Suresh Kumar R @scribnar

@hey_madni Can you give some examples of what you have built with those thousands of agents? I’m really curious seeing this kind of “Run thousands of agents while you sleep “ posts

Well people arguing with each other with different meaning of the word in mind Some like Marc and Elon literally take introspection and others consider the meaning of retrospection(reflect back to fix ) and misunderstand it to be introspection Then X comes live. I love it Without these conversations I would not have

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Maths is amazing

Rāma Śēṣan Chandraśēkaran @maa_bhaishiiH

4 days ago

The power of exponential growth still escapes in the intuition of many people, even those who have a reasonable grasp of basic mathematics. There is a famous Indian legend about the invention of chess that captures this. A king was so delighted by the game that he offered the

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Most people hyping vibe coding don’t develop complex systems They don’t forget gen have an idea about complex systems and architectural problems they present even if the people hyping are some wealthy investors from California If Vibe coding can solve it all then we would have MicroHard already and we will have most of our software needs already fulfilled

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.

The graphics is fantastic to explain the cell A living cell looks like an ecosystem on its own Wow, wow fantastic We can never know whether God created universe or the universe created God along with it - Rig Veda

Divinely Designed @DivinelyDesined

a week ago

The most detailed 3D reconstruction of a cell ever created. Blows my mind every time. But what exactly are we looking at here? The average human cell contains: ~ 15-20 total distinct organelle types, totalling between ~1-10 million working together per cell. All these

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@rohanpaul_ai Indeed and it’s also true for software Products ideas and features using maths and physics concepts Now it’s very cheap and frictionless to tryout new crazy ideas using maths in software which was pretty much impossible earlier

Competition is for losers and finding an opportunity that helps create a product without competition is not that difficult if we are willing to struggle a bit and work without validation as anything without competition is not yet validated by market

@venturetwins And then they complaint that they got burnt (Imran lost all the files) 🤣

Everyone is different. Its how we allow everyone to be part of the society and business life that matters Helping everyone integrate in both by using their advantages and compensating where they lack with others who are good on those areas

Quality of review by Grok build exceeds both Claude and Gemini in terms of security review of code Now I need to start testing for spec and implementation itself I use grok web to research and give to Claude code to spec Codex and Gemini are not good in detailed spec

xAI @xai

2 weeks ago

Grok Build is now available in Beta for all SuperGrok and X Premium+ users. Use Plan Mode, create images and videos with Imagine, and build automations or orchestrators with the CLI. Visit x.ai/cli to get started.

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@xai Finally

Dealing with uncertainty becomes extremely important to travel paths not well paved So it’s not that people don’t have skills required to achieve great things but most don’t want to choose the path with any amount of uncertainty fearing failure and loss of social status

Unwilling to “suffer / bear pain of all shorts / go through difficulties” is why people, families, societies and counties settle for less in life and their goals It’s not that people can’t do great things but mostly rare individuals who are willing to enter unknown territory

Math is becoming even more accessible for everyone with AI which means programmers can write better softwares & financial analysts can do better analysis & forecasting & most importantly mathematicians can solve the problems that that’s not been solved for decades benefitting al

Scott Kominers @skominers

3 weeks ago

Wild that an LLM autonomously disproved the unit-distance conjecture 🤯 But it’s also striking to me that, almost immediately after seeing the construction, a human mathematician was able to improve it further. Speaks to the potential of human–AI collaboration in math, QED 🔲

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AI coding agents have created a new kind of problem I can’t go to sleep without giving them work as otherwise they would be sleeping too. It’s like 24 x 7 shift workers constantly working and you need to ensure you have good work lined up for them and it cannot be slop

Excellent way to put it. Unless you preload your Brian with lot of concepts, the convergence of neural activity required for innovation has nothing to cook

Juliette @juliette_hiyb

3 weeks ago

@paulg The best ideas come from a convergence of neural activity across disparate networks within the brain. (Quite literally, this convergence is marked by a gamma burst.) The only way to have truly great ideas is to therefore build out this substrate with as many intricately detailed

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AI PCs are all the rage but they need become cheaper for real world use cases or we need to radically change the model architecture and math to make them small like in the direction of Liquid AI

Big Brain AI @realBigBrainAI

3 weeks ago

Lisa Su (CEO of AMD) unveils the world's smallest AI development PC, capable of running 200B parameter models locally.

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