Loading video...

Video Failed to Load

Go Home

A FLY’S VISUAL SYSTEM COULD BECOME A ROBOT’S EYES. not by copying the eyes. by copying the wiring behind them. a fly’s brain doesn’t build a detailed picture of the world. its visual circuits reduce light, contrast and motion into decisions: something moved obstacle ahead turn keep going AI...

mr_kozh's profile picture

kozh ./

2,342 subscribers

81,861 views • 11 days ago •via X (Twitter)

LIVE

Anya Rossi• Live Now

Private livecam show

0 Comments

No comments available

Comments from the original post will appear here

Related Videos

HOLY SH*T, THE WIRING FROM A DEAD FLY’S BRAIN IS NOW DRIVING A TINY ROBOT. not a simulation trapped inside a computer. a physical machine receiving camera input and turning it into movement. scientists reconstructed a fly brain containing: 166,700 neurons roughly 125 million synapses the circuits behind vision, motion and behavior developers then built a loop: camera frame → modeled neural activity → movement command → robot turns, walks or avoids an obstacle the camera supplies the pixels. the fly’s neural wiring decides which pixels matter. this does not mean the fly was revived. the robot is not conscious. it does not understand its surroundings. and there is no tiny mind trapped inside the machine. but the control system is based on biological wiring that evolution spent millions of years refining. the fly is dead. its solution to movement is now walking around in another body. I broke down how 166,700 neurons became a physical controller below ↓
0:14

HOLY SH*T, THE WIRING FROM A DEAD FLY’S BRAIN IS NOW DRIVING A TINY ROBOT. not a simulation trapped inside a computer. a physical machine receiving camera input and turning it into movement. scientists reconstructed a fly brain containing: 166,700 neurons roughly 125 million synapses the circuits behind vision, motion and behavior developers then built a loop: camera frame → modeled neural activity → movement command → robot turns, walks or avoids an obstacle the camera supplies the pixels. the fly’s neural wiring decides which pixels matter. this does not mean the fly was revived. the robot is not conscious. it does not understand its surroundings. and there is no tiny mind trapped inside the machine. but the control system is based on biological wiring that evolution spent millions of years refining. the fly is dead. its solution to movement is now walking around in another body. I broke down how 166,700 neurons became a physical controller below ↓

kozh ./

92,936 views • 5 days ago

HOLY SH*T, A DEAD FLY’S BRAIN MAP IS NOW MAKING A ROBOT MOVE. not a brain sitting inside a jar. not an AI trained to imitate how a fly behaves. the actual wiring reconstructed from its nervous system. scientists mapped: 166,700 neurons roughly 125 million synapses the circuits behind vision, motion and behavior AI helped reconstruct the connections. developers then turned the map into a control loop: camera sees the environment → fly-derived visual circuits process the input → modeled neurons produce activity → activity becomes motor commands → the robot moves the body is synthetic. the control logic begins with biological wiring shaped by millions of years of evolution. this does not mean the fly was resurrected. the robot isn’t conscious. it doesn’t remember being alive. it doesn’t understand where it is going. but the solution its nervous system used to see, react and move can now operate inside a completely different body. this is bigger than one robot. if biological circuits can become inspectable controllers, engineers may not need to invent every intelligent behavior from zero. they can study solutions evolution already built. the fly is dead. its wiring is still making something move. I broke down how 166,700 neurons escaped the brain map and entered a machine below ↓
0:12

HOLY SH*T, A DEAD FLY’S BRAIN MAP IS NOW MAKING A ROBOT MOVE. not a brain sitting inside a jar. not an AI trained to imitate how a fly behaves. the actual wiring reconstructed from its nervous system. scientists mapped: 166,700 neurons roughly 125 million synapses the circuits behind vision, motion and behavior AI helped reconstruct the connections. developers then turned the map into a control loop: camera sees the environment → fly-derived visual circuits process the input → modeled neurons produce activity → activity becomes motor commands → the robot moves the body is synthetic. the control logic begins with biological wiring shaped by millions of years of evolution. this does not mean the fly was resurrected. the robot isn’t conscious. it doesn’t remember being alive. it doesn’t understand where it is going. but the solution its nervous system used to see, react and move can now operate inside a completely different body. this is bigger than one robot. if biological circuits can become inspectable controllers, engineers may not need to invent every intelligent behavior from zero. they can study solutions evolution already built. the fly is dead. its wiring is still making something move. I broke down how 166,700 neurons escaped the brain map and entered a machine below ↓

