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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

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203,581 görüntüleme • 1 gün önce •via X (Twitter)

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Gaide Merlot1 gün önce

dev is live now! stream:

Gaide Merlot profil fotoğrafı
Gaide Merlot1 gün önce

dev is live now! stream:

Dan ✦ profil fotoğrafı
Dan ✦1 gün önce

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 profil fotoğrafı
Tijen1 gün önce

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

bitguide profil fotoğrafı
bitguide1 gün önce

Like the 3rd ticker you shilling gtfo

Crypto Zeinab profil fotoğrafı
Crypto Zeinab1 gün önce

100m coded

senda profil fotoğrafı
senda1 gün önce

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

Luigi profil fotoğrafı
Luigi1 gün önce

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

Mitnick profil fotoğrafı
Mitnick1 gün önce

@elonmusk is this dangerous? lol

Based Normye profil fotoğrafı
Based Normye1 gün önce

This is fucking insane Trillions

Fruit fly fefe profil fotoğrafı
Fruit fly fefe1 gün önce

so we got bigger family!

Zees 🐂🀄️ profil fotoğrafı
Zees 🐂🀄️1 gün önce

Fudders will fud, see yall at 10M.

Sgt. Wingflapper profil fotoğrafı
Sgt. Wingflapper1 gün önce

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

NO.12 profil fotoğrafı
NO.121 gün önce

lol

Gux 🥷 profil fotoğrafı
Gux 🥷1 gün önce

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

Dreams profil fotoğrafı
Dreams1 gün önce

Easy $50m

Capolini💰 profil fotoğrafı
Capolini💰1 gün önce

THIS IS INSANE WTF LITERALLY BILLIONS

Francis OG dev profil fotoğrafı
Francis OG dev1 gün önce

0

Crypto profil fotoğrafı
Crypto1 gün önce

Millions

Roddy. profil fotoğrafı
Roddy.1 gün önce

aped some.. goodluck building

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Sebastian Sosa1 gün önce

🪰⚖️ 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:

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Slipstream PR1 gün önce

👀

Capolini💰 profil fotoğrafı
Capolini💰1 gün önce

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

Bald Knower 🧑🏼‍🦲 profil fotoğrafı
Bald Knower 🧑🏼‍🦲1 gün önce

sure why not

Speedy profil fotoğrafı
Speedy1 gün önce

Dont get flylined

MARTIN profil fotoğrafı
MARTIN1 gün önce

Are you going to start a live stream?

Fruit fly fefe profil fotoğrafı
Fruit fly fefe1 gün önce

Fly took over the world soon

Capolini💰 profil fotoğrafı
Capolini💰1 gün önce

THIS IS MASSIVE

Capolini💰 profil fotoğrafı
Capolini💰1 gün önce

This is gonna bounce so hard from here

China Quant Dev profil fotoğrafı
China Quant Dev1 gün önce

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

Pepe profil fotoğrafı
Pepe1 gün önce

Can you detect a Frog with its own Brain 🐸

Capolini💰 profil fotoğrafı
Capolini💰1 gün önce

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

Secret squirrel profil fotoğrafı
Secret squirrel1 gün önce

Oh shit this is big

Benzer Videolar

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

4,986,951 görüntüleme • 2 gün önce

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?
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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⌚️

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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⌚️

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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

233,872 görüntüleme • 6 ay önce

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.
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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 görüntüleme • 8 ay önce

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?
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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⌚️

126,150 görüntüleme • 1 gün önce

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 görüntüleme • 1 yıl önce

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

77,405 görüntüleme • 5 gün önce

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

467,141 görüntüleme • 1 yıl önce

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

41,585 görüntüleme • 5 gün önce

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 görüntüleme • 4 ay önce

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

30,367 görüntüleme • 7 ay önce

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

764,454 görüntüleme • 5 gün önce

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⌚️

99,581 görüntüleme • 4 gün önce

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:
0:37

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 görüntüleme • 2 ay önce

$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

147,636 görüntüleme • 3 gün önce

Physicist: Consciousness DOES NOT Come From The BRAIN The prevailing materialist paradigm asserts that consciousness is a byproduct of neural activity, a mere epiphenomenon of biochemical interactions in the brain. However, this reductionist view crumbles under deeper scrutiny, as it fails to account for the vast spectrum of consciousness, from transcendent mystical states to near-death experiences and non-local awareness. Consciousness is not confined within the brain; rather, the brain is a transceiver, a finely tuned instrument that receives and modulates the vast ocean of awareness permeating the cosmos. Just as a radio does not generate the music it plays but instead decodes signals from an unseen field, the brain is an interface between the physical realm and the infinite, omnipresent field of consciousness. Mystic science, in alignment with ancient wisdom and cutting-edge quantum research, reveals that consciousness is fundamental-an organizing principle of reality itself. Walter Russell's work echoes this truth, demonstrating that mind is primary and matter is a consequence of its rhythmic pulsations. The brain, much like a crystalline matrix, is structured to interpret and shape consciousness into coherent experience, but it does not generate it. In this light, consciousness is not local, nor is it constrained by the physical form. It is the unseen architect behind the rhythms of existence, the hidden intelligence orchestrating the grand cosmic symphony to believe that the brain creates consciousness is akin to believing that the eye creates light or that a mirror generates the image it reflects. It is not the origin but the instrument. As mystic scientists, we recognize thay true awakening lies in shifting our perception from brain-centered awareness to the realization that we are conduits of an eternal intelligence, woven into the very fabric of existence. Consciousness is not inside us—we are inside it. ✨🙌🏾💫 © Dr. Jason Yuan
1:25

