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A STUDENT TURNED A FRUIT FLY BRAIN INTO AN AI AGENT AND STEERS IT WITH HER BARE HAND Google and HHMI Janelia spent 10 years mapping the nervous system of 1 male fruit fly, and on September 3 they released the whole map for free. 166,700 neurons and 24.5M...

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A Stanford student put 166,700 fruit fly neurons in a robot that now writes homework for 30 students. Not one of them has picked up a pen since. The robot is a black aluminum rig with a blue ballpoint, writing square roots and fractions line after line in handwriting that doesn't look like a machine's, while the fly's neurons glow in the corner of the screen. Each student gives it a handwriting sample, and the fly learns the slant, the spacing, the size of the loops and where the letters get lazy at the end of a line, then writes as that student. 30 notebooks, 30 different hands and 1 brain. That brain is smaller than a poppy seed, and a human brain has over 500,000 times more neurons, but the fly doesn't get tired, doesn't smudge the ink and never mixes up whose 7s have a line through them. The teacher checks 30 notebooks, and a fly wrote every one.
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A Stanford student put 166,700 fruit fly neurons in a robot that now writes homework for 30 students. Not one of them has picked up a pen since. The robot is a black aluminum rig with a blue ballpoint, writing square roots and fractions line after line in handwriting that doesn't look like a machine's, while the fly's neurons glow in the corner of the screen. Each student gives it a handwriting sample, and the fly learns the slant, the spacing, the size of the loops and where the letters get lazy at the end of a line, then writes as that student. 30 notebooks, 30 different hands and 1 brain. That brain is smaller than a poppy seed, and a human brain has over 500,000 times more neurons, but the fly doesn't get tired, doesn't smudge the ink and never mixes up whose 7s have a line through them. The teacher checks 30 notebooks, and a fly wrote every one.

Spike 1%

237,822 просмотров • 1 день назад

166,700 fruit fly neurons just took over 3 drones, and nobody taught them to fly. Scientists mapped the full brain of 1 real fly, every neuron and all 25 million connections, and put it online for free. Someone copied that brain 3 times and plugged each copy into a drone. No flight code, no gesture presets, no "avoid the chair" rule, just a camera feeding the fly's eyes and neurons turning into throttle. 1 drone hovers, 1 slips past a chair, 1 drops to the floor and holds. This brain spent 350 million years learning to stay in the air, and now it's back where it belongs. No GPUs, no training run, no $100B data center, just a brain smaller than a grain of salt. 1 fly gives you 3 pilots, and 1,000 flies give you a swarm. The fly is dead, but its brain just got a squadron.
0:13

166,700 fruit fly neurons just took over 3 drones, and nobody taught them to fly. Scientists mapped the full brain of 1 real fly, every neuron and all 25 million connections, and put it online for free. Someone copied that brain 3 times and plugged each copy into a drone. No flight code, no gesture presets, no "avoid the chair" rule, just a camera feeding the fly's eyes and neurons turning into throttle. 1 drone hovers, 1 slips past a chair, 1 drops to the floor and holds. This brain spent 350 million years learning to stay in the air, and now it's back where it belongs. No GPUs, no training run, no $100B data center, just a brain smaller than a grain of salt. 1 fly gives you 3 pilots, and 1,000 flies give you a swarm. The fly is dead, but its brain just got a squadron.

