Hacking Science

China-based developers have introduced Photon Matrix, a LiDAR-guided mosquito defense system capable of detecting and neutralizing up to 30 mosquitoes per second. The device uses Light Detection and Ranging (LiDAR) sensors to scan the environment, identify flying insects, and calculate their size, position, speed, and trajectory within approximately three milliseconds.

Once a mosquito is confirmed as a target, a precision laser is directed toward it for elimination. The system is programmed to avoid larger objects such as humans and pets and is available in versions covering up to 3–6 meters. Developers say the technology could support future mosquito-control and disease-prevention efforts.

More than 10 billion devices run on his idea.
He made $0 from every single one and he planned it that way.
Meet Ajay Bhatt,
- Indian-American engineer. Born 1957 in Vadodara, Gujarat.
- Came to the US with a master's degree and joined Intel in 1990.
- One frustrating night, he couldn't connect a printer for his daughter's homework.
- He asked: why isn't there ONE universal port?
- His boss said it would never work. Told him to drop it.
- He didn't.
- Built it with fellow Intel engineer Bala Cadambi.
- Then united 7 fierce rivals Intel, Microsoft, IBM, Compaq, DEC, NEC, Nortel behind one shared standard.
- Apple fought it with FireWire. USB was cheaper. USB won.
- USB 1.0 launched in 1996. He went on to build USB 2.0 and 3.0.
- Intel made it royalty-free free for the entire planet.
- Bhatt earned not a dime in personal royalties. By choice.
- 2009: Intel made him a rock star in a viral ad played by a hired actor, not him.
- 2025: India finally honored him with the Padma Shri.
The man who connected the world.
And asked for nothing in return.

PewDiePie trained a frontier model at home
Now he released Odysseus, a self-hosted AI workspace

- be PewDiePie
- play games on YouTube and scream 24/7
- become a meme reviewer
- get unfathomably famous
- f**k that, a family guy now
- retire
- move to Japan with beautiful wife
- mfw, a dad now
- as a dad you must do dad things
- scratch that
- "as a dad must do frontier AI research"
- goal is to beat GPT-4o at coding (16% on Aider)
- buys $30000 GPU setup
- reads DeepSeek paper
- decides to start massive GitHub scraping and data augmenting run
- not good enough
- read Magicoder paper
- generate tons of synthetic coding data
- train a new model
- guuuuuh. the data made the model worse
- mfw just wasted months for nothing
- decides to lock in and try again
- makes model worse again ffs
- try again
- finally beating GPT-4o on data (16.1%) few months ago
- not satisfied
- should simply train a reasoning model
- reads more papers
- start experimenting with more synthetic data
- Mhhh something doesn't smell right
- house almost burned down due to power connector
- shrug
- just buy a new one
- mfw computer is now crashing 24/7 generating synthetic data
- new plan: just call DeepSeek API for high quality synthetic data
- train model again
- 17.2%
- performance fluctuates slightly on each eval run
- big brain idea: repeat eval until randomly reach 18%
- s**e actually got 19.6%
- feelsgoodman.png
- nvm the benchmark was contaminated and was training the wrong base model the whole time
- rerun everything again
- new score: 4.4%
- you read that right REEEEEEEE
- almost get a heart attack
- have you tried plugging the device off and back on again?
- change nothing and just retrain again
- 25.3 %
- LETS FU***NG GOOO
- realize that 1/3rd of the benchmark was not running. guuuh
- scared sh*tless it would score below 10% again
- run yet again. the whole thing this time
- Thirty f**king six percent
- accidentally beat Gemini 2.0 Pro Exp and GPT-4.1 mini
- "want moaaaar"
- finds some more post-training data
- 39% babyyyy
- realize at the end that was just benchmaxxing Aider polyglot
- next quest: run SWE-Bench and other coding benchmarks
- " failed a thousand times, but prevailed in the end"
- just a little sad side-quest
- runs huge models locally like Qwen 235B and GPT-OSS 120B
- builds custom web UI because existing ones aren’t good enough
calls it Odysseus
- open sources it on GitHub Odysseus
- even has an AI council where models debate and vote on the best answer
- runs everything offline
- instantly gets thousands of stars

A MIT student told me he can read a 400-page textbook in two hours and retain more than people who read every word.

