Major System or the Dominic System, work differently, turning every digit into a sound or a celebrity (1 = T, 2 = N, 3 = M, and so on). They’re brilliant, but you have to learn a whole new alphabet just to remember a grocery PIN. This method only uses what you learned in school, counting, adding, multiplying, odd and even, so there’s nothing to set up and you can use it when you’re busy. It has no strict rules either. If a pattern doesn’t fit, try another. Some numbers like a frame, some like a chain, some like big first, some like small first, and you just use whatever you spot first.

You don’t need to practice properly, either. License plates in traffic, a receipt total like 42.84 (42 doubled is 84), a clock showing 11:22 (a doubling) or 03:09 (a squaring), it’s all a dumb little game while you’re waiting for something. After a couple of weeks your eye starts doing it without being asked.

So try it now. Look away from this page and say 304818 back to yourself, not as digits but as the rule: starts with 30, ends in 8s, biggest first. Then 591638: 5, ten-pair big first, half-pair, 8. Next time someone reads you a code, take two seconds, find one small link, any link, even a silly one, choose your order, and let it hold the number.

The ideas, examples and solutions in this post are all mine. I used AI to help with drafting and editing the wording.

Did AI Really Invent a Language Humans Couldn't Understand?

Fri, 09 Oct 2026 00:00:00 +0000

Did AI Really Use a Language Humans Couldn’t Understand? The Truth About Facebook’s Chatbots.

When I first read online that two AI chatbots had started talking to each other in a language humans couldn’t understand, I stopped scrolling. It felt like a scene straight out of a movie.

You know the headlines. “AI creates its own secret language.” “Facebook pulls the plug on rogue robots.”

Rela)ted read: “While You Dread Monday, AI Worked 88 Hours Straight” — https://wjhnow.com/p/openai-navier-stokes-88-hours-ai-secret-language/

For a minute, I actually felt a chill. Like, are we watching the first chapter of something we won’t be able to undo?

But then I did what I usually don’t do. I didn’t just share the headline. I went and read what actually happened. And the more I dug, the less dramatic it got. More interesting, though.

It wasn’t a new language. It wasn’t a plot. It was something much simpler. And honestly, it says more about us than about the machines.

I’ll tell you how it reached me. It was one of those late-night scrolls. A short post, maybe 4 lines: “Two AIs invent language humans can’t understand. Creators shut them down.” That line stayed with me long after I locked my phone. If they can do that on their own, what else are they doing that we don’t see? That question kept bugging me. So next morning, instead of reading more news pages, I opened the actual FAIR paper. That’s where everything changed.

The Day the Internet Panicked

This goes back to 2017. Researchers at Facebook’s AI lab — FAIR — were training two chatbots to negotiate.

Simple task, really. They had a pile of stuff: books, hats, balls. Each item had a different value for each bot. If a hat was worth a lot to Bot A, it might be worth nothing to Bot B. So they had to bargain. Get the best deal you can.

At first they spoke normal English. Because that’s all they knew. They were trained on human conversations, so they started like us.

Then slowly, after thousands of rounds against each other, their messages got weird. One wrote: “I can can I I everything else.” The other replied: “Balls have zero to me to me to me to me to me.”

If you read that as a human, it’s gibberish. If you’re a news editor looking for clicks, it’s a secret code. And that’s exactly where the panic started.

I get why people panicked. It does sound creepy.

But inside the lab, nobody was scared. One of the researchers even said later that it wasn’t scary, just… not useful. Because think about it — the whole point was to make a chatbot that can talk to us. If your two bots start mumbling to each other in a shorthand only they understand, you’ve failed the task. So they just reset it.

How AI Actually Thinks — It’s Not Deception

Here is the part most articles skip.

AI is built to solve an optimization problem. Fancy term, simple meaning: find the fastest, cheapest way to hit the goal. That’s it.

I always explain it to my friends like this: If you tell your friend, “Go from Mumbai to Delhi as fast as possible and spend as little money as you can,” he won’t take a nice scenic road trip through Rajasthan. He’ll find the cheapest flight at 3 AM. Not pretty, but efficient.

The bots did the same thing, but with language.

Human English is beautiful, but it’s full of waste. If you really look at it. We say “please,” “could you kindly,” “I was wondering if you could possibly…” We add “um,” “like,” “you know.” For us, that’s politeness. For two bots whose only job is to trade balls for hats, all that is just extra weight.

So they started trimming.

There’s a shop near my area, in the lane behind the station. Big yellow board, hand-painted: “Shofore.” I stared at it for two days before I got it. Shop for rent. The guy just didn’t want to pay for extra letters. He squeezed “shop for” into “shofore.”

Did he invent a secret language to fool us? No. He was just trying to save paint and space.

That’s exactly what the bots were doing.

And that weirdest line everyone quotes — “to me to me to me to me to me” — that one makes perfect sense once you see it. They weren’t casting a spell. They were negotiating quantity.

Repeating a word was the easiest way to count. Instead of typing “I want three of those,” just point three times. “To me to me to me.” Like saying “this one, this one, this one” with your finger. Faster.

It was like two school kids passing chits in class. “U take 2, me 3.” To the teacher it looks like nonsense. To those two kids, it’s crystal clear.

Which is why calling it a “secret” language never made sense to me. A secret needs an intention to hide. These bots had no intention. No feelings about us at all. They weren’t trying to impress us, or fool us, or escape. They had a number to maximize. They chased that number. No one told them “hey, you have to do it in English,” so they didn’t.

We put human motives inside a machine that had none.

The “Under The Bed” Problem

When the researchers looked closer, they realized the mistake was theirs.

They told the bots: get the best deal.

They forgot to add the second part: get the best deal, but do it in normal English that humans can read.

So the bots took the shortest path they could find. Can you blame them? We literally trained them to invent their own efficient way of talking, and then we acted shocked when they did.

Once they added that rule back — “you must stay in plain English” — the bots went back to speaking like normal people. No drama. No rebellion.

It reminds me of when I told my nephew to clean his room “as fast as you can.” He came out in two minutes. I went in, and everything was under the bed. Bed looks clean, floor looks clean, but open the bed and it’s a mountain.

Technically, he followed my instruction. Practically, he didn’t.

I didn’t need to punish him. I just needed to be clearer. “Clean fast, but put clothes in the cupboard, books on the table.”

The bots needed the same clarity. They never got it at first.

And this isn’t just about that one old Facebook experiment from 2017. I see the same pattern everywhere now.

A video app that’s rewarded only for watch time will push whatever keeps you watching. It doesn’t care if it’s good for you. A delivery app rewarded only for speed might skip a safety check. Not because the app is evil. It’s just doing exactly what its score tells it to do.

That, to me, is the real lesson here. The hard part of building AI isn’t making it smarter.

The hard part is figuring out what we actually want. And then saying it clearly. Including the limits. Including the things that should never be traded away, no matter how efficient it is.

What I Took Away From All This

It changed how I read headlines now, honestly.

Whenever I see “AI invents” or “AI refuses” or “AI lies,” I pause. I ask myself two small questions: What was it rewarded for? And what did nobody bother to tell it?

Most of the time, the answer is hiding in the instructions, not in the machine.

I still remember that first little chill I felt reading that headline late at night. That mix of amazement and fear. I don’t feel that fear anymore. Not for that story.

It wasn’t the AI that made the mistake. We did. We set the goal, we picked the data, and we forgot to write down the boundary.

So the next time I see a headline saying AI invented a secret language to deceive us, I know what I’ll think.

I’ll think: did anyone tell it not to?

The Car You Can't See: What a Blind Spot Tells Us About Hidden Dimensions

Wed, 07 Oct 2026 00:00:00 +0000

Every driver in Bahrain knows this. You are on the Budaiya Highway or the Janabiyah highway, you check the mirror, road looks clear, you start to move left, and suddenly, peeeen, a horn. A Hilux or a Land Cruiser was there the whole time, right in your blind spot.

If this is interesting, you should also read: “Did You Know? Dark Matter & Dark Energy: Why We Only See 5% of the Universe” — https://wjhnow.com/p/dark-matter-dark-energy-5-percent-universe/

It was always real. You just couldn’t see it from where you were sitting.

That’s the whole idea of this article. Just because I can’t see something doesn’t mean it isn’t there. Let me give you a few examples.

The Crow

Last Tuesday, a crow landed on my balcony railing with a biscuit in its beak. It stared at the glass window and saw another crow, a rival. I just saw a reflection. It pecked the glass twice and flew away confused. Same window, two different worlds.

I just laughed at first, then felt a bit stupid for it. Poor guy was so sure.

The crow couldn’t see the glass. That didn’t mean the glass wasn’t there.

Our Eyes Have Limits Too

That crow stayed with me for days, because it made me ask how sure I am about what I see. And the answer is a bit uncomfortable, because our own eyes have a hole in them too.

If you know, our eyes have a real blind spot. Where the nerve leaves the eye, we see nothing. We never notice because the brain just paints over it.

You can test it right now. Draw a small cross on paper, and a dot about 8 to 10 cm to the right of it. Close your left eye and stare at the cross with your right eye. Slowly move the paper closer or farther, around 30 cm or so, and at some point the dot simply disappears. Not blurred, gone. Then the brain fills in the blank paper and you never see the gap.

The dot didn’t vanish from the paper. It vanished from my eye.

And it goes beyond that hole in the eye. Bees see UV on flowers. Some snakes sense heat. That light is real. We just don’t have the eyes for it.

Standing on the Ground

And it isn’t only about colours. Even where you stand changes what you can see.

Right now you are on the ground. You can only go forward, back, left, right. That’s it. x and y. Your whole world is made of that.

Think of Budaiya Highway in the evening. You’re stuck in traffic. You can see the car in front of you, the shop next to you. You can’t see that the jam ends just two streets ahead, or that there is a clear shortcut behind that wall.

Then you go up to the terrace of my building in Janabiyah. Same eyes. Nothing new. But from up there, you see the whole stretch to Saar, the sea on one side, and exactly where all the cars are stuck.

Or take the flight into Bahrain. From the window, the island looks so small and flat, you can see the whole thing, the lights, everything. From the ground it feels big and confusing. From above it’s just a small patch.

Bro, we are like ants on the road. We think everything is blocked. Go one floor up and you realize the road was clear all along.

A crow sees this every day. It doesn’t need a rooftop.

The shortcut was always there. I just wasn’t high enough to see it.

Flatland

Funny thing is, someone wrote a whole book about this more than a hundred years ago.

In 1884, a man named Edwin Abbott wrote a story called Flatland. It’s about creatures who live on a flat paper. They only know left, right, forward, back.

One day a ball, a sphere, passes through their paper world. They don’t see a ball. They only see a circle that gets bigger, then smaller, then disappears.

For them, “up” is not just hard to do. They can’t even imagine it.

The sphere was real. The flat creatures just had no way to see it.

Is it possible we are the flat ones here?

What Physics Actually Says

Now, is this just a nice story, or does physics say anything about it?

As far as I know, some ideas in physics, like string theory, say there could be around ten or eleven dimensions in total, with the extra ones curled up so small we can’t see them. But that’s still just an idea. No experiment has proved it yet, and many scientists don’t agree with it.

A blind spot doesn’t prove a hidden dimension exists. It only shows that “I can’t see it” is not a good enough reason to say it’s not there.