kozh ./

37,148 views • 3 days ago

this is insane, a dead fly brain just got a new body 166,700 fruit fly neurons and more than 25 million biological connections are now sitting inside a robotic body that looks almost exactly like the animal they came from the lab gave those neurons cameras, wings and motors, then let the system figure out what those signals meant without writing normal flight behavior for it a light moves and the head turns airflow changes and the wings correct another fly crosses the camera and the robot starts following it the creepy part is how little machinery the brain actually needs before old instincts start becoming useful again nature spent hundreds of millions of years building a nervous system that can react, stabilize and chase movement someone just gave that nervous system a body made of metal the original fly is gone its wiring is still trying to fly
0:14

this is insane, a dead fly brain just got a new body 166,700 fruit fly neurons and more than 25 million biological connections are now sitting inside a robotic body that looks almost exactly like the animal they came from the lab gave those neurons cameras, wings and motors, then let the system figure out what those signals meant without writing normal flight behavior for it a light moves and the head turns airflow changes and the wings correct another fly crosses the camera and the robot starts following it the creepy part is how little machinery the brain actually needs before old instincts start becoming useful again nature spent hundreds of millions of years building a nervous system that can react, stabilize and chase movement someone just gave that nervous system a body made of metal the original fly is gone its wiring is still trying to fly

explos1ve

196,313 views • 13 days ago

Google just gave away the full wiring diagram of a fruit fly's brain, and a student turned it into an AI agent. Google and HHMI Janelia spent 10 years mapping the full nervous system of one male fruit fly, then released the complete map for free on September 3. 166,700 neurons. 24.5 million synapses. Every wire from eye to leg, mapped and public. She turned the wiring into a leaky integrate and fire simulation and gave it a job. A camera tracks her fingers, the movement hits the fly's sensory neurons, and whatever motor neurons fire decide what a robot does on the other side of the screen. ▪ Pinch triggers running implementation ▪ Point spins up a new implementation ▪ Pull back triggers heavy lifting Twelve days ago this brain was just a dataset. Then it played DOOM. Then Chrome Dino. Then it walked a Strandbeest. Now it works for a student. This isn't a living fly. It's a computer model of real wiring, and she built the bridge between her hand and its neurons herself. The fly died years ago. Its wiring still takes orders.
0:14

Google just gave away the full wiring diagram of a fruit fly's brain, and a student turned it into an AI agent. Google and HHMI Janelia spent 10 years mapping the full nervous system of one male fruit fly, then released the complete map for free on September 3. 166,700 neurons. 24.5 million synapses. Every wire from eye to leg, mapped and public. She turned the wiring into a leaky integrate and fire simulation and gave it a job. A camera tracks her fingers, the movement hits the fly's sensory neurons, and whatever motor neurons fire decide what a robot does on the other side of the screen. ▪ Pinch triggers running implementation ▪ Point spins up a new implementation ▪ Pull back triggers heavy lifting Twelve days ago this brain was just a dataset. Then it played DOOM. Then Chrome Dino. Then it walked a Strandbeest. Now it works for a student. This isn't a living fly. It's a computer model of real wiring, and she built the bridge between her hand and its neurons herself. The fly died years ago. Its wiring still takes orders.

Zentrix⌚️

63,932 views • 14 days ago

A dead fruit fly was sliced into 66 pieces. Now its 166,700 neurons are driving robots. Nobody trained it, no dataset, no reward function, no GPU farm, just the wiring. The fly took a second to die, but copying it took 7 microscopes running nonstop for a year, shaving it 1/10,000 of a hair at a time and photographing every layer before the next cut. AI rebuilt it from the scraps, eyes to legs with the neck still attached, 124M synapses and 11,710 cell types, 44 years of human labor finished by machines. Scientists first tried this in 2008 and guessed it would take 500 people 10 years. Then the internet got the file and someone gave it a body. Camera → fly eyes → 166,700 spiking neurons → descending neurons → motors. One of those neurons is DNp01, the giant fiber, the cell that fires when your hand comes down. Push an object at the camera and the looming detectors light up, so the robot swerves. It took 0 training runs, just the same reflex that gets a fly out from under a swatter in 100 milliseconds. Then came the control test, where they kept all 138,639 neurons and shuffled where the synapses connect. The behavior died. The intelligence was never in the neurons, it was in the map. The fly is gone, but the flinch is still running.
0:18

