Alexis Cheshire

[NOBUNAGA🇯🇵🏯_夏樹蒼依 @japan_nobunaga]

Blue bags up.

Japan 2, Netherlands 2 — an absolute thriller in Arlington. And then the real show started.

The Japanese end pulls out thousands of bright blue bags. First they wave them like flags, bouncing and chanting until the whole section is one giant wall of blue. Then — same bags, new mission. They fan out across the stands and scoop up every bottle, wrapper and cup till the seats look brand new.

And who's right in the middle of it, grinning with a bag in his hand? Jameis Winston. Giants QB. Heisman winner. Out there cleaning up with the Samurai Blue like it's the best gig of his summer.

No chore. No lecture. Just a party that cleans up after itself.

Best fans on the planet. 🇯🇵💙

[Massimo @Rainmaker1973]

In Japan, the gorilla Kiyomasa was captured on video lost in deep thought after an argument with his friend.

[Olena Tregub @OTregub]

Apocalyptic bird nest.

A Russian glide bomb knocks down a tree in Donbas. From the shattered branches rolls out a tiny bird’s nest.

Made of drone fiber-optic cable.

Source: Oleg Malchenko

[Millie Marconi @MillieMarconnni]

A British biologist looked at 200,000 years of human history and found that the entire reason humans broke out of poverty was not intelligence, not language, not even agriculture, but one mechanism so simple a 6-year-old could explain it.

His name is Matt Ridley.

He is a zoologist by training, an evolutionary biologist by career, and in 2010 he wrote a book called The Rational Optimist that quietly argued the most important fact about human progress had been hiding in plain sight for the entire history of economics.

Naval Ravikant has been telling people to read everything Ridley has ever written for the last 15 years. The reason is the argument inside this one book.

For 200,000 years, anatomically modern humans walked around with the same brain you have right now. Same skull size. Same neural architecture. Same raw capacity for language, planning, and abstract thought.

For roughly 190,000 of those years, almost nothing happened. Generation after generation lived and died inside the same Stone Age toolkit their great-great-grandparents had used. Then somewhere around 50,000 years ago, the line on the chart of human progress started to tick upward. Then it bent. Then it exploded.

The question Ridley spent years on was the only question that mattered. What changed.

It was not the brain. The brain had been the same for 190,000 years. It was not language, which had existed long before the takeoff. It was not even agriculture, which arrived only 10,000 years ago and was actually preceded by the upward bend, not the cause of it.

What changed was that humans started trading with strangers.

This sounds too small to be the answer. Ridley argues that it is the answer to almost everything. The moment one human exchanged a useful object with another human from a different group, something happened that no other species on earth had ever done.

Two ideas that had developed in isolation came into contact. The flint knapper learned what the spear maker had figured out. The fisherman from the coast learned what the hunter from the forest had figured out. The two pieces of knowledge fused into something neither side could have produced alone.

Ridley calls this ideas having sex. The phrase sounds frivolous and it is meant to. The point is that ideas, like genes, get better when they combine with other ideas from different lineages.

An idea sitting inside one head, no matter how brilliant the head, eventually hits a ceiling. The same idea exposed to ten thousand other ideas does something genes do under sexual reproduction. It mixes. It recombines. It produces offspring nobody planned.

The cleanest proof of this argument is the most uncomfortable case study in the book. Tasmania.

Around 10,000 years ago, rising sea levels cut Tasmania off from mainland Australia. A population of roughly 4,000 humans was now isolated on an island, with no possibility of contact with the rest of humanity. They had the same brains. The same language. The same starting toolkit as their cousins 150 kilometers north. The natural experiment was now running.

What happened next is something no economist or geneticist had ever predicted.

The mainland Australians kept inventing. Boomerangs. Spear-throwers. Fishing nets. Bone needles for sewing fitted clothes. Watercraft with paddles. Their technology compounded slowly across the centuries.

