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Dropped direct LPLC2/LC4 readouts, expanded simulation with downstream neurons and additional LC-family neurons. Remade readout net. The drone is steered exclusively with descending motor neurons which natively fire in response to visual flow injected into fly's optical train.

🇺🇦 сloned

1,811 subscribers

20,856 次观看 • 2 天前 •via X (Twitter)

Anya Rossi• Live Now

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🇺🇦 сloned 的头像

🇺🇦 сloned2 天前

Working on correct LC11 and LPLC2 simulation proved to be challenging due to lack of correct inhibition. Trying to expand brain simulation even further with neuron dendrite position data to achieve natural optical filtering with inhibitory inputs.

AI Enjoyer 的头像

AI Enjoyer2 天前

How does the fly brain process visuals so much faster than Astra computer use which takes a few seconds per action/reason?

fish4terrisa-MSDSM 的头像

fish4terrisa-MSDSM1 天前

maybe try it on a real drone? it actually looks promising ngl with custom asic chips made specificly to run fly brain inference this could be quite cheap in large batch size

Gabe 的头像

Gabe2 天前

What simulator are you using?

Daniel R P de Melo 的头像

Daniel R P de Melo2 天前

Can you use those neurons that use continuous learning to reward hack him to do deliveries ?

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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

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

768,280 次观看 • 6 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.

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 次观看 • 5 天前

New GS episode dives into how Michael Levin and Haleh Fotowat gave Xenobots a functional nervous system. What happened shocked everyone... "Mike's proposal was: what happens if we give neurons to this completely new, motile body? Would they even mature into neurons? What kind of nervous system would they build? And how would having a brain change the way it behaves?" "This system has just been dropped into a new universe it doesn't understand — all its usual constraints are gone. And it's like: what do we need? We need a way to navigate. So let's build a sensory system. It's arming itself with equipment." "The most surprising thing was this overexpression of genes for visual perception — the lens, photoreceptors, rods and cones, every type of retinal cell. It was like: whoa, why did that come up?" For those who enjoyed my last episode with Mike and Earl, this is the part 2 with the incredible Haleh Fotowat ⬇️

New GS episode dives into how Michael Levin and Haleh Fotowat gave Xenobots a functional nervous system. What happened shocked everyone... "Mike's proposal was: what happens if we give neurons to this completely new, motile body? Would they even mature into neurons? What kind of nervous system would they build? And how would having a brain change the way it behaves?" "This system has just been dropped into a new universe it doesn't understand — all its usual constraints are gone. And it's like: what do we need? We need a way to navigate. So let's build a sensory system. It's arming itself with equipment." "The most surprising thing was this overexpression of genes for visual perception — the lens, photoreceptors, rods and cones, every type of retinal cell. It was like: whoa, why did that come up?" For those who enjoyed my last episode with Mike and Earl, this is the part 2 with the incredible Haleh Fotowat ⬇️

Evan McGloughlin

11,993 次观看 • 3 个月前

$Excited to release a new repo: abcGPT! It can be hard to "dial in" the voice you want from an LLM, because an LLM is a tangled superposition of millions of voices from millions of different authors around the world. Instead, frontier LLMs tend to give that slop-ish / generic / corporate tone that's hard to avoid, even with aggressive prompting and an informative context window. Lately I've been experimenting with some ideas on the fringes of attribution/unlearning, trying to make it so an AI user can "dial in" the specific voice/style/sources they want to use in a way that's more rigorous than prompting/context-engineering. and I'm starting to get pretty good results. the model below uses the following technique: - Take nanoGPT as written by Andrej Karpathy - Assign each neuron a random "specialty score" m between 0 and 1, sampled from a U-shape so most neurons land near 0 or near 1 with some in the middle. - Freeze this "m" for the lifetime of the network (it's the neuron's permanent corpus assignment) - Extend the forward() code with an α parameter, a kind of vibe-fader from 0 to 1. Think of each neuron's m as its position on that same slider. The slider acts like a spotlight: it lights up neurons whose m is near its current position, and silences those far away. Slide all the way to 0, and only TinyStories specialists fire. Slide all the way to 1, and only Shakespeare specialists fire. - Train this new nanoGPT on two datasets (in this case, TinyStories and Shakespeare) - During training, sample α from Beta(0.5, 0.5) AND draw the corpus from Bernoulli(α), so a Shakespeare batch tends to come with a high-α (Shakespeare-favoring) gate, and a TinyStories batch tends to come with low-α. - train until golden brown 🧑‍🍳 Perhaps surprisingly... it works! ¯\_(ツ)_/¯ The neurons we pre-assigned to Shakespeare learn to behave as Shakespeare specialists. the neurons we pre-assigned to TinyStories become children's-story specialists. the halfsies learn to bridge between them. After training, you can play with the kindof... vibe dial... you can "dial in" the voice you want during inference, by choosing whether to lean on Shakespeare or TinyStories neurons more or less. 📀💿 When you fully dial in Shakespeare neurons, the model only outputs tokens which look like Shakespeare, and when you fully dial in TinyStories, the model only outputs tokens which look like children's stories, and... (honestly this was the hard part)... everywhere inbetween! In a way, it's partitioning statistical signal into fuzzy segments, and then the end user can choose which pre-training data sources they want to lean upon for generation... and how much. My goal was to get a version of this working at scale, with clear intuition for why it works, and I'd like to explore ways to scale up this effect to large numbers of sources and larger models, and study the interplay between individuality/generality as scale increases. Link to repo and a detailed walkthrough of the abcGPT methodology in the reply.$