A dead fruit fly was sliced into 66 pieces. Now its 166,700 neurons are driving robots. Nobody trained it, no dataset, no reward function, no GPU farm, just the wiring. The fly took a second to die, but copying it took 7 microscopes running nonstop for a year, shaving it 1/10,000 of a hair at a time and photographing every layer before the next cut. AI rebuilt it from the scraps, eyes to legs with the neck still attached, 124M synapses and 11,710 cell types, 44 years of human labor finished by machines. Scientists first tried this in 2008 and guessed it would take 500 people 10 years. Then the internet got the file and someone gave it a body. Camera → fly eyes → 166,700 spiking neurons → descending neurons → motors. One of those neurons is DNp01, the giant fiber, the cell that fires when your hand comes down. Push an object at the camera and the looming detectors light up, so the robot swerves. It took 0 training runs, just the same reflex that gets a fly out from under a swatter in 100 milliseconds. Then came the control test, where they kept all 138,639 neurons and shuffled where the synapses connect. The behavior died. The intelligence was never in the neurons, it was in the map. The fly is gone, but the flinch is still running.

Spike 1%

24,336 views • 8 days ago

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 synapses, every wire from eye to leg. She ran all of it as a leaky integrate-and-fire simulation and then gave it a job. The top-right panel is the brain firing live, the robot on the left is the agent it drives. A camera tracks her fingers, the movements hit the fly's sensory neurons, and whatever motor neurons fire decide what the agent does. Status: IDLE Pinch → RUNNING IMPLEMENTATION Point → SPINNING UP NEW IMPL Pull back → HEAVY LIFTING The catch: this is not a living fly but a computer model of real wiring, and she built the link between her hand and its neurons. 12 days ago this brain was a dataset Then it played DOOM Then Chrome Dino Then it walked a Strandbeest Now it works for a student. The fly died years ago, but its wiring still takes orders.
0:14

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 synapses, every wire from eye to leg. She ran all of it as a leaky integrate-and-fire simulation and then gave it a job. The top-right panel is the brain firing live, the robot on the left is the agent it drives. A camera tracks her fingers, the movements hit the fly's sensory neurons, and whatever motor neurons fire decide what the agent does. Status: IDLE Pinch → RUNNING IMPLEMENTATION Point → SPINNING UP NEW IMPL Pull back → HEAVY LIFTING The catch: this is not a living fly but a computer model of real wiring, and she built the link between her hand and its neurons. 12 days ago this brain was a dataset Then it played DOOM Then Chrome Dino Then it walked a Strandbeest Now it works for a student. The fly died years ago, but its wiring still takes orders.

Spike 1%

105,085 views • 16 days ago

166,700 fruit fly neurons are flying a drone around my bedroom. 1 camera watches my open hand and that's the whole input. The panel in the middle is the brain running live 166,700 neurons, 25 million connections, the full reconstructed map of a fruit fly's head. Camera reads the hand, the brain fires, the output becomes throttle. Open palm and it climbs, fist and it holds, drop the hand and it comes down. 4 propellers the size of a coin hanging off a nervous system that belonged to an insect. The honest part this is a computational model, not a resurrected fly, the wiring is biological, the drone is mine, I built the bridge between them. I never wrote flight logic, no PID tuning, no gesture presets, I gave the neurons somewhere to send the signal and they found the ceiling. 11 seconds in, it holds altitude on its own. The fly is dead and it's still flying.
0:15

166,700 fruit fly neurons are flying a drone around my bedroom. 1 camera watches my open hand and that's the whole input. The panel in the middle is the brain running live 166,700 neurons, 25 million connections, the full reconstructed map of a fruit fly's head. Camera reads the hand, the brain fires, the output becomes throttle. Open palm and it climbs, fist and it holds, drop the hand and it comes down. 4 propellers the size of a coin hanging off a nervous system that belonged to an insect. The honest part this is a computational model, not a resurrected fly, the wiring is biological, the drone is mine, I built the bridge between them. I never wrote flight logic, no PID tuning, no gesture presets, I gave the neurons somewhere to send the signal and they found the ceiling. 11 seconds in, it holds altitude on its own. The fly is dead and it's still flying.