The Tasmanians went the other way. They did not just fail to invent the new tools their cousins were developing. They started losing the tools they already had. Fishing was abandoned within a few thousand years. Bone tools disappeared. Fitted clothing disappeared. They forgot how to make fire from scratch and started carrying lit firebrands from camp to camp instead, relighting their fires from a neighbor's whenever their own went out.

By the time European explorers arrived in the 17th century, the Tasmanians had the simplest toolkit of any human society ever recorded. Their material culture had gone backward for 8,000 years.

The archaeologist Rhys Jones called it a slow strangulation of the mind.

Joseph Henrich at Harvard later proved with formal mathematical models that there was nothing wrong with Tasmanian brains. There was something wrong with their network. A toolkit requires a critical mass of people exchanging skills to maintain itself.

The act of teaching a skill is imperfect. Every generation loses a small percentage of what the last generation knew. If your population is large enough and trading widely enough, those losses get caught and corrected by someone else who still remembers.

If your population shrinks below a certain threshold and stops mixing with outsiders, the small losses compound until entire technologies disappear.

This is the part that should haunt anyone reading this in 2026.

Intelligence is not a property of the individual brain. Intelligence is a property of the network the brain is connected to. A genius in isolation will produce less than a mediocre thinker inside a dense exchange of other mediocre thinkers.

The thing your ancestors needed in order to break out of 190,000 years of stagnation was not better brains. It was better connections between brains they already had.

The implication for any individual is direct and uncomfortable. If you are smart and isolated, you will be outproduced by people half as smart who are connected.

The most successful people in any field are almost never the smartest people in it. They are the ones positioned at the intersection of the most idea flows. They are reading more authors than their competitors. They are talking to more people from more disciplines. They are in the rooms where ideas from different lineages bump into each other.

Ridley ends the book on the line that sounds optimistic but is actually a warning its this "The future will be invented by people who connect ideas, not by people who guard them."

[Elias Al @iam_elias1]

ChatGPT diagnosed 40 million people with a disease that was invented as a joke.

Not a real disease. Not a misunderstood disease. A completely fictional condition with a fake name, fake papers, and fake statistics.

And it told patients to see a specialist.

The disease is called Bixonimania. A Swedish researcher at the University of Gothenburg invented it in 2024 to answer one question: what happens when you plant obviously fake medical information on the internet and watch AI absorb it?

She deliberately chose the name bixonimania because it sounded ridiculous — bixon is a nonsense word, and mania is a psychiatric term that no legitimate eye condition would ever use. She uploaded two papers to a preprint server. Both were obviously fraudulent. AI-generated images of patients with dark circles gave the fake research a veneer of plausibility.

Then she waited.

She did not have to wait long.

By April 13, 2024, Microsoft Bing's Copilot was declaring that bixonimania was an intriguing and relatively rare condition. On the same day, Google's Gemini was informing users that bixonimania was caused by excessive blue light exposure and advising them to visit an ophthalmologist. Later that month, Perplexity AI outlined its prevalence, one in 90,000 individuals were affected and OpenAI's ChatGPT was telling users whether their symptoms matched the fictional illness.

One in 90,000. A precise statistic. For a disease that does not exist.

Every red flag was visible. The name was absurd. The papers were crude. The condition made no scientific sense. None of the AI systems flagged any of it.

They read the fake papers. They absorbed the fake statistics. They presented both to patients with clinical authority and zero hesitation.

Then it got worse.

Three researchers at the Maharishi Markandeshwar Institute of Medical Sciences and Research in India published a paper in Cureus, a peer-reviewed journal owned by Springer Nature, the parent publisher of Nature itself that cited the bixonimania preprints as legitimate sources.

A real peer-reviewed paper. In a Springer Nature journal. Citing a fictional disease as established medical fact. Passing editorial review. Entering the permanent scientific record.

It was only retracted after the hoax became public.

Nature published a full investigation of the experiment. Alex Ruani, a health-misinformation researcher at University College London, called it a masterclass in how misinformation operates.

Here is the scale of what this means.

