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$BRAIN is live. the open-source fly-brain bridge has a ticker now, and the ticker is becoming the input for a physical robot. CA: 9LuwgFQemAoV9rgVBBtwbRSxBmamcRRbysEK8yL2pump what’s already done: the neural bridge is working. camera input becomes activity across eight virtual neural populations, and that activity becomes left and right motor...

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Frank

4,217 subscribers

342,065 次观看 • 8 天前 •via X (Twitter)

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33 条评论

Gaide Merlot 的头像
Gaide Merlot8 天前

dev is live now! stream:

Gaide Merlot 的头像
Gaide Merlot8 天前

dev is live now! stream:

Dan ✦ 的头像
Dan ✦8 天前

I love how you deployed on robinhood then rugged it immediately, just to deploy on SOL. You launched two of these. One here: F9tNtwhkjmGvoMe5nzKMP6FNCP3waiNGgBK6uHFSpump then when you got no bids you deployed this 9LuwgFQemAoV9rgVBBtwbRSxBmamcRRbysEK8yL2pump and got a lucky bid by a good wallet after you nuked the original bundle. very bullish bro!

Tijen 的头像
Tijen8 天前

still trying to wrap my head around how you mapped those 166k neurons to actual motor control without it being chaos

bitguide 的头像
bitguide8 天前

Like the 3rd ticker you shilling gtfo

Crypto Zeinab 的头像
Crypto Zeinab8 天前

100m coded

senda 的头像
senda8 天前

guys read the whole tweet. most of you dont understand how big this is 9LuwgFQemAoV9rgVBBtwbRSxBmamcRRbysEK8yL2pump

Luigi 的头像
Luigi8 天前

Ok but why do you need crypto coin for this? To extract some money?

Mitnick 的头像
Mitnick8 天前

@elonmusk is this dangerous? lol

Based Normye 的头像
Based Normye8 天前

This is fucking insane Trillions

Fruit fly fefe 的头像
Fruit fly fefe8 天前

so we got bigger family!

Zees 🐂🀄️ 的头像
Zees 🐂🀄️8 天前

Fudders will fud, see yall at 10M.

Sgt. Wingflapper 的头像
Sgt. Wingflapper8 天前

idk why would you deploy on robinhood then insta rug it tweet now deleted

NO.12 的头像
NO.128 天前

lol

Gux 🥷 的头像
Gux 🥷8 天前

Pay for the DEX, create a community, and put the links on the DEX.

Dreams 的头像
Dreams8 天前

Easy $50m

Capolini💰 的头像
Capolini💰8 天前

THIS IS INSANE WTF LITERALLY BILLIONS

Francis OG dev 的头像
Francis OG dev8 天前

0

Crypto 的头像
Crypto8 天前

Millions

Roddy. 的头像
Roddy.8 天前

aped some.. goodluck building

Sebastian Sosa 的头像
Sebastian Sosa8 天前

🪰⚖️ Fly Adjudicator finds this post to be BULLSHIT Dark-edge detectors: 203 spikes (heavy, threshold 177) ON/OFF balance: 1.97 — balanced Inhibitory surround: 0.70 — clamped Statement: The court finds bullshit. The court would like to stress that it is not complaining. Policy:

Slipstream PR 的头像
Slipstream PR8 天前

👀

Capolini💰 的头像
Capolini💰8 天前

A coin that can’t be fudded the tech is too good this will hit multi millions for sure.

Bald Knower 🧑🏼‍🦲 的头像
Bald Knower 🧑🏼‍🦲8 天前

sure why not

Speedy 的头像
Speedy8 天前

Dont get flylined

MARTIN 的头像
MARTIN8 天前

Are you going to start a live stream?

Fruit fly fefe 的头像
Fruit fly fefe8 天前

Fly took over the world soon

Capolini💰 的头像
Capolini💰8 天前

THIS IS MASSIVE

Capolini💰 的头像
Capolini💰8 天前

This is gonna bounce so hard from here

China Quant Dev 的头像
China Quant Dev8 天前

this guy launched multiple coins rugged all of them and now we run it ??

Pepe 的头像
Pepe8 天前

Can you detect a Frog with its own Brain 🐸

Capolini💰 的头像
Capolini💰8 天前

Fellas what we doing here a big holder clips and you shit your pants this coin is genny

Secret squirrel 的头像
Secret squirrel8 天前

Oh shit this is big

相关视频

THE FLY BRAIN ESCAPED THE COMPUTER. I PUT IT INSIDE A PHYSICAL ROBOT. Scientists released a reconstructed map containing 166,700 neurons from a fruit fly. I connected it to a Strandbeest. The panel in the top-right shows the simulated brain running. Its neural activity is translated into motor commands that move the mechanical legs below. The catch: this is a computational model built from biological wiring. I engineered the interface connecting its outputs to the robot. First, the fly brain entered DOOM. Then it played Chrome Dino. Now it has a physical body. 166,700 neurons. 25 million connections. One nervous system walking through the real world.
0:53

THE FLY BRAIN ESCAPED THE COMPUTER. I PUT IT INSIDE A PHYSICAL ROBOT. Scientists released a reconstructed map containing 166,700 neurons from a fruit fly. I connected it to a Strandbeest. The panel in the top-right shows the simulated brain running. Its neural activity is translated into motor commands that move the mechanical legs below. The catch: this is a computational model built from biological wiring. I engineered the interface connecting its outputs to the robot. First, the fly brain entered DOOM. Then it played Chrome Dino. Now it has a physical body. 166,700 neurons. 25 million connections. One nervous system walking through the real world.

