James Flint

Writer, privacy sheriff, AI wrangler

[Sam @SamCKx]

Less than 18 hours ago, the brave and dignified father of Henry Nowak appealed to the nation to not use his son’s death to create further division.

“We want to use Henry’s heartbreaking story to make change for the better. We do not want his death to create further division, hatred or tension.”

Nigel Farage clearly has different plans as he exploits that family’s unimaginable loss and pain to further his own political ambitions, and create more of the division his party is thriving on:

“I suggest the rest of us respond to this with pure, cold, rage.”

[Anonymous @YourAnonNews]

Trump literally trumped the law. No one is ever going to see Donald Trump get the justice he deserves for all of the crimes he's committed. Why? It's bad. It's REALLY fucking BAD. Listen:

[Anthony Scaramucci @Scaramucci]

Britain has gotten poorer over the last decade. Brexit didn't work.

Disposable income has fallen more than in other European countries and certainly more than in the US.

They cut their nose off despite their economic face, a populist, nationalist, emotional decision, and now they're stuck because you can't get the same deal with the EU you once had.

People are done with both parties and they want to throw the bums out.

Now Farage is rising. I've met him and think he’s a con.

Go read his economic proposals — some of them are hate-based, some are tribally based.

Farage's broader economic proposals are populist noise that will make the country weaker and hurt the very people supporting him.

[Feargal Sharkey @Feargal_Sharkey]

Oh here we go again. @YorkshireWater claiming they are "INVESTING" £8.3 billion between 2025 - 2030.

That of course turns out to be completely untrue.

The only money I can see that shareholders are putting into the business is paying back the "loan" they took, by March 2027 and the £100 million they were supposed to pay to help reduce sewage dumping by the end of March 2025.

Every other damned penny of that £8.3 billion, that's £8.2 billion is being funded directly out of bill payers' pockets and not a damned thing to do with shareholders.

Why are water companies allowed to tell such utter lies?

Ofwat?

[Today In History @historigins]

In 1997, Brazilian soccer player Roberto Carlos took a free kick from 35 meters against France that seemed to defy the laws of physics as it curved around a wall of defenders at an angle that seemed impossible. ⁠
⁠
This “miracle goal” led a team of French physicists to publish a study in the journal New Journal of Physics, in which they explained that the extreme spin and speed created a “spinning spiral of the ball” that overcame gravity and air resistance.⁠

[Brian Krassenstein @krassenstein]

HOLY CRAP Trump actually accomplished a miracle. Here is what he got out of Iran:

- Reduce its stockpile of enriched uranium by about 98%

- Limit uranium enrichment to 3.67% purity (far below weapons-grade)

- Cut the number of installed centrifuges by roughly two-thirds

- Only enrich uranium at one declared site (Natanz)

- Stop enrichment activities at Fordow and convert it into a research facility

- Redesign the Arak heavy-water reactor so it could not easily produce weapons-grade plutonium

- Ship out or dilute excess enriched uranium
Allow extensive inspections by the International Atomic Energy Agency (IAEA)
Permit continuous monitoring of nuclear facilities and supply chains

- Accept “snap” inspections under expanded monitoring rules

- Avoid building new heavy-water reactors for years

- Stay within strict limits on uranium stockpile size and centrifuge development for set periods ranging from 10–25 years

Ooops, sorry!

That was the JCPOA that Obama signed with Iran, only to have him tear it up, kill 140 kids, get hundreds of Americans injured, 13 killed, and gas prices to surge 50%.

[Clash Report @clashreport]

WATCH: Ex-Google CEO Eric Schmidt got booed by University of Arizona graduates while urging them to embrace AI at their May commencement.

[James Flint @JamesFlint]

https://t.co/Y6zArgJP9I

[James Flint @JamesFlint]

Want to know if the EU Act affects you (and what to do about it?). I wrote a blog... check it out.