Excited to release a new repo: abcGPT! It can be hard to "dial in" the voice you want from an LLM, because an LLM is a tangled superposition of millions of voices from millions of different authors around the world. Instead, frontier LLMs tend to give that slop-ish / generic / corporate tone that's hard to avoid, even with aggressive prompting and an informative context window. Lately I've been experimenting with some ideas on the fringes of attribution/unlearning, trying to make it so an AI user can "dial in" the specific voice/style/sources they want to use in a way that's more rigorous than prompting/context-engineering. and I'm starting to get pretty good results. the model below uses the following technique: - Take nanoGPT as written by Andrej Karpathy - Assign each neuron a random "specialty score" m between 0 and 1, sampled from a U-shape so most neurons land near 0 or near 1 with some in the middle. - Freeze this "m" for the lifetime of the network (it's the neuron's permanent corpus assignment) - Extend the forward() code with an α parameter, a kind of vibe-fader from 0 to 1. Think of each neuron's m as its position on that same slider. The slider acts like a spotlight: it lights up neurons whose m is near its current position, and silences those far away. Slide all the way to 0, and only TinyStories specialists fire. Slide all the way to 1, and only Shakespeare specialists fire. - Train this new nanoGPT on two datasets (in this case, TinyStories and Shakespeare) - During training, sample α from Beta(0.5, 0.5) AND draw the corpus from Bernoulli(α), so a Shakespeare batch tends to come with a high-α (Shakespeare-favoring) gate, and a TinyStories batch tends to come with low-α. - train until golden brown 🧑‍🍳 Perhaps surprisingly... it works! ¯\_(ツ)_/¯ The neurons we pre-assigned to Shakespeare learn to behave as Shakespeare specialists. the neurons we pre-assigned to TinyStories become children's-story specialists. the halfsies learn to bridge between them. After training, you can play with the kindof... vibe dial... you can "dial in" the voice you want during inference, by choosing whether to lean on Shakespeare or TinyStories neurons more or less. 📀💿 When you fully dial in Shakespeare neurons, the model only outputs tokens which look like Shakespeare, and when you fully dial in TinyStories, the model only outputs tokens which look like children's stories, and... (honestly this was the hard part)... everywhere inbetween! In a way, it's partitioning statistical signal into fuzzy segments, and then the end user can choose which pre-training data sources they want to lean upon for generation... and how much. My goal was to get a version of this working at scale, with clear intuition for why it works, and I'd like to explore ways to scale up this effect to large numbers of sources and larger models, and study the interplay between individuality/generality as scale increases. Link to repo and a detailed walkthrough of the abcGPT methodology in the reply.

⿻ Andrew Trask

134,902 次观看 • 3 个月前

Introducing FlyOCR 🪰 - I trained a fly brain to read a PDF It uses the full MaleCNS v1.0 fruit fly connectcome. The architecture is inspired by doomfly by The fly splits a pdf image into individual glyphs, maps pixels into receptor activations, runs simplified current-based dynamics across the 166k neurons and 25m edges in the circuit, applies a compact readout model on the downstream spikes, and concatenates everything into the parsed output. On reading an actual Microsoft 10-k, the fly gets ~86% over the balance sheet heading, but is largely able to read the numeric values correctly. Over 1.7k+ sampled glyphs (chars+digits) it gets 87% accuracy. With enough training it might match some of the latter-generation MNIST models! Maybe eventually we’ll replace our doc parsing VLMs with flies. Full video below. Repo with full code + report:

Introducing FlyOCR 🪰 - I trained a fly brain to read a PDF It uses the full MaleCNS v1.0 fruit fly connectcome. The architecture is inspired by doomfly by The fly splits a pdf image into individual glyphs, maps pixels into receptor activations, runs simplified current-based dynamics across the 166k neurons and 25m edges in the circuit, applies a compact readout model on the downstream spikes, and concatenates everything into the parsed output. On reading an actual Microsoft 10-k, the fly gets ~86% over the balance sheet heading, but is largely able to read the numeric values correctly. Over 1.7k+ sampled glyphs (chars+digits) it gets 87% accuracy. With enough training it might match some of the latter-generation MNIST models! Maybe eventually we’ll replace our doc parsing VLMs with flies. Full video below. Repo with full code + report:

Jerry Liu

37,048 次观看 • 3 天前

166,700 neurons from a dead fruit fly are loose in a living room, and nobody wrote a single thing they do. Scientists sliced a male fruit fly's nervous system into electron microscope images and traced every wire by hand: 166,700 neurons, 25.6 million synapses, brain and nerve cord, the whole animal, published for free. Most people read that paper, and 1 team downloaded the fly and gave it a body. The brain runs on a Mac, the fly lives in AR glasses, and the room itself became its senses, because Spectacles already map surfaces and depth and hands, so eyes turned into a 16 × 8 retina on each fly's head, distance into 12 rays against the world mesh, and smell into Gemini naming a tea cup, flowers, a water bottle while depth drops every label into 3D where the brain can reach it. A hand moving too fast became fear. They read the decisions out of the exact neurons a real fly uses, where DNa02 turns it, MDN walks it backwards, MN9 feeds and the giant fiber fires the escape. The CPU crawled, 50 ms of brain time in 238 ms, so they vibe coded a Metal GPU kernel with Claude Code and got identical spikes in 41 ms. The first builds stuttered until a Perfetto trace showed the GPU nearly idle while the main thread choked, and squashing the board text from 67 draw calls to 2 unlocked 55 to 60 fps with 2 brains running at once. In the video a fly lands on an open palm while another peels off a hand coming in fast. That dodge is in no script anywhere, and they only gave it eyes.

166,700 neurons from a dead fruit fly are loose in a living room, and nobody wrote a single thing they do. Scientists sliced a male fruit fly's nervous system into electron microscope images and traced every wire by hand: 166,700 neurons, 25.6 million synapses, brain and nerve cord, the whole animal, published for free. Most people read that paper, and 1 team downloaded the fly and gave it a body. The brain runs on a Mac, the fly lives in AR glasses, and the room itself became its senses, because Spectacles already map surfaces and depth and hands, so eyes turned into a 16 × 8 retina on each fly's head, distance into 12 rays against the world mesh, and smell into Gemini naming a tea cup, flowers, a water bottle while depth drops every label into 3D where the brain can reach it. A hand moving too fast became fear. They read the decisions out of the exact neurons a real fly uses, where DNa02 turns it, MDN walks it backwards, MN9 feeds and the giant fiber fires the escape. The CPU crawled, 50 ms of brain time in 238 ms, so they vibe coded a Metal GPU kernel with Claude Code and got identical spikes in 41 ms. The first builds stuttered until a Perfetto trace showed the GPU nearly idle while the main thread choked, and squashing the board text from 67 draw calls to 2 unlocked 55 to 60 fps with 2 brains running at once. In the video a fly lands on an open palm while another peels off a hand coming in fast. That dodge is in no script anywhere, and they only gave it eyes.

Spike 1%

10,860 次观看 • 3 天前

$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 commands IMU data feeds movement back into the model. smoothing, speed limits and a 500 ms watchdog are already built in the synthetic demo runs locally today. the ESP32 scaffold is ready for hardware integration the entire project is open source and MIT licensed github: what’s next, in order: the on-chain listener goes live. every $BRAIN transaction becomes a stimulus sent directly into the neural model then the physical robot connects. the wallet address determines which neurons activate, the amount determines the strength of the impulse, and the brain converts that reaction into movement then the livestream. every transaction, neural impulse and physical response visible in real time then user commands. spend $BRAIN to trigger a stronger reflex, request movement, temporarily control the robot, name a neuron or place your name on the stream the loop, plain: a transaction enters the neurons react the robot moves a Proof of Reflex is created with the transaction hash, neural activity map and video of the physical response the current brain is a small fly-inspired simulation. the bridge and local demo work today. the blockchain connection, physical robot and livestream are being built now the internet becomes its sensory organ. the blockchain becomes its nervous system. $BRAIN makes the body move.