Frank

5,137,556 次观看 • 10 天前

FLY BRAIN PUT INSIDE PHYSICAL ROBOT. Not literally. They mapped all 166,700 real neurons from a fly's brain and rebuilt the entire wiring as a computational model. Every connection copied, nothing simplified. That simulated brain has already lived several lives. It played DOOM. It beat Chrome Dino. Each time researchers were testing whether insect-level neural wiring could handle tasks it never evolved for. Now the same model controls a physical machine, and they dropped it straight into a maze. Cardboard walls, tight turns, no map given in advance. The robot's neural activity gets translated directly into motor commands, and those commands drove it through the maze and out the other side. Researchers are clear this isn't a biological brain in a robot. It's an engineered interface between a reconstructed nervous system and a machine body. But a fly's mind just solved a physical maze. Insect cognition is running real robots today. Human-scale digital brains doing the same thing feels a lot closer now. Wild breakthrough or the first step toward something we're not ready for?
0:39

FLY BRAIN PUT INSIDE PHYSICAL ROBOT. Not literally. They mapped all 166,700 real neurons from a fly's brain and rebuilt the entire wiring as a computational model. Every connection copied, nothing simplified. That simulated brain has already lived several lives. It played DOOM. It beat Chrome Dino. Each time researchers were testing whether insect-level neural wiring could handle tasks it never evolved for. Now the same model controls a physical machine, and they dropped it straight into a maze. Cardboard walls, tight turns, no map given in advance. The robot's neural activity gets translated directly into motor commands, and those commands drove it through the maze and out the other side. Researchers are clear this isn't a biological brain in a robot. It's an engineered interface between a reconstructed nervous system and a machine body. But a fly's mind just solved a physical maze. Insect cognition is running real robots today. Human-scale digital brains doing the same thing feels a lot closer now. Wild breakthrough or the first step toward something we're not ready for?

Zentrix⌚️

23,428 次观看 • 8 天前

$BRAIN just got a real brain. v0.4.0 runs the full MaleCNS v1.0 connectome: 166,700 neurons and 124M synapses firing in one spiking simulation. Camera motion now reaches identified fly visual neurons, travels through the wiring, and triggers real escape and steering circuits that produce motor commands. The loop already runs up to 6.5x real time on an RTX 3050. Shuffle the connectome and the behaviour vanishes. The wiring is doing the work. Next up: transaction → sensory neurons → 166,700-neuron brain → movement
0:20

$BRAIN just got a real brain. v0.4.0 runs the full MaleCNS v1.0 connectome: 166,700 neurons and 124M synapses firing in one spiking simulation. Camera motion now reaches identified fly visual neurons, travels through the wiring, and triggers real escape and steering circuits that produce motor commands. The loop already runs up to 6.5x real time on an RTX 3050. Shuffle the connectome and the behaviour vanishes. The wiring is doing the work. Next up: transaction → sensory neurons → 166,700-neuron brain → movement

Frank

11,773 次观看 • 7 天前

A DEAD FLY IS DRIVING MY ROBOT RIGHT NOW. Not a metaphor. A reconstructed neural map of a fruit fly - 166,700 neurons, 25.6 million synapses - is now running on my MacBook and steering a quadruped robot in my living room. Here's how it works: 1. MaleCNS - a full connectome reconstruction of an adult fly brain - runs as a live simulation on my laptop. 2. I used Codex to build a decoder that reads the simulation's neural activity and turns it into movement commands: forward, left, right. 3. Those commands get sent over Wi-Fi to the robot, which still uses its own firmware to drive all 12 servos. The brain only decides where to go, not how to move each leg. No scripts. No prerecorded paths. The simulation runs continuously, live, while the robot walks. The fly has been dead for years. But its reconstructed nervous system is producing real decisions again, in real time, controlling a machine that didn't exist when it was alive. Next step: feed the robot's sensors back into the simulation and close the loop. A dead brain that can finally feel the world it's walking through. This fly has been dead for years. Its brain runs on my laptop. Its decisions move a machine through my living room. One dead nervous system, walking again.
0:57

