MIT researchers have been digging into the "brains" of 60 different scientific AI models, and have stumbled upon something wild. It turns out, whether an AI is reading text or looking at 3D atoms, they are all starting to agree on the same hidden truth about our universe. Here is the pattern you can't unsee. 🧵 1/ First, the premise. We have AI models for everything now. • Some read protein sequences (like text). • Some look at 3D crystal structures (like vision). • Some predict forces in materials. They are built differently. They are trained differently. They should think differently. 2/ But a new paper from MIT just asked a massive question: "Are these models actually learning the same physics?" The answer is yes. And it’s kind of spooky. 3/ The researchers took nearly 60 models—from LLMs reading SMILES strings to complex 3D potentials—and peered inside their latent spaces (their internal "thoughts"). They found that as models get smarter, their internal representations of matter start to look identical. 4/ Think of it like this: If you ask a poet and a physicist to describe a sunset, they use different languages. But if they are both experts, they are describing the exact same reality. The AI models are converging on a "Universal Representation of Matter." 5/ This chart in the paper is the smoking gun. It shows that an LLM (trained on text) and a 3D Atomistic Model (trained on geometry) align almost perfectly when looking at molecules. The text model "hallucinated" the 3D structure implicitly. It learned the physics just by reading the chemistry. 6/ But that's not even the most interesting part. This convergence gives us a new way to spot "fake" intelligence. The researchers found that high-performing models all cluster together in this "truth" space. But the weak models? They scatter. 7/ It’s the Anna Karenina principle of AI: "All happy (smart) models resemble one another; every unhappy (dumb) model is unhappy in its own way." If a model diverges from the pack on standard data, it hasn't learned a new trick. It’s just lost in a local sub-optimum. 8/ However, there is a catch. When the researchers threw "out-of-distribution" data at the weak models (stuff they hadn't seen before), the behavior flipped. Instead of scattering, the weak models collapsed. They all started making the same low-information mistakes. 9/ This reveals a massive problem in Materials Science AI specifically. The study shows these models are currently "data-governed." They are memorizing their specific training sets rather than learning universal laws. They aren't "foundational" yet. They are just really good parrots. 10/ So, what does this mean for the future of Science? Efficiency: We don't need massive, expensive, symmetry-enforcing architectures. We can "distill" the knowledge from big models into simple, fast ones. Truth: We can use "alignment" to fact-check AI. If a model disagrees with the consensus of other top models, it's likely wrong. 11/ The most profound takeaway? pattern-matching And the fact that different AIs are independently deriving the same laws suggests that these models aren't just pattern matching. They are uncovering reality. 12/ If this research holds up, in 5 years we won't distinguish between "protein models" and "materials models." We will just have "Matter Models." One foundation to simulate it all. 13/ This paper is a dense but rewarding read. It fundamentally changes how I think about "generality" in AI. If you want to dive deeper, grab the PDF here: [Link to 2512.03750v1.pdf] And SUBSCRIBE to me for more breakdowns of the science that is quietly changing the world.

Yes I will vouch the same, I worked on same prototype, by spending around a hundred bucks to generate meaningful knowledge graph before hand. The right chunking of text, with embeddings will auto cluster content in vector db. Knowledge graph is later part,tbf redundant.

The folks who get maximal benefit from AI and vibe coding are the ones who understand technical concepts and know some basic programming The can tell when the agent is hallucinating or making mistakes and can nudge it in the right direction They also understand how to design correctly and prompt it to build what they want