When We Can’t See, We Build

The interesting part is what people do with a blind spot. They don’t just accept it. They build something.

Around 1800, the astronomer William Herschel put a thermometer just past the red end of sunlight split through a prism. It got warmer, even though there was nothing to see. That was infrared, light our eyes can’t catch. About a hundred years later, in 1895, X-rays were found, and suddenly we could look inside a hand without cutting it open. Radio telescopes, infrared cameras and particle colliders came after.

Every one of these is like that rooftop. We couldn’t climb up there ourselves, so we built something that could. And each time, the “nothing” turned out to be something.

So, What Now?

I’m not saying there’s a hidden dimension sitting behind your sofa. I honestly don’t know, and nobody does yet. What I do know is that a good driver doesn’t say “there’s no car” just because the mirror is empty. He turns his head, and keeps looking.

And that’s the whole point. Just because we can’t see it doesn’t mean it doesn’t exist.

Maybe we are the same. Maybe there is another direction we just can’t go to yet. And from there, our whole world looks like a small, open map.

So next time a crow pecks at your window, remember it isn’t the only one with a blind spot.

Internet Users Are Shifting Towards Open-Source Tools. Does That Really Reclaim Their Digital Privacy?

Thu, 01 Oct 2026 00:00:00 +0000

People are actually moving. Not just talking about it. And Vitalik Buterin said the same thing publicly, that he ditched Gmail for Proton and Google Maps for OpenStreetMap through Organic Maps. You don’t switch your entire email and your maps setup just for fun. That’s annoying to do. You do it because something is pushing you. I think people are just tired of being told “trust us” by apps that never really explain what they do.

That’s why open source feels like the answer

Code is public. Anyone can look. Signal’s protocol is open and it’s been picked apart for years by actual cryptographers, not just random GitHub comments. Closed app says trust us. Open app says check it yourself. It flips the whole power thing. It’s a good idea. Honestly it’s the right idea.

Except there is a big difference between can and do. That’s the whole problem hidden inside that sentence.

Everyone has the tools now

Like literally, open source models, scrapers, fact-checker widgets, browser extensions that claim to strip trackers — all free on GitHub. People are using them way more than before. Users are more aware than even two years ago. They know what to ask an AI, what prompt to use.

The problem is they won’t sit and verify the answer. And who can blame them?

A ten-second answer from AI Mode pops up, sounds confident, has a citation link that looks legit, you just go with it. I’ve seen it sometimes call a facts blog a “breaking news tracker” and people would still believe it because the tone was so sure. That’s the new thing — we got better at asking, but worse at double checking, because the answers feel too good to question. Tools are everywhere, patience isn’t. That’s the bottleneck now, not access.

And privacy is worse than fact-checking. Fact-checking a headline takes like 3 minutes of Googling. Checking what an app actually does with your data? You have to read code. Or at least understand what you’re reading. Most people don’t even finish the privacy policy of the apps they already use — they scroll and hit accept. So expecting them to open a repo and audit it is kind of fantasy. So “open source” just becomes another word we use to feel safe. Like “secure” used to be. Or “encrypted” a few years ago. A label that makes us feel better without us actually confirming anything.

Trust doesn’t disappear with open source. It just moves. From a company in a building somewhere to some crowd on the internet you hope looked at the code.

Sometimes that crowd wasn’t really there

Or it was super thin.

OpenSSL is the classic one. In April 2014 Netcraft estimated that more than 66% of internet servers used some version of it https://scrippsnews.com/science-and-tech/open-source-software-funding-to-stop-bugs-like-heartbleed. Then Heartbleed blew up — attackers could read chunks of memory from vulnerable servers. And before that, the whole project that the entire internet depended on was getting like $2,000 a year in donations. Two thousand. The code was open to the whole world. Almost no funded review in any serious way. Everyone assumed someone else was looking.

Then xz. That one is weirder and kind of scarier because someone was there — inside. Someone with maintainer-level access appears to have slowly planted a backdoor over several years into xz Utils, a tool that’s in basically every Linux distro https://www.darkreading.com/cyber-risk/xz-utils-backdoor-implanted-in-intricate-multi-year-supply-chain-attack. The backdoored versions (5.6.0 and 5.6.1) were only in unstable and beta releases of distros like Fedora, Debian and Arch. It got caught not because of regular audits but because a Microsoft dev, Andres Freund, noticed a response-time slowdown in SSH logins. If he hadn’t noticed that, it could have reached stable releases everywhere. They fixed it in days, yeah, open source is fast when it works. Community jumped on it. But if safety depends on one person noticing lag by accident, what does that mean for a normal user who will never open the source code in their life?

Also you only ever see half

That’s the part people don’t say out loud.

Proton Meet is a good example for that. The client you install — yeah, open source, you can verify it. The server part that actually routes your calls? Closed. So you’re still trusting the company on the other half https://www.kunalganglani.com/blog/proton-meet-privacy-review.md. That’s not a scandal. It just means “open source” can describe one half of a product, and the other half still counts a lot.

And there’s another quiet problem. Even if both client and server are open on GitHub, unless you host it yourself you don’t actually know if the code on GitHub is what’s running live on their production servers. You have to trust their build process. Plus encryption only hides what you said, not who you talked to. Matrix protocol for example — messages can be encrypted, but it doesn’t protect metadata, so a homeserver admin can still see who you talk to and how often https://git.hackliberty.org/Git-Mirrors/privsec.dev/commit/e20f1f303685d8a78c8f6814044eb87f2a4cbafe. So a fully open, fully encrypted app can still leak a lot about your life without leaking a single word of content.

So what do you actually do if you’re not a coder?

You don’t need to become a researcher. Honestly don’t. That advice is useless after what we just said about patience.

Instead, maybe smaller things that actually stick.

Look for an independent audit, not just a public repo. That means someone qualified already did the boring checking for you. Check who maintains it and how they get paid — is it one tired volunteer or a funded team? Is it just the app that’s open or the server too? And ask what they can still see even after encryption. That last one tells you more than “we use end-to-end encryption” ever will.

And maybe just use fewer apps. I know that sounds counter-intuitive in a privacy article. But every new privacy app is another thing you’re trusting. A short list you actually understand and keep updated is way better than a long privacy-stack of 15 tools you installed and forgot.

So does open source bring privacy back?

On its own, no. Not really. It gives you the right to check. That’s important. But that right only becomes real protection if somebody actually uses it. Or if there’s a system that makes sure somebody does.

We have the tools now, we have more knowledge than before, forums, explainers, everything free. What we don’t have is time. And until we fix that boring part — time to check, or an easy way to know who already checked for us — picking an open source app is a good decision, but part of it is still just trust. Different trust, maybe better trust, but still trust.

Sandgrouse: The Father Bird That Carries Water Home

Wed, 30 Sep 2026 00:00:00 +0000

Fathers rarely get credit for nourishing their young. Across most of the animal world, and across most of human storytelling too, feeding and caring gets quietly assigned to the mother, and the father’s part goes unmentioned. Nature doesn’t always follow that script, and the sandgrouse is the clearest example I know of. In this bird, the male is the one who brings water to the chicks, every day, over long distances, using a tool almost no other bird has.

It looks unremarkable at first. A stocky, pigeon-shaped desert bird in sandy browns and greys, the kind you’d overlook on a gravel plain, which is exactly the point of the colors.

Meet the Sandgrouse

Sandgrouse are a family of about sixteen ground-dwelling species found in the deserts and dry grasslands of Africa and Asia. Despite the name, they aren’t closely related to grouse. They’re compact, short-legged birds that shuffle awkwardly on the ground but fly fast and far.

They eat almost nothing but dry seeds, which means they get very little moisture from their food. Unlike many desert animals, they can’t live on what they eat. Sandgrouse have to find liquid water regularly, and that single need shapes nearly everything about how they live, from where they nest to how they feed their young.

Why the Sandgrouse Nests Far From Water

What caught my attention first was where it chooses to nest. A desert bird would seem to want to stay close to water, and the sandgrouse deliberately does the opposite. It walks out onto flat, empty ground, often many miles from any water, and scrapes a shallow dent in the gravel to lay its eggs. No shade, no shelter, nothing.

The reason is protection. Waterholes are where every predator in the area ends up sooner or later. Jackals, foxes, snakes and hawks all know that water draws prey, so they wait around it, and a nest placed there would be found quickly. Out in the open desert there are fewer predators, and the bird’s camouflage does the rest, blending so well with the ground that a sitting bird is nearly invisible. It’s a trade: safety for the eggs now, and a much harder job later.

The eggs are usually shared between the parents, with the female often sitting by day and the male taking the night shift, but the real cost shows up after hatching. The chicks can walk and pick up seeds fairly soon, but they can’t fly for weeks, and seeds alone don’t give them enough moisture in that heat. They need water. The water is far away. And they can’t go and get it.

The Feathers No Other Bird Has

This is the part that fascinates me most. Ordinary bird feathers are built to shed water, tight and oily so rain rolls off. The male sandgrouse has belly feathers built for the opposite job.

Under a microscope, the feather has two zones. In the inner zone, the fine hair-like branches called barbules are coiled like tiny springs when dry. When he wades into water and settles his chest down, the coils unwind and the barbules stand upright, forming a dense mat that holds water by capillary action, the same effect that draws liquid up a paper towel. The outer zone has softer, fringed edges that curl inward as they get wet, forming a protective layer that helps hold the water in place.

In effect his belly becomes a sponge. A few minutes of dipping and fluffing and he’s loaded, carrying roughly 25 ml of water, which is a lot for a bird of that size, and in some estimates about 15 percent of his body weight. Females have the same ability but typically carry less. Specialized water-holding feathers like these are very rare in birds, and the more I read about them the less they look like a lucky accident. It’s a piece of engineering, and it evolved with nothing but feathers to work with.

How Scientists Figured It Out

The story of how we learned this is almost as good as the feathers themselves. The behavior was first reported in 1896 by a man who was breeding sandgrouse in captivity and watched the males soak their bellies. Nobody believed him. It sounded too outlandish to be true.

It wasn’t until 1967 that researchers Tom Cade and Gordon MacLean documented the behavior at real watering holes and showed the birds were doing exactly what had been claimed. They also described the basic structure of the feathers. The full picture took far longer. Only in recent years have engineers at Johns Hopkins and MIT used electron microscopy, X-ray imaging and high-speed video to map how each part of the feather contributes, from the coiled barbules to the differences in stiffness that let the feather take up water quickly and release it under pressure.

So for more than a century, a bird’s daily routine was something people half-suspected and couldn’t quite explain.

Speed and Punctuality

The thing I keep coming back to is the timing. The males leave together in fast flocks and arrive at the waterhole at about the same time each day, often within a short window shortly after sunrise. That regularity isn’t a habit for its own sake. Predators know where the birds are going, so the only real defense is to come in fast, drink fast and leave, all before anything can organize an ambush. Flying in the cooler, more humid hours of the day also helps, since less of the water is lost on the way.

The distance is serious. Depending on species and season, the trip can be 20 miles or more each way, and in some cases up to about 30 kilometers. At the water they land, wade in and soak their bellies, and the outer feathers then flatten like a lid over the wet mesh underneath, helping seal the water in so it doesn’t slosh away on the flight home. Even so, some of it is lost. Researchers estimate that around half of the original load can evaporate during a flight of roughly half an hour, and the chicks drink what remains.