A DEAD FLY IS DRIVING MY ROBOT RIGHT NOW. Not a metaphor. A reconstructed neural map of a fruit fly - 166,700 neurons, 25.6 million synapses - is now running on my MacBook and steering a quadruped robot in my living room. Here's how it works: 1. MaleCNS - a full connectome reconstruction of an adult fly brain - runs as a live simulation on my laptop. 2. I used Codex to build a decoder that reads the simulation's neural activity and turns it into movement commands: forward, left, right. 3. Those commands get sent over Wi-Fi to the robot, which still uses its own firmware to drive all 12 servos. The brain only decides where to go, not how to move each leg. No scripts. No prerecorded paths. The simulation runs continuously, live, while the robot walks. The fly has been dead for years. But its reconstructed nervous system is producing real decisions again, in real time, controlling a machine that didn't exist when it was alive. Next step: feed the robot's sensors back into the simulation and close the loop. A dead brain that can finally feel the world it's walking through. This fly has been dead for years. Its brain runs on my laptop. Its decisions move a machine through my living room. One dead nervous system, walking again.

kiruwaaaa

75,565 次观看 • 8 天前

For the first time in history, GPT-6 Astra created a fly that is simply a fly. Not a chatbot pretending to be a fly. Not a simplified animation. A digital fly running its own connectome brain, perceiving a real room through its own senses and interacting with the environment using the power of AR. The entire loop is surprisingly wild: Spectacles capture the retina, light rays, hands, and the surrounding room → the room is converted into an inventory → sensory information is fed into the fly’s neural system → the MaleCNS connectome processes it → descending neurons and motor pools generate actions → the commands return to the Spectacles → wings, legs, and head respond. In other words, the fly sees through its own eyes, processes the world through its own simulated brain, and acts through its own virtual body. We are moving from AI that generates outputs to AI that actually has a body, senses, a brain, and a closed perception-action loop. The 🪰 is no longer just simulated. It is experiencing a world The developer Pavlo Tkachenko is pinned below in the post
0:28

For the first time in history, GPT-6 Astra created a fly that is simply a fly. Not a chatbot pretending to be a fly. Not a simplified animation. A digital fly running its own connectome brain, perceiving a real room through its own senses and interacting with the environment using the power of AR. The entire loop is surprisingly wild: Spectacles capture the retina, light rays, hands, and the surrounding room → the room is converted into an inventory → sensory information is fed into the fly’s neural system → the MaleCNS connectome processes it → descending neurons and motor pools generate actions → the commands return to the Spectacles → wings, legs, and head respond. In other words, the fly sees through its own eyes, processes the world through its own simulated brain, and acts through its own virtual body. We are moving from AI that generates outputs to AI that actually has a body, senses, a brain, and a closed perception-action loop. The 🪰 is no longer just simulated. It is experiencing a world The developer Pavlo Tkachenko is pinned below in the post

Zentrix⌚️

321,163 次观看 • 10 天前

The full video from Cortical Labs explaining how they put 200,000 brain cells onto a silicon chip and had it play Doom is wild: “When a demon appears on the left of the screen, specific electrodes stimulate the sensory area of the neural culture on the left side. The neurons react to that stimulation. We then listen to their response, the spikes, and interpret that activity as motor commands. If the neurons fire in a specific pattern, the Doom guy shoots.”
6:15

The full video from Cortical Labs explaining how they put 200,000 brain cells onto a silicon chip and had it play Doom is wild: “When a demon appears on the left of the screen, specific electrodes stimulate the sensory area of the neural culture on the left side. The neurons react to that stimulation. We then listen to their response, the spikes, and interpret that activity as motor commands. If the neurons fire in a specific pattern, the Doom guy shoots.”

Trung Phan

234,032 次观看 • 6 个月前

From the moment a baby is born, their brain develops first and foremost through movement. Every stage is not just motor skills—it’s the creation of new neural pathways in the brain.
0:35

From the moment a baby is born, their brain develops first and foremost through movement. Every stage is not just motor skills—it’s the creation of new neural pathways in the brain.

Metabolic Factor

94,586 次观看 • 8 个月前

166,700 fruit fly neurons are walking a robot cat across a desk right now. In September 2026, Google Research and HHMI Janelia mapped every neuron and every connection in an adult male fruit fly's full nervous system, brain, optic lobes, and ventral nerve cord together, and published it in Cell. The number is staggering on its own: 166,700 neurons, roughly 125 million synapses, the most complete nervous system map of any animal ever built. Now that exact wiring is running live, simulated neuron for neuron, inside a small robotic quadruped equipped with a camera and motion sensors. Movement comes in through the sensors, the biological circuitry fires the way it always did, and the output drives the robot's legs directly. No one wrote the balance logic. No one coded the gait. The insect's own nervous system does that job, just on legs it never had. To be clear about what this is and isn't: nothing biological is alive in this loop. It's a full computational reconstruction of real neural wiring, controlling hardware instead of an insect body. The brain is real. The body is borrowed. Remarkable proof that a mapped nervous system can drive any body you give it, or a preview of something people aren't ready to think through yet?
0:24