They say - DevOps is dead, SRE is dead. - AI agents will be managing/troubleshooting your Kubernetes clusters. - Infrastructure-as-Code will be fully automated! - CI/CD pipelines will be built and managed by AI! I've been hearing these dramatic predictions since ChatGPT launched. After 3 years of actually using AI( their top models) in my daily work, I can tell you this: the reality is very different from the headlines. Yes, AI can write Terraform code(not production ready though), kubernetes manifest(incomplete), basic pipelines and scripts people already posted in github, stack overflow. But try getting it to: - Debug a complex Kubernetes networking issue - Handle multi-region failover scenarios - Design scalable microservices architecture - Manage security compliance across cloud providers - Use newly released, cloud services and security implementation. - Work with cross teams, negotiating, managing conflicts and keep things running. Even autonomous AI agents fall short: - They can't maintain context across your entire infrastructure - Struggle with real-world edge cases - Can't understand company-specific requirements - Limited by their training data when facing novel problems If your job is just copying Terraform templates, copy-pasting code from Stack Overflow you should be concerned. But if you understand distributed systems, security implications, and complex infrastructure patterns - AI will amplify your capabilities, not replace them. The winners will be engineers who can: - Think deeply about systems architecture - Solve novel infrastructure challenges - Use AI to automate routine work - Focus on high-impact engineering decisions Stop believing the hype. Start focusing on becoming a better engineer who knows how to use AI as another tool in their arsenal. The future isn't AI replacing DevOps engineers. It’s the human who understand technology in depth, remember the issues they faced last year and can make better decision when production is down instead of hallucinating.

Vision(the literal sense of sight) and format still is big driver for any type of(human/animal/ai) cognition. Can we imagine civilization growth or innovation with out vision so far?

If you feel like giving up, you must read this never-before-shared story of the creator of PyTorch and ex-VP at Meta, Soumith Chintala. > from hyderabad public school, but bad at math > goes to a "tier 2" college in India, VIT in Vellore > rejected from all 12 universities for US masters despite 1420 on the GRE > fuckit.jpg > goes to the US anyway on a J-1 visa to CMU with no plan > applies for masters (again) to 15 universities > rejected from all except USC and with late admissions, NYU in 2010 > finds this guy called Yann LeCun (before he was famous) > starts getting into open source > rejected from all jobs including DeepMind > only job is Amazon as test engineer > his PhD mentor helps him get a job at a small startup (MuseAmi) > rejected from DeepMind > couldn't get H-1B because of J-1 home return issue; gets waiver through months of approval with USCIS and US State Dept > very low on confidence > In 2011/12 builds one of the fastest AI inference engines on phones > rejected from DeepMind > emailed Yann again and joins FAIR because of Torch7 open-source work > scrapes through bootcamp at Facebook, struggling on an HBase task > L8/L9 engineers at Facebook struggle to get ImageNet working > figures out numerics / hyperparam issue as an L4 > first big win! > FAIR goes well, runs 3 person torch7 team and co-creates PyTorch > because of politics, management wants to shut down PyTorch > cries-at-bar.jpg, literally > eventually some people save PyTorch and it launches in 2017 > gets a EB-1 green card! > the rest is history... Think about that. He went to a tier 2 college. Was rejected from all Masters programs 2x. Rejected from every single job except Amazon test engineering. Rejected from DeepMind 3x. Nearly had his baby project shut down. Struggled with visa issues. After 12 years of failures (2005-17), he eventually rose to became a VP at Meta one of the most influential people in AI! Soumith's story is one of resilience and he's living proof that no matter how down in the dumps you are, there's always hope.