He doesn't skim.

He interrogates the book like a witness by using a free tool called NotebookLM.

I didn't believe him either. Then he showed me exactly what that means.

Most people open a textbook on page one and march forward. Chapter by chapter. Page by page. They finish exhausted, retain maybe 20% of it, and feel like the problem is their focus or their memory.

It's not. The problem is the approach.

A witness doesn't volunteer everything they know. You have to drag it out of them with the right questions. The same is true of a textbook.

Here's his exact process.

He uploads the entire textbook into NotebookLM before touching a single page. Then he runs one prompt before reading a word.

"What is the central argument this book is making, and what would a reader need to believe for that argument to fall apart?"

That question does something most students never do before opening chapter one. It hands him the skeleton of the entire book in under two minutes. He knows where it's going, what it's trying to prove, and exactly where to look for the cracks.

Now he's not a reader. He's a cross-examiner.

He goes into the book with three targeted questions NotebookLM helped him generate.

"What do I already believe about this subject that this book directly challenges?"
"Which chapters contain the evidence the author relies on most heavily?"
"Where is the author's argument weakest?"

He reads only the sections that answer those questions. Everything else he skips without guilt.

Then comes the move that makes it genuinely unfair.

Every time he finishes a section that matters, he closes the book and runs one more prompt.

"Based on what I just read, what question would expose a student who understood the surface of this argument but missed the logic underneath it?"

He answers that question from memory before moving on. If he can't answer it, he goes back. If he can, the concept is locked.

He told me most students highlight because it feels like learning. NotebookLM helped him realize highlighting is just decoration on top of forgetting. The only thing that actually works is being forced to reconstruct what you read without looking at it.

By the end of two hours he hasn't read every word. He's built a complete map of the book's argument, identified exactly where his prior beliefs were wrong, and retained every section that mattered because he was forced to reconstruct it himself.

The students who read every word finished the same book.

But they were passengers.
He was driving.

There's a difference between covering material and owning it.

Most students spend four years covering it.

A British scientist invented the single most valuable piece of technology in human history, then signed a document that legally guaranteed he would never make a cent from it, and he did it on purpose while every university around him was racing to patent everything they could.

His name is Tim Berners-Lee, and the invention was the World Wide Web (WWW).

Not the internet, which already existed as a way to connect computers, but the actual web of pages and links you are using to read this right now. HTML. HTTP. The URL. He built all three while working at CERN, a physics lab in Switzerland, between 1989 and 1991.

He wrote the first browser on a NeXT computer and stuck a label on it that said "DO NOT POWER IT DOWN" because if anyone unplugged it, the entire web would vanish.

Here is the part that should stop you cold.

CERN owned the invention. Under the rules of the time, the lab could have licensed it, charged a fee for every installation, and collected a royalty on every server that ever came online.

His colleague Robert Cailliau confirmed they actively discussed exactly this, because in the early 1990s patenting university inventions and squeezing money out of them was the standard move.

They could have charged for every search. Every upload. Every page load on Earth, forever.

Berners-Lee fought to give it away instead.

He pushed CERN to release the source code into the public domain with no patent and no fee of any kind. On April 30, 1993, two CERN directors signed a half-page document that relinquished all intellectual property rights to the World Wide Web. A few signatures on a single sheet of paper.

That was the moment nobody came to own the thing that now connects more than five billion people.

His reasoning was not sentimental. It was mechanical.

He understood something most inventors never grasp. The value of the web was not in the code. It was in the network. And a network only grows if everyone can join without asking permission.

The second you charge a toll, people route around you, and you end up with a hundred tiny incompatible webs instead of one universal one. He said it plainly years later.