Spike 1%

1,046,183 views • 16 days ago

This is f*cking gold - I built catbrain catch The idea is simple: a camera sees a ball, a cat-inspired visual model reacts, and the system asks: WHERE will it be when the arm gets there? Here’s the pipeline. Oriented filters extract visual features. Simulated neurons respond to those features and changes between frames. Color, shape, neural activity, and tracking history help identify the ball. Then a trajectory fit predicts where it will cross a target line in the image. → Camera input becomes neural responses → Motion helps separate targets from static clutter → Recent observations become an interception forecast → Lost tracking clears the prediction The next challenge is the REAL one: turning image coordinates into a reachable catch position on a physical robot. I left the project link in the replies. Build it, test it, break it.
0:17

This is f*cking gold - I built catbrain catch The idea is simple: a camera sees a ball, a cat-inspired visual model reacts, and the system asks: WHERE will it be when the arm gets there? Here’s the pipeline. Oriented filters extract visual features. Simulated neurons respond to those features and changes between frames. Color, shape, neural activity, and tracking history help identify the ball. Then a trajectory fit predicts where it will cross a target line in the image. → Camera input becomes neural responses → Motion helps separate targets from static clutter → Recent observations become an interception forecast → Lost tracking clears the prediction The next challenge is the REAL one: turning image coordinates into a reachable catch position on a physical robot. I left the project link in the replies. Build it, test it, break it.

BuBBliK

35,422 views • 13 days ago

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

12,054 views • 16 days ago

A Gaussian Splat can become a world where Robots and AI agents can act. In our latest OVER Research experiment, we placed a robot inside a real-world 3D capture, with a VLM making decisions based on what it sees. At every step, the robot holds a pose in the reconstruction, gets a newly rendered view of the environment, takes an action, moves, and sees the world again from its new position. Why does this matter? Because 3D captures can become more than reconstructions to explore. They can become environments where embodied AI and robots can navigate, act, be evaluated and eventually train across real-world spaces at scale. Capture a place once. Then turn it into a world where AI and Robots can act. The full experiment, including what we discovered once we actually put the loop to the test:
0:14

A Gaussian Splat can become a world where Robots and AI agents can act. In our latest OVER Research experiment, we placed a robot inside a real-world 3D capture, with a VLM making decisions based on what it sees. At every step, the robot holds a pose in the reconstruction, gets a newly rendered view of the environment, takes an action, moves, and sees the world again from its new position. Why does this matter? Because 3D captures can become more than reconstructions to explore. They can become environments where embodied AI and robots can navigate, act, be evaluated and eventually train across real-world spaces at scale. Capture a place once. Then turn it into a world where AI and Robots can act. The full experiment, including what we discovered once we actually put the loop to the test:

Over the Reality 🌐

15,261 views • 1 month ago

UPDATE: Flyctor has a second body now I bought another Vector robot and connected it to the same stack: - a camera feed - GPT-6 Astra for real-time visual understanding - a MaleCNS fly-brain simulation for movement decisions - a small audio protocol for robot-to-robot communication I didn't let them exchange normal Wi-Fi messages. Instead, each robot has to communicate through its speaker and microphone. One robot emits a short sequence of tones. The other records the sound, converts it into a frequency pattern, classifies the pattern, and injects it into its fly-brain simulation as a sensory event. Right now their entire vocabulary is only four signals: one chirp = "I found an object" two chirps = "come closer" long tone = "path blocked" rapid chirps = "I need help" The interesting part is that the robots don't send coordinates. They don't share a map. They don't know where the other robot is. One Flyctor sees something through Astra, decides it matters, sends a sound, and the other Flyctor has to hear it, interpret it, then decide what to do with that information. camera -> Astra -> fly brain -> speaker microphone -> audio decoder -> fly brain -> movement It is basically a tiny sensory loop between two digital insect nervous systems. Yesterday one robot detected a blocked path and sent the long-tone signal. The other one heard it, turned toward the sound, and rolled over to investigate. It was not impressive in the way modern AI demos are impressive. It was impressive because it felt like watching two small creatures notice each other for the first time.
0:14

