Shiv

@ AI engineer

[Shiv @iam_openAI]

Why Every Nation Needs Its Own Intelligence

The question is: Who controls intelligence?

China recognized this early and invested heavily in chips, data centers, AI research, local datasets, and national As a result, it can continue to Advance AI under eternal restriction.

[Sridhar Vembu @svembu]

This is big: all access to Mythos and Fable AI models disabled for everyone outside America.

First thoughts:

1. Technology is the ultimate weapon. National sovereignty, national security, all of it is now about technology.

2. Globalization is dead and Bharat must find her own way ahead.

We must keep these two ideas in mind.

What can our government do right now? Ensure that orgs in India embrace smaller models, both Indian and Chinese open source ones. With a bit of effort, we can make them work. Anyway, why pay money to people who don't even want to sell to you?

We must deepen our R&D. Sarvam has been on it and we have been on it but remember that the latest models cost not only huge GPU budgets to train, the GPUs themselves are restricted. So we can't afford the scale of money (of the order of $100+ billion to even get in the game!) and even if we could come up with the money, we can't get all the GPUs. I would not like to ask the government to fund tens of billions of dollars on this anyway - the money has far better uses.

Zoho has been pursuing alternative R&D approaches that are far, far less expensive but by its nature cutting edge R&D takes time and we are patient. I am confident we will get there.

Any remaining people in India who have delusions about globalization should wake up now.

[Shiv @iam_openAI]

@svembu the India needs to understand USA and China has their own compute powers if they shutdown intelligence & resources and make control
china can build their own but us we depend from oil to GPU's how can we survive ? No support private R&D's from govt. our talent is empowering USA

[Shiv @iam_openAI]

Can I tell you why Telugu people are obsessed with foreign countries because since our childhood from a Serial to Movies all they obsessed and promote like getting job or living their real privilege like America alludu, america mogudu or settled Switzerland.

[Himanshu Jain @HemanNamo]

Anshul Kuncha.

Hyderabad boy.
Master’s in Business Analytics.
MNC job in Philadelphia.
Weekend pizza deliveries for extra income.

He did exactly what millions of Indian families teach:
Study hard. Work hard. Build a future.

Lured to a vacant apartment.
Shot in the back of the head.
Nothing stolen. Cold-blooded.

His sister’s heartbreaking message to every Indian parent:
“Do not send your kids to the US.”

For decades, Indians have powered America’s hospitals, universities, tech, and economy — paying billions in taxes.

The least they deserve is safety.

Justice for Anshul Kuncha.
Protection for every Indian abroad. Now.

🇮🇳💔🇺🇸

#JusticeForAnshul #IndianLivesMatter

[Shiv @iam_openAI]

@GCherkupally My friend from Telangana went to study anime at some college in Kadapa Andhra Pradesh first day of college few girls and boys came and asked only one thing kamma or kapu he said I'm from BC -B padmashali then what happened next till his 4 years of degree they never speak to him

[Millie Marconi @MillieMarconnni]

A Dutch computer scientist gave one lecture in 1988 arguing that programming is unlike anything humans have ever tried to do before, and the reason most software on earth is broken is that we are still teaching it as if it were a hobby.

His name was Edsger Dijkstra. He won the Turing Award in 1972. He invented the shortest path algorithm that every GPS on earth still runs on.

He wrote the paper that killed the goto statement in modern programming languages.

He spent 50 years quietly being one of the most consequential thinkers in the entire history of computer science, and he was in a very bad mood by the time he stood up at the ACM Computer Science Conference in 1988 to deliver the lecture that almost nobody at the conference wanted to hear.

The lecture was called On the Cruelty of Really Teaching Computer Science.

It is now one of the most cited papers in the entire history of computing education. It was filed in his archive as EWD1036, handwritten in his careful fountain-pen calligraphy because he refused to use a typewriter and famously refused to use email for the rest of his life.

The argument was simple and uncomfortable.

Programming, Dijkstra said, is a radical novelty. Not a new tool. Not a new skill. Not a faster version of something humans already knew how to do. A genuinely new category of intellectual activity that has no real precedent in the entire history of the human species, and our brains have not been built to handle it.

Here is what he meant by that.

When a programmer writes a line of high-level code and presses run, that single line might trigger a billion operations at the level of the silicon.

The ratio between the abstraction you are working in and the physical events you are actually causing is roughly one billion to one. No engineer in history before computing ever had to reason about a system spanning that kind of ratio inside their own head.