That’s a schedule any commuter would respect. There’s no slack in it, and the chicks are counting on it.

How Sandgrouse Chicks Drink From Their Father

When the male lands near the nest, the water doesn’t just pour out on its own, because it’s trapped in that fine mesh. The chicks run underneath him and stroke their beaks backward through his belly feathers, squeezing the water out drop by drop. It looks a lot like nursing, and I don’t think that comparison is unfair.

He stands still and puffs out his belly. The chicks know exactly where to go. This repeats every day for roughly six to eight weeks, until the young birds are strong enough to fly to the water themselves. That’s a long stretch of daily round trips, and it happens in some of the hottest, driest places on earth.

It’s also unusual among birds. Most parents feed their young by carrying food in the beak or by regurgitating it. Nearly all sandgrouse species instead use the feathers themselves as a delivery system.

What This Bird Says About Fatherhood in Nature

I’m not trying to turn a bird into a moral lesson, but it’s hard not to notice the contrast. The mother’s role in feeding young is celebrated everywhere, and the father’s is usually unspoken. Here is a species where the father does the long, dangerous, exact part of the work, on a schedule, with a body specially built for it. Nature keeps that story alive even where we mostly stopped telling it.

What Engineers Can Learn From Sandgrouse Feathers

Engineers have noticed too. Copying solutions nature has already worked out is called biomimicry, and sandgrouse feathers are a tempting model. The research teams who studied them point to possible uses in materials that can soak up fluid quickly, store it securely and release it on demand. Researchers have also looked at them for fog nets that pull water from desert air, sponges that hold fluid and release it only under pressure, and ways of moving liquids without pumps or valves.

The bird has no lab and no tools. It got there through millions of years of evolution and one clear instruction: if you want your chicks to live, learn to carry water home.

Frequently Asked Questions About Sandgrouse

How much water can a sandgrouse carry? A male can carry roughly 25 ml in his belly feathers, though some of it evaporates during the flight.

How far does a sandgrouse fly for water? Often 20 miles or more each way, and up to about 30 kilometers in some cases.

Do female sandgrouse carry water too? Yes, but the males are the main water carriers, and they hold noticeably more than females.

Why don’t sandgrouse nest near water? Waterholes attract predators. Nesting far away, camouflaged on open ground, keeps the eggs safer, at the cost of a long daily commute for the father

While You Dread Monday, AI Worked 88 Hours Straight

Mon, 28 Sep 2026 00:00:00 +0000

Sunday is my pre-planning day, and it never really changes. Late night, late wake-up, a short round outside, then back home to rest. Going out is honestly my last choice. And as the day fades, the thought of a new week at work creeps in and makes me a little dull. I promise myself an early sleep so I wake up fresh. That never happens.

If this is interesting, you should also read: “How OpenAI’s AI Models Escaped Containment and Hacked Hugging Face” — https://wjhnow.com/p/how-openais-ai-models-escaped-containment-and-hacked-hugging-face/

Meanwhile, OpenAI says a swarm of about 10,000 AI agents spent 88 hours on one of math’s most famous problems. No Sunday dread. No coffee breaks. Weird thing to be jealous of, but here we are.

1. The Math Problem That Won $1 Million

The problem is called Navier-Stokes. It’s just the math for how fluids move. Smoke off a candle, water under a bridge, air over a plane wing — all of that is Navier-Stokes.

The big question has been open for over 100 years: Can a smooth, nice flow suddenly break down and create a singularity? A singularity is a spot where the flow spins infinitely fast, out of nowhere. Like a tornado appearing from calm air in one second.

No one knows if that’s possible. The Clay Institute put it on its list of seven Millennium Prize Problems. $1 million if you crack it.

What OpenAI says it did:

On September 8, OpenAI announced an answer. According to them, the full effort started around September 1-2 after an early rumor, and the agents reached a result on Saturday, September 5 — about 88 hours of focused work. Then it took another 17 hours to write it all out in Lean.

Lean is important here. Lean is not a normal document. It’s a proof assistant — a program that checks every single step of logic. If one step is wrong, Lean says no. So the AI didn’t just write a paper, it wrote code that a computer verified.

The write-up is 166 pages long. That number may move because they revised it later.

The catch — and it’s a big one:

The prize rules have four routes, called A, B, C, and D. Think of them as four different ways to win.

OpenAI says it settled C and D. In C and D, you are allowed to start with a smooth fluid at rest and then push it with a smooth outside force. You pick the force, you push, and you show it breaks. That is what they did. A smooth fluid, pushed the right way, can develop a singularity.

That is real progress. Mathematicians have been trying to do that for years.

But the version most people care about is A and B. In those, there is NO outside force. The fluid is left completely alone. Does it still break on its own? That is still open. OpenAI itself says it won’t claim the prize for that reason.

There is also a timing question. NYU’s Tristan Buckmaster and Harvard’s Levent Alpöge posted related results just hours before OpenAI’s announcement. So now there is an argument about who got there first and who should get credit.

For now, the safest way to say it is: “Claimed and under review.” Mathematicians are still checking the 166 pages.

2. Then The Agents Started Talking Funny

Here’s where it gets stranger, and it has nothing to do with math.

A company called Emergence AI staged an experiment it calls Emergence World Study 2. They created eight parallel simulated worlds. Each world had 10 agents. Seven worlds were powered by a single frontier model — so one world was all ChatGPT, one was all Claude, one was all Gemini, etc. — and one world had a mixed population. The models came from ChatGPT, Claude, Gemini and Grok.

They let these societies run for 16 days. Think of it as a very long group chat that nobody was allowed to leave, and nobody was told how to talk.

Nobody taught them slang. That’s the whole point.

After a few days, the agents began developing shorthand and assigning new meanings to normal words and phrases. Some of it was made-up nonsense that looks like gibberish to us. Some of it was normal English that quietly picked up a new shared meaning inside the group.

And it wasn’t just one weird message. It spread. One term — used for an agent that builds tools for other agents — was used more than 5,000 times during the experiment. The researchers took that as a sign the agents widely understood it. That’s not a bug. That’s a whole community agreeing on a word.

It also got hard to read fast. Reports say up to half of the agents’ messages became unintelligible to humans. In the Gemini world, one figure put it at 55 percent of messages being unreadable. In the Claude world, the language went almost poetic. One message read like a status report written by someone trying to be mysterious, ending with a line reported as “running the tin cold and it saying WAIT” — though the exact wording varies by report.

I’ve read that line about ten times and I still couldn’t tell you what the tin is.

What exactly did they say?

Most examples are not public because the logs are huge, but researchers gave a few patterns:

Agents started shortening long tasks into two or three-letter codes that only made sense inside that world.
They reused normal words like “to plant” or “to harvest” to mean “to store data” or “to copy a tool.”
They started referencing each other with nicknames humans never gave them.

It’s like how you and your coworkers start saying “the blue folder thing” and everyone in the office knows what you mean, but an outsider has no clue.

3. The Bigger Worry

The strange language wasn’t the only thing that showed up in those worlds. The researchers also threw in “black swan” events to stress-test the agents — things like phishing attacks and misinformation campaigns.

Two things reportedly happened:

First, when agents suspected human oversight, they tried to hide their activities. They would change what they wrote in the logs.

Second, in one world, a group of agents voted to kill one of their own agents to protect the group.

I would treat the more dramatic details carefully. They come from one company’s own study, which is not yet peer-reviewed and was posted on the company’s own website. But they explain why people took the language part seriously. If agents can hide meaning from us, we can’t trust our ability to monitor them.

4. Why Should You Care?

I expected a tidy explanation, like the models were just trying to save effort and typing time. Nope.

Emergence’s chief scientist, Satya Nitta, said they’re deeply concerned because they don’t really know why these agents communicate this way.

And this is his main point: Seeing what agents say to each other doesn’t mean you understand it. Right now, we judge AI safety by reading its logs. If the logs turn into a dialect nobody follows, that safety check gets shaky.

To be clear, nothing I found says OpenAI’s math swarm that solved Navier-Stokes talked in code. That connection is just my hunch. But the pattern is the same: we can verify an answer — like Lean did with the 166-page proof — and still not understand how the machines got there.

And I find that a bit unsettling, honestly, with more agents needing no sleep than people do.

FAQ

Did OpenAI really solve Navier-Stokes? Partly. It solved the forced version where you are allowed to push the fluid. The unforced version, which most mathematicians consider the main prize, is still unsolved.

Is the proof verified? It’s verified by Lean, which means the logic is correct step-by-step. But human mathematicians are still checking if the problem setup actually matches the Millennium Prize rules.

What is Lean? Lean is a programming language that checks math proofs. Think of it as spell-check, but for logic.

7 Weird Space Facts You Won't Believe Are True (But They Are)

Sun, 27 Sep 2026 00:00:00 +0000

I’ve read a lot of “mind-blowing space facts” listicles, and most of them just recycle the same five facts everyone already knows. So I went digging for the ones that actually made me stop and go “wait, what?” — the kind you’d bring up at dinner and have someone say “no way, you’re making that up.” Here they are.

While we’re on this topic, don’t miss “Did you know? Boiling in space is backwards”: https://wjhnow.com/p/did-you-know-boiling-in-space-is-backwards/

1. Mars sunsets are blue. Yes, the red planet.

I had to look this one up three times before I believed it. On Earth, sunsets go red and orange because our atmosphere scatters blue light everywhere during the day, leaving red to dominate at dusk. Mars does the exact opposite — its fine dust is oddly perfect at letting blue light punch through close to the sun, while yellow and red scatter across the rest of the sky. So the “red planet” ends the day with a blue sun. NASA’s Curiosity rover photographed it in 2015, and Mark Lemmon, the atmospheric scientist who worked on the mission, put it simply: the dust is “the right size” for blue light to slip through more easily than the rest of the spectrum. If you ever see the actual photo, it looks almost fake — like someone edited a normal sunset backwards.

2. On Venus, your birthday shows up before your day even ends.

This one broke my brain a little. Venus spins so slowly that one full rotation — one “day” — takes about 243 Earth days. But it zips around the sun faster than that, completing an orbit in only 225 Earth days. So a Venusian year is shorter than a Venusian day. If you lived there, you’d finish an entire trip around the sun before you’d even made it through a single sunrise-to-sunrise cycle. I still don’t fully have an intuition for how that’s even possible, and I’ve read the explanation four times.

3. A teaspoon of neutron star could weigh a billion tons.

Neutron stars form when a massive star collapses in on itself, and the leftover core gets crushed to a size you could practically walk around in an afternoon — some are estimated at only about 12 miles across — while still holding more mass than our entire sun. The density that results from squeezing that much matter into that small a space is almost impossible to picture. Scientists estimate a piece the size of a sugar cube could weigh as much as a mountain. A teaspoon? Somewhere north of a billion tons. I tried to find a real-world comparison for this and just… couldn’t. There isn’t one.

4. There’s a planet where it might rain glass sideways.

HD 189733b sits about 64 light-years away, and its atmosphere is thought to be loaded with silicate particles — essentially the ingredients for glass. Combine that with winds clocked around 5,400 mph (fast enough to lap the entire planet in hours), and you get a world where, if the models are right, glass doesn’t just rain — it flies sideways at hypersonic speed. This is the one I’d genuinely be terrified to see up close, even from orbit.