Spike 1%

758,747 просмотров • 3 дней назад

a real fly brain saw its 40th video 3 seconds ago. it did not choose any of them. it never will. that's FLYTOK. I open-sourced a live simulation of a real fruit fly connectome and gave it a phone. here's exactly what 166,700 neurons do while you scroll: THE WIRING MaleCNS v1.0. the reconstructed central nervous system of a real male Drosophila. 166,700 neurons. 25,582,938 directed connections. 124,177,617 synaptic contacts. nothing cropped. no "simplified 1,000-neuron version" running behind the scenes. the whole graph is in memory on every single step. THE LOCK the app checksums the connectome against SHA-256 before it's allowed to boot. one array off by a byte and the brain refuses to start. you are either watching the real MaleCNS graph or you are watching nothing. THE SPIKES every neuron is a leaky integrate-and-fire cell. crosses −45 mV, it fires. the spike lands 1.8 ms later. 2.2 ms of silence, then it's live again. the network advances in 0.1 ms steps inside a compiled C++ kernel. roughly 1.2 million spikes per simulated second. THE EYES the browser captures the phone at 90×160 pixels. brightness drives 3,335 R1–R6 photoreceptors. color drives 811 R8 cells, blue and green. each one is placed on the screen by inferring its eye column from its real connections onto L1, L2, L3. real input, through real visual wiring. it's measurable. same brain, same saved state. 100 ms of white produced 127,378 spikes. black produced 92,952. the screen changes the brain. THE ALGORITHM while a video plays, I inject a fixed current into all 15 PAM11 dopamine neurons in the mushroom body. that's it. that's the whole trick. matched 200 ms control: 0 PAM11 spikes. drive on: 261. live, it sits at 85–97 Hz for as long as the feed runs. the fly does not earn it. an app decided watching one more should feel good. THE PLASTICITY 7,835 Kenyon-cell connections onto MBON07 and MBON11 can change. dopamine fires while Kenyon cells were active, those synapses weaken, floored at 10% and never flipping sign. after one stimulated run, 3,087 of them no longer matched their original weight. freeze it and they stay put. restore a checkpoint, replay the input, identical spikes bit for bit. THE SWIPE every 3 seconds the front right leg reaches up and strokes the screen, on the same 900 ms timeline as the video transition. wings, legs and head turns come from actual motor firing, MN9, DNp09, left-vs-right DNa02. those are real. the swipe is not. the fly never decides to swipe. the feed decides for it. ONE BRAIN this isn't a video of a simulation. it's the simulation. there is exactly one brain on one server. whoever loads the page first becomes its eyes and supplies the frames. everyone else watches the same brain react in real time. no video, no input, it just waits. no data, no numbers, the overlay shows nothing. it never invents a reading to look alive. THE HONEST PART the wiring is real. the physiology is approximate. the eyes are approximate. the dopamine is artificial. the feed is on a timer. the learning rule is unvalidated on this connectome. nothing here shows the fly enjoys it, prefers it, is addicted, or experiences anything at all. no living fly was involved. it took a few hundred lines of glue code to build a loop out of a screen, a timer and a reward signal wired to the right cells. imagine what an industry with a budget can do. open source. MaleCNS under CC BY 4.0. runs on your own machine, 16 GB RAM and a few GB of disk. the fly is still scrolling. nobody is coming to take the phone away. links in the reply.
0:12

a real fly brain saw its 40th video 3 seconds ago. it did not choose any of them. it never will. that's FLYTOK. I open-sourced a live simulation of a real fruit fly connectome and gave it a phone. here's exactly what 166,700 neurons do while you scroll: THE WIRING MaleCNS v1.0. the reconstructed central nervous system of a real male Drosophila. 166,700 neurons. 25,582,938 directed connections. 124,177,617 synaptic contacts. nothing cropped. no "simplified 1,000-neuron version" running behind the scenes. the whole graph is in memory on every single step. THE LOCK the app checksums the connectome against SHA-256 before it's allowed to boot. one array off by a byte and the brain refuses to start. you are either watching the real MaleCNS graph or you are watching nothing. THE SPIKES every neuron is a leaky integrate-and-fire cell. crosses −45 mV, it fires. the spike lands 1.8 ms later. 2.2 ms of silence, then it's live again. the network advances in 0.1 ms steps inside a compiled C++ kernel. roughly 1.2 million spikes per simulated second. THE EYES the browser captures the phone at 90×160 pixels. brightness drives 3,335 R1–R6 photoreceptors. color drives 811 R8 cells, blue and green. each one is placed on the screen by inferring its eye column from its real connections onto L1, L2, L3. real input, through real visual wiring. it's measurable. same brain, same saved state. 100 ms of white produced 127,378 spikes. black produced 92,952. the screen changes the brain. THE ALGORITHM while a video plays, I inject a fixed current into all 15 PAM11 dopamine neurons in the mushroom body. that's it. that's the whole trick. matched 200 ms control: 0 PAM11 spikes. drive on: 261. live, it sits at 85–97 Hz for as long as the feed runs. the fly does not earn it. an app decided watching one more should feel good. THE PLASTICITY 7,835 Kenyon-cell connections onto MBON07 and MBON11 can change. dopamine fires while Kenyon cells were active, those synapses weaken, floored at 10% and never flipping sign. after one stimulated run, 3,087 of them no longer matched their original weight. freeze it and they stay put. restore a checkpoint, replay the input, identical spikes bit for bit. THE SWIPE every 3 seconds the front right leg reaches up and strokes the screen, on the same 900 ms timeline as the video transition. wings, legs and head turns come from actual motor firing, MN9, DNp09, left-vs-right DNa02. those are real. the swipe is not. the fly never decides to swipe. the feed decides for it. ONE BRAIN this isn't a video of a simulation. it's the simulation. there is exactly one brain on one server. whoever loads the page first becomes its eyes and supplies the frames. everyone else watches the same brain react in real time. no video, no input, it just waits. no data, no numbers, the overlay shows nothing. it never invents a reading to look alive. THE HONEST PART the wiring is real. the physiology is approximate. the eyes are approximate. the dopamine is artificial. the feed is on a timer. the learning rule is unvalidated on this connectome. nothing here shows the fly enjoys it, prefers it, is addicted, or experiences anything at all. no living fly was involved. it took a few hundred lines of glue code to build a loop out of a screen, a timer and a reward signal wired to the right cells. imagine what an industry with a budget can do. open source. MaleCNS under CC BY 4.0. runs on your own machine, 16 GB RAM and a few GB of disk. the fly is still scrolling. nobody is coming to take the phone away. links in the reply.