A bridge builder reasons about steel beams and the physics of weight. A surgeon reasons about organs and the physics of tissue. A chemist reasons about molecules and the physics of bonds.

All of them are working inside ratios of physical scale where the largest and smallest things they need to think about are within a few orders of magnitude of each other.

A programmer routinely writes one line that orchestrates a billion physical events on a chip, and is expected to predict the behavior of all of them in advance.

Dijkstra argued that the human brain was simply not built for this. Every intuition we have evolved over hundreds of thousands of years comes from a world of medium-sized objects behaving in continuous ways. Computing is the opposite. It is discrete, not continuous.

A program that runs perfectly a billion times can crash on the billion-and-first iteration because of a single bit. A single character missing from a line of code can take down a power grid. There is no margin. There is no graceful degradation. The system either works or does not, and the only way to know is to actually run it.

This was the part of the lecture where Dijkstra made everyone in the room uncomfortable.

He said the way computer science was being taught in universities was a quiet disaster. Professors were teaching programming the way carpenters teach woodworking. With examples. With metaphors. With analogies to things students already understood. Files are like folders. Memory is like a desk. A function is like a recipe.

Dijkstra said this was actively making it harder for students to think clearly. The whole point of a radical novelty is that there is nothing in your past experience to compare it to.

The moment you start reaching for metaphors, you are smuggling in old intuitions that do not apply, and those intuitions will betray you the first time you try to reason about a system the metaphor was not built to describe.

His exact line was this: the usual way in which we plan today for tomorrow is in yesterday's vocabulary. And yesterday's vocabulary, he argued, was killing the field.

The reason most software is broken is downstream of this single misunderstanding. Programmers are taught to think of code as a craft. Something you get a feel for.

Something you pick up through practice. Something where intuition gets sharper with experience.

Dijkstra said this is exactly backwards. Programming is not a craft. It is closer to mathematics than to carpentry, and the moment you treat it as a craft, you guarantee that the software you produce will be full of the kind of bugs that craftsmanship cannot catch.

The fix, in his view, was to teach programming the way mathematics is taught. You should be able to prove your program correct before you run it.

You should reason about your code formally, the way a mathematician reasons about a theorem, not the way a carpenter feels their way through a joint. The students who learned this way, he said, would walk out of their classes with a kind of confidence that no amount of typing practice could produce.

The lecture was published in Communications of the ACM in 1989. The field did not listen. Universities kept teaching programming the same way.

Software kept getting bigger. Bugs kept compounding. By 2026, almost every piece of software on earth has known security vulnerabilities, undefined behaviors, and edge cases that nobody has ever proven safe. The doom that Dijkstra warned about in 1988 is now the default condition of the digital world we have built.

The deeper lesson is the one most readers miss the first time through.

Dijkstra was not just talking about software. He was making a much bigger point about how humans learn anything that is genuinely new. The instinct to translate the unfamiliar into the familiar is the most natural thing in the world.

It is also the single biggest obstacle to actually understanding something that has no precedent. If you keep reaching for analogies, you will never see the new thing clearly. You will only see your old framework projected onto it.

This is happening right now with AI. The same instinct that made people learn programming through metaphors of files and folders is making people understand large language models through metaphors of brains and people.

Almost every framework being used to describe AI in 2026 is borrowed from a previous domain. None of them quite fit. The few people who are actually building useful intuitions about how these systems work are the ones who have done what Dijkstra recommended forty years ago.

They have set down the old vocabulary. They have looked at the new thing on its own terms. They have accepted that the radical novelty is radical for a reason.

You are not slow. You were taught a discipline as if it were a hobby. The cruelty is real.

The fix is still available.

[Andrew Ng @AndrewYNg]

One of the new, buzzy jobs in Silicon Valley is the AI Forward Deployed Engineer (FDE), an engineer who is embedded within a client organization to help customize solutions, such as building and tuning agentic workflows that suit the client’s particular needs. I’ve heard from people who are wondering anew about the FDE career path since OpenAI and Anthropic started building new teams to place FDEs within client organizations.

The rise of FDEs for AI workloads is one way AI is creating new jobs (and why the jobpolcalypse narrative of upcoming job market collapse is false -- there will be many AI and non-AI jobs). However, I believe there will be far more AI Engineer jobs than FDEs, as I explain below.

The FDE role was pioneered about two decades ago by Palantir, which sent engineers to government locations to work on secure, air-gapped networks. In addition to having good technical skills, FDEs need communication skills and sometimes business skills. For example, they may need to speak with clients to understand their needs, formulate a strategy to prioritize projects, explain complex technology, and respectfully push back if a client asks for something unrealistic. They’re enjoying a resurgence because of the amount of work involved in taking an off-the-shelf LLM and building it into a custom agentic workflow that fits particular business needs.