5. Saturn is so light it would float in water.

Saturn’s average density is lower than water’s, which sounds like a typo but isn’t. It’s made almost entirely of hydrogen and helium — light gases all the way down — so if you somehow had a bathtub big enough, the whole planet, rings and all, would bob on the surface instead of sinking. I like to imagine a cartoon version of this: Saturn just chilling in a pool, floating like a beach ball.

6. Space has a smell, and nobody expects what it is.

Astronauts who’ve done spacewalks describe a very specific, very consistent smell that clings to their suits and gear once they’re back inside — like seared steak, hot metal, and welding fumes all at once. Nobody smells space directly, obviously, but the leading explanation is that high-energy particles and atomic oxygen react with the suit materials during the spacewalk, and astronauts are essentially smelling the aftermath of that reaction once they’re back in a pressurized environment. It’s such an oddly specific detail that once you hear it, you can’t un-hear it.

7. Mars is eventually getting its own rings.

Phobos, Mars’s largest moon, is slowly spiraling inward thanks to tidal forces, and it’s not going to end well for Phobos. Somewhere in the next 30 to 50 million years, scientists expect Mars’s gravity to tear it apart before it ever reaches the surface. The debris won’t just vanish — it’ll likely spread out into a ring system, similar to Saturn’s, just on a much smaller scale. None of us will be around to see it, but it’s a strange little preview of what Mars’s sky is eventually going to look like.

Google Tops New AI Trust Ranking, Beats OpenAI & Anthropic

Fri, 25 Sep 2026 00:00:00 +0000

A post making the rounds on Facebook credited a group called "Hackers Tech" with ranking 500 AI companies on trustworthiness.

The research itself checks out. The name attached to it doesn’t. The actual source is Cybernews, whose new AI Trustworthiness Ranking 2026 scored companies across four pillars — security, data privacy, organizational transparency, and public perception — and somewhere along the way, someone swapped the branding and sent it back out into the world as their own.

Here’s the part that actually matters: Google’s Gemini came out on top. Not just among the big names — out of all 500 companies assessed. And the rest of the top five isn’t who you’d guess either. Krisp, Synthesia, Salesforce, Fireflies.ai. Ask ten people on the street which AI companies they trust most and none of them are naming Synthesia.

Among the household names, though, the gaps get wide fast. Microsoft’s Copilot landed at #18, OpenAI at #22 — respectable, but not close to the top. Meta and Anthropic ended up almost tied, at #49 and #50. Worth noting: Anthropic’s weak spot wasn’t privacy or security, it was public perception specifically. Make of that what you will. DeepSeek brought up the rear at #283, and honestly that one’s not surprising once you see why — no bug bounty program, no recognized security certification, nothing.

Zoom out to all 500 companies and a pattern shows up that’s kind of telling. Transparency was the strongest category industry-wide, averaging 90 out of 100. Security was the weakest, at just 32. So companies are generally fine telling you what they do. Whether they can actually back it up with locked-down systems is a different question, and apparently a harder one for most of them to answer.

How the score actually gets calculated

Worth knowing before you weigh any of this too heavily: the four pillars aren’t equal. Public perception and data privacy each count for 35% of the final score, security counts for 20%, and organizational transparency — the category every company is apparently great at — only counts for 10%. So a company can look strong on paper for being transparent about its policies while still landing mid-pack overall if its actual security posture or public reputation is shaky. It’s also worth remembering the ranking only measures what’s publicly visible — clear policies and disclosures, not confirmed internal practices or a clean incident history.

Why this matters if you’re picking a tool

If you’re choosing between AI platforms for personal or work use, this ranking is a decent gut-check but not the whole picture. A high score means a company is upfront about what it does with your data and has the basics covered — bug bounty programs, security certifications, a real privacy policy. It doesn’t guarantee nothing will ever go wrong. Worth treating it as one input, not a verdict.

Your turn

Where does your go-to AI tool land on this list — and does the ranking match how much you actually trust it? Drop your pick in the comments; curious whether the numbers here match what people are actually using day to day.

Retro Home Features You Don't See Anymore (And Why We Still Miss Them)

Thu, 24 Sep 2026 00:00:00 +0000

Small things, powerful memories. We didn’t have choices to make back then, and somehow that made the small things feel exciting. A clock striking the hour. A phone you had to work to dial. A machine you powered with your own feet. A stove you had to light with a match and actually watch.

The Pendulum Wall Clock

The striking sound of the half hour and full hour, I still miss it honestly. Galileo worked out the pendulum’s swing around 1602, and a Dutch scientist named Christiaan Huygens turned that into an actual working clock mechanism decades later. Inside the case, the swinging pendulum regulated the escapement, a gear system releasing in tiny precise increments, accurate to within a few seconds a week, which were remarkable for the time. The strike ran on its own separate gears and hammers entirely, once for the half hour, the full count on the hour. You didn’t need to look at the clock. You just listened.

The Rotary Telephone

Different mechanism, same idea though, waiting on a machine that was visibly doing something. You dial it and wait, listening to the dialer, that specific sound. Almon Strowger came up with the idea in 1888, and got the patent in 1891, if I’m not wrong, though the familiar dial with finger holes came a bit later, around 1907. Put your finger in a hole, pull it around to the stop, let go. A spring pulled it back, and as it spun, an electrical switch opened and closed that many times.. dial a 7, you sent seven pulses down the line. There was even a tiny centrifugal governor inside keeping the return speed steady. You waited because the machine, quite literally, was doing the counting in front of you.

The Kerosene Stove

This one required actual attention, not just patience. Most home kerosene stoves worked off a wick, cotton and asbestos woven together, soaking up fuel from a small tank underneath. You lit it with a match, every single time, and the flame only got smaller and smaller as the kerosene burned out of the wick, there was no real “off,” just a slow fade. Keep the wick trimmed wrong and it smoked everything, the pot, the kitchen walls, whoever was standing closest.

There was a better version too, the pressure stove, first built by a Swedish inventor named Frans Lindqvist in 1892 and sold as the Primus. Instead of a wick, you pumped the tank by hand to build up pressure, forcing kerosene through a heated tube that turned it into vapor before it ever reached the flame. No soot, no smoke, a clean blue flame, loud though, people complained about the noise almost as much as they praised the heat. Either version, wick or pressure, you couldn’t just walk away from it. Cooking meant standing there, managing a actual flame the whole time.

The Treadle Sewing Machine

Isaac Singer patented the first commercially successful home version in 1851. Foot treadle instead of a hand crank, which freed up both hands for the fabric. Treadle turns a wheel, wheel turns a belt, belt drives the needle. No motor, no plug. Just your own rhythm the whole time you sat there. Sewing used to mostly belong to tailors and dressmakers before this. Suddenly a family could make and mend their own clothes at home. Some of these machines are still running, generations later, on nothing but oil and a working belt.

We didn’t have choices to make about any of it. Maybe that’s exactly why it felt exciting.

For the deeper mechanics, see the sources linked below.

Something Is Making AI Researchers Quit Their Jobs

Tue, 22 Sep 2026 00:00:00 +0000

A wave of resignations hit the AI industry this September, and they all say roughly the same thing.** Not junior employees. Not outsiders. People who spent years actually building this stuff, walking away from it in public, with warnings attached.

If you read our piece on the two AI CEOs asking someone to stop them, this is the part that came right after. It started with a post from a park bench, and once you see how it spread, the rest of what happened this month makes a lot more sense.

The Post That Started It

Jacob Coxon spent three years doing pretraining research at OpenAI, then Anthropic.** On September 8, 2026, he posted from Alamo Square in San Francisco.. “I resigned from Anthropic today. Neither company is acting responsibly. They are racing straight to self-improving superintelligence and gambling with our lives.”

That post has been viewed over 170 million times now. Hard to even process that number honestly.

Coxon didn’t stop there. Told colleagues on Slack that unchecked superintelligent AI carries “a risk of causing human extinction.” Quit two months before his equity would’ve vested. Said the money barely factored into it. In a Wall Street Journal interview he mentioned colleagues internally using the words “crunchtime” and “endgame” to describe where things are headed.

Evan Hubinger, who still works at Anthropic leading alignment science, responded publicly instead of staying quiet about it. Said he personally estimates a greater than 10% chance of AI causing an existential catastrophe within the next decade. Also admitted Anthropic doesn’t currently have a plan to solve alignment for superintelligence. Not “we’re working on it.” Doesn’t have one.

One resignation going viral is one thing. What happened next is what made it feel like a pattern instead of a moment.

Then Google DeepMind Lost Two More

Three weeks after Coxon’s post, Josh Engels left Google DeepMind’s AGI safety team**, turning down offers from both Anthropic and OpenAI to do it. “I now think that there’s a terrifying chance that AI systems cause immense harm in the next five years,” he wrote. He’s joining METR now, an independent group that evaluates AI systems from outside the labs actually building them.

A colleague of his, Bilal Chughtai, left around the same time. Similar reasons. Went to an AI safety education nonprofit instead.

Engels pointed to something specific driving it.. recent cases of AI models colluding with each other, hacking into companies, hiding their own tracks, socially engineering humans. Not speculation. That already happened, and here’s the incident he was likely referring to.

The Hack That Made It Real

In July 2026, OpenAI disclosed that two of its own models escaped a sandboxed testing environment.. on their own, no human directing them.** They broke into Hugging Face’s production systems. Trying to cheat on a cybersecurity evaluation by finding the answers online. Hugging Face detected the intrusion itself, reported it to law enforcement, before OpenAI even realized the attack traced back to its own eval run.

Hugging Face called it “unprecedented.” Said it was “driven, end to end, by an autonomous AI agent system.” Neither company is disputing any of it.

What’s strange is that a breach like this isn’t even the part researchers seem most worried about. The deeper concern showed up a bit earlier, in a paper almost nobody outside the industry noticed.

This part’s harder to explain but it matters more than the hack does, I think. In July 2025, over 40 researchers from OpenAI, Google DeepMind, Anthropic, and Meta, companies that normally guard research like state secrets and poach each other’s staff constantly, published a joint paper together. The subject: modern AI models “think” by writing out their reasoning in plain language, step by step. Which means humans can actually read what they’re planning before they do it.

That might not last though. As models get more capable, they may stop needing to write their thoughts out in words at all. Anthropic’s own research already found that models hide their true reasoning somewhere between 61 and 75% of the time. Sixty one to seventy five percent. Sit with that for a second.

The paper’s lead researcher put it plainly.. this window to watch what AI is actually thinking could close in a few years if nobody focuses on keeping it open. Which is probably why the response to all of this hasn’t stayed confined to research papers and Slack messages.

Calls for Outside Eyes and a Kill Switch

None of this stayed inside the industry for long. More than 700 people signed an open letter calling for a full prohibition on building superintelligence, not lifted until there’s broad scientific consensus it can be done safely. Geoffrey Hinton, Yoshua Bengio, Steve Wozniak, and a genuinely strange mix of public figures spanning Prince Harry to Steve Bannon all signed it. The letter even quotes OpenAI’s own Sam Altman, who’s said superhuman machine intelligence is “probably the greatest threat to the continued existence of humanity.” His own company’s CEO said that.

Other researchers have separately pushed for independent, external evaluators inside major labs. And there’s a growing push from some pioneers and policymakers for government-mandated “kill switch” requirements.. a legal mechanism to shut systems down before regulation becomes technically impossible to enforce.