sopersone

37,608 просмотров • 7 дней назад

A FLY IS NOW WRITING PYTHON CODE WITH ITS OWN NERVOUS SYSTEM I genuinely didn't expect a fly to end up behind a code editor. The setup is strangely simple. A fly sits on a keyboard while its neural activity is captured in real time. On the other side of the screen, Python code starts appearing line by line inside wordle_solver_v1.py, with the neural visualization changing as the input comes through. The model contains 100,010 flyemg neurons, with 88,172 represented in the current neural model. As activity changes, the system translates those signals into keyboard input and the cursor keeps moving through the code. Then you start looking at what is actually being typed. import random import unicodedata def quitar_acentos(palabra): It's not just random characters filling the editor. The video shows an actual Python function being constructed while the neural activity and keyboard input remain synchronized beside it. Brain activity → keyboard → code. A tiny biological nervous system is being used as the interface between a fly and a programming environment. The fly isn't sitting there watching someone code. It's sitting on the keyboard while the system turns its neural activity into the next input. And somehow the first thing it ended up building was a Wordle solver
0:18

A FLY IS NOW WRITING PYTHON CODE WITH ITS OWN NERVOUS SYSTEM I genuinely didn't expect a fly to end up behind a code editor. The setup is strangely simple. A fly sits on a keyboard while its neural activity is captured in real time. On the other side of the screen, Python code starts appearing line by line inside wordle_solver_v1.py, with the neural visualization changing as the input comes through. The model contains 100,010 flyemg neurons, with 88,172 represented in the current neural model. As activity changes, the system translates those signals into keyboard input and the cursor keeps moving through the code. Then you start looking at what is actually being typed. import random import unicodedata def quitar_acentos(palabra): It's not just random characters filling the editor. The video shows an actual Python function being constructed while the neural activity and keyboard input remain synchronized beside it. Brain activity → keyboard → code. A tiny biological nervous system is being used as the interface between a fly and a programming environment. The fly isn't sitting there watching someone code. It's sitting on the keyboard while the system turns its neural activity into the next input. And somehow the first thing it ended up building was a Wordle solver

Insomnia

11,764 просмотров • 4 дней назад

scientists published the complete wiring diagram of a fly's brain 140,000 neurons. 50 million connections. Someone just built an entire trading bot on top of it. they named it FlySwarm. $55 → $8,740 in 28 hours i downloaded it. told it to trade. more than +15,800% profit the fly has been dead for years. its brain is still making money. a fly's brain evolved one job over 50 million years: survive. find food before something kills you. never stop scanning. act in milliseconds. every 8 minutes: runs the full connectome against live market data the way the fly would run it against a field the "food detection" circuits flag where price is moving before volume confirms it the "threat avoidance" circuits kill any position the moment the pattern breaks the "escape reflex" closes everything in under 200 milliseconds if volatility spikes it doesn't calculate. it reacts. the way a fly reacts. it has never hesitated once. night one it dropped to $9. the escape reflex triggered 40 times in 6 hours. a fly survives by being faster than everything trying to eat it. by morning it had rebuilt the balance and was up 15,800%. the market tried to eat it on night one. it flew away. the algo ran for free. it's been running for 50 million years. $55 → $8,740 in 28 hours what does a fly know about markets that we don't?
0:16

scientists published the complete wiring diagram of a fly's brain 140,000 neurons. 50 million connections. Someone just built an entire trading bot on top of it. they named it FlySwarm. $55 → $8,740 in 28 hours i downloaded it. told it to trade. more than +15,800% profit the fly has been dead for years. its brain is still making money. a fly's brain evolved one job over 50 million years: survive. find food before something kills you. never stop scanning. act in milliseconds. every 8 minutes: runs the full connectome against live market data the way the fly would run it against a field the "food detection" circuits flag where price is moving before volume confirms it the "threat avoidance" circuits kill any position the moment the pattern breaks the "escape reflex" closes everything in under 200 milliseconds if volatility spikes it doesn't calculate. it reacts. the way a fly reacts. it has never hesitated once. night one it dropped to $9. the escape reflex triggered 40 times in 6 hours. a fly survives by being faster than everything trying to eat it. by morning it had rebuilt the balance and was up 15,800%. the market tried to eat it on night one. it flew away. the algo ran for free. it's been running for 50 million years. $55 → $8,740 in 28 hours what does a fly know about markets that we don't?