[James Flint @JamesFlint]

@POTUS https://t.co/F2cuXwrH4s

[Volodymyr Zelenskyy / Володимир Зеленський @ZelenskyyUa]

Our responses to Russia’s prolongation of the war and its attacks on our cities and communities are entirely justified. This time, Ukrainian long-range sanctions reached the Moscow region, and we are clearly telling the Russians: their state must end its war. Ukrainian drone and missile manufacturers continue their work. I am grateful to the Security Service of Ukraine and all the Defense Forces of Ukraine for their precision. The distance from Ukraine’s state border is over 500 km. The concentration of Russian air defense in the Moscow region is the highest. But we are overcoming it. Glory to Ukraine!

[Elon Musk @elonmusk]

Where will AI be in 1, 2 or 3 years?

[Haider. @haider1]

Yann LeCun says LLMs are strongest in domains where language itself is the substrate of reasoning, like math and code

They can solve problems, prove theorems, and write programs — but they are not creative mathematicians, software architects, or computer scientists

"their role is to help humans build"

[James Flint @JamesFlint]

@elonmusk Sorry in what way has Hollywood been destroyed exactly? Seems pretty hearty to me.

[James Flint @JamesFlint]

@elonmusk Hate to tell you Elon and please don't have a stroke but in the movie the Trojans aren't played by Turks and Odysseus isn't acted by a Greek dude either (unless I'm wrong about Matt Damon). Keep calm now...

[Led By Donkeys @ByDonkeys]

Immigration makes Britain brilliant.

[Marina Purkiss @MarinaPurkiss]

Angela Rayner was dragged through every front page for weeks...

HMRC's verdict: NOT deliberate. NOT even careless.

Farage pockets £5 million and chooses not to declare it

So where's the wall-to-wall coverage?

Tell me this isn't a rigged game.

[Sky News @SkyNews]

BREAKING: Nigel Farage bought a £1.4 million property in cash, shortly after receiving a £5m personal gift from billionaire donor Christopher Harborne, Sky News learns.

Sky's political correspondent @AliFortescue has this exclusive story ⬇️ 

https://t.co/LKwzx3IN3x

[Aakash Gupta @aakashgupta]

Yann LeCun closed $1.03B for AMI Labs on March 10. Three days later, this paper dropped from his NYU collaborators.

15M parameters. Single GPU. A few hours of training.

LeWorldModel is the first JEPA that trains end-to-end from raw pixels. Two loss terms: predict the next embedding, keep the latent space Gaussian. Previous JEPAs needed exponential moving averages or pretrained encoders to avoid representation collapse. LeWM doesn't.

Six hyperparameters down to one.

The numbers are the story. Foundation-model-based world models require hundreds of millions of parameters and serious compute to plan a control task. LeWM plans up to 48x faster while staying competitive on 2D and 3D benchmarks. The whole thing fits on a laptop GPU.

Look at the trajectory. Yann announced his Meta departure in November 2025 after 12 years and called founding FAIR his "proudest non-technical accomplishment." On March 10, 2026, AMI Labs closed the largest seed round in European history at a $3.5B pre-money valuation. Bezos, Nvidia, Samsung, and Toyota all wrote checks.

Three days later: a paper showing that JEPA-from-pixels is no longer fragile and no longer compute-heavy. The engineering scaffolding that made it look like an academic curiosity is gone.

The authors sit at Mila, NYU, Samsung SAIL, and Brown. None at Meta.

Yann's bet was that the path to machine intelligence runs through world models, not language models. He left a public company to build it. Each JEPA paper from his network resets the assumed cost structure for that bet. This one makes world modeling laptop-cheap.

Meta still has the GPUs. The architecture left.

[Ihtesham Ali @ihtesham2005]

A Hungarian psychologist raised three daughters to prove that any child could become a chess grandmaster through early specialization. He succeeded. Two of them became grandmasters. One became the greatest female chess player who ever lived.

Then a sports scientist looked at the data and found something nobody wanted to hear.

His name is David Epstein. The book is called "Range."

The Polgar experiment is one of the most famous case studies in the history of deliberate practice. Laszlo Polgar wrote a book before his daughters were even born arguing that geniuses are made, not born. He homeschooled all three girls in chess from age four. By their teens, Susan, Sofia, and Judit were dominating tournaments against grown men. Judit became the youngest grandmaster in history at the time, breaking Bobby Fischer's record. The story became the gospel of early specialization. Pick a domain young, drill it hard, and you can manufacture excellence.