UPDATE: Flyctor has a second body now I bought another Vector robot and connected it to the same stack: - a camera feed - GPT-6 Astra for real-time visual understanding - a MaleCNS fly-brain simulation for movement decisions - a small audio protocol for robot-to-robot communication I didn't let them exchange normal Wi-Fi messages. Instead, each robot has to communicate through its speaker and microphone. One robot emits a short sequence of tones. The other records the sound, converts it into a frequency pattern, classifies the pattern, and injects it into its fly-brain simulation as a sensory event. Right now their entire vocabulary is only four signals: one chirp = "I found an object" two chirps = "come closer" long tone = "path blocked" rapid chirps = "I need help" The interesting part is that the robots don't send coordinates. They don't share a map. They don't know where the other robot is. One Flyctor sees something through Astra, decides it matters, sends a sound, and the other Flyctor has to hear it, interpret it, then decide what to do with that information. camera -> Astra -> fly brain -> speaker microphone -> audio decoder -> fly brain -> movement It is basically a tiny sensory loop between two digital insect nervous systems. Yesterday one robot detected a blocked path and sent the long-tone signal. The other one heard it, turned toward the sound, and rolled over to investigate. It was not impressive in the way modern AI demos are impressive. It was impressive because it felt like watching two small creatures notice each other for the first time.

kiruwaaaa

21,000 views • 11 days ago

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.
0:12

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 views • 3 months ago

this DIY camera stabilizer costs roughly $30–45 to build. not because it beats DJI. because it doesn’t try to. it uses a 3D-printed frame, an ESP32, an IMU, and two $4 micro servos to keep a lightweight action camera roughly level. for comparison: DJI’s RS 4 Mini starts at $309. the $260+ difference is not just branding. DJI gives you three brushless axes, smooth motion, better mechanics, calibration, battery management, and a product that works out of the box. this build gives you something else: a working feedback-control system you can hold in your hand. $40 buys the lesson. $309 buys the polished result.
0:14

this DIY camera stabilizer costs roughly $30–45 to build. not because it beats DJI. because it doesn’t try to. it uses a 3D-printed frame, an ESP32, an IMU, and two $4 micro servos to keep a lightweight action camera roughly level. for comparison: DJI’s RS 4 Mini starts at $309. the $260+ difference is not just branding. DJI gives you three brushless axes, smooth motion, better mechanics, calibration, battery management, and a product that works out of the box. this build gives you something else: a working feedback-control system you can hold in your hand. $40 buys the lesson. $309 buys the polished result.

ard

55,497 views • 1 month ago

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 views • 20 days ago

This is not camera footage. It is a Blender character with 8K skin, detailed wrinkles, wet eyes, facial controls and enough micro-detail to make your brain keep waiting for the person to behave like a person. HumanPro packages that skin workflow into a Blender add-on instead of making artists rebuild it from scratch every time. The interesting AI angle is not “AI made a realistic girl.” A reusable 3D human can keep the same face across thousands of shots, then be relit, reposed, animated and dropped into completely different scenes without the identity drifting every six frames. Add Claude through Blender MCP and the workflow gets stranger: the model can help assemble scenes, adjust cameras and lighting, inspect renders and correct obvious visual problems, while the character system handles the skin and facial structure. It still does not remove the artist. Someone has to control expression, motion, lighting and the exact moment realism quietly turns into a very expensive mannequin. Most AI influencer projects are still fighting prompt consistency one image at a time. A rigged digital human is less magical, but probably much closer to how this becomes an actual production system.
0:18

This is not camera footage. It is a Blender character with 8K skin, detailed wrinkles, wet eyes, facial controls and enough micro-detail to make your brain keep waiting for the person to behave like a person. HumanPro packages that skin workflow into a Blender add-on instead of making artists rebuild it from scratch every time. The interesting AI angle is not “AI made a realistic girl.” A reusable 3D human can keep the same face across thousands of shots, then be relit, reposed, animated and dropped into completely different scenes without the identity drifting every six frames. Add Claude through Blender MCP and the workflow gets stranger: the model can help assemble scenes, adjust cameras and lighting, inspect renders and correct obvious visual problems, while the character system handles the skin and facial structure. It still does not remove the artist. Someone has to control expression, motion, lighting and the exact moment realism quietly turns into a very expensive mannequin. Most AI influencer projects are still fighting prompt consistency one image at a time. A rigged digital human is less magical, but probably much closer to how this becomes an actual production system.