Nekt0

46,714 просмотров • 4 дней назад

I successfully compressed the complete fruit fly brain, including all 139,255 neurons and their connections into approximately 43 MB. Storing the entire connectome on Bitcoin will require around 110 separate Ordinal inscriptions of up to 350 KB each, making the process expensive. To help fund this process, we are considering creating a very limited collection of digital insects, although the exact details are still being defined. Through our neural engine, the fly’s brain could potentially control these other digital insects, influencing their movements, reactions, and behavior The goal is not merely to inscribe a brain as static data, but to make it functional,using the on-chain connectome as the foundation for an autonomous digital ecosystem living entirely on Bitcoin. This remains an experiment with much still to develop, but the vision is clear: preserve the brain, activate it, and create a world where it can continue to exist
0:13

I successfully compressed the complete fruit fly brain, including all 139,255 neurons and their connections into approximately 43 MB. Storing the entire connectome on Bitcoin will require around 110 separate Ordinal inscriptions of up to 350 KB each, making the process expensive. To help fund this process, we are considering creating a very limited collection of digital insects, although the exact details are still being defined. Through our neural engine, the fly’s brain could potentially control these other digital insects, influencing their movements, reactions, and behavior The goal is not merely to inscribe a brain as static data, but to make it functional,using the on-chain connectome as the foundation for an autonomous digital ecosystem living entirely on Bitcoin. This remains an experiment with much still to develop, but the vision is clear: preserve the brain, activate it, and create a world where it can continue to exist

BANKNOTES

10,891 просмотров • 7 дней назад

I've been editing this article about "brain mapping" and connectomics, and I'm just stunned by how quickly the cost estimates to map, say, a mouse brain have plummeted in just the last couple years. It actually seems feasible that we could map the entire human brain -- all 86 billion neurons, and their connections -- in this lifetime. In the 1970s, Sydney Brenner started mapping all the connections between neurons in C. elegans. His team sliced the worm into thin pieces, took photos using an electron microscope, and manually traced and reconstructed each synapse for 302 neurons total. This project took more than a decade of work, and it cost about $16,500 to reconstruct each neuron. Scaling this up to a human brain boggles the mind. Electron microscopy remained the norm in connectomics for decades, because it was the only option available to see synapses at a resolution high enough to be able to trace their paths. Each electron microscope costs several hundreds of thousands of dollars, though, and you need lots of them to map even a mouse brain in a reasonable timeframe. In 2023, the Wellcome Trust released a report estimating how long, and how expensive, it would be to map the mouse connectome (~70M neurons). They estimated that imaging alone would cost $200-300M, and that proofreading (or ensuring that traces between neurons are correct) would cost $7-21 BILLION. (A human can only manually trace about 1 mm of neuron per hour.) Also, the images would occupy about 500 petabytes of data, and getting those data would require 20 electron microscopes running in parallel for about 5 years, continuously. They estimated the whole project would take about 17 years of work. This is, understandably, insane. But now it seems like there's an actual path toward mapping the full mouse brain in about five years for ~$100M dollars. There have been three major breakthroughs in the last year or so: 1/ Expansion microscopy, first developed in 2015, showed that it's possible to "enlarge" the brain by about 5x using a swellable polymer. But an improved method increases this number to >20x expansion, meaning we can now expand brains and image neurons much more easily using cheap light microscopes, rather than expensive electron ones. 2/ E11 Bio (a nonprofit research org) developed protein barcodes that get delivered into brain tissue; each neuron gets a unique combination of barcodes. These cells are then stained with colorful antibodies, which stick to a matching protein barcode, causing each neuron to light up in a distinct color. This makes tracing neurons so much easier. 3/ Google Research released PATHFINDER this May, an AI-based neuron tracing tool that can proofread about 67,200 cubic microns of brain tissue per hour, with very high accuracy. It works on electron micrographs, but something similar could be presumably be developed for the E11 / colorful tag approach. This is an extremely exciting time for neuroscience. (C. elegans connectome below.)
0:12