166,700 fruit fly neurons are walking a robot cat across a desk right now. In September 2026, Google Research and HHMI Janelia mapped every neuron and every connection in an adult male fruit fly's full nervous system, brain, optic lobes, and ventral nerve cord together, and published it in Cell. The number is staggering on its own: 166,700 neurons, roughly 125 million synapses, the most complete nervous system map of any animal ever built. Now that exact wiring is running live, simulated neuron for neuron, inside a small robotic quadruped equipped with a camera and motion sensors. Movement comes in through the sensors, the biological circuitry fires the way it always did, and the output drives the robot's legs directly. No one wrote the balance logic. No one coded the gait. The insect's own nervous system does that job, just on legs it never had. To be clear about what this is and isn't: nothing biological is alive in this loop. It's a full computational reconstruction of real neural wiring, controlling hardware instead of an insect body. The brain is real. The body is borrowed. Remarkable proof that a mapped nervous system can drive any body you give it, or a preview of something people aren't ready to think through yet?

Zentrix⌚️

229,457 次观看 • 8 天前

Using monkey brain data to operate robotic arms! 🙉 Project Jenkins explores brain-computer interfaces by decoding neural activity into robotic movement and generating synthetic brain data from motion patterns. Using neural recordings from a macaque monkey named Jenkins, researchers developed models to decode brain signals into robotic arm movements and encode movement data into simulated neural activity. The project utilizes robotic arms and an interactive web console for real-time brain data generation from joystick inputs. The research paves the way for adaptive brain-computer interfaces by modeling brain activity more flexibly. Congrats to authors Dima Yanovsky, and Andrii Zahorodnii! 🦾
0:26

Using monkey brain data to operate robotic arms! 🙉 Project Jenkins explores brain-computer interfaces by decoding neural activity into robotic movement and generating synthetic brain data from motion patterns. Using neural recordings from a macaque monkey named Jenkins, researchers developed models to decode brain signals into robotic arm movements and encode movement data into simulated neural activity. The project utilizes robotic arms and an interactive web console for real-time brain data generation from joystick inputs. The research paves the way for adaptive brain-computer interfaces by modeling brain activity more flexibly. Congrats to authors Dima Yanovsky, and Andrii Zahorodnii! 🦾

Lukas Ziegler

21,627 次观看 • 1 年前

They are now making fruit flies write. They are even using them to create entire fonts. The method is surprisingly fascinating. First, an image of a letter is projected into the fruit fly’s eye. The visual information is transmitted through its visual neurons and processed by a trained neural network connected to its neural circuitry. The resulting signal is then sent to the motor neurons controlling the fly’s right foreleg. Those motor neurons activate the leg muscles, causing the fly to move its foreleg in a specific pattern that traces the shape of the letter. A tiny black pen is attached to the foreleg, allowing the movement to leave an actual written mark on the surface. In other words, visual information enters through the fly’s eyes, is translated into neural commands, and ultimately becomes physical handwriting through its own muscles. Researchers have even used this system to create fonts based on the letters drawn by fruit flies. At this point, I’m becoming increasingly curious about what else fruit flies could be made to do.
0:28

They are now making fruit flies write. They are even using them to create entire fonts. The method is surprisingly fascinating. First, an image of a letter is projected into the fruit fly’s eye. The visual information is transmitted through its visual neurons and processed by a trained neural network connected to its neural circuitry. The resulting signal is then sent to the motor neurons controlling the fly’s right foreleg. Those motor neurons activate the leg muscles, causing the fly to move its foreleg in a specific pattern that traces the shape of the letter. A tiny black pen is attached to the foreleg, allowing the movement to leave an actual written mark on the surface. In other words, visual information enters through the fly’s eyes, is translated into neural commands, and ultimately becomes physical handwriting through its own muscles. Researchers have even used this system to create fonts based on the letters drawn by fruit flies. At this point, I’m becoming increasingly curious about what else fruit flies could be made to do.

IGNIS

80,255 次观看 • 12 天前

this is insane - I made a robot that plays ping-pong using CAT-INSPIRED vision A camera watches the ball. 20,480 artificial neurons process what it sees. Then a mechanical arm predicts where the ball is going - and moves to intercept it. Here’s what’s happening under the hood: → 20,480 ARTIFICIAL NEURONS process visual signals using a leaky integrate-and-fire model → 8 ORIENTATION CHANNELS detect edges and structure inspired by mechanisms studied in feline vision → The system tracks the ball and predicts where it will be around 80 MS into the future. → The FULL pipeline now works: camera → neural activity → tracking → trajectory → robotic arm movement What started as a weird experiment is now a WORKING physical system. A cat-inspired visual model is literally controlling a robot arm to chase a ping-pong ball. GitHub is in the reply. BOOKMARK this - I’ll keep posting updates as I push catbrain further.
0:47

this is insane - I made a robot that plays ping-pong using CAT-INSPIRED vision A camera watches the ball. 20,480 artificial neurons process what it sees. Then a mechanical arm predicts where the ball is going - and moves to intercept it. Here’s what’s happening under the hood: → 20,480 ARTIFICIAL NEURONS process visual signals using a leaky integrate-and-fire model → 8 ORIENTATION CHANNELS detect edges and structure inspired by mechanisms studied in feline vision → The system tracks the ball and predicts where it will be around 80 MS into the future. → The FULL pipeline now works: camera → neural activity → tracking → trajectory → robotic arm movement What started as a weird experiment is now a WORKING physical system. A cat-inspired visual model is literally controlling a robot arm to chase a ping-pong ball. GitHub is in the reply. BOOKMARK this - I’ll keep posting updates as I push catbrain further.

