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1/4 LLMs solve research grade math problems but struggle with basic calculations. We bridge this gap by turning them to computers. We built a computer INSIDE a transformer that can run programs for millions of steps in seconds solving even the hardest Sudokus with 100% accuracy

Christos Tzamos

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🇺🇸 NVIDIA CEO JENSEN HUANG: WE HAD TO INVENT THE COMPUTER BEFORE THE PROBLEM EXISTED Jensen reflected on Nvidia’s early days, revealing how the company’s mission in 1993 seemed almost impossible, building a computer for problems that didn’t yet exist. He said they had to “create the problem” before they could solve it, a gamble that shaped modern computing: “When Nvidia started in 1993, we were trying to create this new computing approach. The question is, what’s the killer app? The company wanted to create a new type of computer that could solve problems normal computers couldn’t. But the applications that existed in 1993 were ones that normal computers could solve. We had a mission statement for a company that had no chance of success. I didn’t know that in 1993. It just sounded like a good idea.” Source: Joe Rogan

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Milestone! We (robotic arms for gadgets assembly) finished the first commercial order, which brought the first revenue. Here are some learnings from this: The customer was a smart toy manufacturer. The task was to add a heatsink to Raspberry Pi. We received parts from them and returned the assembled modules back. Currently, it's done by teleoperation. Later it will be done by a remote employee via the Internet. Then it will be automated action by action, reducing the operator's time on this and making the task profitable. ps. If you have an assembly task that we can do for you asynchronically - leave a comment below. Learning 1. It's possible! This task which is usually done by the human arm with 5 fingers can be done with a two-finger gripper with the addition of a couple of simple tooling. The task was not simplified. We peeled off thin films from stickers, unpacked paper boxes, moved PCB boards full of components, etc. And no unsolvable problems have been encountered yet. Challenges: 1) The paper box shifted during the opening Solved with the plastic walls that you can lean against 2) Heat pad, stuck to the gripper instead of heat sync. Can be solved by gripper with a pump, but this time solved with the patience of the operator 3) The film on the pad is very thin. Turned out that sub-millimeter arm precision is enough to peel it off with just a regular gripper. 4) The working area has not enough space. You'll only know this by doing real tasks in bulk. This could be solved by an extra pair of long arms, but in this case, solved with the patience of the operator. I think that in the end, we will have 5-10 types of universal tooling and 5-10 types of grippers to solve almost all the problems in such assembly tasks. Learning 2. It's slow. It took 5 times more time, than doing it with human hands. But the good news is there's a lot of room for improvement. We now have specific “time for task” metrics, which we will decrease with iterations. The main reasons for slowness: 1) To rotate the gripper to a steep angle you are forced to control one robot arm with two hands instead of using both arms. We can fix this by just making more room for rotations. 2) Grabbing PCB board with two arms is hard. A slight difference in rotation can break the board, and it's hard to control these angles visually. To solve this, the best way is to use force feedback so you can feel the pressure applied to the item. 3) Accuracy and steadiness is still can be improved We will try a metal version and double the motors to do this. 4) It is physically difficult for the human hands to move with such precision To solve this, we will add a pad for the hands like in surgical robots Learning 3. It's a good business model The "Factory in the cloud" is a good business model for this stage. You send us parts and we send back assembled modules. Currently, it's more convenient than sending a robot to your place, as we can iterate/fix the robot quickly and utilize it 100% of the time. When we polish the set-up over time - we can send robots to your place. So if we can assemble something for you in the USA with Chinese prices by using modern automation - leave a comment below.

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Deepa Soni, the executive vice president and CIO of New York Life Insurance Company sees AI as a "strategic enabler." “It’s not technology waiting to solve a business problem, but there are actual business problems that we can solve now with AI that we could not before.”

Deepa Soni, the executive vice president and CIO of New York Life Insurance Company sees AI as a "strategic enabler." “It’s not technology waiting to solve a business problem, but there are actual business problems that we can solve now with AI that we could not before.”

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The list of all the problems in mathematics that Terence Tao *could* solve is absolutely enormous. The problem is we can't make copies of him, dump millions of dollars of inference compute into each copy, and have them each tackle a different Millennium Prize Problem for a hundred subjective years. But we can do that with AI. So in that way, we may be underrating how significant it will be when we have AIs that are not superintelligent but rather simply as good as the best humans today. It’s actually an extremely powerful property of AI that by just running copies in parallel, you can immediately fill in the waterline of accomplishments that are feasible with a given level of capabilities.

The list of all the problems in mathematics that Terence Tao could solve is absolutely enormous. The problem is we can't make copies of him, dump millions of dollars of inference compute into each copy, and have them each tackle a different Millennium Prize Problem for a hundred subjective years. But we can do that with AI. So in that way, we may be underrating how significant it will be when we have AIs that are not superintelligent but rather simply as good as the best humans today. It’s actually an extremely powerful property of AI that by just running copies in parallel, you can immediately fill in the waterline of accomplishments that are feasible with a given level of capabilities.

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In this simple example, we show that Claude Code can read the iMessage database on the latest version of macOS, even with a leading EDR running on the system, illustrating the impact of an adversary who can remotely control the agent. We do this using Terminator, an internal research tool we built while studying the security implications of computer use agents. In this setup, the terminal application has previously been granted FDA, a subtle misconfiguration that effectively gives the agent access to unexpected context.

In this simple example, we show that Claude Code can read the iMessage database on the latest version of macOS, even with a leading EDR running on the system, illustrating the impact of an adversary who can remotely control the agent. We do this using Terminator, an internal research tool we built while studying the security implications of computer use agents. In this setup, the terminal application has previously been granted FDA, a subtle misconfiguration that effectively gives the agent access to unexpected context.

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Dr. Phil Goes Off Script With Stunning Criticism of the US Government “We got people in charge of solving problems that don’t really want to solve them because their job depends on having that problem. “They need that problem to justify their existence. They need that problem to maintain this huge bureaucracy, to maintain this big budget, this big agency. They need that problem to exist. I think we’ve got people in charge of solving problems that are not problem solvers.”

Dr. Phil Goes Off Script With Stunning Criticism of the US Government “We got people in charge of solving problems that don’t really want to solve them because their job depends on having that problem. “They need that problem to justify their existence. They need that problem to maintain this huge bureaucracy, to maintain this big budget, this big agency. They need that problem to exist. I think we’ve got people in charge of solving problems that are not problem solvers.”

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✨🇨🇳In China, first-grade children are having a math competition on an interactive whiteboard. They compete in a fun game by solving arithmetic problems, making math learning super engaging!

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