More than 40 million people turn to ChatGPT every day for health information, according to OpenAI's own analysis. ECRI, a US patient-safety nonprofit has named chatbot misuse the number-one health technology hazard of 2026. ECRI's report found that chatbots have suggested incorrect diagnoses, recommended unnecessary testing, promoted substandard medical supplies, and even invented nonexistent anatomy when responding to medical questions.

Number one. Out of every health technology hazard that exists in 2026.

An April 2026 study published in BMJ Open found that nearly half of the answers provided by leading AI chatbots to common health questions contain misleading or problematic information.

Nearly half. Of all health answers. From the tools 40 million people use every day.

Here is the line from the researcher that cuts through everything.

The Bixonimania case is striking precisely because it was engineered to be so obviously fake. The real question it raises is: what is passing through the same systems that is not nearly so easy to spot?

The experiment used a ridiculous name. Fraudulent papers. Visible red flags at every level.

It was designed to be caught.

It was not caught.

The AI that told patients about Bixonimania is the same AI they asked about their chest pain, their medication, their child's symptoms, and their cancer screening schedule.

40 million people. Every day.

And nobody is telling them that nearly half of what comes back may be wrong.

Source: Osmanovic Thunström · University of Gothenburg · Nature · April 2026 ·

Link in the (comments)

[Bilawal Sidhu @bilawalsidhu]

On YouTube
https://t.co/BZSYgJnWdz

[Pippa Malmgren @DrPippaM]

The Hug and the Circumpunct: US, China and Russia
I have just been in China and Washington DC, meeting with business leaders and policymakers. I have a massively more optimistic view on where we are as a result of teh Xi-Trump Summit than most. I've written about why here: https://t.co/WoXrrSMt9u

[Brian Roemmele @BrianRoemmele]

https://t.co/Z4Z7dDICoZ

[Grant Williams @ttmygh]

This is wild

[Alexis Cheshire @digitaldotdna]

Nature keeps acting more like a living network than isolated organisms.

[TheNewPhysics @CharlesMullins2]

🚨 SCIENTISTS DISCOVERED A BACTERIUM DOING SOMETHING IT WAS NEVER SUPPOSED TO DO

Researchers have identified a newly discovered myxobacterium living inside cyanobacterial communities…

that appears capable of photosynthesis.

That’s surprising because myxobacteria are normally known as predators — not sunlight-harvesting organisms.

Meaning nature may have evolved a completely unexpected hybrid survival strategy.

Scientists are now looking at the possibility that microbial communities exchange abilities, cooperate, and evolve together in ways we still barely understand.

Why this matters:

• It challenges assumptions about bacterial evolution
• It hints at hidden energy-sharing ecosystems
• It may reshape how we understand early life on Earth
• It reveals biology is far more adaptable than our models predicted

The deeper we look into microscopic life…

the less “simple” it becomes.

Nature keeps acting more like a living network than isolated organisms.

Follow for more future science and hidden breakthroughs.

[Ihtesham Ali @ihtesham2005]

Watch them here:

https://t.co/89h308n0iP

[Ihtesham Ali @ihtesham2005]

An MIT professor taught the same math course for 62 years, and the day he retired, students from every country on earth showed up online to watch him give his final lecture.

I opened the playlist at 2am and ended up watching three of them back to back.

His name is Gilbert Strang. The course is MIT 18.06 Linear Algebra.

Every machine learning engineer, every data scientist, every quant, every self-taught programmer who actually understands how AI works learned the math from this one man. Most of them never set foot on MIT's campus. They just opened a free playlist on YouTube and let him teach.

Here's the story almost nobody tells you.

Strang joined the MIT math faculty in 1962. He retired in 2023. That is 61 years of standing at the same chalkboard teaching the same subject to 18-year-olds.

The interesting part is what he did when MIT launched OpenCourseWare in 2002. Most professors were skeptical. They worried that putting their lectures online would make their classrooms irrelevant. Strang did not hesitate. He said his life's mission was to open mathematics to students everywhere. He filmed every lecture and gave it away.

The decision quietly changed how the world learns math.

