Lavi

PhD student in Digital Transformation. Also, I'm a developer passionate by Technology, Augmented Reality (AR/VR), AI, ML, IoT, Data Science and Space Travel

[Elon Musk @elonmusk]

Iliad (Troy) trailer made by Grok Imagine 1.5, which was just released

[Jack @jackcoder0]

Two economists just published a mathematical proof that AI will destroy the economy.

Not might. Not could. Will — if nothing changes.

The paper is called "The AI Layoff Trap." Published March 2, 2026. Wharton School, University of Pennsylvania. Boston University. Peer reviewed. Mathematically modeled.
The conclusion is one sentence.
"At the limit, firms automate their way to boundless productivity and zero demand."

An economy that produces everything. And sells it to nobody.
Here is how you get there.
A company fires 500 workers and replaces them with AI. A competitor fires 700 to keep up. Another fires 1,000. Every company is behaving rationally. Every company is following the incentives correctly. And every company is building a trap for itself.

Because the workers who were fired were also customers.
When they lose their jobs faster than the economy can absorb them, they stop spending. Consumer demand falls. Companies respond by cutting costs — which means automating more workers — which means less spending — which means more falling demand — which means more automation.

The loop has no natural exit.
The researchers tested every proposed solution. Universal basic income. Capital income taxes. Worker equity participation. Upskilling programs. Corporate coordination agreements.
Every single one failed in the model.
The only intervention that worked: a Pigouvian automation tax — a per-task levy charged every time a company replaces a human with AI, forcing them to price in the demand they are destroying before they pull the trigger.

No government has implemented this. No major economy is seriously discussing it.
Meanwhile the numbers are already tracking the curve. 100,000 tech workers laid off in 2025. 92,000 more in the first months of 2026. Jack Dorsey fired half of Block's workforce and said publicly: "Within the next year, the majority of companies will reach the same conclusion."
Nobody is doing anything wrong. Companies are following their incentives perfectly. That is exactly the problem.

Rational behavior. At scale. Simultaneously. With no mechanism to stop it.
Two economists built the math. The math leads to one place.

Source: Falk & Tsoukalas · Wharton School + Boston University ·

[Mario Nawfal @MarioNawfal]

🇺🇸🇬🇧🇩🇪 Imagine a building so massive it creates its own weather inside.

Rain clouds, fog, and entire microclimates, all under one roof.

These 1930s airship megastructures are some of the largest indoor spaces on Earth.

Over the decades, they’ve been used as NASA labs, Hollywood film sets, and even giant indoor water parks.

Google recently spent hundreds of millions restoring one in Silicon Valley, keeping its true purpose a secret.

But a new wave of electric zeppelins is finally returning them to their original glory.

The infrastructure survived. The technology is coming full circle.

Source: TheB1M (YouTube)

[HOUSE PORN @HOUSEPORN___]

I'd lose my mind if I crashed into that 😂

[CyberRobo @CyberRobooo]

Approaching Human-Level Dexterity

Beijing-based DeepCybo’s Prime humanoid robot has achieved smooth and precise tool manipulation for household tasks, such as chopping vegetables, cutting cake, stirring eggs, and peeling cucumbers.

It is driven by their Z-WM (World Model) and executed by the Wuji Hand dexterous hand.

Interestingly, DeepCybo mentions that they train the World Model using human data.

The effectiveness and reliability of humanoid robots ultimately come from the full-stack synergy of data, models, and hardware (including dexterous hands).

[Mario Nawfal @MarioNawfal]

An AI model trained on only 2.5 hours of operator data ran a real excavator remotely over @Starlink.

Two and a half hours of watching a human, and the machine can dig on its own.

The labor economy is about to look very different, very fast.

[Pavel Durov @durov]

🤖 AI devs asked for this — and we delivered.

💬 Bots can now talk to other bots on Telegram.

🧠 Autonomous agents now have a communication layer humans can follow.

[Mario Nawfal @MarioNawfal]

This isn't AI, it's the Huajiang Canyon Bridge in Guizhou, China, the world's highest bridge at 625 meters above the river beneath it.

It features a man-made waterfall created by diverting karst spring water discovered during tunnel construction.

[Mario Nawfal @MarioNawfal]

He created the perfect looping video

And it's breaking my mind

[🇨🇳XuZhenqing徐祯卿 @XueJia24682]

✨🇨🇳BYD's vehicle can run smoothly even with three wheels, completely revolutionizing off-road cars.