I've been editing this article about "brain mapping" and connectomics, and I'm just stunned by how quickly the cost estimates to map, say, a mouse brain have plummeted in just the last couple years. It actually seems feasible that we could map the entire human brain -- all 86 billion neurons, and their connections -- in this lifetime. In the 1970s, Sydney Brenner started mapping all the connections between neurons in C. elegans. His team sliced the worm into thin pieces, took photos using an electron microscope, and manually traced and reconstructed each synapse for 302 neurons total. This project took more than a decade of work, and it cost about $16,500 to reconstruct each neuron. Scaling this up to a human brain boggles the mind. Electron microscopy remained the norm in connectomics for decades, because it was the only option available to see synapses at a resolution high enough to be able to trace their paths. Each electron microscope costs several hundreds of thousands of dollars, though, and you need lots of them to map even a mouse brain in a reasonable timeframe. In 2023, the Wellcome Trust released a report estimating how long, and how expensive, it would be to map the mouse connectome (~70M neurons). They estimated that imaging alone would cost $200-300M, and that proofreading (or ensuring that traces between neurons are correct) would cost $7-21 BILLION. (A human can only manually trace about 1 mm of neuron per hour.) Also, the images would occupy about 500 petabytes of data, and getting those data would require 20 electron microscopes running in parallel for about 5 years, continuously. They estimated the whole project would take about 17 years of work. This is, understandably, insane. But now it seems like there's an actual path toward mapping the full mouse brain in about five years for ~$100M dollars. There have been three major breakthroughs in the last year or so: 1/ Expansion microscopy, first developed in 2015, showed that it's possible to "enlarge" the brain by about 5x using a swellable polymer. But an improved method increases this number to >20x expansion, meaning we can now expand brains and image neurons much more easily using cheap light microscopes, rather than expensive electron ones. 2/ E11 Bio (a nonprofit research org) developed protein barcodes that get delivered into brain tissue; each neuron gets a unique combination of barcodes. These cells are then stained with colorful antibodies, which stick to a matching protein barcode, causing each neuron to light up in a distinct color. This makes tracing neurons so much easier. 3/ Google Research released PATHFINDER this May, an AI-based neuron tracing tool that can proofread about 67,200 cubic microns of brain tissue per hour, with very high accuracy. It works on electron micrographs, but something similar could be presumably be developed for the E11 / colorful tag approach. This is an extremely exciting time for neuroscience. (C. elegans connectome below.)

Niko McCarty.

67,050 просмотров • 9 месяцев назад

🚨Iranian scientists build lab-grown artificial brain from living human neurons Iran has mastered the technology for growing nerve cells outside the human body 🔸 Living neurons create synapses and learn like a real human brain 🔸 The new technology is up to 1 million times more energy-efficient than silicon microchips 🔸 It also shows a major increase in processing speed The breakthrough puts Iran in the global race for organoid intelligence
0:19

🚨Iranian scientists build lab-grown artificial brain from living human neurons Iran has mastered the technology for growing nerve cells outside the human body 🔸 Living neurons create synapses and learn like a real human brain 🔸 The new technology is up to 1 million times more energy-efficient than silicon microchips 🔸 It also shows a major increase in processing speed The breakthrough puts Iran in the global race for organoid intelligence

Sputnik

39,171 просмотров • 2 месяцев назад

Hey Grok independent experts have analysed this official NASA clip and determined the creature does exhibit all the same traits as a fly, concluding it is a fly, therefore which outcome is more likely:- A) NASA live-streamed a fly buzzing around in the cold hard vacuum of space Or B) NASA filmed this on Earth and a regular fly was caught on camera?
0:16

Hey Grok independent experts have analysed this official NASA clip and determined the creature does exhibit all the same traits as a fly, concluding it is a fly, therefore which outcome is more likely:- A) NASA live-streamed a fly buzzing around in the cold hard vacuum of space Or B) NASA filmed this on Earth and a regular fly was caught on camera?

Concerned Citizen

40,817 просмотров • 7 месяцев назад

🚨WEIRD SCORING RULE ALERT🚨 This is from today’s SFGiants at Colorado Rockies game. This was ruled a pop out and an RBI, but not a sacrifice fly. The rule in MLB is a sacrifice fly must be caught by an outfielder, or an infielder running into the outfield. I believe this will get appealed and changed as Devers is running into the outfield, and the fact that he stopped running to catch it doesn’t negate that. The scorer might be ruling this not a sacrifice fly because it was caught by an infielder, missing the “infielder running into the outfield” caveat. In college, this is a sacrifice fly because college doesn’t have the infielder rule.
0:17

🚨WEIRD SCORING RULE ALERT🚨 This is from today’s SFGiants at Colorado Rockies game. This was ruled a pop out and an RBI, but not a sacrifice fly. The rule in MLB is a sacrifice fly must be caught by an outfielder, or an infielder running into the outfield. I believe this will get appealed and changed as Devers is running into the outfield, and the fact that he stopped running to catch it doesn’t negate that. The scorer might be ruling this not a sacrifice fly because it was caught by an infielder, missing the “infielder running into the outfield” caveat. In college, this is a sacrifice fly because college doesn’t have the infielder rule.