If he had demanded fees, there would be no World Wide Web. There would be lots of small webs, and none of them would have mattered.

So the thing that made the web worth trillions is the exact same thing that guaranteed he would never personally capture any of it. Openness was not a sacrifice he made against the invention's success. Openness was the success. The free part was the product.

People who made far less consequential things became billionaires off the platform he built. He watched it happen and kept running a nonprofit standards body out of an office at MIT, setting the rules that keep the web working for everyone, paid like a normal professor.

When an interviewer once asked him why he never cashed in, he refused the premise of the question. He said that framing only makes sense if you measure a person's worth by their net worth. People are what they have done and what they stand for, not what sits in their bank account.

The man who could have owned a piece of every click ever made chose to own none of it, because he understood that the only way to give the world something this big was to make sure he could never take it back.

The most valuable thing ever built belongs to everyone, and that was the entire point.

Karpathy just joined Anthropic and Wall Street added $50 BILLION to their IPO valuation overnight.

One man, $50 Billion added to Anthropic’s valuation.

An MIT mathematician sat down in 1950 and wrote a small, non-technical book aimed at the general public. He was not predicting the future. He was warning them. He said machines would eventually replace human work, optimize ruthlessly for the wrong goals, and quietly turn human beings into components inside systems they did not control.
Almost nobody listened. 75 years later, every warning he made has come true.
His name was Norbert Wiener. The book is called The Human Use of Human Beings.
The textbook story of AI ethics says the field began in the 2010s, when Stuart Russell, Nick Bostrom, and a small group of researchers started writing about the dangers of intelligent machines. That story is wrong. The first serious book about the ethics of AI was published in 1950, by a man who had personally invented the science that AI would eventually be built on, and who saw exactly what was coming with a clarity nobody else managed to match for the next 70 years.
Here is the story almost nobody tells you.
Norbert Wiener was a child prodigy. He graduated from Harvard at 14. He had a PhD in mathematics by 17. He became an MIT professor before he turned 30. During World War II he was assigned to work on anti-aircraft fire control systems. The problem was simple and impossible. How do you aim a gun at a fast-moving plane that will not be where it is by the time the shell arrives.
His answer turned into a new science. He called it cybernetics, from the Greek word for steersman. In 1948 he published a technical book by that name. Cybernetics was the foundation of modern control theory, robotics, and almost everything that became artificial intelligence. The book was dense. Most readers could not get past the math. The ideas inside it were too important to leave trapped in equations.
So in 1950 Wiener sat down and wrote a second book aimed at ordinary people. No equations. No jargon. Just consequences. He titled it The Human Use of Human Beings. It is barely 200 pages. It is one of the most prescient documents ever written about technology.
The first thing he warned about was automation.
He predicted, in 1950, that machines would replace human work across every industry. Not just factory work. Not just manual labor. Any task that could be reduced to a procedure would eventually be automated. He specifically said white-collar work would not be safe. Bookkeeping. Translation. Drafting. Calculation. Anything where a human was being paid to follow a defined process would eventually be done by a machine for a fraction of the cost.
He was not celebrating this. He was warning about it. He said the social consequences would be enormous, that entire industries would collapse, that the value of human labor itself would be undermined for tasks where humans had been useful for centuries. He wrote this 75 years before ChatGPT made every white-collar professional check their job description twice.
The second thing he warned about was the alignment problem. He did not call it that. The phrase did not exist. But he described it precisely.
He said that machines optimize for the goal you give them. They do not optimize for what you meant. They optimize for what you wrote down. If the goal is poorly specified, the machine will pursue the literal version of it with terrifying efficiency, and the result will be a disaster the builders did not foresee.
He used the metaphor of the magic monkey's paw from a horror story by W.W. Jacobs. A grieving father wishes his dead son alive again. The paw grants the wish. Something climbs back out of the grave that is technically the son. The wish was granted exactly as stated. The outcome is hell.
Modern AI safety researchers use almost the same metaphor 75 years later. They call it specification gaming, reward hacking, mesa-optimization. The names are new. The problem Wiener described in 1950 is exactly the same.
The third thing he warned about was the loss of human agency.
He predicted that humans would gradually surrender their decision-making to systems they did not understand. Not because the systems would force them to. Because the systems would be more convenient, more accurate, and more profitable than human judgment. People would offload their navigation, their reading, their relationships, and eventually their thinking to optimization processes designed by companies whose interests were not aligned with their users.
He said something in 1950 that I cannot stop thinking about. He said the more efficiently a society delegates its decisions to machines, the less able it becomes to make decisions at all. The atrophy is gradual. By the time anyone notices, the capacity to choose is gone, and what remains is people executing decisions that were made for them, by systems they did not build, in service of goals they were never asked about.
Look at modern social media feeds, recommendation algorithms, dating apps, navigation systems, news aggregators, and you are looking at exactly what he described.
The fourth thing he warned about was the easiest one to ignore at the time and the most disturbing now.
He warned that authoritarian regimes would use the new computing technology to track, manipulate, and control populations at a scale never previously possible. Not in the future. Soon. He said the same techniques that made cybernetics useful for guiding missiles would be used to guide societies, and that the small, incremental decisions about what to optimize, who to surveil, and how to feed information back into the system would compound into a kind of soft control that did not need force to function. People would do what the system wanted because the system would shape what they wanted in the first place.
He saw modern surveillance states 75 years before they existed.
The strangest thing about reading the book in 2026 is realizing how few of these problems have been seriously addressed.
Wiener was not anti-technology. He had personally helped build it. He was not nostalgic for a pre-machine age. He was warning that any tool powerful enough to amplify human capability is also powerful enough to amplify human stupidity, greed, and indifference, and that the dangers were not in the machines themselves but in the unwillingness of human institutions to ask hard questions about who the machines were being built for.
He died in 1964. He never lived to see most of his predictions come true. He never used a personal computer. He never followed a hyperlink. He never saw a modern recommendation algorithm.
He just wrote down, in 1950, in plain English, what the world would look like when the technology he had helped invent was built out by people who had not read his warnings.
The book is around 200 pages. It is in print. Used copies are everywhere for under ten dollars. It reads like science fiction in which the author already knows how the story ends.
The first serious book about the ethics of AI was published before there was any AI to be ethical about. Almost nobody who works on the problem today has read it.
The warnings are the same. The author has been dead for 60 years.