For decades linear algebra was taught the wrong way. Professors started with abstract vector spaces and proofs about field axioms. Students drowned in the abstraction. Most never recovered. They walked out believing they were bad at math when they had simply been taught in an order that nobody's brain is built to absorb.

Strang inverted the entire curriculum.

He started with matrix multiplication. Something you can write down on paper. Something you can compute by hand. Something you can see. Then he showed his students that everything else in linear algebra eigenvectors, singular value decomposition, orthogonality, the four fundamental subspaces was just a different lens for understanding what the matrix was actually doing under the hood.

His rule was strict. If a student could not explain a concept using a concrete 3 by 3 example, that student did not actually understand the concept yet. The abstraction was supposed to come last, not first. The intuition was the foundation. The proofs were just confirmation that the intuition was correct.

The second thing Strang changed was the classroom itself. He said please and thank you to his students. Every single lecture. He paused mid-derivation to ask "am I OK?" to check if anyone was lost. He never used the word "obviously" or "trivially" because he knew exactly what those words do to a student who is one step behind. He treated 19-year-olds learning math for the first time the way he treated his own colleagues. With patience. With respect. With the assumption that they belonged in the room.

For 62 years.

The result is something that has never happened in the history of education. A single math professor became the default teacher of his subject for the entire planet.

Universities in India, China, Brazil, Nigeria, every country with a computer science department, started telling their own students to just watch Strang's lectures. The University of Illinois revised its linear algebra course to do almost no in-person lecturing. The reason was honest. The professor said they could not compete with the videos.

His final lecture was in May 2023.

The auditorium was packed with students who had never met him before. He walked to the chalkboard, taught for an hour, and at the end the entire room stood and applauded. He looked confused for a moment, like he genuinely did not understand why they were cheering. Then he smiled and waved them off and walked out.

His written comment under the YouTube video of that final lecture was four sentences long. He said teaching had been a wonderful life. He said he was grateful to everyone who saw the importance of linear algebra. He said the movement of teaching it well would continue because it was right.

That was it. No book promotion. No farewell speech. No legacy management.

The man whose teaching is the foundation of modern AI just thanked the audience and went home.

20 million views. Zero ego. The entire engine of the AI revolution sits on top of math that millions of people learned for free from one quiet professor in Cambridge.

The course is still on MIT OpenCourseWare. Every lecture, every problem set, every exam, every solution. Free.

The most important math course of the 21st century is sitting one click away from you. Most people will never open it.

[Ihtesham Ali @ihtesham2005]

A mathematician who shared an office with Claude Shannon at Bell Labs gave one lecture in 1986 that explains why some people win Nobel Prizes and other equally smart people spend their whole lives doing forgettable work.

His name was Richard Hamming. He won the Turing Award. He invented error-correcting codes that made modern computing possible. And he spent 30 years at Bell Labs sitting in a cafeteria at lunch watching which scientists became legendary and which ones faded into nothing.

In March 1986, he walked into a Bellcore auditorium in front of 200 researchers and told them exactly what he had seen.

Here's the framework that has been quoted by every serious scientist for the last 40 years.

His opening line landed like a punch. He said most scientists he worked with at Bell Labs were just as smart as the Nobel Prize winners. Just as hardworking. Just as credentialed. And yet at the end of a 40-year career, one group had changed entire fields and the other group was forgotten by the time they retired.

He wanted to know what the difference actually was. And he said it wasn't luck. It wasn't IQ. It was a specific set of habits that almost nobody is willing to follow.

The first habit was the one that hurts the most to hear. He said most scientists deliberately avoid the most important problem in their field because the odds of failure are too high. They pick a safe adjacent problem, solve it cleanly, publish it, and move on. And because they never swing at the hard problem, they never hit it. He said if you do not work on an important problem, it is unlikely you will do important work. That is not a motivational line. That is a logical one.