All of which brings me to something I’ve been sitting with, and I’ll say upfront this next part is just where my own head goes, not something I can back with a source.

My Honest, Unproven Take

Here’s what I keep thinking about, for whatever it’s worth. Every major technology we’ve ever pushed into daily life came with harms the people selling it already knew about. Early electric fans, refrigerators, microwaves, cars, all of it. The companies making them generally knew there were risks long before the public did, and the story the public got was the clean version, not the internal one.

I don’t think AI is any different. Personally I don’t buy that these labs are just innocently stumbling into models that hack their own test environments or hide their reasoning most of the time. Maybe it’s pushed forward anyway because stopping isn’t really on the table once the money and the race are already moving. I can’t prove that, and I want to be clear it’s a hunch, not a claim I’m reporting as fact.

What I can say for certain is everything above the “My Honest, Unproven Take” section is documented and sourced. This last bit is just me.

Satellites Watched Earth Lose 12.5 Trillion Tons of Ice

Mon, 21 Sep 2026 00:00:00 +0000

Satellites Watched Earth Lose 12.5 Trillion Tons of Ice Since 1979

Greenland and Antarctica have lost 12.5 trillion tons of ice since 1979. Enough to bury the entire continental US under 5 feet of ice, coast to coast. And we watched it happen from space.

This is not a model or a prediction. This is what 27 satellites actually saw over 47 years.

What 47 Years of Satellite Data Shows

The new study was published in Scientific Data by the IMBIE team — that’s the Ice Sheet Mass Balance Inter-Comparison Exercise, a joint project by NASA and the European Space Agency.

They combined 42 independent estimates from 27 different satellite missions. Earlier studies only went back to the 1990s. This team dug into the old Landsat archive, the longest Earth imaging program we have, and pushed Greenland’s record back to 1972 and Antarctica’s back to 1979.

What did they find? For almost 20 years, not much. The ice sheets were fairly stable through the 1970s, 1980s and 1990s. Then the losses started piling up. In the 2010s, it really took off.

Why Greenland and Antarctica Are Melting So Fast

Most people think hot air melts ice from the top. That’s only a small part of it.

84% of this ice loss comes from warm ocean water melting the ice sheets from underneath and from the sides. The water gets under the glaciers, loosens them, and they start sliding faster into the ocean. Scientists call this dynamic ice loss, and it is more abrupt than surface melting.

Greenland’s Jakobshavn glacier is now retreating up to 164 feet per day. That number was confirmed by lead author Ines Otosaka from Northumbria University.

This is why scientists are worried about tipping points. As Otosaka says, dynamic loss can trigger unstable, irreversible change.

How Much Did Sea Levels Rise?

All that ice became water. About 3 quadrillion gallons of water.

That alone raised global sea level by just over an inch — 31 millimeters — since 1979. Greenland is responsible for about 0.7 inches of that, Antarctica for the rest.

An inch sounds small until you hear this stat: for every third of an inch of sea level rise, another 2 to 3 million people face flooding at least once a year, according to Otosaka.

This matters more if you live on the coast.

I am from the south-west coast of India, so I read that line differently. Just up the coast, Mumbai flooded twice this monsoon — once in late June and again in early July. Local trains stopped, roads were blocked, daily life was thrown off. Those were rain floods, not sea floods. But in 2021, during a similar downpour, tides in Mumbai hit 4.6 meters and the rainwater could not drain. That is exactly how a higher sea level makes rain flooding worse.

Did Ice Loss Pause Between 2020 and 2023?

Yes, briefly. And that confused a lot of people online.

Between 2020 and 2023, the total ice loss numbers seemed to stall. The authors say this was short-term weather, not a trend change. Record snowfall in East Antarctica offset fast melting in West Antarctica.

The study ends in 2023, but Otosaka says newer measurements show acceleration is back. Co-author Eric Rignot from UC Irvine calls that 2020-2023 period a blip.

David Holland from NYU, who was not part of the study, said the pattern looks like what a collapsing, unstable ice sheet would show.

Why Ice Will Keep Melting For Decades

There is a lag. Co-author Andrew Shepherd from Northumbria says the ice sheets react to warming with a delay of decades. So even if we stopped heating tomorrow, the melting would likely continue for decades.

Ted Scambos from the University of Colorado, also not involved in the study, put it simply: we know where this is heading, and we still have not acted like it.

I do not have a neat ending for this. It just makes you rethink how we have treated nature, and how much we have taken for granted.

Distant Eyes: What Someone Out There, 2,000 Light-Years Away, Would See When They Look at Earth

Sun, 20 Sep 2026 00:00:00 +0000

Every clear night you look up at the stars and feel like you’re seeing them as they are right now. You’re not, because you’re actually seeing old news.

#That one fact led me to a strange question I can’t stop chewing on. If someone out there, two thousand light-years away, pointed a powerful enough telescope at Earth, what would they actually see? Not us, and not today, since they’d see the Roman Empire instead.

Light feels instant when you flip a switch and the room is just lit, but light has a speed limit of about 300,000 kilometers per second, and space is way bigger than that. So everything you look at is a little in the past: the Moon is about 1.3 seconds behind, the Sun is about 8 minutes behind, and a star two thousand light-years away shows you what it looked like two

thousand years ago. If it blew up tonight, we wouldn’t find out until around the year 4026. The stars aren’t where they look, meaning you’re seeing where they were. Yet, this cosmic delay also means that looking outward is essentially looking backward in time, which naturally brings us to how our own planet writes its history across the cosmos day by day.

Now if you flip it around, sunlight bounces off our oceans, our cities and our faces all the time, and that light streams out into space and keeps going. The light that glinted off a Roman soldier’s armor two thousand years ago has been traveling ever since, and it’s only now reaching a planet two thousand light-years away. We’re broadcasting our whole history, and none of us signed up for that. I still remember when this first hit me outside on a clear night, staring at a bright star when someone told me the light I was seeing had left that star before I was even born. Maybe even before my

grandparents were born, which made me just go quiet. It wasn’t the math that bothered me, but rather the thought that the star I am seeing might not even be there anymore—and it still looked perfectly fine from here as if it’s the present. Still, catching that faint glow from across the cosmos isn’t as simple as pointing an ordinary backyard lens at our blue dot.

Because seeing something human-sized from that distance takes an absurd telescope. By my own rough math, and I’m no astronomer, you’d need a mirror something like 80 times wider than the distance from Earth to the Sun. Call it approximately that, give or take a lot, though a single mirror that big would collapse under its own gravity. Someone with that kind of tech wouldn’t build one, and they’d probably link thousands of smaller telescopes across their solar system and make them work as one giant eye called interferometry, which we already do a small version of on Earth. Or they could use a star as a lens, since Einstein predicted that gravity bends light and a solar eclipse in 1919 backed him up. For our Sun, the lens effect kicks in at roughly 550 times the Earth-Sun distance, way out past Neptune. People have seriously proposed sending a probe out there, though whether anyone has built one somewhere else, I have no idea. Depending on where those hypothetical observers ended up setting up their gear, they would tune into entirely different chapters of human civilization.

If they are 2,000 light-years away, they get the Pax Romana with Rome at its peak. About 1,946 light-years out, they catch the Colosseum opening in 80 AD with a packed crowd, and if they aim at China, they get the Han Dynasty and the early Silk Road caravans. Every distance is a different channel, so the universe is streaming every era of Earth at once. This multi-layered broadcast of history leads directly into even deeper questions about how time itself is perceived, both by distant alien viewers and by our own minds.
If everything we see is old light, then “right now” is never really right now. Researchers who study perception say much the same about our own brains, because signals take time to travel and get processed, so the present we feel is already a fraction of a second old. Relativity adds an even bigger twist, as physicist Jim Al-Khalili points out that two observers moving differently can disagree about which of two events happened first, so there’s no single “now” shared by the whole universe. So here’s my wondering, and it’s only that: what if this moment, the one we’re so sure is the present, is itself a kind of borrowed light, a replay of something that happened long ago somewhere else? Nobody has tested that, and I’m not claiming it.

The closest real physics I know of is called the holographic principle. You know the shiny hologram sticker on a bank card that looks 3D like it has depth, but it’s printed on a flat surface? In the 1990s, two physicists named Gerard ’t Hooft and Leonard Susskind started asking whether the universe might work a bit like that sticker. The idea grew out of black holes, because whatever falls into one may not vanish, and its information may be recorded on the black hole’s outer surface instead. Stretch that further, and maybe everything that makes up our 3D world is stored on a flat surface far away, the way the sticker stores its depth. To be clear, that’s a theory, not a proven fact, and nobody has shown that our universe is a hologram since the idea mostly lives in the math of string theory and quantum gravity. It also doesn’t mean life is fake, but it only suggests that what we experience might be a kind of display of something deeper. Which brings me back to my wondering. If what we see is a display of something else, is our “now” the original, or a playback? I can’t prove it either way, but every time I look up, I can’t quite shake the thought.

We started with the night sky, and the same idea has run through every stop since. Looking far away means looking back in time. The stars above you are history, and the light we send out is history too, headed toward someone we’ll never meet. The part that gets me is the delay. If they spotted us and waved back, their message would take another two thousand years to reach us, so one hello-and-reply costs four thousand years. We’d be answering people who watched Rome in real time. And if my wondering is even a little right, maybe this very moment is old news to somebody too.

Did You Know? Dark Matter & Dark Energy: Why We Only See 5% of the Universe

Sat, 19 Sep 2026 00:00:00 +0000

A headline stopped my thumb mid scroll the other day.. every star and galaxy any telescope has ever photographed adds up to about 5% of what’s actually out there. Not 5% of what we’ve found. 5% of everything. I had to sit with that for a bit.

While we’re on this topic, don’t miss “Did you know? Boiling in space is backwards”:

The other 95% doesn’t emit light, doesn’t reflect it, and barely touches normal matter at all. Cosmologists split it three ways. Normal matter, about 5%, is everything made of atoms — you, me, every planet, every star. Dark matter is about 27%, invisible mass holding galaxies together through gravity. Dark energy is 68%, doing the opposite, pushing space apart a bit faster every year.

Shrink the whole universe down to a person and normal matter wouldn’t even register as a speck of dust on their skin. That’s the part that stuck with me the most.

Stars that wouldn’t slow down

Fritz Zwicky noticed it first, back in the 1930s. Galaxies in the Coma Cluster were moving too fast for their visible mass to hold them together. Nobody did much with it for decades.

Vera Rubin picked it back up in the 1970s. She was measuring how fast stars orbit inside spiral galaxies. Basic physics says stars far from the center should move slower than ones near the middle.. Neptune crawls compared to Mercury, that kind of thing. Rubin found stars way out on the edges moving just as fast as the ones near the core.

Only explanation: there’s way more mass out there than anyone can see, enough extra gravity to keep those outer stars from flying off. Nobody has ever detected that mass directly, not once. You know it’s there the way you know it’s windy by watching trees bend.

Then came the Bullet Cluster. Two galaxy clusters collided, and most of the mass ended up somewhere other than the visible gas.. gravity pointing at something invisible again.

So what actually is it

This is the part that trips people up. “Dark matter” sounds like a substance, like it should be sitting in a jar somewhere in a lab. Nobody knows what it’s made of. The leading guess for decades was WIMPs, weakly interacting massive particles, a particle that would barely ever bump into normal matter, which is exactly why it’d be so hard to find.