Physicist: Consciousness DOES NOT Come From The BRAIN The prevailing materialist paradigm asserts that consciousness is a byproduct of neural activity, a mere epiphenomenon of biochemical interactions in the brain. However, this reductionist view crumbles under deeper scrutiny, as it fails to account for the vast spectrum of consciousness, from transcendent mystical states to near-death experiences and non-local awareness. Consciousness is not confined within the brain; rather, the brain is a transceiver, a finely tuned instrument that receives and modulates the vast ocean of awareness permeating the cosmos. Just as a radio does not generate the music it plays but instead decodes signals from an unseen field, the brain is an interface between the physical realm and the infinite, omnipresent field of consciousness. Mystic science, in alignment with ancient wisdom and cutting-edge quantum research, reveals that consciousness is fundamental-an organizing principle of reality itself. Walter Russell's work echoes this truth, demonstrating that mind is primary and matter is a consequence of its rhythmic pulsations. The brain, much like a crystalline matrix, is structured to interpret and shape consciousness into coherent experience, but it does not generate it. In this light, consciousness is not local, nor is it constrained by the physical form. It is the unseen architect behind the rhythms of existence, the hidden intelligence orchestrating the grand cosmic symphony to believe that the brain creates consciousness is akin to believing that the eye creates light or that a mirror generates the image it reflects. It is not the origin but the instrument. As mystic scientists, we recognize thay true awakening lies in shifting our perception from brain-centered awareness to the realization that we are conduits of an eternal intelligence, woven into the very fabric of existence. Consciousness is not inside us—we are inside it. ✨🙌🏾💫 © Dr. Jason Yuan

🧬Maxpein🧬

45,371 görüntüleme • 11 ay önce

The brain is not the mind. The mind is the spiritual brain. The brain is just its physical expression. As above, so below. As within, so without. 🧠 Thoughts are the language of the mind. 💓 Emotions are the language of the soul. Your heart reflects your soul, just as your brain reflects your mind. Every thought creates a frequency. Every emotion sends a signal. Understand this — and you unlock reality. 📘
1:04

The brain is not the mind. The mind is the spiritual brain. The brain is just its physical expression. As above, so below. As within, so without. 🧠 Thoughts are the language of the mind. 💓 Emotions are the language of the soul. Your heart reflects your soul, just as your brain reflects your mind. Every thought creates a frequency. Every emotion sends a signal. Understand this — and you unlock reality. 📘

vegastar

31,838 görüntüleme • 1 yıl önce

This robot assistant from the NVIDIA CES Keynote on Monday is going viral. Nader Khalil🍊 explains all the hottest emerging AI trends in one demo: AI applications in 2026 will be multi-model, multi-modal, hybrid cloud/local, use open source models as well as proprietary models, control robots and embedded devices in the physical world, and have voice interfaces. (And the demo had a cute robot *and* a cute dog. Gold.) The demo was built with Pipecat AI. NVIDIA posted a really nice technical walk-through and complete code. The Reachy Mini robot from Hugging Face is open source hardware. (You can order it now, I have one!). You can run the assistant locally on your own hardware, in the cloud, or both.
2:26

This robot assistant from the NVIDIA CES Keynote on Monday is going viral. Nader Khalil🍊 explains all the hottest emerging AI trends in one demo: AI applications in 2026 will be multi-model, multi-modal, hybrid cloud/local, use open source models as well as proprietary models, control robots and embedded devices in the physical world, and have voice interfaces. (And the demo had a cute robot *and* a cute dog. Gold.) The demo was built with Pipecat AI. NVIDIA posted a really nice technical walk-through and complete code. The Reachy Mini robot from Hugging Face is open source hardware. (You can order it now, I have one!). You can run the assistant locally on your own hardware, in the cloud, or both.

kwindla

49,032 görüntüleme • 8 ay önce

A Neural Network Can Grow New Neurons Where It Is Confused? In 1994, Bernd Fritzke published A Growing Neural Gas Network Learns Topologies. He introduced a network that starts small, follows incoming data, and inserts new neurons where its error is highest. In the animation, the fog is the drifting data. The glowing nodes are neurons. The fibers are learned connections. The network grows into a living skeleton of the manifold.
0:30

A Neural Network Can Grow New Neurons Where It Is Confused? In 1994, Bernd Fritzke published A Growing Neural Gas Network Learns Topologies. He introduced a network that starts small, follows incoming data, and inserts new neurons where its error is highest. In the animation, the fog is the drifting data. The glowing nodes are neurons. The fibers are learned connections. The network grows into a living skeleton of the manifold.

Mathelirium

38,306 görüntüleme • 4 ay önce