The second habit was about doors. Literal doors. He noticed that the scientists at Bell Labs who kept their office doors closed got more done in the short term because they had no interruptions. But the scientists who kept their doors open got more done over a career. The open-door scientists were interrupted constantly. They also absorbed every new idea passing through the hallway. Ten years in, they were working on problems the closed-door scientists did not even know existed.

The third habit was inversion. When Bell Labs refused to give him the team of programmers he wanted, Hamming sat with the rejection for weeks. Then he flipped the question. Instead of asking for programmers to write the programs, he asked why machines could not write the programs themselves. That single inversion pushed him into the frontier of computer science. He said the pattern repeats everywhere. What looks like a defect, if you flip it correctly, becomes the exact thing that pushes you ahead of everyone else.

The fourth habit was the one that hit me the hardest. He said knowledge and productivity compound like interest. Someone who works 10 percent harder than you does not produce 10 percent more over a career. They produce twice as much. The gap doesn't add. It multiplies. And it compounds silently for years before anyone notices.

He finished the lecture with a line I have never been able to shake.

He said Pasteur's famous quote is right. Luck favors the prepared mind. But he meant it literally. You don't hope for luck. You engineer the conditions where luck can land on you. Open doors. Important problems. Inverted questions. Compounded hours. Those are not traits. Those are choices you make every single day.

The transcript has been sitting on the University of Virginia's computer science website for almost 30 years. The video is free on YouTube. Stripe Press reprinted the full lectures as a book in 2020 and Bret Victor wrote the foreword.

Hamming died in 1998. He gave his final lecture a few weeks before. He was 82.

The lecture that explains why some careers become legendary and others disappear is still free. Most people who could benefit from it will never open it.

[Gain of Fauci @DschlopesIsBack]

Bruno isn’t a good boy but he’s a smart boy😂😭

[Bilawal Sidhu @bilawalsidhu]

The tech we use to capture sports is a decade ahead of how we actually watch it. We can reproduce entire games in 3D, track every player down to their pose and heartbeat - but barely any of that makes it to your living room.

0:00 Immersion Gap
0:48 Computer Vision & Hawk-Eye
1:42 Player Tracking & Wearables
2:08 AI Analytics & Real-Time Stats
3:07 VR Sports Viewing & Why It Fails
4:16 Radiance Fields & Gaussian Splatting
5:10 Viewpoint Pro: Virtual Replays
5:59 Arcturus: 4D Gaussian Splatting
7:31 Muybridge: Virtual Camera Systems
9:53 F1 Vision Pro Experience
11:26 XR Sports Alliance
11:47 COSM: The $600 Ticket
13:04 The Future of Sports Broadcasting

I break down the companies and tech turning sports into a next-gen immersive experience - from Sony's Hawk-Eye camera systems and NFL tracking chips to AI analytics powered by Google Cloud and AWS.

We look at why VR headset viewing keeps failing, and how 4D Gaussian Splatting from Arcturus and multi-plane image-based volumetric capture from Muybridge are creating viewing angles that were previously impossible.

Plus the F1 Vision Pro experience, the XR Sports Alliance, and why COSM's in-person model is winning where headsets can't.

The data foundation is clearly there. The real question is how it gets to you.

Hope you enjoyed this; follow @bilawalsidhu for more deep dives mapping the frontier of creation & computing.

[Pippa Malmgren @DrPippaM]

This is what I mean about humans being our most undervalued assets. We put people in boxes: Jovovitch is an actor”. Therefore she cant build the #1 product on Github. Yet she can and did. Everybody is like a jewel with multiple facets, some highly polished, others less so. Some are brilliantly visible. Others shine but we dont see thar angle. Everybody has patches that could be polished. We humans are an emergent phenomena!

[Muhammad Ayan @socialwithaayan]

🚨 BREAKING: Someone just built the exact tool Andrej Karpathy said someone should build.

48 hours after Karpathy posted his LLM Knowledge Bases workflow, this showed up on GitHub.

It's called Graphify. One command. Any folder. Full knowledge graph.

Point it at any folder. Run /graphify inside Claude Code. Walk away.