$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 commands IMU data feeds movement back into the model. smoothing, speed limits and a 500 ms watchdog are already built in the synthetic demo runs locally today. the ESP32 scaffold is ready for hardware integration the entire project is open source and MIT licensed github: what’s next, in order: the on-chain listener goes live. every $BRAIN transaction becomes a stimulus sent directly into the neural model then the physical robot connects. the wallet address determines which neurons activate, the amount determines the strength of the impulse, and the brain converts that reaction into movement then the livestream. every transaction, neural impulse and physical response visible in real time then user commands. spend $BRAIN to trigger a stronger reflex, request movement, temporarily control the robot, name a neuron or place your name on the stream the loop, plain: a transaction enters the neurons react the robot moves a Proof of Reflex is created with the transaction hash, neural activity map and video of the physical response the current brain is a small fly-inspired simulation. the bridge and local demo work today. the blockchain connection, physical robot and livestream are being built now the internet becomes its sensory organ. the blockchain becomes its nervous system. $BRAIN makes the body move.

Frank

222,838 次观看 • 2 天前

How your ability to focus is dependent on your diet: Being fasted reduces parasympathetic "rest and digest" activation, keeping you alert. "Half as much caffeine will give you just as much lift as twice as much caffeine will give you on a full belly of pasta." Neurons thrive on glucose. A study published in Neuron found that "the precision with which neurons in the brain will represent things in our environment is actually much greater when there is sufficient glucose in the brain." Very low blood glucose sharpens concentration through alertness; sufficient blood glucose sharpens concentration through improved neural encoding. Overeating collapses both. Delay the first caffeine intake to 90 to 120 minutes after waking to avoid an afternoon energy crash. Do a first 90-minute focus block fasted with water, electrolytes, and caffeine. After eating, expect a dip. A second 90-minute block in the afternoon is viable if the midday meal is modest enough to avoid pushing the nervous system into parasympathetic mode. Andrew D. Huberman, Ph.D.

How your ability to focus is dependent on your diet: Being fasted reduces parasympathetic "rest and digest" activation, keeping you alert. "Half as much caffeine will give you just as much lift as twice as much caffeine will give you on a full belly of pasta." Neurons thrive on glucose. A study published in Neuron found that "the precision with which neurons in the brain will represent things in our environment is actually much greater when there is sufficient glucose in the brain." Very low blood glucose sharpens concentration through alertness; sufficient blood glucose sharpens concentration through improved neural encoding. Overeating collapses both. Delay the first caffeine intake to 90 to 120 minutes after waking to avoid an afternoon energy crash. Do a first 90-minute focus block fasted with water, electrolytes, and caffeine. After eating, expect a dip. A second 90-minute block in the afternoon is viable if the midday meal is modest enough to avoid pushing the nervous system into parasympathetic mode. Andrew D. Huberman, Ph.D.

Huberman Lab Clips

10,692 次观看 • 2 个月前

"This is how GPT-4 sees and hears consciousness" I used GPT-4 to describe consciousness. Then I used its description to generate an image, a video based on this image, and a soundtrack. Tools I used: GPT-4, Midjourney, Kaiber AI, Mubert, RunwayML This is the description I used that GPT-4 had of consciousness as a prompt to text-to-image, image-to-video, and text-to-music. I put the video and sound together in RunwayML: "Consciousness could be visually represented as a complex, multi-dimensional web of interconnected neurons. Imagine a vast, shimmering network of glowing points of light, with each point representing a neuron. These neurons are connected by delicate, pulsating strands of energy, representing the synapses through which information and thoughts flow. This neural network would be constantly evolving and changing, with new connections forming and old ones dissolving, reflecting the dynamic nature of consciousness. The colors of the points and connections could shift through a rich spectrum, symbolizing different emotions, experiences, and cognitive processes. Surrounding this intricate web, there could be a swirling, luminous mist of vibrant colors, representing the ineffable and mysterious nature of consciousness. The mist would ebb and flow, occasionally coalescing into recognizable patterns and forms, hinting at the emergence of thoughts and ideas"

"This is how GPT-4 sees and hears consciousness" I used GPT-4 to describe consciousness. Then I used its description to generate an image, a video based on this image, and a soundtrack. Tools I used: GPT-4, Midjourney, Kaiber AI, Mubert, RunwayML This is the description I used that GPT-4 had of consciousness as a prompt to text-to-image, image-to-video, and text-to-music. I put the video and sound together in RunwayML: "Consciousness could be visually represented as a complex, multi-dimensional web of interconnected neurons. Imagine a vast, shimmering network of glowing points of light, with each point representing a neuron. These neurons are connected by delicate, pulsating strands of energy, representing the synapses through which information and thoughts flow. This neural network would be constantly evolving and changing, with new connections forming and old ones dissolving, reflecting the dynamic nature of consciousness. The colors of the points and connections could shift through a rich spectrum, symbolizing different emotions, experiences, and cognitive processes. Surrounding this intricate web, there could be a swirling, luminous mist of vibrant colors, representing the ineffable and mysterious nature of consciousness. The mist would ebb and flow, occasionally coalescing into recognizable patterns and forms, hinting at the emergence of thoughts and ideas"

Kris Kashtanova

423,420 次观看 • 3 年前

End-to-end neural networks racing drones in Abu Dhabi! 🚁 Check out the drone racing team from Delft University of Technology! A completely end-to-end neural network solution, from pixels to direct motor commands. No Kalman filters. No computer vision feature detectors. Just neurons flying the drone. The challenge is extreme. These drones fly at high speeds and need split-second decisions with minimal onboard resources: a single rolling-shutter camera and an IMU. Their approach is called SkyDreamer, based on the Dreamer-v3 reinforcement learning algorithm. First, a world model is trained in simulation. Then, the neural network learns how to fly in its dreams through reinforcement learning. The network's internal state can be read out to see where it thinks it is on the track or how fast it's going. Even better, the drone estimates some of its own body characteristics during flight, like the camera angle relative to the body, eliminating time-consuming manual calibration. The system uses only a single camera and the gyros from the IMU, ignoring the accelerometers, just like human FPV pilots do. ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