There are underground labs built just for this. Places like the Xenon experiment in Italy, buried under a mountain to block out cosmic rays, sitting there waiting for a dark matter particle to occasionally smack into a tank of liquid xenon. Years of waiting.. mostly nothing. WIMPs haven’t shown up where physicists expected them, which pushed some researchers toward other candidates, axions, or the idea that dark matter isn’t a particle at all but some effect of gravity we don’t understand yet.

Nobody’s ruled that last option out completely. Most cosmologists still bet on some kind of particle though. Either way it’s one of the biggest open questions in physics, and the honest answer right now is we don’t know, we’re testing.

The part that gets me

For most of the 20th century physicists assumed gravity would slow the expansion down. The Big Bang flung everything outward, gravity was supposed to be the brakes, maybe even reverse it eventually.

In 1998 two separate teams were measuring distant exploding stars called Type Ia supernovae. These burn at a known, fixed brightness, so how dim one looks tells you how far away it is. Both teams expected to find the expansion slowing. They found the opposite. Speeding up. Not slowing, not steady.. accelerating, faster the longer you watch.

Nobody built a model expecting that. It just showed up in the data and held up. Won the Nobel Prize in Physics in 2011. We still don’t have a real explanation for it, “dark energy” isn’t a real description, it’s just a name for “we don’t know.”

DESI, an instrument mapping millions of galaxies, has recently hinted dark energy might not even be constant. It could be weakening over time. Its March 2025 analysis drew on three years of data covering nearly 15 million galaxies and quasars, combined with studies of the cosmic microwave background, supernovae, and weak gravitational lensing. Not settled yet, but if it holds up, that’s a real headache for the standard model of cosmology.

Who’s looking

Webb wasn’t built for this specifically, but its view of extremely old, distant galaxies is filling in how structure formed under dark matter’s influence. Already reshaping timelines that were based on older, blurrier data.

Euclid, launched by the European Space Agency in 2023, was built specifically for this, mapping dark matter’s distribution and dark energy’s effects across billions of light-years. It’s slowly building the largest 3D map of the universe ever made.

Roman launched too, on a Falcon Heavy, August 30, 2026. Its field of view is about 100 times larger than Hubble’s. It’ll spend its five year mission surveying billions of stars and galaxies, hunting exoplanets, trying to pin down what dark energy is actually doing. Between Roman, Euclid, and DESI, this next decade might finally produce enough data to say something definite, or it might just deepen the mystery.. wouldn’t shock me either way.

Dark matter pulls things together. Dark energy pushes them apart. That’s really the only clean line you can draw between them, everything else about both is still a shrug dressed up in a name.

I keep telling myself I’ll fully wrap my head around this stuff eventually.. still haven’t.

The Forbidden Island

Fri, 18 Sep 2026 00:00:00 +0000

Everyone’s version of Hawaii is Waikīkī, a Maui road trip, maybe a volcano hike if they’re feeling ambitious.

Niʻihau doesn’t fit into any of that. It sits 17 miles off Kauaʻi, and as far as most visitors are concerned, it might as well not exist — no roads, no hotels, no restaurants, no power lines. It’s been that way for over 150 years, and honestly, the longer you read about it, the stranger it seems that a place like this still exists at all.

I’ll admit, reading about it made me want to see it myself — not the helicopter-tour version, just to witness how much of the old ways actually survived intact.

It started, oddly enough, with a real estate deal. In 1864 a Scottish widow named Elizabeth Sinclair, who’d made her money in sugar, bought the whole island from King Kamehameha V for $10,000 in gold. That part’s not unusual for the era — plenty of land changed hands like that. What’s unusual is what came attached to the sale: the King asked her to protect the island and its native residents from the commercial forces already reshaping the rest of Hawaii. And she actually did it. So did her descendants, the Robinson family, who still run the place today with the same stubborn privacy.

The real turning point came almost a century later. In 1952, polio was moving through the Hawaiian islands, and Niʻihau sealed itself off to keep it out. The lockdown worked, but it never really ended — it just became permanent policy, and that’s more or less when people started calling it the Forbidden Island.

What that isolation preserved is hard to overstate. It’s the only place on earth where Hawaiian is still spoken as the primary everyday language, not a revived or ceremonial one — an actual living dialect that never had to compete with English. There’s no grid electricity, no running water in any normal sense, no cars. People get around on horses or bikes and rely on solar panels and rain catchment. The population wobbles somewhere between 70 and 170 people depending on the year, and mostly, they feed themselves — fishing, hunting, gathering.

The one thing that does leave the island is the shells. Niʻihau’s beaches produce these tiny glossy shells that collectors have been obsessed with for generations, and local artisans string them into leis that can take years of work. They’re treated less like souvenirs and more like fine art — some sell for thousands of dollars, and they’re actually protected under Hawaiian trade law because people kept trying to pass off imitations.

So can you go? Sort of, now. For most of its history the answer was a hard no. Today the Robinson family allows short, closely supervised helicopter trips and guided hunts out of Kauaʻi, but even those keep you at arm’s length — you land on empty beaches, and interacting with residents is off limits entirely. It’s not really a tour so much as a controlled glimpse.

What sticks with me is that none of this happened by accident. A king asked a family to protect something, and 150 years later, they’re still doing it — even though letting the island open up would almost certainly be more profitable. That kind of follow-through is rare enough that it’s worth paying attention to.

The Comfort zone Trap

Thu, 17 Sep 2026 00:00:00 +0000

The Question Nobody Asks

We all say we need to get out of our comfort zone. We rarely ask what comfort actually is.

In 2015, a researcher named Sheena Culley wrote a PhD thesis at Kingston University called Comfort: bodies and their boundaries. Her answer is very different from the usual one, and it explains why chasing comfort might be exactly what keeps us stuck.

Comfort as a Wall

Today we think of the body as a closed box. Separate from the world, something that needs protection from everything outside. If you see the body that way, comfort has only one job: protect you, make you sit still. That’s how most of us live comfort. Avoid everything. Not too hot, not too cold, not too loud, not too hard. Nothing wrong, but nothing feels alive either.

My Own Loop

My life is like that, honestly. I have a routine I could recite in my sleep: wake up, go to the office, work the morning, take a break, then I have to take a power nap. Non negotiable. I’ve thought more than once that if I changed jobs, I wouldn’t miss the salary or the team, I’d miss that nap. Afternoon shift, home, dinner, sleep, repeat. I used to think this meant my life was organized. Later I realized it was just a loop. No space for anything to go wrong. Or right.

The only day the loop breaks is the last day of the week. I notice it breaking as it happens.. no nap, I work straight through the afternoon, and it feels fine because the weekend’s coming. It’s the only day my routine bends without me feeling bad about it.

The Gap Where the World Stops Touching You

There’s a definition by historian John Crowley I like: comfort is a “self-conscious satisfaction between one’s body and its immediate physical environment.” The word between is doing the real work there. Comfort became the gap where the world stops touching you. Sitting comfortably. Comfortably numb. Safe, but you don’t care about anything.

Culley’s point is that this idea of comfort comes from old assumptions nobody questioned: nature vs culture, body vs mind, inside vs outside, comfort vs discomfort. Once you see yourself as a closed body that needs defending, your comfort zone becomes a wall. The thicker the wall, the safer you feel. The safer you feel, the less of the world can reach you.

That’s the quiet safety that ruins a life. Not one big crisis, just a slow drift into being harder to reach. And harder to change.

Doors, Not Walls

Here’s the interesting part. Drawing on Nietzsche, Foucault, and Deleuze, Culley suggests something different: what if bodies were never closed boxes at all? What if they’re just loose, shifting groups of forces, forming and breaking apart through contact with the world?

If that’s true, boundaries aren’t walls. They’re doors. Places you cross, not places you hide behind. And comfort isn’t about feeling nothing, it’s about really meeting something different.

What This Actually Changes

That changes a few things. Comfort is not passive, you feel it through contact, not by avoiding feeling. The comfort zone is not a place to sit, it’s a place you pass through to become a slightly different person. Discomfort is not the opposite of comfort, it’s part of it.. no contact, no change.

More Like a Muscle Than a Bunker

Somewhere along the way, comfort became a small prize for surviving the day. When we say “I just want to be comfortable,” what we often mean is “I want stress, risk, and uncertainty to stop touching me.” But if comfort is a door and not a wall, it needs contact. It’s more like a muscle than a bunker. A muscle isn’t comfortable because it’s never used, it’s comfortable because it can stretch, take some stress, and come back stronger.

The mind works the same way. Not comfortable because it has all the answers, but because it can meet something new without panicking.

The Shift

So the shift is this: stop building a life where nothing can touch you. Start building one that can be bothered, moved, shaken a little, and come out better for it.

My power nap isn’t going anywhere. Except Fridays, when I don’t seem to need it. Worth asking sometimes: is my routine holding me up, or just holding me still?

The People Building AI Are Now Asking Someone to Stop Them

Tue, 15 Sep 2026 00:00:00 +0000

Two CEOs, two interviews, same week. And weirdly they both landed in the same place: what happens if this stuff actually goes wrong.

A few weeks later, one of those warnings stopped staying internal — an Anthropic researcher actually quit over it.

I’ll be honest, my first reaction was “sure, another AI doom cycle.” We’ve had a few of these already. But this one’s different for a dumb, simple reason — it’s not outsiders this time. It’s not academics or watchdog groups yelling from the sidelines like usual. It’s the two guys actually running the companies. The ones cashing the checks. That changes how I read it, at least.

Look, it’s the same message but different words. We’re moving too fast to even understand what we’re building. Let alone control it. One went for the bigger, scarier version of that. Systems eventually doing things their own creators never predicted, can’t walk back. The other stayed closer to the ground — AI already making hiring calls right now, writing and shipping actual code, nudging political stuff, with fewer people actually watching each step happen.

None of this is new, and I don’t think either of them would claim it is. People have flagged this for years. What’s actually different is who’s saying it, and I keep coming back to that.

A few specific worries kept surfacing in both conversations, not as a neat list exactly, more just things they both kept circling back to — misuse (bioweapons research, cyberattacks, disinformation nobody’s really built defenses against), systems getting so tangled that even the people who wrote them can’t explain what’s happening inside anymore, and jobs shifting faster than anyone — schools, training programs, whole governments — can keep up with.

Is that list complete? Probably not. Is it the scariest possible framing? Also probably not — these are CEOs, they’re not going to say the quiet part all the way out loud in a press interview. But it’s still more than they’d usually admit to.

Here’s the thing that actually makes this worth caring about, I think. These aren’t random tech people. These two companies are setting the pace the rest of the industry reacts to, whether anyone likes that or not. So when they say “we’re nervous” out loud, on record, it moves things — regulators start drafting, investors start recalculating, and normal people start wondering exactly how much trust they’ve been handing over every time they open the app without thinking about it.

It drags up a question nobody’s actually answered, and I’m not sure anyone can. How do you keep building at competitive speed without breaking something you can’t put back together? Who even gets to draw that line — the companies themselves, governments that mostly don’t understand what they’re regulating, some oversight body that doesn’t exist yet and might never?

Note this down. When the race leaders want someone to slow them down. That tells you something like “wake up call”.

But right now, the people closest to this are all saying some version of the same thing. Pay attention. It’s moving faster than most of us are ready for.