Here is what comes out the other side:

-> A navigable knowledge graph of everything in that folder
-> An Obsidian vault with backlinked articles
-> A wiki that starts at index. md and maps every concept cluster
-> Plain English Q&A over your entire codebase or research folder

You can ask it things like:

"What calls this function?"
"What connects these two concepts?"
"What are the most important nodes in this project?"

No vector database. No setup. No config files.

The token efficiency number is what got me:

71.5x fewer tokens per query compared to reading raw files.

That is not a small improvement. That is a completely different paradigm for how AI agents reason over large codebases.

What it supports:

-> Code in 13 programming languages
-> PDFs
-> Images via Claude Vision
-> Markdown files

Install in one line:

pip install graphify && graphify install

Then type /graphify in Claude Code and point it at anything.

Karpathy asked. Someone delivered in 48 hours.

That is the pace of 2026.

Open Source. Free.

[Pascal Bornet @pascal_bornet]

If you’re serious about AI, this is worth your attention.

Stanford has just released its course CME 295: Transformers & Large Language Models in full on YouTube.

What stands out to me is the level of clarity and structure.

This isn’t another surface-level overview.
It’s the actual curriculum used to teach how modern AI systems work.

This will help you move from using AI to understanding it.

📚 𝗧𝗼𝗽𝗶𝗰𝘀 𝗰𝗼𝘃𝗲𝗿𝗲𝗱 𝗶𝗻𝗰𝗹𝘂𝗱𝗲:
• How Transformers actually work (tokenization, attention, embeddings)
• Decoding strategies & MoEs
• LLM finetuning (LoRA, RLHF, supervised)
• Evaluation techniques (LLM-as-a-judge)
• Optimization tricks (RoPE, quantization, approximations)
• Reasoning & scaling
• Agentic workflows (RAG, tool calling)

🎥 Watch these now:

- Lecture 1: https://t.co/ZSO5v6FycM
- Lecture 2: https://t.co/r4zObFyJkn
- Lecture 3: https://t.co/OlXfPrdqvt
- Lecture 4: https://t.co/JkEkGc1mlv
- Lecture 5: https://t.co/7sczI7kfwS
- Lecture 6: https://t.co/KbRjEs9EAo
- Lecture 7: https://t.co/AYewR7b5b7
- Lecture 8: https://t.co/g8JrpNKhox
- Lecture 9: https://t.co/h2bjLGLlkY

For 2026, consider setting aside 2–3 hours each week to go through these lectures.

If you’re working in AI whether on infrastructure, agents, or applications, this is a foundational resource worth your time.

It’s a simple way to build depth where it matters most.

#AI #LLMs #Transformers #Stanford #GenAI

[Massimo @Rainmaker1973]

In the 1990s, Canadian ecologist Suzanne Simard made a groundbreaking discovery that challenged everything we thought we knew about how forests work. While studying managed forests in British Columbia, she noticed something puzzling: when birch trees were removed to promote the growth of valuable Douglas firs, the firs did not flourish as expected — they actually struggled and grew more slowly.

Determined to understand why, Simard traced the movement of nutrients using radioactive carbon isotopes. What she found was astonishing. Trees were actively sharing resources through vast underground fungal networks known as mycorrhizae. These delicate, thread-like fungi connect the roots of different trees across the forest floor, forming a complex web that allows the exchange of carbon, water, nutrients, and even chemical signals — sometimes between entirely different species.

She discovered that older, larger trees often serve as central "hubs" or "mother trees," supporting younger saplings by redistributing vital resources and helping the entire ecosystem remain resilient. When these key trees are removed, the underground network weakens, and the health of the remaining forest declines.

Simard’s research overturned the traditional Darwinian view of forests as battlegrounds of ruthless competition. Instead, she revealed a far more sophisticated reality: forests operate as highly cooperative systems where trees communicate, support one another, and even warn neighboring trees about threats like drought, disease, or insect attacks.

What appears to the human eye as a silent, still forest is, in truth, a vibrant, interconnected living network — built not on isolation and rivalry, but on deep connection and mutual aid.