MNG

👨‍👦💼🇭🇰 Father of a VACTERL superstar

[Anatoli Kopadze @AnatoliKopadze]

instead of watching 2 hours of Netflix tonight, watch this Stanford lecture

it's the clearest explanation I've seen of how ChatGPT and Claude actually work

useful whether you've never touched AI in your life or have been using it every day for the past year

I took the key ideas and turned them into a practical guide on how to actually get 100% out of Claude

find it below

[Michael Bloch @michaelxbloch]

https://t.co/Sbr574u8fu

[Curiosity @CuriosityonX]

This creature lives inside of you and controls everything you do.

And you are scared of ghosts...

[AI Edge @aiedge_]

I've taught dozens of people AI from scratch using this exact roadmap.

Zero technical experience required - works every single time.

How to learn AI the right way:

[Jasmin @AI_with_jasmin]

🚨 BREAKING: Passive studying is dead!

Claude can train your brain harder than most professors ever will.

Here are 10 Claude prompts to learn anything 10× faster

[Mark Cuban @mcuban]

I’m going to tell you how much worse it was at the start of the PC Revolution for white collar workers trying to adapt, vs today with AI

Today, presumably every white collar worker has access to a smart phone and/or a PC/laptop.

Back then, a PC cost $4,995 , an off brand was $3,995. 5k in 1984 is about $16k today. It was really expensive.

The only reason I could learn how to code and support software is because my job let me take home a PC to learn. By reading the software manual. Literally. RTFM. Or pay to go to training. Classes that started at hundreds of dollars then. It was expensive. It absolutely limited who could get ahead.

Today, ANYONE can go to their browser, to the AI LLM website of their choice, and type in the words “I’m a novice with zero computer background, teach me how to create an agent that reads my email and …”

That concept applies to LEARNING ANYTHING

Think about what this means. Any employee of any company can say “ I need to learn how to xyz for my job , which is to do the following: Tell me what more information do you need to help me be more efficient, productive and promotable”. Or “ what new skills can you teach me that will help me reduce my chances of getting laid off “. Or “what suggestions do you have for me to communicate to my boss, who I barely know, to help my chances of staying employed “

These aren’t great prompts. But they are a start that anyone can take.

Think about how incredible that is.

Back in the day was so much harder for white collar workers. It was harder for new grads because unless they took comp sci, they probably had never used a PC.

Big Companies are going to cut jobs. No question about it. Small companies is are going to need more and more AI literate thinkers who can help them compete or get an edge

What I tell every entrepreneur, and it’s more crucial today. “ when you run with the elephants there are the quick and the dead. Adopt tech quickly , you can out maneuver big companies. “

[Ihtesham Ali @ihtesham2005]

I accidentally discovered how to compress a semester of learning into 48 hours.

A grad student at MIT showed me his NotebookLM setup. I thought he was just organized. Then I watched him pass a qualifying exam on a subject he'd never studied before.

Here's exactly what he did:

First: he didn't upload a textbook.

He uploaded 6 textbooks, 15 research papers, and every lecture transcript he could find on the subject.

Then he asked NotebookLM one question:

"What are the 5 core mental models that every expert in this field shares?"

Not "summarize this." Not "explain this topic."
Mental models. The stuff that takes professors years to develop.

But the next part is what broke my brain.

He followed up with:

"Now show me the 3 places where experts in this field fundamentally disagree, and what each side's strongest argument is."

In 20 minutes he had a map of the entire intellectual landscape of the field:
the debates, the consensus, the open questions.

Most students spend a full semester just figuring out what those debates even are.

Then he did something I've never seen before.

He asked:

"Generate 10 questions that would expose whether someone deeply understands this subject versus someone who just memorized facts."

He spent the next 6 hours answering those questions using the source material. Every wrong answer triggered a follow-up:

"Explain why this is wrong and what I'm missing."

By hour 48, he could hold a conversation with his thesis advisor without getting destroyed.

The tool didn't change. The questions did.

Most people treat NotebookLM like a fancy highlighter.
These students are using it like a private tutor who has read everything ever written on the subject.

The difference between a semester and 48 hours isn't the amount of content.

It's knowing which questions to ask.

[Alex Prompter @alex_prompter]

🚨 BREAKING: OpenAI just mathematically proved that no AI model will ever stop hallucinating.

Their paper demonstrates that the way language models generate text, predicting one word at a time based on probability, creates a mathematical floor of error that cannot be engineered away. The generative error rate will always be at least double the classification error rate. That’s not a bug report. That’s a proof.

Worse: they examined 10 major AI benchmarks and found 9 of them score “I don’t know” the same as a wrong answer. Zero points. The entire evaluation system trained these models to guess confidently rather than admit uncertainty.