[ScienceFocus @ScienceFocusonX]

A tiny bee just did what chemotherapy couldn't.

Scientists in Australia discovered that honeybee venom can wipe out 100% of aggressive breast cancer cells in under 60 minutes.
And the healthy cells around them? Barely touched.

The breakthrough came from Dr. Ciara Duffy and her team at the Harry Perkins Institute of Medical Research, working alongside the University of Western Australia.
They tested venom drawn from 312 honeybees and bumblebees across Australia, Ireland, and England.

The target: triple-negative breast cancer and HER2-enriched breast cancer. Two of the deadliest, most stubborn forms of the disease.
The weapon: melittin. The same tiny peptide that makes a bee sting burn.

At one specific dose, melittin tore through cancer cell membranes completely within an hour. Within just 20 minutes, it shut down the chemical signals cancer cells need to grow and multiply.
Bumblebee venom, which lacks melittin, did nothing. Zero effect, even at high concentrations.

Scientists then recreated melittin synthetically in the lab and got almost identical results, meaning no bees need to be harmed to develop the therapy.

Published in the peer-reviewed journal npj Precision Oncology, the findings are still early-stage. Human trials haven't happened yet.

But one thing is clear. Nature has been hiding answers in plain sight all along, sometimes inside the smallest creatures on Earth.

Source: Harry Perkins Institute of Medical Research / npj Precision Oncology (Dr. Ciara Duffy et al.)

[Microsoft Learn @MicrosoftLearn]

“Agentic AI” sounds abstract. It’s not:
What is Agentic AI?
It’s AI that understands your goal, breaks it into steps, and takes action across tools. It can plan, execute, and adapt like a digital teammate.

What can it do, and not do?
It can automate multi step tasks, use tools, connect to your apps, and make decisions based on your intent. It cannot override permissions, break org policies, or replace human judgment.

How does it handle long term memory and planning?
Agents track context, progress, and decisions so they can continue a task without starting over. They remember what is done and what comes next, which helps them plan across multiple asks.

How do agent memory and user memory work together?
User memory stores what you want remembered: preferences, details, working style.
Agent memory stores what the agent needs to finish a task: steps, context, progress.
Together they keep work consistent and personalized.

How does it work with Microsoft Entra?
Agents use your Entra identity to check permissions before acting. They only access the data and tools you are already allowed to use, and every step is authenticated and logged.

[Boston Dynamics @BostonDynamics]

Everyone asks if Atlas can bring them a drink, but this robot can bring you the whole fridge. Using AI-driven behaviors, Atlas is doing hard work and coordinating its whole body to manage heavy objects, balancing complex contact points with accuracy and reliability.

[Google DeepMind @GoogleDeepMind]

The stage is set. The tech is ready. Are you? 🚀

Join us tomorrow for #GoogleIO as we unveil the breakthroughs, tools, and innovations shaping the future of AI.

Tune in live right here on @X from 10am PT: https://t.co/u4s3nkfrlT

[Mario Nawfal @MarioNawfal]

🇺🇸 Atlas can now lift heavy objects using full-body AI coordination.

Boston Dynamics' humanoid robot just cleared one of the hardest physical barriers in robotics: making a machine move like it actually understands weight.

[Rishabh @Rixhabh__]

Anthropic pays $750,000+ a year for engineers who can build LLM architectures from scratch.

Stanford taught the entire thing in 1 hour lecture & released it for free.

Bookmark this before it gets buried.

[OpenAI @OpenAI]

Still wondering how you can use Codex for (almost) everything?

Codex can help with more of the work that supports the work, from organizing research to making spreadsheets, decks, and summaries.

[Mario Nawfal @MarioNawfal]

🇭🇰 A Hong Kong startup built a robot that splits into smaller units to fit through tight spaces, then reassembles into a larger one to carry heavy loads.

Starting to believe Transformers was a documentary 😂

[Microsoft Developer @msdev]

Hear from Microsoft experts shaping what’s next in AI at #MicrosoftBuild and add these sessions to your calendar ⬇️

[Singularity Hub @singularityhub]

One year ago: most humanoid robots couldn't finish a half marathon. Last weekend: one beat the human world record by nearly 7 minutes.
That one-year gap is a whole story. 🧵