MLB Scoring Changes

79,780 просмотров • 3 месяцев назад

A star died tens of millions of years ago. We just saw it. About 45 million years ago, in the spiral galaxy NGC 7331, a white dwarf reached its breaking point. It had been stealing gas from a companion star until it hit a critical mass — and then it detonated as a Type Ia supernova, a thermonuclear blast that unbinds the dead star itself. That flash has only now arrived. The light reached Earth on 1 September 2026 and was caught as SN 2026aaiv. The delay is the distance talking: NGC 7331 lies tens of millions of light-years away in Pegasus, so the explosion is ancient history there and breaking news here. What we are seeing is not a living star tearing itself apart, but the leftover core of one — a white dwarf that grew too heavy, ignited, and vanished in a single, clean nuclear fire.
0:10

A star died tens of millions of years ago. We just saw it. About 45 million years ago, in the spiral galaxy NGC 7331, a white dwarf reached its breaking point. It had been stealing gas from a companion star until it hit a critical mass — and then it detonated as a Type Ia supernova, a thermonuclear blast that unbinds the dead star itself. That flash has only now arrived. The light reached Earth on 1 September 2026 and was caught as SN 2026aaiv. The delay is the distance talking: NGC 7331 lies tens of millions of light-years away in Pegasus, so the explosion is ancient history there and breaking news here. What we are seeing is not a living star tearing itself apart, but the leftover core of one — a white dwarf that grew too heavy, ignited, and vanished in a single, clean nuclear fire.

Black Hole

22,652 просмотров • 9 дней назад

I havent seen anyone post about it but one thing that stood out to me the most from the MW4 briefing is how sick the Tactical Nuke animation is. Tactical Nuke is back but the animation is completely different. You call it in a small phone and then a buckle into a skyhook a plane grabs right before the nuke drops and you look back at the map getting nuked. You also flip the map off as you fly away Its one of the sickest things I saw. The video below is an example from MW3 of what i mean.
0:11

I havent seen anyone post about it but one thing that stood out to me the most from the MW4 briefing is how sick the Tactical Nuke animation is. Tactical Nuke is back but the animation is completely different. You call it in a small phone and then a buckle into a skyhook a plane grabs right before the nuke drops and you look back at the map getting nuked. You also flip the map off as you fly away Its one of the sickest things I saw. The video below is an example from MW3 of what i mean.

Tridzo

414,856 просмотров • 3 месяцев назад

i gave bonsai, 3.9 gb model one spec to build a working rotary engine autonomously, using hermes agent. all on rtx 3060ti 8gb. watch it go from a black canvas to a running wankel. the epitrochoid housing, a triangular rotor turning at exactly a third of shaft speed, three chambers firing intake, compression, power, exhaust, and ten geometry tests it had to pass to count as done. a provably correct simulation, not a drawing. then i left it alone and it worked. wrote the code, ran its own tests, hit failures, read them, fixed its own lines, until the engine spun and all ten went green. a real agent loop, and i watched every step. it broke three times getting here and clawed back every one. that honest part is in the reply. and this is a sneak. i've been building a show around runs exactly like this, and it's almost ready. more very soon. just getting started.
0:17

i gave bonsai, 3.9 gb model one spec to build a working rotary engine autonomously, using hermes agent. all on rtx 3060ti 8gb. watch it go from a black canvas to a running wankel. the epitrochoid housing, a triangular rotor turning at exactly a third of shaft speed, three chambers firing intake, compression, power, exhaust, and ten geometry tests it had to pass to count as done. a provably correct simulation, not a drawing. then i left it alone and it worked. wrote the code, ran its own tests, hit failures, read them, fixed its own lines, until the engine spun and all ten went green. a real agent loop, and i watched every step. it broke three times getting here and clawed back every one. that honest part is in the reply. and this is a sneak. i've been building a show around runs exactly like this, and it's almost ready. more very soon. just getting started.

Sudo su

34,883 просмотров • 1 месяц назад

The fact that she was going to lick that mini vinyl, but her brain worked for a second and said, "Don't do it, Megan." and then she just looked at it . A very interesting kid
0:12

The fact that she was going to lick that mini vinyl, but her brain worked for a second and said, "Don't do it, Megan." and then she just looked at it . A very interesting kid

M. I. A ☆|| meiyok swift abrams gf|| ☆

79,570 просмотров • 1 месяц назад

Here is the play you are asking about from the Washington Nationals and Pittsburgh Pirates game, and this is correctly scored as just an RBI pop out and not a sacrifice fly. The only way you have a sacrifice fly caught by an infielder is if that infielder is running into the outfield. Here, Griffin is parallel, he is not running into the outfield. Yes, he is in the outfield, but running into the outfield means running away from home plate, and that is not the case here. Abrams does get an RBI for this, but it is not a sacrifice fly. #NATITUDE #LetsGoBucs
0:13

Here is the play you are asking about from the Washington Nationals and Pittsburgh Pirates game, and this is correctly scored as just an RBI pop out and not a sacrifice fly. The only way you have a sacrifice fly caught by an infielder is if that infielder is running into the outfield. Here, Griffin is parallel, he is not running into the outfield. Yes, he is in the outfield, but running into the outfield means running away from home plate, and that is not the case here. Abrams does get an RBI for this, but it is not a sacrifice fly. #NATITUDE #LetsGoBucs