An Indian who had been living in Japan for more than a yr noticed something strange : his Japanese friends were polite & helpful, yet none of them ever invited him to their home, not even for a cup of tea. Confused & hurt, he finally asked one Japanese friend why this was so. After a long silence, the friend replied, "We are taught Indian history… not for inspiration, but as a warning." The Indian, astonished, asked, "A warning? Indian history is taught as a warning? Please explain why." The Japanese friend asked, "How many English ruled India?" The Indian replied, "Maybe… about 10,000?" The Japanese person nodded seriously & asked, "At that time, weren’t there over 300 million Indians?" "So who committed the atrocities on your people? Who followed the orders to whip, torture, & shoot them?" He asked emphatically, "When General Dyer ordered the firing at Jallianwala Bagh, who pulled the trigger? Was it the English soldiers? No, it was Indians." "Why didn’t anyone point their rifle at General Dyer, not a single person?" He continued, "The slavery you talk about—this was your real slavery. Not of the body, but of the soul."* The Indian stood motionless, silent, & ashamed. The Japanese friend continued, "How many Mughals came from Central Asia? Maybe a few thousand? And yet they ruled you for centuries." "The Mughals did not rule India through their numbers; it was your own people who bowed to them, obeyed them, betrayed their own, and showed loyalty to the Mughals. Either to survive or for silver coins." "Your own people converted to their religions." "Your own people betrayed your heroes to the English. Who betrayed Chandrashekhar Azad? Who informed the English about his hiding place in Alfred Park?" "Bhagat Singh was not easily executed without the permission of those people (Gandhi-Nehrus) who called themselves patriots." "You Indians do not need foreign enemies. Your own people repeatedly betray you for power, position, and personal gain. That is why we keep distance from Indians." "When the English came to Hong Kong and Singapore, not a single local joined their army. But in India, you did not just join the enemy’s army—you served them. You worshipped them. You killed your own people to please them." "Even today, you have not changed. You have learned no lessons from history. Even for a little free electricity, a bottle of alcohol, or a blanket—you sell your vote, your conscience, and your voice without thinking." "You chant slogans, protest, but when the country needs your sacrifice, where are you? Your first loyalty is still to your home, family, wife, children, and wealth. The rest—country —can go to hell." After saying this, the Japanese left, and the Indian stood there, head bowed, frozen in shame.

We’re officially #1 in Social Networking on the App Store! Big thanks to every single Arattai user for making this possible. 💛 #StayConnected #Arattai

China 🇨🇳 📌 Sources: WGC, SAFE, industry estimates Govt Reserves: ~2,300 tonnes People-owned: ~31,000 tonnes Total: ~33,300 tonnes (some analysts say closer to 36,000 t) China quietly built a massive household gold reserve, much of it in bars and jewelry, making gold a saving

💯

Why stop the tesla cars vision to human eye level only when you've done amazing things like bringing down a skyscraper from the edge of space safely and made the first electric super car and a bunch of tesla cars that can withstand a granade attack or bullet proof. 🚗💨🚀 #TeslaCarVision #SpaceExploration #ElectricSuperCar #BulletProof

Java,javascript,python, xslt,scala

Indian student Divya Tyagi at Penn State University has cracked a 100-year-old math problem, which will enable higher efficiency in wind turbines.

A couple reflections on the quantum computing breakthrough we just announced... Most of us grew up learning there are three main types of matter that matter: solid, liquid, and gas. Today, that changed. After a nearly 20 year pursuit, we’ve created an entirely new state of matter, unlocked by a new class of materials, topoconductors, that enable a fundamental leap in computing. It powers Majorana 1, the first quantum processing unit built on a topological core. We believe this breakthrough will allow us to create a truly meaningful quantum computer not in decades, as some have predicted, but in years. The qubits created with topoconductors are faster, more reliable, and smaller. They are 1/100th of a millimeter, meaning we now have a clear path to a million-qubit processor. Imagine a chip that can fit in the palm of your hand yet is capable of solving problems that even all the computers on Earth today combined could not! Sometimes researchers have to work on things for decades to make progress possible. It takes patience and persistence to have big impact in the world. And I am glad we get the opportunity to do just that at Microsoft. This is our focus: When productivity rises, economies grow faster, benefiting every sector and every corner of the globe. It’s not about hyping tech; it’s about building technology that truly serves the world.

Can you find the Easter eggs we've hidden in our RAGEN codebase? 🔍There's some fun surprises hidden there!🎮 github.com/ZihanWang314/rage…

We’ve given free Perplexity Pro to all students and faculty and staff of IIT Madras, where I did my undergrad. Super excited to start there as we begin our expansion for Indian campuses.

RT @prabhas: If Einstein born today, what would he become in future? With all the social media gaints along with technology growth around…

If Einstein born today, what would he become in future? With all the social media gaints along with technology growth around including ai?

BREAKING: Elon Musk hosted India Global Forum Business Delegation at SpaceX's Starbase facility in Texas.