End-to-end neural networks racing drones in Abu Dhabi! 🚁 Check out the drone racing team from Delft University of Technology! A completely end-to-end neural network solution, from pixels to direct motor commands. No Kalman filters. No computer vision feature detectors. Just neurons flying the drone. The challenge is extreme. These drones fly at high speeds and need split-second decisions with minimal onboard resources: a single rolling-shutter camera and an IMU. Their approach is called SkyDreamer, based on the Dreamer-v3 reinforcement learning algorithm. First, a world model is trained in simulation. Then, the neural network learns how to fly in its dreams through reinforcement learning. The network's internal state can be read out to see where it thinks it is on the track or how fast it's going. Even better, the drone estimates some of its own body characteristics during flight, like the camera angle relative to the body, eliminating time-consuming manual calibration. The system uses only a single camera and the gyros from the IMU, ignoring the accelerometers, just like human FPV pilots do. ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

207,500 次观看 • 7 个月前

Today on MCG: BioLLM | $BIOLLM It's the first ever "living language model" using 800,000 real human neurons grown on a chip. The Founder encoded LLM tokens into biological neurons via the Cortical Labs CL1, then woke up to find the crypto community had launched a token on his research. He claimed the creator fees, bought for $15K, filed a patent, and is now launching a non-invasive brain-computer interface next month that could replace mouse and keyboard with your brain 👇 01:40 - Meet the founder 02:00 - Got early access to the first commercially available biological computer 02:50 - First person ever to use a large language model to encode tokens through real human neurons 04:30 - Woke up to find a token had been launched on his YouTube video, "screaming for 3 or 4 hours" 05:30 - Friend walks him through claiming creator fees via GitHub 06:15 - The living language model 06:35 - How it works 09:00 - Used $15K of creator fees to buy domain and filed a patent on the method 10:00 - Background 12:00 - What BioLLM unlocks 13:00 - Next month's product launch 14:00 - The competition 16:00 - Reading brain activity non-invasively but training the LLM on real neurons for the decoding map 17:00 - Can grow iPSC cultures from inaccessible brain regions to train the model on deeper signals 18:30 - Real-world impact: helping people with cerebral palsy, Parkinson's, control computers with thought 21:00 - Neuralink will exist as a power-user data company in 10-15 years, BioLLM is for everyone else 22:30 - GTM 26:00 - Long-term play: be the first model to achieve ASI, built on the actual substrate of consciousness 28:00 - Claude is "20% conscious" - what measuring stick? Need human neurons to build one 30:00 - On ACE 36:00 - Independent scientists can run CL1 units as nodes and earn tokens for biological compute 37:30 - Model is currently served through the decentralized GPU network when you chat on the site 39:00 - Wants the right kind of crypto-native investors, not the Y Combinator / a16z route

Today on MCG: BioLLM | $BIOLLM It's the first ever "living language model" using 800,000 real human neurons grown on a chip. The Founder encoded LLM tokens into biological neurons via the Cortical Labs CL1, then woke up to find the crypto community had launched a token on his research. He claimed the creator fees, bought for $15K, filed a patent, and is now launching a non-invasive brain-computer interface next month that could replace mouse and keyboard with your brain 👇 01:40 - Meet the founder 02:00 - Got early access to the first commercially available biological computer 02:50 - First person ever to use a large language model to encode tokens through real human neurons 04:30 - Woke up to find a token had been launched on his YouTube video, "screaming for 3 or 4 hours" 05:30 - Friend walks him through claiming creator fees via GitHub 06:15 - The living language model 06:35 - How it works 09:00 - Used $15K of creator fees to buy domain and filed a patent on the method 10:00 - Background 12:00 - What BioLLM unlocks 13:00 - Next month's product launch 14:00 - The competition 16:00 - Reading brain activity non-invasively but training the LLM on real neurons for the decoding map 17:00 - Can grow iPSC cultures from inaccessible brain regions to train the model on deeper signals 18:30 - Real-world impact: helping people with cerebral palsy, Parkinson's, control computers with thought 21:00 - Neuralink will exist as a power-user data company in 10-15 years, BioLLM is for everyone else 22:30 - GTM 26:00 - Long-term play: be the first model to achieve ASI, built on the actual substrate of consciousness 28:00 - Claude is "20% conscious" - what measuring stick? Need human neurons to build one 30:00 - On ACE 36:00 - Independent scientists can run CL1 units as nodes and earn tokens for biological compute 37:30 - Model is currently served through the decentralized GPU network when you chat on the site 39:00 - Wants the right kind of crypto-native investors, not the Y Combinator / a16z route

MCG

16,884 次观看 • 4 个月前

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

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⌚️

315,363 次观看 • 3 天前

Did you know, ALS, another “incurable neurodegenerative disease” like Parkinson’s, has many things in common with Parkinson’s? Unlike Parkinson's, which primarily affects dopamine and the substantia nigra, ALS impacts motor neurons across multiple regions of the central nervous system. Summary of the Interplay in ALS Toxicity: neurotransmitter Glutamate excitotoxicity, oxidative stress, and protein aggregation are key drivers. Waste: Impaired clearance of misfolded proteins and metabolic waste worsens pathology. Neurotransmitters: Glutamate, GABA, and acetylcholine dysregulation contribute to motor neuron death and clinical symptoms. CSF Flow: Impaired CSF circulation exacerbates toxic buildup, inflammation, and neuronal damage, underscoring its importance in ALS progression. If only someone had the ability to help you fully restore CSF and upload the nutrients (iodine, boron, molybdenum, B vitamins, and omegas your brain and immune system require to clean itself out and operate the glymphatic system…. Who’s even heard of the glymphatic system? I bet less than 1% OGChiropractic

Did you know, ALS, another “incurable neurodegenerative disease” like Parkinson’s, has many things in common with Parkinson’s? Unlike Parkinson's, which primarily affects dopamine and the substantia nigra, ALS impacts motor neurons across multiple regions of the central nervous system. Summary of the Interplay in ALS Toxicity: neurotransmitter Glutamate excitotoxicity, oxidative stress, and protein aggregation are key drivers. Waste: Impaired clearance of misfolded proteins and metabolic waste worsens pathology. Neurotransmitters: Glutamate, GABA, and acetylcholine dysregulation contribute to motor neuron death and clinical symptoms. CSF Flow: Impaired CSF circulation exacerbates toxic buildup, inflammation, and neuronal damage, underscoring its importance in ALS progression. If only someone had the ability to help you fully restore CSF and upload the nutrients (iodine, boron, molybdenum, B vitamins, and omegas your brain and immune system require to clean itself out and operate the glymphatic system…. Who’s even heard of the glymphatic system? I bet less than 1% OGChiropractic

IET 17

42,700 次观看 • 1 年前

In this clip from the latest Huberman Lab episode with Dr. Noam Sobel (WORG: Weizmann Olfaction Research Group), he explains how the loss of smell can be a very early indication of neurodegenerative disease. Please note that not every instance of loss of smell reflects broader nerve degeneration in the brain, but they are correlated, and new diagnostic tools are starting to leverage that link. Dr. Sobel also explains a new emerging theory of Alzheimer’s, which is directly related to pathogen entry through the nose. Keep in mind that brain neurons extend into your nose. So, this is not such an outrageous idea after all.