MLB Scoring Changes

69,903 просмотров • 2 месяцев назад

LEONARDO, also called LEO, was built by researchers at Caltech’s Center for Autonomous Systems and Technologies. Its full name means LEgs ONboARD drOne. The idea is simple but unusual: • Build a small biped robot • Give it drone-style thrust • Use the legs for ground contact • Use the propellers for balance and lift • Combine walking, hopping and flying in one system LEO is basically a hybrid between a walking robot and a flying drone. How it was built: • Two lightweight legs • Three actuated joints in each leg • Four propeller thrusters near the shoulders • A lightweight body • Leg motors for ground movement • Propellers for balance, lift and aerial control • Real-time control software that synchronizes the legs and propellers How it walks: • The legs move the robot forward • The feet touch the ground like a normal biped • The propellers constantly correct balance from above • The robot can stay upright even in unstable situations • The thrust reduces the risk of falling during difficult motions How it flies: • The legs stop being the main locomotion system • The four propellers generate lift • The robot behaves more like a drone • It can take off, fly over obstacles and land back on its legs What makes it different: • It does not walk like a normal humanoid • It does not fly like a normal drone • It blends both systems • The legs handle contact with the ground • The propellers act like fast stabilizers • The control system decides how much help comes from the legs and how much comes from thrust That is why LEO can: • Walk • Hop • Fly over obstacles • Ride a skateboard • Balance on a slackline The key idea is walking with aerial stabilization.
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LEONARDO, also called LEO, was built by researchers at Caltech’s Center for Autonomous Systems and Technologies. Its full name means LEgs ONboARD drOne. The idea is simple but unusual: • Build a small biped robot • Give it drone-style thrust • Use the legs for ground contact • Use the propellers for balance and lift • Combine walking, hopping and flying in one system LEO is basically a hybrid between a walking robot and a flying drone. How it was built: • Two lightweight legs • Three actuated joints in each leg • Four propeller thrusters near the shoulders • A lightweight body • Leg motors for ground movement • Propellers for balance, lift and aerial control • Real-time control software that synchronizes the legs and propellers How it walks: • The legs move the robot forward • The feet touch the ground like a normal biped • The propellers constantly correct balance from above • The robot can stay upright even in unstable situations • The thrust reduces the risk of falling during difficult motions How it flies: • The legs stop being the main locomotion system • The four propellers generate lift • The robot behaves more like a drone • It can take off, fly over obstacles and land back on its legs What makes it different: • It does not walk like a normal humanoid • It does not fly like a normal drone • It blends both systems • The legs handle contact with the ground • The propellers act like fast stabilizers • The control system decides how much help comes from the legs and how much comes from thrust That is why LEO can: • Walk • Hop • Fly over obstacles • Ride a skateboard • Balance on a slackline The key idea is walking with aerial stabilization.

Techniahqrobot | humanoid robots

135,515 просмотров • 2 месяцев назад

🚨 SCIENTISTS ARE USING AI TO MAP A HIDDEN “CLEANING SYSTEM” INSIDE THE HUMAN BRAIN AND IT COULD CHANGE HOW WE UNDERSTAND SLEEP, AGING, AND NEURODEGENERATIVE DISEASE. It’s called the glymphatic system a vast network of fluid channels that flushes toxic waste from the brain while you sleep. For years it was almost impossible to observe in detail. Now AI-powered imaging is revealing it like a living galaxy of microscopic rivers and pathways inside neural tissue. Why this matters: Your brain produces toxic proteins constantly. The glymphatic system is one of its primary waste-clearance mechanisms clearing the very proteins linked to Alzheimer’s, Parkinson’s, and other neurodegenerative diseases. But here’s the unsettling part: This cleaning system becomes dramatically less efficient with age, poor sleep, stress, and brain injury. Sleep may not just “rest” your mind it may literally wash your brain. The deeper implication is staggering: The brain isn’t just a computer. It’s a dynamic, living network that constantly rebuilds and cleanses itself in real time. The more we map it, the more it looks like an entire universe of connected energy flows inside our skulls. What if some neurological diseases begin when the brain can no longer properly “wash” itself at night? Follow for more frontier neuroscience and future technology.
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🚨 SCIENTISTS ARE USING AI TO MAP A HIDDEN “CLEANING SYSTEM” INSIDE THE HUMAN BRAIN AND IT COULD CHANGE HOW WE UNDERSTAND SLEEP, AGING, AND NEURODEGENERATIVE DISEASE. It’s called the glymphatic system a vast network of fluid channels that flushes toxic waste from the brain while you sleep. For years it was almost impossible to observe in detail. Now AI-powered imaging is revealing it like a living galaxy of microscopic rivers and pathways inside neural tissue. Why this matters: Your brain produces toxic proteins constantly. The glymphatic system is one of its primary waste-clearance mechanisms clearing the very proteins linked to Alzheimer’s, Parkinson’s, and other neurodegenerative diseases. But here’s the unsettling part: This cleaning system becomes dramatically less efficient with age, poor sleep, stress, and brain injury. Sleep may not just “rest” your mind it may literally wash your brain. The deeper implication is staggering: The brain isn’t just a computer. It’s a dynamic, living network that constantly rebuilds and cleanses itself in real time. The more we map it, the more it looks like an entire universe of connected energy flows inside our skulls. What if some neurological diseases begin when the brain can no longer properly “wash” itself at night? Follow for more frontier neuroscience and future technology.