Did you know CT imaging exists because of the Beatles?
The inventor of CT is Godfrey Hounsfield, a British electrical engineer who worked on radar systems at EMI.
EMI was The Beatles' record label company. Because they were flush with profits due to The Beatles' recent success, they were willing to provide funds for Hounsfield's experimental idea of creating an image of an object with sliced X-ray imaging back in 1967.
By 1969, Hounsfield built a prototype head scanner and tested it first on a preserved human brain.
On October 1, 1971, the first live patient was successfully examined with a CT scanner: a woman with a suspected brain tumor.
It's amazing how disparate events can come together pun intended : to change the world and save lives!

Mind-blowing reality check: 🙂

78% of humanity (6.47 BILLION people) has NEVER used AI.

- 78% (6.47B) Never used AI
- 21.1% (1.75B) Free chatbots only
- 0.72% (60M) Paying $20/mo
- 0.12% (10M) Advanced coding scaffolds

We’re deep in the AI ivory tower, obsessing over models while most of the world hasn’t even tried a free chatbot.

Yet we’re already facing compute shortages.
Imagine when the green/yellow/red segments grow.

The real explosion is just getting started.
Only 0.12% (10M) use advanced coding scaffolds.

If this true, We are so early. (Source Terrible-Priority-21)

CLAUDE Cracks 9-Year-Old Locked Bitcoin Wallet 🤯

Bro had 5 BTC locked in a wallet for 9 years

dumped his old college computer into Claude as a hail mary

Claude found the wallet file, debugged btcrecover's password logic, decrypted the keys, converted to WIF, recovered the funds