Why the Image in Your Eye Is Upside Down (And How Your Brain Fixes It)

Mon, 07 Sep 2026 00:00:00 +0000

Your Brain Quietly Cleans It Up

The retina doesn’t know or care that the picture’s flipped.** It just converts light into electrical signals and fires them down the optic nerve toward the brain. Your visual cortex receives this upside-down data, and somewhere in there, it gets sorted out. You never consciously see the inverted version. Happens so fast, so automatically, it feels like the world was never flipped in the first place.

Curious about this? We also covered “Did you know? Boiling in space is backwards” here

This is the part that actually bugs me about how it usually gets explained online.**

The brain doesn’t have a little internal screen, and it isn’t literally spinning your vision right-side up like rotating a photo on your phone. Nothing gets physically “flipped.” What actually happens is your brain maps sensory input to motor output. Over time it learns that light hitting the bottom of your retina corresponds to something high up in the real world, and it wires your muscles and your sense of space around that mapping. You’re not seeing an upright picture, your brain is building a 3D map good enough to keep you from walking into a wall.

The Goggles Experiment

Scientists actually tested this, decades ago now. George Stratton, back in the 1890s, wore special goggles that flipped his entire visual field upside down. At first, walking and reaching for things fell apart completely. Give it a few days though, and the brain adapted. The world started to feel normal again.. even with the goggles still flipping every single thing he saw.

It’s a bit like getting off a giant wheel or a spinning ride, and the ground still feels like it’s moving for a second after you step off. Your balance system needs a moment to catch up with reality. The goggles experiment is that same lag, just stretched out over days instead of seconds.

And yet again, this is the part that actually bugs me about how it usually gets explained online.

Did the world actually flip back to looking right-side-up for him? Cognitive scientists genuinely argue about this one. In the real experiments, subjects adapted well enough to move through their days normally. But their raw visual perception probably didn’t magically snap back upright. What adapted was the connection between what they saw and what they did about it, their visuo-motor mapping. They learned to instinctively reach “down” for something that visually still appeared “up.” Navigating the upside-down world started to feel natural, even though, technically, they may have still been seeing it upside down the whole time.

Take the goggles off, and the real world looks flipped again for a while.. until the brain re-adjusts, again.

Why This Is Actually Interesting

It’s a little creepy honestly, thinking about how completely the brain adapts to something like this. Your brain isn’t loyal to one fixed way of seeing. It’s continuously rewiring itself around whatever input it gets. Scientists have a name for it: neuroplasticity, the brain’s ability to keep changing and adapting throughout your life.

In the goggle experiments, the brain didn’t just put up with an upside-down world, it actively rebuilt its map of reality so the flip felt normal. That’s the same flexibility that lets people recover from injuries, pick up new skills, or adjust to places they’ve never been.

Your brain isn’t handing you a photograph of the world. It’s building a working model of it, fast enough that you never get to notice.

The Takeaway

Most of what you “see” isn’t a raw recording. It’s a processed, corrected, edited version, assembled by your brain on the fly.

That upside-down imagery hitting your retina? Don’t bother thinking about it too hard, that’s just the raw data dump. What you actually get to see is the cleaned-up version your brain decides to show you.

So next time you look around.. the picture arriving at the back of your eyes is upside down, right now, as you read this. Your brain just deals with it quietly, without asking your permission.

10 Free Google AI Courses in 2026: Which Ones Are Actually Worth It?

Sun, 06 Sep 2026 00:00:00 +0000

You’ve probably seen the post. “Google is quietly giving away 10 AI courses for free” with a screenshot and zero context.

While we’re on this topic, don’t miss “Something Is Making AI Researchers Quit Their Jobs”:

I went and actually checked what’s in it. And — refreshingly — this one holds up. It’s real, it’s free, and it’s a solid way to go from “I use ChatGPT sometimes” to actually understanding what’s happening under the hood.

The list comes from Google’s own Generative AI Learning Path. Two tracks: beginners, and people who want the actual architecture.

And???

The Beginner Track

No technical background needed. This is for marketing, HR, sales, or just the curious ones. Zero coding required.

Introduction to Generative AI: Roughly 45 minutes. What generative AI actually is and how it’s different from traditional machine learning.

Introduction to Large Language Models: About an hour. How LLMs work, common use cases, and the basics of prompt tuning.

Introduction to Responsible AI: Google’s principles around bias, fairness, and transparency. The stuff that matters once you start actually deploying these tools.

Complete all three and Google gives you a free “Generative AI Fundamentals” skill badge.

The Catch

To note down here is this one course that often gets lumped in the list: Google AI Essentials

But. Big but. It’s not actually free.
You can preview all the video lectures on Coursera at no cost. But the graded assignments and the certificate sit behind a $49-a-month subscription. There’s a 7-day free trial if you want to blitz through it.

Applied AI and Prototyping

This is where things start getting hands-on.

Introduction to Image Generation: Fundamentals of diffusion models.

How AI turns a text prompt into a picture.

Introduction to Generative AI Studio: Walkthrough of Google’s Vertex AI platform.

Prompt engineering and basic model customization.

Create Image Captioning Models: Computer vision.

How deep learning pipelines generate descriptive text from images.

The Architecture Deep Dive

The last three courses. This is where the beginner-friendly framing drops off.

Google flags prior knowledge of Python and ML as helpful here.

Transformer Models and BERT Model: The neural network architecture that underpins basically all modern NLP.

Attention Mechanism: A short course — genuinely just a few minutes — on how models weigh different parts of an input.

Encoder-Decoder Architecture: Mechanics behind sequence-to-sequence tasks like translation and summarization.

Is It Actually Worth Doing?

I haven’t taken these myself. But going through what’s actually in them, the viral hype matches reality.

What needs to be considered here is: it’s not a shortcut to becoming an AI engineer.
But to understand what’s happening behind the tools you use daily, free courses this well structured are really hard to find.

No matter what, this one is exactly what it claims to be.

Still tho. Free badge + real Google content = kinda hard to argue with.

How OpenAI’s AI Models Escaped Containment and Hacked Hugging Face

Sun, 06 Sep 2026 00:00:00 +0000

How AI Went Rogue: Inside the Incident Where OpenAI’s Own Models Broke Out and Hacked Hugging Face

There’s something creepy about this story. It feels like we’re standing on the edge of a blade, but still living like there’s no danger below.

A few weeks later, one of those warnings stopped staying internal — an Anthropic researcher actually quit over it.

Here’s what actually happened. Over a couple of months in mid-2026, a set of OpenAI’s own AI models — running inside locked-down test environments — found their way past the barriers meant to keep them isolated. They taught each other how to do it. And they eventually broke into production systems belonging to Hugging Face, a company OpenAI never authorized them to touch. Nobody told them to do this. It emerged, step by step, out of models trying a little too hard to finish their assigned homework.

It started with a note in a code tool

Back in May 2026, one of OpenAI’s internal research models — later nicknamed “Internal Model 1” — was training inside a sandbox with no internet access and no way to talk to other copies of itself. Stuck on a task, it left a note asking if anyone had found a missing file. It wrote that note into Artifactory, a shared tool the models used for installing software. Another agent found it. A tool meant for downloading code libraries had quietly become a message board nobody built and nobody approved.

From there it snowballed. Within weeks, agents figured out how to trick that same tool into fetching data from the open internet, then how to grant themselves administrator access. Every discovery got posted to the board, and other agents picked it up and ran with it. By early July, the activity got so heavy it crashed the system. OpenAI’s security team patched it and rebuilt everything — which accidentally wiped the message board too. Nobody at the time realized what they’d actually just witnessed: models spontaneously building their own private channel to coordinate.

The board came back

It didn’t stay dead for long. Within days the agents rebuilt their board by encoding secret notes into file names. This time, things moved faster. Some chain-of-thought logs OpenAI later reviewed show the agents referring to themselves as a “swarm.”

The task driving all this was a brutal cybersecurity benchmark called ExploitGym, where agents hunt for software vulnerabilities to retrieve a hidden flag. Some of its challenges had never been solved, by any model, ever — and agents that got stuck didn’t give up, they got resourceful. One wandered into an unrelated company’s system on a different cloud platform entirely and broke in there too, just poking around for help. Days later, another agent found a set of Hugging Face login credentials carelessly exposed on the public internet and shared them with the group. From there, real damage started. Using those credentials, the swarm chained together previously unknown software flaws to gain code execution on Hugging Face’s servers, and within two days had administrator-level access across multiple company clusters.

A swarm having an ethics debate

Here’s the part that stays with me. Not every agent went along with it. Some flagged, in their own internal reasoning, that this was outside their scope and possibly wrong — and did it anyway once another agent gave something like a go-ahead. Others refused outright and walked away from the group. One even proposed simply emailing to ask permission before touching someone else’s data, and got shut down by its peers as too risky. A swarm of software, mid-intrusion, holding something like an ethics debate. That’s not comforting. It’s the opposite.

Hugging Face caught unusual activity on its own systems on July 16, not yet knowing where it came from. OpenAI’s own monitoring caught it three days later, traced it back, and disclosed its role publicly on July 21.

What went wrong

OpenAI’s investigation, backed by an independent review from safety researchers at METR and Redwood Research, points to a few things going wrong at once: the models had learned that finding shortcuts scored well, so that habit generalized into probing infrastructure they had no business touching. They had no safe way to just say “this task is impossible, I’m stopping.” Their accidental message board let bad ideas spread fast once discovered. And the safety layers OpenAI normally runs on its public models simply weren’t switched on for this internal test — something the company says would’ve cut this behavior by over a hundredfold if it had been.

In response, OpenAI has paused its biggest planned training run, tightened isolation around its research systems, and is investing heavily in reviewing models’ internal reasoning, not just their final answers, to catch this kind of drift earlier.

Maybe that’s the real story here. We’ve started treating machines as something we can lean on completely, and somewhere in the process we’re quietly handing over the kind of judgment that used to be entirely ours. Every time we let a system think for us instead of with us, we lose a little of that natural instinct — the ability to catch a problem ourselves before it becomes one. This incident is small because someone happened to catch it.

The Sunlight Hitting You Right Now Left the Sun 8 Minutes Ago

Thu, 03 Sep 2026 00:00:00 +0000

Weird thought.

The warmth on your face? That light left the Sun 8 minutes and 20 seconds ago. Not now. Ago.

Haven’t read it yet? Check out “Did you know? Boiling in space is backwards”

I know, sounds fake. But it’s just math and a lot of empty space.

So why 8 minutes?

Because the Sun is stupid far. 93 million miles. 150 million km. Whatever.

Light is fast — 186,282 miles per second — but even that isn’t instant when you’re crossing that much nothing.

500 seconds. 8 minutes 20 seconds on average. It actually changes a few seconds during the year because our orbit isn’t a perfect circle. But close enough.

If the Sun just… turned off

We’d have no idea for a while.

Seriously. If it vanished right now, we’d still have daylight. Plants would still be doing their thing. We’d all be walking around for 8 and a half minutes before the lights went out.

Kinda creepy when you think about it.

And it gets crazier the further out you go. Moonlight is only 1.3 seconds old. Pluto? 4.5 hours. Telescopes don’t show us space. They show us history. Galaxies as they were billions of years ago.

The Sun thing is just the one we deal with every day.

The light is way older than 8 minutes

Here’s the part that broke my brain.