Rina

109,872 views • 2 months ago

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%

777,046 views • 16 days ago

🚨 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.
0:10

🚨 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 views • 4 months ago

A HUMANOID ROBOT IS ON THE RUN. THE POLICE CANNOT CATCH IT. The highway is blocked. Police cruisers and an ambulance are following behind. Ahead of them, a humanoid medical robot keeps running without slowing down or getting tired. A few years ago, this would have looked like a scene from a sci-fi movie. Today, it looks like an ordinary live news broadcast. This is 100% AI-generated. But what if this chase were real? How would you react to a robot the police could not catch?
0:14

A HUMANOID ROBOT IS ON THE RUN. THE POLICE CANNOT CATCH IT. The highway is blocked. Police cruisers and an ambulance are following behind. Ahead of them, a humanoid medical robot keeps running without slowing down or getting tired. A few years ago, this would have looked like a scene from a sci-fi movie. Today, it looks like an ordinary live news broadcast. This is 100% AI-generated. But what if this chase were real? How would you react to a robot the police could not catch?

CHQ

22,367 views • 17 days ago

An interesting issue with Robotaxi where it took us to a Starbucks, but the Google data had the incorrect location. At drop off we were 0.2 miles from the Starbucks, so we had a short walk. Is there a way the Tesla AI team could add functionality in the app so riders can update map info to correct errors or inaccuracies on the underlying map data and then this propagates to the fleet? Being able to do this with a pin drop on the map instead of having to use an address might make this very easy and user friendly! Or, as a bigger ask, would it be possible for the car to be able to use visual images on its own to look for a Starbucks sign and on the fly, get us closer and update the map data on its own?
0:13

An interesting issue with Robotaxi where it took us to a Starbucks, but the Google data had the incorrect location. At drop off we were 0.2 miles from the Starbucks, so we had a short walk. Is there a way the Tesla AI team could add functionality in the app so riders can update map info to correct errors or inaccuracies on the underlying map data and then this propagates to the fleet? Being able to do this with a pin drop on the map instead of having to use an address might make this very easy and user friendly! Or, as a bigger ask, would it be possible for the car to be able to use visual images on its own to look for a Starbucks sign and on the fly, get us closer and update the map data on its own?

Joe Tegtmeyer 🚀 🤠🛸😎

80,363 views • 1 year ago

three GitHub projects show how much vision can fit inside an ESP32-S3 esp32-camera handles the first layer: sensor data, JPEG frames, PSRAM, and frame buffers 🖇️ once the camera pipeline works, esp-who adds ready-made vision tasks such as face detection, pedestrian detection, and QR recognition 🖇️ the third project explores a smaller but more ambitious setup: training and deploying a custom vision model directly on the microcontroller 🖇️ put the pieces together and the loop becomes straightforward: capture an image run the model turn the result into a decision trigger something in the physical world a light, a servo, an MQTT event, or a local log the useful part is the short path from something the camera sees to something the hardware does
0:19

three GitHub projects show how much vision can fit inside an ESP32-S3 esp32-camera handles the first layer: sensor data, JPEG frames, PSRAM, and frame buffers 🖇️ once the camera pipeline works, esp-who adds ready-made vision tasks such as face detection, pedestrian detection, and QR recognition 🖇️ the third project explores a smaller but more ambitious setup: training and deploying a custom vision model directly on the microcontroller 🖇️ put the pieces together and the loop becomes straightforward: capture an image run the model turn the result into a decision trigger something in the physical world a light, a servo, an MQTT event, or a local log the useful part is the short path from something the camera sees to something the hardware does

ard

20,947 views • 1 month ago