However, I believe the number of AI Engineer jobs will be far larger. A company might accept a few FDEs to be embedded within its organization. But most companies will want far more of their own employees working on their projects. While my organizations do hire FDEs, we hire far more AI Engineers! Also, a common client concern is that it is hard to find vendor-neutral FDEs — they are, after all, there to deeply integrate a particular vendor’s product into a company. In this moment when it’s hard to predict which AI service will be the best one in a year’s time, optionality (the ability to pick whatever vendor turns out to fit best in the future) is very valuable. In contrast, letting FDEs tightly bind a company’s processes significantly reduces optionality.

Right now, I see surging demand for AI Engineers who can build software applications using AI software components (like LLM prompting, agentic frameworks, evals, etc.) and effectively use AI coding agents (like Claude Code, Codex, Antigravity CLI, and OpenCode). As the AI Engineer role matures, I expect it to fragment into more specialized roles, like the generic Software Engineer role from decades ago fragmented into frontend, backend, mobile, data engineering, devops, and so on.

What will be the future, specialized AI engineering roles? I don’t know. Perhaps there will be AI FDEs, LLMOps Engineers, Evals Engineers, AI Data Engineers, Harness Engineers, and other roles we don’t have names for yet. But for now, I see a lot of AI engineers who are generalists create a lot of value. Skilled AI Engineers are in very high demand! As our field continues to mature over the coming decade, I look forward to new specializations within AI Engineering that create even more job opportunities.

[Original text: The Batch newsletter]

[Shiv @iam_openAI]

@TheDispatch01 @talk2anuradha see what's going to happen in future! One day you don't have people to fight for caste or reservations there is only muslims and christians. So called Hindus (Brahmins to shudhras) all are busy fighting and discriminating each other but forgot to unite as Hindus

[Vala Afshar @ValaAfshar]

It is incredible to hear Albert Einstein’s voice as he explains formula E=mc²

[Night Sky Now @NightSkyNow]

🚨 Quantum physics is quietly reshaping how we see not just the universe but life and death themselves.

A growing idea called biocentrism dares to suggest that life and consciousness aren’t random accidents of creation, but the very foundation of reality.

According to this view, death may not be the end. What we call “dying” could simply be a shift in awareness — a transition into another layer of existence within a vast multiverse of possibilities. In other words, reality might not exist out there at all, but within the very act of perception itself.

Supporters of biocentrism often point to strange quantum effects like entanglement, where particles stay connected across galaxies, or the observer effect, where simply measuring something changes its behaviour. Even the eerie idea of retrocausality, where events in the present seem to influence the past, adds to the mystery. Together, these experiments hint at something unsettling — that consciousness might play a deeper role in shaping the universe than we ever imagined.

But here’s the catch: as fascinating as it sounds, biocentrism is not proven science. Most physicists see it as philosophy — an imaginative bridge between quantum mechanics and the mystery of consciousness. The experiments are real, but connecting them to life after death remains speculation.

Still, the idea is impossible to ignore. What if our consciousness doesn’t vanish, but simply changes channel? What if death is not a full stop, but a doorway into another version of existence, beyond time and space?

Whether it’s science or spirituality, biocentrism forces us to ask the most haunting question of all:
If consciousness shapes reality, what really happens when it lets go of one world and awakens in another?

#Quantumphysics #QUANTUM

[Felix Prehn 🐶 @felixprehn]

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[Shiv @iam_openAI]

@CristianoXtra_ What about this💀

[Shiv @iam_openAI]

@CristianoXtra_ 💀

[Shiv @iam_openAI]

@kylianarena @nocontextfooty

[Shiv @iam_openAI]

@NalinisKitchen Don't marry an employed man
marry a jobless man then we say women life is challenging

[Shiv @iam_openAI]

@CristianoXtra_ Crying fans ❎️ lying fans ✅️

[Shiv @iam_openAI]

@NoodleHairCR7 THIS ONE ☠️

[Shiv @iam_openAI]

@NalinisKitchen Ask your father....

[Shiv @iam_openAI]

@SunRisers SRH defended 118 against mumbai indians

[Physics In History @PhysInHistory]

Wanna Learn Mathematics for Free?

Here are some of the Best freely available playlists from Linear Algebra to Number theory that you can use to excel at Mathematics.

A Thread 👇