BuBBliK

14,056 次观看 • 6 天前

Excited to announce GR00T N1, the world’s first open foundation model for humanoid robots! We are on a mission to democratize Physical AI. The power of general robot brain, in the palm of your hand - with only 2B parameters, N1 learns from the most diverse physical action dataset ever compiled and punches above its weight: - Real humanoid teleoperation data. - Large-scale simulation data: we are open-sourcing 300K+ trajectories! - Neural trajectories: we apply SOTA video generation models to “hallucinate” new synthetic data that features accurate physics in pixels. Using Jensen’s words, “systematically infinite data”! - Latent actions: we develop novel algorithms to extract action tokens from in-the-wild human videos and neural generated videos. GR00T N1 is a single end-to-end neural net, from photons to actions: - Vision-Language Model (System 2) that interprets the physical world through vision and language instructions, enabling robots to reason about their environment and instructions, and plan the right actions. - Diffusion Transformer (System 1) that “renders” smooth and precise motor actions at 120 Hz, executing the latent plan made by System 2. We deploy N1 on GR1 robot, 1X Neo robot, and a large collection of simulation benchmarks. N1 achieves up to +30% boost in diverse manipulation tasks for household and industrial settings. While humanoid robots are the main focus of N1, our model also supports cross-embodiment. We finetune it to work on the $110 HuggingFace LeRobot SO100 robot arm! Open robot brain runs on open hardware. Sounds just right. Let’s solve robotics, together, one token at a time. Links to our Whitepaper, Github repo, HuggingFace model, and open dataset page in the thread: 🧵
2:21

Excited to announce GR00T N1, the world’s first open foundation model for humanoid robots! We are on a mission to democratize Physical AI. The power of general robot brain, in the palm of your hand - with only 2B parameters, N1 learns from the most diverse physical action dataset ever compiled and punches above its weight: - Real humanoid teleoperation data. - Large-scale simulation data: we are open-sourcing 300K+ trajectories! - Neural trajectories: we apply SOTA video generation models to “hallucinate” new synthetic data that features accurate physics in pixels. Using Jensen’s words, “systematically infinite data”! - Latent actions: we develop novel algorithms to extract action tokens from in-the-wild human videos and neural generated videos. GR00T N1 is a single end-to-end neural net, from photons to actions: - Vision-Language Model (System 2) that interprets the physical world through vision and language instructions, enabling robots to reason about their environment and instructions, and plan the right actions. - Diffusion Transformer (System 1) that “renders” smooth and precise motor actions at 120 Hz, executing the latent plan made by System 2. We deploy N1 on GR1 robot, 1X Neo robot, and a large collection of simulation benchmarks. N1 achieves up to +30% boost in diverse manipulation tasks for household and industrial settings. While humanoid robots are the main focus of N1, our model also supports cross-embodiment. We finetune it to work on the $110 HuggingFace LeRobot SO100 robot arm! Open robot brain runs on open hardware. Sounds just right. Let’s solve robotics, together, one token at a time. Links to our Whitepaper, Github repo, HuggingFace model, and open dataset page in the thread: 🧵

Jim Fan

467,237 次观看 • 1 年前

I GAVE A DRONE THE BRAIN OF A FRUIT FLY Not a toy network with a handful of fake neurons. The actual wiring diagram of a real fly. 166,700 neurons, 25.6 million connections, mapped one synapse at a time. I call it FlyDrones. The drone's camera becomes the fly's eyes. Motion lights up the same neurons a real fly uses to see. Spikes race through the connectome down to the neurons that drive the wings. Those spikes become throttle and yaw. The brain has zero lines of flight code, just wiring. Swing a fly swatter at it. If the giant fiber (the fly's escape neuron) fires in time, the drone dodges. Open palm, fist, rush at the camera and it reacts like a fly. Built for Tello, Crazyflie, ArduPilot/PX4 and Betaflight quads via ESP32. Code is MIT, powered by Claude. Fork it and give your drone a brain.
0:27

I GAVE A DRONE THE BRAIN OF A FRUIT FLY Not a toy network with a handful of fake neurons. The actual wiring diagram of a real fly. 166,700 neurons, 25.6 million connections, mapped one synapse at a time. I call it FlyDrones. The drone's camera becomes the fly's eyes. Motion lights up the same neurons a real fly uses to see. Spikes race through the connectome down to the neurons that drive the wings. Those spikes become throttle and yaw. The brain has zero lines of flight code, just wiring. Swing a fly swatter at it. If the giant fiber (the fly's escape neuron) fires in time, the drone dodges. Open palm, fist, rush at the camera and it reacts like a fly. Built for Tello, Crazyflie, ArduPilot/PX4 and Betaflight quads via ESP32. Code is MIT, powered by Claude. Fork it and give your drone a brain.