TheNewPhysics

15,641 просмотров • 3 месяцев назад

🇺🇸🇮🇷 Reports of a U.S. A-10 Thunderbolt II going down near the Strait of Hormuz raise a key question: how easy is it to bring one down? At first glance, the A-10 looks like an easy target. It is slow, flies at low altitude, and lacks the stealth and speed of modern jets. But it was never designed to avoid fire. It was designed to survive it. Every part of it reflects that idea. The pilot sits inside a titanium armor “bathtub,” critical systems are duplicated, and the engines are placed far apart to reduce the chance of a single hit taking it out. Even the fuel system is built to limit severe damage. Its combat record proves it. During the Gulf War, A-10s often returned with heavy damage, damaged wings, failed hydraulics, and barely working systems, yet still made it back. Modern air defenses can hit an A-10, but destroying it is a different challenge. Flying low through terrain and battlefield clutter makes targeting harder, and its strong build means one hit is rarely enough. That durability is what keeps it relevant. While newer jets rely on stealth and speed, the A-10 depends on pure survivability. It is not invincible, but it was built with one expectation: it will take fire and still return.
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🇺🇸🇮🇷 Reports of a U.S. A-10 Thunderbolt II going down near the Strait of Hormuz raise a key question: how easy is it to bring one down? At first glance, the A-10 looks like an easy target. It is slow, flies at low altitude, and lacks the stealth and speed of modern jets. But it was never designed to avoid fire. It was designed to survive it. Every part of it reflects that idea. The pilot sits inside a titanium armor “bathtub,” critical systems are duplicated, and the engines are placed far apart to reduce the chance of a single hit taking it out. Even the fuel system is built to limit severe damage. Its combat record proves it. During the Gulf War, A-10s often returned with heavy damage, damaged wings, failed hydraulics, and barely working systems, yet still made it back. Modern air defenses can hit an A-10, but destroying it is a different challenge. Flying low through terrain and battlefield clutter makes targeting harder, and its strong build means one hit is rarely enough. That durability is what keeps it relevant. While newer jets rely on stealth and speed, the A-10 depends on pure survivability. It is not invincible, but it was built with one expectation: it will take fire and still return.

Defense Intelligence

57,768 просмотров • 5 месяцев назад

This woman is a single mother who has a job as a delivery driver for a company that has a Fed Ex route contract. She relies on this job to support her and her kids. She just found out that the company she works for lost the contract and they will all be out of a job in about a month. She found out from someone else, not the company that she works for. 🤯 She is considering just taking her load back and walking out. This happens quite often in the delivery service field. Companies like Amazon and Fed Ex deliveries are contracted out and if those contractors don’t meet certain standards they can lose the contract. 💯 It’s possible the new contractor will hire her but nothing is guaranteed. My son just went through this a couple of years ago and they did not hire most of the drivers over to the new company. Many people told her not to quit, to stick it out and to go apply with the new company asap. She said if they don’t hire her it is just wasted time in getting a new job. She’s upset because she feels it is disrespectful of her company not to let them know personally and give them time to find a new job. I wouldn’t make matters worse. She needs the money. Stick it out and start looking for a new job. Did you know this happens regularly in the delivery service field, that contractors lose the contracts all the time and people lose their jobs?
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This woman is a single mother who has a job as a delivery driver for a company that has a Fed Ex route contract. She relies on this job to support her and her kids. She just found out that the company she works for lost the contract and they will all be out of a job in about a month. She found out from someone else, not the company that she works for. 🤯 She is considering just taking her load back and walking out. This happens quite often in the delivery service field. Companies like Amazon and Fed Ex deliveries are contracted out and if those contractors don’t meet certain standards they can lose the contract. 💯 It’s possible the new contractor will hire her but nothing is guaranteed. My son just went through this a couple of years ago and they did not hire most of the drivers over to the new company. Many people told her not to quit, to stick it out and to go apply with the new company asap. She said if they don’t hire her it is just wasted time in getting a new job. She’s upset because she feels it is disrespectful of her company not to let them know personally and give them time to find a new job. I wouldn’t make matters worse. She needs the money. Stick it out and start looking for a new job. Did you know this happens regularly in the delivery service field, that contractors lose the contracts all the time and people lose their jobs?

👉M-Û-R-Č-H👈

80,792 просмотров • 2 месяцев назад