Epstein opens his book by telling that story honestly and then quietly demolishing the conclusion most people drew from it.

Chess works that way. Most things do not.

Here is the distinction that took him four years of research to articulate, and that almost nobody who quotes the 10,000 hour rule has ever read.

There are two kinds of environments in which humans develop expertise. Psychologists call them kind and wicked. A kind environment has clear rules, immediate feedback, and patterns that repeat reliably. Chess is the cleanest example. Every game ends with a winner and a loser. Every move is recorded. The board never changes shape. The pieces never invent new ways to move. A child who plays ten thousand games will see most of the patterns that exist in the game, and pattern recognition is exactly what chess mastery is built on.

A wicked environment is the opposite. Feedback is delayed or misleading. Rules shift. The patterns that worked yesterday may be exactly the wrong patterns to apply tomorrow. Most of the real world looks like this. Medicine is wicked. Investing is wicked. Building a company is wicked. Scientific research is wicked. Almost every job that involves a complex changing system with humans in it is wicked.

The Polgar sisters trained in the kindest environment any human can train in. Their success was real and the method was correct. The mistake was generalizing the method to fields where the underlying structure of the environment is completely different.

Epstein's research is what made the implication impossible to ignore.

He looked at the careers of elite athletes outside of chess and golf and found that the pattern was almost the inverse of what people assumed. The athletes who reached the very top of their sports were overwhelmingly people who had played multiple sports as children, specialized late, and often switched disciplines well into their teens. Roger Federer played squash, badminton, basketball, handball, tennis, table tennis, and soccer before tennis became his focus. The kids who specialized in tennis at age six and trained year-round for a decade mostly burned out, got injured, or topped out at lower levels of the sport.

The same pattern showed up everywhere he looked outside of kind environments. Inventors with the most patents had worked in multiple unrelated fields before their breakthrough work. Comic book creators with the longest careers had drawn for the most different genres before settling. Scientists who won Nobel Prizes were dramatically more likely than their peers to be serious amateur musicians, painters, sculptors, or writers.

The skill that mattered in wicked environments was not depth in one pattern. It was the ability to recognize when a pattern from one domain applied unexpectedly in another. That kind of thinking cannot be built by drilling a single subject. It can only be built by accumulating mental models from many subjects and learning to move between them.

The deeper finding is the one that should change how you think about your own career.

Specialists in wicked environments often get worse with experience, not better. Epstein cites studies of doctors, financial analysts, intelligence officers, and forecasters showing that years of experience in a narrow domain frequently produce more confident judgments without producing more accurate ones. The expert builds elaborate mental models that feel comprehensive and turn out to be increasingly disconnected from the actual structure of the problem. They stop noticing what does not fit their framework. They mistake fluency for understanding.

Generalists do better in wicked domains for a reason that sounds almost mystical until you understand the mechanism. They have less invested in any single mental model, so they abandon broken models faster. They are used to being a beginner, so they are not threatened by the discomfort of not knowing. They have seen enough different domains that they can usually find an analogy from one field that unlocks a problem in another. The technical name for this is analogical thinking, and the research on it is one of the most underrated bodies of work in cognitive science.

The single most useful sentence in the entire book is the one Epstein puts almost as a throwaway.

Match quality matters more than head start.

A person who tries six different fields in their twenties and finds the one that genuinely fits them will outperform a person who picked one field at fourteen and stuck to it on willpower alone. The lost years were not lost. They were the search process that produced the match. Every field they walked away from taught them something they later imported into the field they finally chose.

The reason this is so hard to accept is cultural, not empirical. We tell children to pick a path early. We reward the prodigy who knew at six. We treat the late bloomer as someone who failed to launch on time, when the data suggests they were running an entirely different and often more effective optimization process underneath.

The Polgar sisters were not wrong. The conclusion the world drew from them was.

If your environment is genuinely kind, specialize early and drill hard. If it is wicked, and almost every interesting human problem is, then the people who win are the ones who refused to specialize until they had seen enough to know what was actually worth specializing in.

You are not behind. You were running the right experiment all along.