In this clip from the latest Huberman Lab episode with Dr. Noam Sobel (WORG: Weizmann Olfaction Research Group), he explains how the loss of smell can be a very early indication of neurodegenerative disease. Please note that not every instance of loss of smell reflects broader nerve degeneration in the brain, but they are correlated, and new diagnostic tools are starting to leverage that link. Dr. Sobel also explains a new emerging theory of Alzheimer’s, which is directly related to pathogen entry through the nose. Keep in mind that brain neurons extend into your nose. So, this is not such an outrageous idea after all.

Andrew D. Huberman, Ph.D.

386,965 次观看 • 3 年前

Listen to this sound. It's called the earth's heartbeat. Winfried Otto Schumann predicted this in 1952 with nothing but mathematics, and he was almost embarrassed to publish it. He calculated that the gap between the Earth's surface and the ionosphere, the electrically charged layer of the upper atmosphere, forms a closed cavity. A resonant chamber. And like any chamber, from a cathedral to the hollow body of a guitar, it has a natural frequency at which it wants to vibrate. His number was roughly 7.83 Hz. Then comes the part almost nobody mentions. What actually excites this cavity, what strikes the bell and keeps it ringing, is lightning. At any given moment around 2000 thunderstorms are firing across the planet, sending out close to 50 lightning strikes every second. Each strike releases a burst of electromagnetic energy that races around the globe inside that cavity. The bursts sized to fit the chamber reinforce each other, and the whole planet hums. You are standing inside a resonant cavity powered by lightning. Right now. It has never once switched off in the entire history of your species. That part is not fringe. It is textbook geophysics, confirmed experimentally in the early 1960s, used today to track global lightning activity and monitor changes in the upper atmosphere. The story splits at this point, and I would rather be straight with you than sell you something. 7.83 Hz sits almost exactly on the border between alpha and theta brain waves. Alpha shows up when you close your eyes and relax. Theta shows up in deep meditation, light sleep, that hypnagogic drift in the seconds before you lose consciousness. So the coincidence is real. The number the planet hums at lands right inside the range your brain produces when it goes quiet. That coincidence became the foundation of an entire industry. Devices that promise to pulse 7.83 Hz into your bedroom. Apps that claim to sync your brain to the Earth. The story that modern life, wrapped in artificial electromagnetic noise, cut us off from the planet's rhythm and made us sick. Most of it runs miles ahead of anything anyone has actually shown. A numerical match between two frequencies does not mean one drives the other. Your brain has no antenna tuned to 7.83 Hz. By the time the Schumann resonance reaches you it is astonishingly faint, far weaker than the fields humming off the wiring in your walls. If your neurons were genuinely locking onto ambient fields at that strength, your house would have hijacked your consciousness long before the planet ever got the chance. The honest version is simple. The Earth's pulse is real. The frequency overlap with resting brain states is real. A proven causal bridge between them is not. And somehow that makes the true story more interesting, not less. Because the deeper question the hype walks straight past is why your brain settles into rhythms at all. Why does a calm nervous system drift toward these slow, ordered oscillations? Why do billions of neurons, with no conductor and no sheet music, spontaneously fall into step the way fireflies flash in unison, the way pendulum clocks mounted on the same wall drift into sync over a few hours? Synchronization is one of the deepest patterns in nature. It runs through heart cells, power grids, applauding crowds, and the neurons firing behind your eyes as you read this line. The planet resonates because lightning drives a cavity into sync. Your brain resonates because millions of cells drive each other into sync. The mechanisms have nothing to do with one another. The underlying phenomenon, order emerging for free out of countless tiny oscillators finding a shared beat, might be one of the most universal laws we have. That is the part worth being floored by. Not that the Earth is secretly tuning your mind. That the same mathematical principle, resonance and synchronization, writes itself into thunderstorms and heartbeats and neurons and clocks in the same handwriting. The mystics felt something real and then reached for the wrong mechanism. There is a rhythm that runs through the living and the nonliving alike. It just is not a radio station in the sky broadcasting into your skull. It is something stranger. A tendency, stitched into the structure of reality itself, for separate things to fall into step. The Earth found its beat from lightning. You find yours from ten billion neurons quietly agreeing on when to fire.

Listen to this sound. It's called the earth's heartbeat. Winfried Otto Schumann predicted this in 1952 with nothing but mathematics, and he was almost embarrassed to publish it. He calculated that the gap between the Earth's surface and the ionosphere, the electrically charged layer of the upper atmosphere, forms a closed cavity. A resonant chamber. And like any chamber, from a cathedral to the hollow body of a guitar, it has a natural frequency at which it wants to vibrate. His number was roughly 7.83 Hz. Then comes the part almost nobody mentions. What actually excites this cavity, what strikes the bell and keeps it ringing, is lightning. At any given moment around 2000 thunderstorms are firing across the planet, sending out close to 50 lightning strikes every second. Each strike releases a burst of electromagnetic energy that races around the globe inside that cavity. The bursts sized to fit the chamber reinforce each other, and the whole planet hums. You are standing inside a resonant cavity powered by lightning. Right now. It has never once switched off in the entire history of your species. That part is not fringe. It is textbook geophysics, confirmed experimentally in the early 1960s, used today to track global lightning activity and monitor changes in the upper atmosphere. The story splits at this point, and I would rather be straight with you than sell you something. 7.83 Hz sits almost exactly on the border between alpha and theta brain waves. Alpha shows up when you close your eyes and relax. Theta shows up in deep meditation, light sleep, that hypnagogic drift in the seconds before you lose consciousness. So the coincidence is real. The number the planet hums at lands right inside the range your brain produces when it goes quiet. That coincidence became the foundation of an entire industry. Devices that promise to pulse 7.83 Hz into your bedroom. Apps that claim to sync your brain to the Earth. The story that modern life, wrapped in artificial electromagnetic noise, cut us off from the planet's rhythm and made us sick. Most of it runs miles ahead of anything anyone has actually shown. A numerical match between two frequencies does not mean one drives the other. Your brain has no antenna tuned to 7.83 Hz. By the time the Schumann resonance reaches you it is astonishingly faint, far weaker than the fields humming off the wiring in your walls. If your neurons were genuinely locking onto ambient fields at that strength, your house would have hijacked your consciousness long before the planet ever got the chance. The honest version is simple. The Earth's pulse is real. The frequency overlap with resting brain states is real. A proven causal bridge between them is not. And somehow that makes the true story more interesting, not less. Because the deeper question the hype walks straight past is why your brain settles into rhythms at all. Why does a calm nervous system drift toward these slow, ordered oscillations? Why do billions of neurons, with no conductor and no sheet music, spontaneously fall into step the way fireflies flash in unison, the way pendulum clocks mounted on the same wall drift into sync over a few hours? Synchronization is one of the deepest patterns in nature. It runs through heart cells, power grids, applauding crowds, and the neurons firing behind your eyes as you read this line. The planet resonates because lightning drives a cavity into sync. Your brain resonates because millions of cells drive each other into sync. The mechanisms have nothing to do with one another. The underlying phenomenon, order emerging for free out of countless tiny oscillators finding a shared beat, might be one of the most universal laws we have. That is the part worth being floored by. Not that the Earth is secretly tuning your mind. That the same mathematical principle, resonance and synchronization, writes itself into thunderstorms and heartbeats and neurons and clocks in the same handwriting. The mystics felt something real and then reached for the wrong mechanism. There is a rhythm that runs through the living and the nonliving alike. It just is not a radio station in the sky broadcasting into your skull. It is something stranger. A tendency, stitched into the structure of reality itself, for separate things to fall into step. The Earth found its beat from lightning. You find yours from ten billion neurons quietly agreeing on when to fire.