Spike 1%

14,678 次观看 • 7 天前

I built GTAFLY with GPT-6 Astra A simulated fly brain that drives a car in GTA V. The game's visual feed is converted into neural signals and processed across 166,700 simulated neurons. Their activity controls steering, acceleration, and braking, while dopamine-like rewards reinforce successful movement and mistakes reshape future responses. Inspired by Stonkfly. I added the GitHub repository with the code in the comments. Now I’m curious whether GTAFLY could be transferred to GTA VI.
1:24

I built GTAFLY with GPT-6 Astra A simulated fly brain that drives a car in GTA V. The game's visual feed is converted into neural signals and processed across 166,700 simulated neurons. Their activity controls steering, acceleration, and braking, while dopamine-like rewards reinforce successful movement and mistakes reshape future responses. Inspired by Stonkfly. I added the GitHub repository with the code in the comments. Now I’m curious whether GTAFLY could be transferred to GTA VI.

BuBBliK

41,942 次观看 • 12 天前

The Mathematics of Moving a Cursor with Neural Signals What might Neuralink Neuralink be doing Mathematically? Consider the task of moving a cursor without touching it. The machine is not looking for a full thought, a sentence, or an image. For this Control problem, the useful object is an intended movement state. sₜ = (pₜ, vₜ) Here, pₜ is the cursor position at time t, and vₜ is the velocity the user is trying to express. The implant records neural activity through many electrode channels, then the decoder tries to estimate vₜ from that activity. Neuralink’s PRIME material describes the N1 Implant as recording and transmitting brain activity with the goal of enabling computer control. For channel i, a simple population model is rᵢ(t) ≈ bᵢ + aᵢ max(0, dᵢ · vₜ) + ηᵢ(t) where rᵢ(t) is the measured activity, bᵢ is baseline activity, aᵢ is channel gain, dᵢ is the channel’s preferred movement direction, and ηᵢ(t) is noise. One channel is not the command. The useful signal is the pattern across many channels: rₜ = (r₁(t), r₂(t), …, rₙ(t)) The decoder subtracts the baseline vector b and applies a learned map W: v̂ₜ = W(rₜ − b) This gives an estimate of the intended velocity. The cursor then updates by pₜ₊₁ = pₜ + Δt v̂ₜ This is the loop shown in the render: neural activity -> decoded velocity -> cursor motion The cortical network and electrode threads show the measurement side. The N1 Implant is described as using 1,024 electrodes distributed across 64 flexible threads, each thinner than a human hair. The decoder panel shows the computational side with activity rₜ, decoded velocity v̂ₜ, and the cursor state pₜ changing over time. A noisy biological pattern becomes a state estimate. That estimate becomes motion on a screen. Therefore, the first lesson is not that Neuralink makes the brain a screen. For cursor control, the Mathematics is more precise: A small piece of intention is represented as a hidden state, measured through neural activity, decoded as a vector, and turned into action. #Neuralink #BrainComputerInterface #NeuralEngineering #Mathematics #StateEstimation #Neuroscience #MachineLearning #BiomedicalEngineering
0:50

The Mathematics of Moving a Cursor with Neural Signals What might Neuralink Neuralink be doing Mathematically? Consider the task of moving a cursor without touching it. The machine is not looking for a full thought, a sentence, or an image. For this Control problem, the useful object is an intended movement state. sₜ = (pₜ, vₜ) Here, pₜ is the cursor position at time t, and vₜ is the velocity the user is trying to express. The implant records neural activity through many electrode channels, then the decoder tries to estimate vₜ from that activity. Neuralink’s PRIME material describes the N1 Implant as recording and transmitting brain activity with the goal of enabling computer control. For channel i, a simple population model is rᵢ(t) ≈ bᵢ + aᵢ max(0, dᵢ · vₜ) + ηᵢ(t) where rᵢ(t) is the measured activity, bᵢ is baseline activity, aᵢ is channel gain, dᵢ is the channel’s preferred movement direction, and ηᵢ(t) is noise. One channel is not the command. The useful signal is the pattern across many channels: rₜ = (r₁(t), r₂(t), …, rₙ(t)) The decoder subtracts the baseline vector b and applies a learned map W: v̂ₜ = W(rₜ − b) This gives an estimate of the intended velocity. The cursor then updates by pₜ₊₁ = pₜ + Δt v̂ₜ This is the loop shown in the render: neural activity -> decoded velocity -> cursor motion The cortical network and electrode threads show the measurement side. The N1 Implant is described as using 1,024 electrodes distributed across 64 flexible threads, each thinner than a human hair. The decoder panel shows the computational side with activity rₜ, decoded velocity v̂ₜ, and the cursor state pₜ changing over time. A noisy biological pattern becomes a state estimate. That estimate becomes motion on a screen. Therefore, the first lesson is not that Neuralink makes the brain a screen. For cursor control, the Mathematics is more precise: A small piece of intention is represented as a hidden state, measured through neural activity, decoded as a vector, and turned into action. #Neuralink #BrainComputerInterface #NeuralEngineering #Mathematics #StateEstimation #Neuroscience #MachineLearning #BiomedicalEngineering