Their own numbers confirm it. On factual queries, o1 hallucinated 16% of the time. o3 hit 33%. o4-mini reached 48%. The newer reasoning models performed worse, not better.
Most coverage stopped there. “AI is broken, be afraid.”

We read it differently.

If the model is permanently unreliable by default, then the prompt becomes the entire quality control layer. How you structure the input, what constraints you set, what verification steps you build in, that’s not a nice-to-have anymore. That’s the difference between usable output and fabricated nonsense.

This paper doesn’t discredit AI. It proves that the gap between people who prompt well and people who don’t is about to become the most consequential skill divide in tech.

We’ve spent years building 30,000+ prompts engineered for exactly this reality. Structured inputs. Built-in verification. Context-rich architectures that minimize where the model needs to guess.

The model was never the variable. The prompt always was.

[Adam KP @AdamKPx]

This is what learning looks like in spatial computing 👀

[Dustin @r0ck3t23]

Dario Amodei just gave his first interview since the Pentagon blacklisted his company. The toll is visible on his face.

He was asked one question. What would you say to the President right now?

He didn’t hesitate.

Amodei: “We are patriotic Americans. Everything we have done has been for the sake of this country.”

Anthropic built their models to defend America. They were the first AI lab cleared for classified military systems. They wanted to help the warfighter.

But the Pentagon demanded unrestricted access to fully autonomous weapons and mass surveillance of American citizens.

Amodei drew the line.

The government responded with emergency Cold War powers. A supply chain designation normally reserved for foreign adversaries. A six-month federal phaseout ordered from Truth Social.

Amodei: “When we were threatened with supply chain designation and Defense Production Act, which are unprecedented intrusions into the private economy, we exercised our classic First Amendment rights to speak up and disagree with the government.”

The administration framed Anthropic’s refusal as anti-American.

Amodei’s response dismantled that framing in one sentence.

Amodei: “Disagreeing with the government is the most American thing in the world.”

Here is the deeper paradox nobody in Washington wants to say out loud.

We are in a geopolitical race against autocratic adversaries who use AI for mass surveillance of their own citizens and autonomous weapons with no human oversight.

The Pentagon demanded that Anthropic build those exact capabilities for America.

Amodei: “The red lines we have drawn, we drew because we believe that crossing those red lines is contrary to American values.”

You cannot defeat authoritarianism by adopting its methods.

You cannot defend the open society by forcing private companies to build its antithesis under threat of wartime emergency powers.

Anthropic held the line. Got blacklisted for it. And came out the other side saying the same thing they said going in.

That is what it actually looks like to mean it.

[Elon Musk @elonmusk]

You can just take a picture of your medical data or upload the file to get a second opinion from Grok

[DAN KOE @thedankoe]

https://t.co/CdLm1xoOAb

[Ray Dalio @RayDalio]

https://t.co/tjmbT5ytUN

[Gaurav Dalmia @gdalmiathinks]

A must-read article.

[Mario Nawfal @MarioNawfal]

ELON: X MONEY WILL BE THE SOURCE OF ALL TRANSACTIONS

“It's intended to be the place where all the money is.

The central source of all monetary transactions.

It's really going to be a game-changer.”

Source: @elonmusk, xAI All-Hands, February 10, 2026

[MNG @mnghl12]

World models = machines reasoning about your rooms/objects/routines. Your home becomes intelligent coordination, not just gadgets. #WorldModels

[MNG @mnghl12]

🚀 "This year, your life quietly changed."

New essay: AI as your invisible upgrade—smarter mornings, coordinated homes, human work. From a physical AI founder.

Robots that understand your space. Time for what matters.

Read: https://t.co/OA5ilguvRU

[Haider. @haider1]

Andrew Ng says the concept of AGI has become meaningless because everyone defines it differently

The original definition was AI that could do any intellectual task a person can — essentially, AI as intelligent as humans

"by that measure, we're decades away"

[Bilawal Sidhu @bilawalsidhu]

I tested Google’s world model Genie 3... Then DeepMind told me everything

00:00 - Intro & Authoring Workflow
00:27 - Genie 3 Playtesting & Demos
05:33 - Interview w/ Google DeepMind (Genie 3 co-lead @jparkerholder and Sr. PM Diego Rivas)
06:54 - Wildest emergent behaviors observed
09:02 - The "inception" moment genie simulating itself
10:14 - Use cases for filmmaking, gaming & education
13:08 - Exerting control & promptable world events
15:28 - Solving for spatial memory and longer generation time
17:27 - The future of interactive, branching media feeds
18:57 - Open-endedness & community-driven evolution
20:04 - What's next for Genie 4? Hints!
21:30 - How Nano Banana Pro leveled up Genie 3