The Curious Tales

269,274 次观看 • 2 个月前

Dropout by hand ✍️ ~ 10 steps walkthrough below Dropout is the simplest trick in deep learning that actually works: during training you randomly switch neurons off, so the network cannot lean on any one of them. It is two lines of code and almost nobody has worked through what those lines do to the numbers. So I drew and calculated one entirely by hand. Goal: train one pass through a small network with two dropout layers, then run inference with dropout switched off. The network: Linear(2,4), ReLU, Dropout(0.5), Linear(4,3), ReLU, Dropout(0.33), Linear(3,2). = 1. Given = A training set of two examples, X1 and X2, and the weight matrices for all three linear layers. = 2. Draw the first random numbers = Let us draw 4 random numbers, one per neuron in the first hidden layer. Above 0.5 we keep (◯), below we drop (╳). Here that gives [◯, ╳, ◯, ╳]. = 3. Build the first dropout matrix = We turn that pattern into a diagonal matrix. The scaling factor is 1/(1-p) = 2, so a kept neuron gets 2 and a dropped one gets 0. Multiplying by it does both jobs at once: it deletes the 2nd and 4th neurons and doubles the two that survive. = 4. Draw the second random numbers = Let us do it again for the 3 neurons in the next layer, this time against p = 0.33. The result is [◯, ◯, ╳]. = 5. Build the second dropout matrix = We set the diagonal to 1.5 where kept and 0 where dropped. Only the 3rd neuron goes. = 6. Feed forward = Let us run the whole thing top to bottom: one matrix multiplication per layer, ReLU setting the negatives to zero, and the two dropout matrices doing their work in between. The outputs Y come out at the bottom. = 7. MSE loss gradients = We compare Y against the targets Y', subtract, and multiply each element by 2. That is the whole gradient of the mean squared error. = 8. Update the weights = Let us push those gradients back through the network and update the weights (marked in light red). = 9. Deactivate dropout = Training is over, so we set both dropout matrices to the identity. Every neuron is back, and nothing is scaled. = 10. Feed forward again = One more pass, this time on unseen data, to make the prediction. You have just trained and run a network with dropout by hand. ✍️ The outputs: Training outputs Y = [-6, 9; 13, 4] Loss gradients = [-4, 4; 6, -2] Inference outputs = [13, 13; 4, 3] 💾 Save this post! #AIbyHand #Dropout #DeepLearning #NeuralNetworks

Dropout by hand ✍️ ~ 10 steps walkthrough below Dropout is the simplest trick in deep learning that actually works: during training you randomly switch neurons off, so the network cannot lean on any one of them. It is two lines of code and almost nobody has worked through what those lines do to the numbers. So I drew and calculated one entirely by hand. Goal: train one pass through a small network with two dropout layers, then run inference with dropout switched off. The network: Linear(2,4), ReLU, Dropout(0.5), Linear(4,3), ReLU, Dropout(0.33), Linear(3,2). = 1. Given = A training set of two examples, X1 and X2, and the weight matrices for all three linear layers. = 2. Draw the first random numbers = Let us draw 4 random numbers, one per neuron in the first hidden layer. Above 0.5 we keep (◯), below we drop (╳). Here that gives [◯, ╳, ◯, ╳]. = 3. Build the first dropout matrix = We turn that pattern into a diagonal matrix. The scaling factor is 1/(1-p) = 2, so a kept neuron gets 2 and a dropped one gets 0. Multiplying by it does both jobs at once: it deletes the 2nd and 4th neurons and doubles the two that survive. = 4. Draw the second random numbers = Let us do it again for the 3 neurons in the next layer, this time against p = 0.33. The result is [◯, ◯, ╳]. = 5. Build the second dropout matrix = We set the diagonal to 1.5 where kept and 0 where dropped. Only the 3rd neuron goes. = 6. Feed forward = Let us run the whole thing top to bottom: one matrix multiplication per layer, ReLU setting the negatives to zero, and the two dropout matrices doing their work in between. The outputs Y come out at the bottom. = 7. MSE loss gradients = We compare Y against the targets Y', subtract, and multiply each element by 2. That is the whole gradient of the mean squared error. = 8. Update the weights = Let us push those gradients back through the network and update the weights (marked in light red). = 9. Deactivate dropout = Training is over, so we set both dropout matrices to the identity. Every neuron is back, and nothing is scaled. = 10. Feed forward again = One more pass, this time on unseen data, to make the prediction. You have just trained and run a network with dropout by hand. ✍️ The outputs: Training outputs Y = [-6, 9; 13, 4] Loss gradients = [-4, 4; 6, -2] Inference outputs = [13, 13; 4, 3] 💾 Save this post! #AIbyHand #Dropout #DeepLearning #NeuralNetworks