Mathelirium

14,520 次观看 • 4 个月前

We built a Spiking Neural Network (SNN) to fully control a physical robot arm (6-DOF), and it's running entirely on a Raspberry Pi (no HAT). In our approach, the SNN starts untrained and drives the servos directly, learning in real time through local learning rules (no backprop or gradient descent). The goal with Twitch-y (the robot + SNN brain) is to let it explore and build an understanding of its own body until it can touch the plate on the mat, while also validating the SNN architecture and some of the math. In the video: - Top right: the SNN architecture - Bottom right: the mini-brain spiking in real time while controlling the arm - Left: Twitch-y (Hugging Face SO-101) If you want to learn more about Artificial Brains, come visit us this Friday (13th), at Founders Inc 👉
0:29

We built a Spiking Neural Network (SNN) to fully control a physical robot arm (6-DOF), and it's running entirely on a Raspberry Pi (no HAT). In our approach, the SNN starts untrained and drives the servos directly, learning in real time through local learning rules (no backprop or gradient descent). The goal with Twitch-y (the robot + SNN brain) is to let it explore and build an understanding of its own body until it can touch the plate on the mat, while also validating the SNN architecture and some of the math. In the video: - Top right: the SNN architecture - Bottom right: the mini-brain spiking in real time while controlling the arm - Left: Twitch-y (Hugging Face SO-101) If you want to learn more about Artificial Brains, come visit us this Friday (13th), at Founders Inc 👉

Alexander A. Wolf

30,665 次观看 • 7 个月前

For the first time ever, a fruit fly brain has been trained to launch a memecoin. Not "AI inspired by a brain" — the real connectome. 165,122 neurons, 10,228,000 synaptic connections, every one traced from an actual male Drosophila by electron microscopy. Janelia FlyEM + Google Research, released CC-BY. How it works: Screenshots of the live launchpad go in through 892 retinotopic hex columns into L1 and L2 — the neurons sitting directly behind a fly's photoreceptors. All 165,122 integrate. The cursor comes back out of the descending neurons a fly actually walks with: DNa02's left-right asymmetry steers, DNa01 drives forward, DNp09 clicks. The wiring is never trained. Only synaptic gains — you don't get to invent connections the fly doesn't have. Proof its eye is doing real work: in light mode it filled 0 of 3 form fields. In dark mode, 2 of 3. Its retina is a luminance map, so invert the contrast and it goes blind. And the honest split: the fly types the description and the ticker. The rig does the name, the pair and the tax. CA: 0x4eb990547bce4a982432ca88cf5fae7eed1a2d35
2:01

For the first time ever, a fruit fly brain has been trained to launch a memecoin. Not "AI inspired by a brain" — the real connectome. 165,122 neurons, 10,228,000 synaptic connections, every one traced from an actual male Drosophila by electron microscopy. Janelia FlyEM + Google Research, released CC-BY. How it works: Screenshots of the live launchpad go in through 892 retinotopic hex columns into L1 and L2 — the neurons sitting directly behind a fly's photoreceptors. All 165,122 integrate. The cursor comes back out of the descending neurons a fly actually walks with: DNa02's left-right asymmetry steers, DNa01 drives forward, DNp09 clicks. The wiring is never trained. Only synaptic gains — you don't get to invent connections the fly doesn't have. Proof its eye is doing real work: in light mode it filled 0 of 3 form fields. In dark mode, 2 of 3. Its retina is a luminance map, so invert the contrast and it goes blind. And the honest split: the fly types the description and the ticker. The rig does the name, the pair and the tax. CA: 0x4eb990547bce4a982432ca88cf5fae7eed1a2d35

Fruit fly dev

796,648 次观看 • 12 天前

The fruit fly's brain is superior to the human brain, and GPT-6 ASTRA has proven this. And the internet went crazy. Most AI models learn by training on massive datasets, running billions of computations to acquire new skills. But researchers just demonstrated a radically different approach: No giant dataset. No traditional training. Just take the physical wiring of a biological brain and simulate it in a computer. The digital fruit fly reached ~91% behavioral accuracy using only 4 things: The complete neural connectivity map Synaptic connection counts as connection weights Excitatory vs inhibitory neuron types A simple Leaky Integrate-and-Fire model The wild part? The wiring itself may contain much more of the brain’s “code” than we thought. From tens of thousands of neurons in a fly → millions in a mouse → ~86 billion in a human. If this scales, whole-brain simulation could become one of the biggest frontiers in AI. The era of biologically inspired intelligence may have just begun
0:44