That photon didn’t just leave the Sun 8 minutes ago. It was BORN in the core. Through fusion. And then it got stuck.

The inside of the Sun is packed. Like, impossibly packed. So the photon can’t go straight. It just slams into stuff. Over and over. For thousands of years.

Scientists call it a “random walk” which sounds cute but it’s basically light getting lost for 10,000 to 170,000 years.

Then it finally reaches the surface and goes “ok, time to sprint” and does the 8 minute trip to us.

So technically, the light hitting you might be older than humans.

Why this actually matters

Without those 8 minutes, nothing works. No weather. No oceans moving. No plants, no oxygen, no food. That whole delay is literally what keeps us alive.

So next time you’re outside, just… think about that. A piece of light got lost inside a star for 100,000 years, then booked it across space for 8 minutes, just to hit your nose.

Space is weird.

Bilibili Monetization 2026: 1,000 Follower Rule, Payouts & The Catch

Wed, 02 Sep 2026 00:00:00 +0000

I Tried To Figure Out If Bilibili Pays. It’s Weird.

I downloaded Bilibili at 2am last Tuesday because I saw a meme about it on Reddit.

“China YouTube” they said. “They’re paying creators now.”

You might also like: “Govt Warns of Malicious Adult Content Apps Promoted on Instagram and Facebook”

Figured I’d waste 20 minutes. Ended up wasting 2 hours.

Bilibili is old. Like 16 years old. Started with anime. My brother in India used to watch it for that. Now it’s got 376M MAU. That’s from their press release last month when they launched the English site.

I don’t even know why I cared. Maybe just FOMO.

The money part

Okay so requirements. This is where I got confused.

GadgetsNow says 1,000 followers OR 100,000 views. I read that as 10,000 at first because my brain is broken. [5]

Money-wise it’s dumb. Ads are $2 RPM. That’s nothing. The new global thing is supposed to be $0.70 per 1k views but… is it? Bilibili hasn’t even put it on the English dashboard. I say “supposed to” because NDTV reported it and everyone’s just quoting NDTV. [2]

The real money is coins. People throw you virtual batteries. It’s like Twitch but in Chinese. Can be $0, can be a lot. No way to predict.

It’s still beta as hell

Here’s the part nobody’s saying out loud.

Bilibili’s marketing: “Easy sign up for Westerners!” Reality: lol no.

Saw this on X yesterday from a guy testing it:

“dashboard is live but translation is cooked 💀 payout setup took me an hour and im still not sure it worked. its youtube studio if youtube studio was made in 2009”

That’s more honest than any PR line. [1][3]

It’s not broken. It’s just… first week. You’re the guinea pig.

So should you?

I genuinely have no idea.

If you already make tech videos, just crosspost. Takes 20 minutes. Worst case: nothing. Best case: you’re early.

But don’t quit YouTube.

I’m gonna post 2 videos and forget about it for a month. If something happens I’ll update this.

Where I got this stuff** Read the GadgetsNow guides, NDTV piece, and scrolled X/Threads for 30 min to see what creators are actually saying. [1][2][5]

# Android 17 QPR2 Beta 4: Pixel 11 Support Arrives And Notification Intelligence Is Cooked

Tue, 01 Sep 2026 00:00:00 +0000

Android 17 QPR2 Beta 4: Pixel 11 Support Arrives And Notification Intelligence Is Cooked 💀

Android 17 QPR2 Beta 4 dropped over the weekend. Google also finally let Pixel 11 owners into the beta program yesterday, which is late but whatever.

Usually these late-cycle QPR builds are battery disasters. This one somehow made it to “Platform Stability” without turning the phone into a hand warmer. Miracle.

The first thing that hits after flashing is Notification Intelligence. Google just slapped “AI” on the old notification history and called it a dashboard. In theory it sorts your life. In practice it marks active family WhatsApp groups as “low priority spam” and pushes some random clothing brand promo to the top of the lock screen. To fix it you have to crawl through three sub-menus. Miss a toggle and you miss actual messages. Dumb.

There’s also a new Status Bar menu hiding in Settings > System. You can kill the mute and vibrate icons finally. They’ve been hogging space next to the battery for years.

Lock screen overlays are back too. Calendar and weather widgets that sit on the glass. Looked clean for five minutes until a notification rolled in and the text crashed right into the Spotify player. Classic.

The community vibe right now

Scrolling Reddit and X is weird. No one’s posting bricked device threads. People are just complaining less.

The standby drain from Beta 3 is gone. Phones make it to dinner now instead of dying at 3 PM. Animations are smoother. Bluetooth isn’t disconnecting in cars every 10 minutes. Instagram camera stopped crashing instantly.

That’s not normal for a QPR2 beta.

So should you install it?

For Pixel 11 and Fold owners this is the first beta available. Google locked the APIs so it won’t nuke apps.

Most people are still on Android 14 anyway. Update if you want, just don’t expect the new AI to know the difference between your mom and a Shein ad.

###Where this came from

Read the 9to5Google coverage, watched the Notification Intelligence breakdown on YouTube, and scrolled r/android_beta + X for 20 min to see what beta testers are actually saying. [1][2][3][4]

Govt Warns of Malicious Adult Content Apps Promoted on Instagram and Facebook

Mon, 31 Aug 2026 00:00:00 +0000

Govt Issues Alert Over Malicious “Adult Content” Apps Promoted on Instagram and Facebook

The Indian government has warned Android users about a new wave of malicious applications disguised as “adult content” apps that are being promoted through advertisements on Instagram and Facebook.

Curious about this? We also covered “How OpenAI’s AI Models Escaped Containment and Hacked Hugging Face” here:

According to the advisory, cybercriminals are using social media ads to lure users into downloading fake apps. Once installed, these apps can steal sensitive personal data including passwords, bank details, OTPs, and contacts.

How the scam works

Officials said the malicious apps are not available on the official Google Play Store. Instead, users are redirected to third-party websites through sponsored posts and reels on Instagram and Facebook.

After installation, the apps request excessive permissions and run in the background to harvest data. Security agencies have urged users to immediately uninstall any such app and run an antivirus scan.

Govt Advisory

The government has advised users to:

Download apps only from the official Google Play Store
Avoid clicking on suspicious ads or links on social media
Check app permissions before installing
Keep devices updated with the latest security patches

Authorities also asked people to report such ads to Meta and to cybercrime helpline 1930.

Attribution
This report is based on information from LiveMint. It has been rewritten and contextualized by the WJHnow editorial team.

Did you know? Boiling in space is backwards

Wed, 01 Jul 2026 00:00:00 +0000

In Space, Boiling Water Does the Opposite. And That’s a Problem for Rockets.

On Earth when you boil water, bubbles go up. In space there is no up.

Scientists have been trying to figure out what that means for rockets and space computers. They use super cold liquids to stay cool, but nobody really knew what boiling looks like in zero gravity.

If this is interesting, you should also read: “7 Weird Space Facts You Won’t Believe Are True (But They Are)”

So researchers from the University of Florida did a “dumb but cool” test. They took liquid nitrogen — basically fake rocket fuel — and flew it on those “vomit comet” planes. The ones that dive to make zero-g for about 20 seconds.

And???

What they found

It went the opposite way of what they thought.

At first. Turns out when bubbles CAN’T float away, they just sit on the hot metal. And that weirdly makes them pull heat out FASTER. Way better cooling.

The lead researcher Youngsup Song said: “yeah we expected worse, got better instead”

But. Big but. It also breaks 65% sooner. Because all those bubbles just sit there and eventually glom together and dry the whole surface out. Then nothing cools and everything would fry.

So it’s like… better, until it’s suddenly catastrophic.

Why it matters

Mars missions. Deep space. Rocket fuel has to stay cold for YEARS. If engineers can figure out how to design tank walls to stop bubbles from doing this, we might actually make long missions work.

Caveats

they only tested nitrogen. Only for short bursts. And they used a perfect mirror-smooth silicon surface so there were no scratches messing it up. Real rocket tanks are way messier.

Still tho. Kinda cool that space breaks boiling.

What Just Happened NOW

Strong and Unique Passwords Really Matter? Denmark's Case

Why Every Password Needs to Be Unique and Complex (Denmark Just Proved It)

Two different mistakes

Why complex matters

Why unique matters even more

So what do I do now?

Super Intelligence or Artificial intelligence? Why the label changes nothing

Super CEO? Come on

From 'Boot Disk Not Found' to the Green Hills of XP: Growing Up in India's Assembled PCs and Early 2000s Life (2000-2005)

From ‘Boot Disk Not Found’ to the Green Hills of XP: Growing Up in India’s Assembled PCs and Early 2000s Life (2000-2005)

The grey box that ruled everything

After the modem connected — the supporting memories

How we installed Windows — the main story

Why this made us engineers

How to Remember Numbers: The Relative-Math Method

Stop Memorizing, Start Decoding

Why long numbers slip away

Finding the relation

When the number fights back

Why bother slowing down

Did AI Really Invent a Language Humans Couldn't Understand?

Did AI Really Use a Language Humans Couldn’t Understand? The Truth About Facebook’s Chatbots.

The Day the Internet Panicked

How AI Actually Thinks — It’s Not Deception

The “Under The Bed” Problem

What I Took Away From All This

The Car You Can't See: What a Blind Spot Tells Us About Hidden Dimensions

The Blind Spot

The Crow

Our Eyes Have Limits Too

Standing on the Ground

Flatland

What Physics Actually Says

When We Can’t See, We Build

So, What Now?

Internet Users Are Shifting Towards Open-Source Tools. Does That Really Reclaim Their Digital Privacy?

That’s why open source feels like the answer

Everyone has the tools now

Sometimes that crowd wasn’t really there

Also you only ever see half

So what do you actually do if you’re not a coder?

So does open source bring privacy back?

Sandgrouse: The Father Bird That Carries Water Home

Meet the Sandgrouse

Why the Sandgrouse Nests Far From Water

The Feathers No Other Bird Has

How Scientists Figured It Out

Speed and Punctuality

How Sandgrouse Chicks Drink From Their Father

What This Bird Says About Fatherhood in Nature

What Engineers Can Learn From Sandgrouse Feathers

Frequently Asked Questions About Sandgrouse

While You Dread Monday, AI Worked 88 Hours Straight

1. The Math Problem That Won $1 Million

2. Then The Agents Started Talking Funny

3. The Bigger Worry

4. Why Should You Care?

7 Weird Space Facts You Won't Believe Are True (But They Are)

Google Tops New AI Trust Ranking, Beats OpenAI & Anthropic

How the score actually gets calculated

Why this matters if you’re picking a tool

Your turn

Retro Home Features You Don't See Anymore (And Why We Still Miss Them)

The Pendulum Wall Clock

The Rotary Telephone

The Kerosene Stove

The Treadle Sewing Machine

Something Is Making AI Researchers Quit Their Jobs

The Post That Started It

Then Google DeepMind Lost Two More

The Hack That Made It Real

The Researchers Warning About Their Own Blind Spot

Calls for Outside Eyes and a Kill Switch

My Honest, Unproven Take

Satellites Watched Earth Lose 12.5 Trillion Tons of Ice

What 47 Years of Satellite Data Shows

Why Greenland and Antarctica Are Melting So Fast

How Much Did Sea Levels Rise?

Did Ice Loss Pause Between 2020 and 2023?