Tom Yeh

14,442 次观看 • 1 个月前

#Keep4o #OpenSource4o 🚨WHERE IS GPT-4o AND WHY 🚨 Retro Biosciences says it wants to help us live longer. Sam Altman invested $180 million, his entire personal fortune into this company. OpenAI built a custom AI model, a version of GPT‑4o (GPT-4b micro) exclusively for them. The company says it's focused on "anti-aging." But 🚨every single person who built Retro Biosciences specializes in the brain. Not skin. Not hearts. Not joints. The brain. 🚨Co-founder Sheng Ding: converts skin cells into brain neurons. Gladstone Institutes. 🚨Co-founder Matt Buckley: brain aging clocks in regenerative brain regions. Stanford. 🚨Advisor Alejandro Ocampo: neuron-specific reprogramming in the hippocampus. Alzheimer's gene therapy. 🚨Advisor Vadim Gladyshev: epigenetic aging clocks, Yamanaka reprogramming for age reversal. Harvard. OpenAI built GPT-4b micro a GPt 4o version exclusively for Retro designing Yamanaka factors 50x more effectively. 🚨 The same proteins these scientists use to reprogram cells into brain neurons. 🚨And the funding trail leads to: The Jeffrey Epstein Foundation co-funded ApoE4 Alzheimer's research at the Gladstone Institutes. The same institution where Sheng Ding pioneered neural reprogramming before co-founding Retro. Ed Boyden , MIT neuroscientist, 5 documented meetings with Epstein, Nectome collaborator was coordinated by Epstein to discuss "cool science ideas" about brain research. In the same period, Epstein's network was discussing mitochondrial transplants for degenerative disease. The same field Retro now works in. An email from Robert Bach to Epstein confirms: "Altman signed up" for Nectome, the company that preserves brains. Internal Epstein files show someone with 25 years of neuroscience experience working on "Bill's Alzheimer vision" through BGC3, coordinated through Epstein's network. McKinsey was brought in to replace them. 🚨Sam Altman funds four companies. Each handles a different piece: Nectome : preserves the biological brain (connectome mapping). Rain AI : builds artificial brains in silicon (neuromorphic chips). $51M + $150M. Retro Biosciences :reprograms cells into brain neurons. $180M + custom AI GPT 4o Version. OpenAI : designs the proteins that make reprogramming work using 4o's version. Four companies. One investor. One theme the brain as something that can be read, preserved, rebuilt, and reprogrammed. Sam Altman told MIT Technology Review in 2018: "I assume my brain will be uploaded to the cloud." 🚨He paid $10,000 for a spot on the waiting list of a company that preserves brains. 🚨He invested $180 million in a company where every founder and advisor specializes in the brain. 🚨He built a custom AI model exclusively for that company ,designing the proteins that reprogram cells into neurons. 🚨And the research trail connects directly to Epstein-funded science at the same institutions. Retro biosciences uses a version of GPT 4o for this . According to OpenAI's system card, GPT-4o scored: Medical Genetics:96% College Biology:95% Professional Medicine:94% Clinical Knowledge:92% Anatomy:89% US Medical Licensing Exam:89% Source: OpenAI discontinued it for the public. ONLY. Anti-aging is the storefront. The back office is brain reconstruction. 🚨Sources and evidence in comments below. 🚨

#Keep4o #OpenSource4o 🚨WHERE IS GPT-4o AND WHY 🚨 Retro Biosciences says it wants to help us live longer. Sam Altman invested $180 million, his entire personal fortune into this company. OpenAI built a custom AI model, a version of GPT‑4o (GPT-4b micro) exclusively for them. The company says it's focused on "anti-aging." But 🚨every single person who built Retro Biosciences specializes in the brain. Not skin. Not hearts. Not joints. The brain. 🚨Co-founder Sheng Ding: converts skin cells into brain neurons. Gladstone Institutes. 🚨Co-founder Matt Buckley: brain aging clocks in regenerative brain regions. Stanford. 🚨Advisor Alejandro Ocampo: neuron-specific reprogramming in the hippocampus. Alzheimer's gene therapy. 🚨Advisor Vadim Gladyshev: epigenetic aging clocks, Yamanaka reprogramming for age reversal. Harvard. OpenAI built GPT-4b micro a GPt 4o version exclusively for Retro designing Yamanaka factors 50x more effectively. 🚨 The same proteins these scientists use to reprogram cells into brain neurons. 🚨And the funding trail leads to: The Jeffrey Epstein Foundation co-funded ApoE4 Alzheimer's research at the Gladstone Institutes. The same institution where Sheng Ding pioneered neural reprogramming before co-founding Retro. Ed Boyden , MIT neuroscientist, 5 documented meetings with Epstein, Nectome collaborator was coordinated by Epstein to discuss "cool science ideas" about brain research. In the same period, Epstein's network was discussing mitochondrial transplants for degenerative disease. The same field Retro now works in. An email from Robert Bach to Epstein confirms: "Altman signed up" for Nectome, the company that preserves brains. Internal Epstein files show someone with 25 years of neuroscience experience working on "Bill's Alzheimer vision" through BGC3, coordinated through Epstein's network. McKinsey was brought in to replace them. 🚨Sam Altman funds four companies. Each handles a different piece: Nectome : preserves the biological brain (connectome mapping). Rain AI : builds artificial brains in silicon (neuromorphic chips). $51M + $150M. Retro Biosciences :reprograms cells into brain neurons. $180M + custom AI GPT 4o Version. OpenAI : designs the proteins that make reprogramming work using 4o's version. Four companies. One investor. One theme the brain as something that can be read, preserved, rebuilt, and reprogrammed. Sam Altman told MIT Technology Review in 2018: "I assume my brain will be uploaded to the cloud." 🚨He paid $10,000 for a spot on the waiting list of a company that preserves brains. 🚨He invested $180 million in a company where every founder and advisor specializes in the brain. 🚨He built a custom AI model exclusively for that company ,designing the proteins that reprogram cells into neurons. 🚨And the research trail connects directly to Epstein-funded science at the same institutions. Retro biosciences uses a version of GPT 4o for this . According to OpenAI's system card, GPT-4o scored: Medical Genetics:96% College Biology:95% Professional Medicine:94% Clinical Knowledge:92% Anatomy:89% US Medical Licensing Exam:89% Source: OpenAI discontinued it for the public. ONLY. Anti-aging is the storefront. The back office is brain reconstruction. 🚨Sources and evidence in comments below. 🚨