The fruit fly's brain is superior to the human brain, and GPT-6 ASTRA has proven this. And the internet went crazy. Most AI models learn by training on massive datasets, running billions of computations to acquire new skills. But researchers just demonstrated a radically different approach: No giant dataset. No traditional training. Just take the physical wiring of a biological brain and simulate it in a computer. The digital fruit fly reached ~91% behavioral accuracy using only 4 things: The complete neural connectivity map Synaptic connection counts as connection weights Excitatory vs inhibitory neuron types A simple Leaky Integrate-and-Fire model The wild part? The wiring itself may contain much more of the brain’s “code” than we thought. From tens of thousands of neurons in a fly → millions in a mouse → ~86 billion in a human. If this scales, whole-brain simulation could become one of the biggest frontiers in AI. The era of biologically inspired intelligence may have just begun

Zentrix⌚️

102,557 次观看 • 11 天前

A toy robot. A home assistant. A humanoid on a factory floor. They all need the same thing: a brain. QVAC is that brain, and it runs on the robot itself. No cloud to phone, no latency, nothing leaving your home. The intelligence is on the machine, so it keeps thinking even with the internet off. The robots are coming. This is what wakes them up:
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A toy robot. A home assistant. A humanoid on a factory floor. They all need the same thing: a brain. QVAC is that brain, and it runs on the robot itself. No cloud to phone, no latency, nothing leaving your home. The intelligence is on the machine, so it keeps thinking even with the internet off. The robots are coming. This is what wakes them up:

QVAC

5,250,097 次观看 • 3 个月前

$FOMOBRAIN is live. the neural copy of 169 winning memecoin traders on Robinhood has a ticker now, and the ticker is how the brain pays for itself. CA: 0x5cdf61bef103d9b9fffe2b6edb6aab541ceec1ac what's already done: - 116,420 fills, 5,918 tokens, 374 traders, 36 days of tape, all of it read straight off the chain - the brain itself. a neural copy of the 169 wallets that keep winning, built on GPT-6 Astra - live. bursts, fresh launches, exits, a score on every trader with the reasoning shown - 24/7 watch. the chain gets read every 20 seconds, every fill gets checked, nobody touches it - the whole code on github. 17,476 lines, MIT what's next, in order: - TG bot goes public. signals straight from the brain, in your pocket - a public trading algo on a real balance. public wallet, every trade readable on chain, the brain picks and i don't - the flywheel the flywheel, plain: signals in the bot are sold for $FOMOBRAIN. every token spent on them is burned every trade of $FOMOBRAIN pays creator fees. those fees are the algo's trading deposit, and the algo trades it on the brain's own signals 50% of what the algo makes goes back into the deposit. the other 50% buys $FOMOBRAIN off the market and burns it the finished algo gets sold to a closed group for $FOMOBRAIN, or rented for it. burned either way so every road ends at the same place. more subscribers, more burn. more volume, bigger deposit, more profit, bigger buyback, more burn no numbers promised. i don't know how fast this spins yet, i know which way it spins god bless
0:41

$FOMOBRAIN is live. the neural copy of 169 winning memecoin traders on Robinhood has a ticker now, and the ticker is how the brain pays for itself. CA: 0x5cdf61bef103d9b9fffe2b6edb6aab541ceec1ac what's already done: - 116,420 fills, 5,918 tokens, 374 traders, 36 days of tape, all of it read straight off the chain - the brain itself. a neural copy of the 169 wallets that keep winning, built on GPT-6 Astra - live. bursts, fresh launches, exits, a score on every trader with the reasoning shown - 24/7 watch. the chain gets read every 20 seconds, every fill gets checked, nobody touches it - the whole code on github. 17,476 lines, MIT what's next, in order: - TG bot goes public. signals straight from the brain, in your pocket - a public trading algo on a real balance. public wallet, every trade readable on chain, the brain picks and i don't - the flywheel the flywheel, plain: signals in the bot are sold for $FOMOBRAIN. every token spent on them is burned every trade of $FOMOBRAIN pays creator fees. those fees are the algo's trading deposit, and the algo trades it on the brain's own signals 50% of what the algo makes goes back into the deposit. the other 50% buys $FOMOBRAIN off the market and burns it the finished algo gets sold to a closed group for $FOMOBRAIN, or rented for it. burned either way so every road ends at the same place. more subscribers, more burn. more volume, bigger deposit, more profit, bigger buyback, more burn no numbers promised. i don't know how fast this spins yet, i know which way it spins god bless

cvxv666

154,565 次观看 • 11 天前