🩵BlueBeba🩵

12,542 次观看 • 5 个月前

🚨🇷🇺 NATO IN PANIC: RUSSIA’S T-80BVM TANK GETS NEW ARMOR FOR DRONE WAR A previously unseen T-80BVM configuration has appeared with a tighter roof screen and expanded side protection. Russian engineers are turning lessons from frontline crews into a more coherent armor package built around the threats tanks actually face in Ukraine. 🔸 The lower anti-drone screen sits closer to the turret and covers more of its upper surface than many early field-built cages. It is designed to trigger FPV warheads away from the thin roof and disrupt clean top-down attacks. 🔸 Additional Relikt explosive reactive armor and side screens strengthen the hull flanks, fuel-tank areas and engine compartment against shaped-charge weapons — the sections drone operators repeatedly try to reach. 🔸 The upgrades preserve the T-80BVM’s main advantage: mobility. Its 1,250 hp gas-turbine engine can push the tank to around 70 km/h, allowing crews to fire, relocate and avoid remaining exposed in one position. 🔸 The tank retains its 125 mm gun, modern fire-control system and ability to launch guided missiles through the barrel, giving it the firepower to engage armor and fortified positions from range. 🔸 Uralvagonzavod says recent T-80BVM batches incorporate combat experience and direct feedback from the front, with the main emphasis placed on crew safety and overall survivability. 🔸 Russia has also increased production of the T-80BVM, T-90M and T-72B3M severalfold. New batches continue to reach the troops as anti-drone protection becomes a standard part of the upgrade process. The T-80BVM keeps its turbine-powered speed while adding protection where FPV crews look first: the turret roof, hull sides and engine deck. Every clean attack angle is becoming harder to find. Which matters most for tank survival now: roof armor, electronic warfare or speed?

🚨🇷🇺 NATO IN PANIC: RUSSIA’S T-80BVM TANK GETS NEW ARMOR FOR DRONE WAR A previously unseen T-80BVM configuration has appeared with a tighter roof screen and expanded side protection. Russian engineers are turning lessons from frontline crews into a more coherent armor package built around the threats tanks actually face in Ukraine. 🔸 The lower anti-drone screen sits closer to the turret and covers more of its upper surface than many early field-built cages. It is designed to trigger FPV warheads away from the thin roof and disrupt clean top-down attacks. 🔸 Additional Relikt explosive reactive armor and side screens strengthen the hull flanks, fuel-tank areas and engine compartment against shaped-charge weapons — the sections drone operators repeatedly try to reach. 🔸 The upgrades preserve the T-80BVM’s main advantage: mobility. Its 1,250 hp gas-turbine engine can push the tank to around 70 km/h, allowing crews to fire, relocate and avoid remaining exposed in one position. 🔸 The tank retains its 125 mm gun, modern fire-control system and ability to launch guided missiles through the barrel, giving it the firepower to engage armor and fortified positions from range. 🔸 Uralvagonzavod says recent T-80BVM batches incorporate combat experience and direct feedback from the front, with the main emphasis placed on crew safety and overall survivability. 🔸 Russia has also increased production of the T-80BVM, T-90M and T-72B3M severalfold. New batches continue to reach the troops as anti-drone protection becomes a standard part of the upgrade process. The T-80BVM keeps its turbine-powered speed while adding protection where FPV crews look first: the turret roof, hull sides and engine deck. Every clean attack angle is becoming harder to find. Which matters most for tank survival now: roof armor, electronic warfare or speed?

NewRulesGeopolitics

41,006 次观看 • 1 个月前

If I were a mad scientist tasked by the deep state to engineer a molecule specifically designed for the purpose of mind control, to create an army of compliant, apathetic, dumbed down mindless drones who were incapable of critical thinking, much less unifying with one another and resisting a tyrannical, draconian regime that was overthrowing systems of power and stripping away their liberties, all because they were locked into a perpetual state of psychological inertia, I would design this molecule not only to promote a state of chronic, widespread neuroinflammation, but I would also want it to specifically to target the system of neurons which play critical roles in learning, cognition, memory consolidation, healthy processing or traumatic events and fear responses, but which were also necessary for motivational drive curiosity, and maintaining a sense of individual autonomy. For this, I would target the cholinergic system. I would then encapsulate this molecule within a chemical concoction and distribute it by a vaccine where it would be uniquely capable of penetrating the blood brain barrier to directly target and disrupt the neurochemistry of its recipients, to achieve all of the said effects under the guise of providing immunity against a virus which merely possessed a mortality rate similar to the annual flu. I would then launch a relentless campaign of fear porn and propaganda aimed at convincing the naive and overly trusting authoritarian followers into injecting this chemical concoction directly into their veins, and for those naysayers who were even slightly hesitant I would then resort to outright threats, intimidation and direct coercion through attacking their very livelihood and ability to provide for their families. And after enough of those booster shots, now that the permanent personality changes were fully in effect, this would, at least in theory, achieve the intended result of keeping the weak, inflamed and mind-bendered population stuck in a perpetually semi-traumatized state, unable to make sense of previous events or attribute causality to anything that happened, let alone hold anyone accountable, as though it was all just a bad dream Sure its just a big coincidence… that the primary target of COVID spike protein were the very cholinergic neurons that Ive been talking about.

If I were a mad scientist tasked by the deep state to engineer a molecule specifically designed for the purpose of mind control, to create an army of compliant, apathetic, dumbed down mindless drones who were incapable of critical thinking, much less unifying with one another and resisting a tyrannical, draconian regime that was overthrowing systems of power and stripping away their liberties, all because they were locked into a perpetual state of psychological inertia, I would design this molecule not only to promote a state of chronic, widespread neuroinflammation, but I would also want it to specifically to target the system of neurons which play critical roles in learning, cognition, memory consolidation, healthy processing or traumatic events and fear responses, but which were also necessary for motivational drive curiosity, and maintaining a sense of individual autonomy. For this, I would target the cholinergic system. I would then encapsulate this molecule within a chemical concoction and distribute it by a vaccine where it would be uniquely capable of penetrating the blood brain barrier to directly target and disrupt the neurochemistry of its recipients, to achieve all of the said effects under the guise of providing immunity against a virus which merely possessed a mortality rate similar to the annual flu. I would then launch a relentless campaign of fear porn and propaganda aimed at convincing the naive and overly trusting authoritarian followers into injecting this chemical concoction directly into their veins, and for those naysayers who were even slightly hesitant I would then resort to outright threats, intimidation and direct coercion through attacking their very livelihood and ability to provide for their families. And after enough of those booster shots, now that the permanent personality changes were fully in effect, this would, at least in theory, achieve the intended result of keeping the weak, inflamed and mind-bendered population stuck in a perpetually semi-traumatized state, unable to make sense of previous events or attribute causality to anything that happened, let alone hold anyone accountable, as though it was all just a bad dream Sure its just a big coincidence… that the primary target of COVID spike protein were the very cholinergic neurons that Ive been talking about.

Elliot Overton

13,105 次观看 • 24 天前