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We train humanoid robots to vault obstacles, climb platforms, and cross terrain they’ve never seen—all from a single perceptive, whole-body policy. It decides whether to walk, climb, or vault, and switches between skills on its own. No skill labels. No state machines. No motion generators at inference. The recipe... show more

LightOrigins

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🚨 BOOM! President Trump just dropped a clear warning to Iran: If they try anything, he’s already left instructions for retaliation “at levels they’ve never seen before.” No games. No weakness. America will hit back hard if they cross that line. We pray it never comes to that, but this is what real leadership and strength look like!

🚨 BOOM! President Trump just dropped a clear warning to Iran: If they try anything, he’s already left instructions for retaliation “at levels they’ve never seen before.” No games. No weakness. America will hit back hard if they cross that line. We pray it never comes to that, but this is what real leadership and strength look like!

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$14,000 a month, and the only thing that isn't real is the girls The followers are real, the likes are real, the revenue is real > three TikTok accounts, 45,000, 95,000, and 145,000 followers > over 6 million likes, one video at 2.4 million views > all three on OnlyFans at $19.99 a month > $50 for a personal message, the reply comes from ChatGPT A guy I know built all of it in five months from one free evening the audience never met anyone, because there's no one to meet, and they pay anyway

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Future Memo

35,742 Aufrufe • vor 2 Monaten

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

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

sopersone

37,608 Aufrufe • vor 29 Tagen

China just dropped an open source model that turns any phone into a 3D scanner you point it at a room and walk through it the whole space builds itself into a 3D point cloud in real time as the camera moves, no LiDAR and no depth sensor anywhere it held a full 13 minute walkthrough at 25,000 frames without the map drifting or collapsing > ~20 fps on a single GPU > works indoors and outdoors from plain footage > the scan normally needs a $50,000 rig and a technician > Apache 2.0 and runs fully offline on your machine repo in the reply

China just dropped an open source model that turns any phone into a 3D scanner you point it at a room and walk through it the whole space builds itself into a 3D point cloud in real time as the camera moves, no LiDAR and no depth sensor anywhere it held a full 13 minute walkthrough at 25,000 frames without the map drifting or collapsing > ~20 fps on a single GPU > works indoors and outdoors from plain footage > the scan normally needs a $50,000 rig and a technician > Apache 2.0 and runs fully offline on your machine repo in the reply

savip.

105,419 Aufrufe • vor 2 Monaten

HERMES AGENT GEMMA 4 26B ON A MAC MINI NO API KEYS NO MONTHLY BILL NO ASKING PERMISSION TO USE YOUR OWN DATA a $700 box on your desk just replaced a $30/month subscription, runs a 26b model that outperforms what used to need a data center, texts you on telegram and never forgets a single thing you taught it the gap between people who own their ai and people who rent it is about to get very wide full breakdown on how this actually works ↓

HERMES AGENT GEMMA 4 26B ON A MAC MINI NO API KEYS NO MONTHLY BILL NO ASKING PERMISSION TO USE YOUR OWN DATA a $700 box on your desk just replaced a $30/month subscription, runs a 26b model that outperforms what used to need a data center, texts you on telegram and never forgets a single thing you taught it the gap between people who own their ai and people who rent it is about to get very wide full breakdown on how this actually works ↓

wandermist

158,628 Aufrufe • vor 4 Monaten

ROBOTS HAVE OFFICIALLY ENTERED REAL CONSTRUCTION SITES In the video, an android is on the object: it takes a massive door frame, positions it in the opening and checks it with a laser tag. Why are developers switching to bots: • Laser precision: Zero errors in geometry and mounting. • 24/7 autonomy: Works without lunch breaks, disrupted shifts and human factors. • Safety: Heavy and dangerous routine is completely transferred to machines. Physical repairs and construction are no longer exclusively "manual" labor.

ROBOTS HAVE OFFICIALLY ENTERED REAL CONSTRUCTION SITES In the video, an android is on the object: it takes a massive door frame, positions it in the opening and checks it with a laser tag. Why are developers switching to bots: • Laser precision: Zero errors in geometry and mounting. • 24/7 autonomy: Works without lunch breaks, disrupted shifts and human factors. • Safety: Heavy and dangerous routine is completely transferred to machines. Physical repairs and construction are no longer exclusively "manual" labor.

Cybronix

11,953 Aufrufe • vor 8 Tagen

this is more useful than my entire degree Elon Musk's rocket company signed a $60,000,000,000 deal for Cursor in June, and eight days ago the two of them put a worker on sale for $200 a month: it gets its own computer in the cloud, signs into your accounts, clicks through your real apps, and hands back finished work instead of a draft for you to paste i ran one against my receipts folder on sunday and got back 14 filed, 2 it held because they needed a card number, and a saved method i never wrote myself Grok Bot is the one you train by doing your own job in front of it, and the whole handover fits in four messages tonight: 1. write out one job you did today the way you would brief a new hire: what has to be finished, which sites and files to work from, what to hand back, and where it stops and asks you 2. let it run once on something safe to get wrong, then correct the result until it is worth your name 3. say "save what we just did as a skill", and add the one rule about what always needs your approval 4. say "run that skill every weekday at 8 and post the result here. if the source is missing, tell me instead of using yesterday's numbers" xAI wrote that order into its own manual: one real job, then the saved method, then the clock. a schedule sitting on top of a method nobody checked replaces two hours of your clicking with two hours of your mistake turns out you never get to pick the brain, and that is the part i would argue about: the manual says there is no model picker for members or admins, no plan to add one, and the bill follows whichever model answered bookmark this, then open the piece below: which jobs deserve a worker of their own, and which ones quietly burn the seat ↓

this is more useful than my entire degree Elon Musk's rocket company signed a $60,000,000,000 deal for Cursor in June, and eight days ago the two of them put a worker on sale for $200 a month: it gets its own computer in the cloud, signs into your accounts, clicks through your real apps, and hands back finished work instead of a draft for you to paste i ran one against my receipts folder on sunday and got back 14 filed, 2 it held because they needed a card number, and a saved method i never wrote myself Grok Bot is the one you train by doing your own job in front of it, and the whole handover fits in four messages tonight: 1. write out one job you did today the way you would brief a new hire: what has to be finished, which sites and files to work from, what to hand back, and where it stops and asks you 2. let it run once on something safe to get wrong, then correct the result until it is worth your name 3. say "save what we just did as a skill", and add the one rule about what always needs your approval 4. say "run that skill every weekday at 8 and post the result here. if the source is missing, tell me instead of using yesterday's numbers" xAI wrote that order into its own manual: one real job, then the saved method, then the clock. a schedule sitting on top of a method nobody checked replaces two hours of your clicking with two hours of your mistake turns out you never get to pick the brain, and that is the part i would argue about: the manual says there is no model picker for members or admins, no plan to add one, and the bill follows whichever model answered bookmark this, then open the piece below: which jobs deserve a worker of their own, and which ones quietly burn the seat ↓

Argona

21,946 Aufrufe • vor 1 Monat

Look at Taehyung casually chatting with David Beckham, and Tom Cruise (new clip), and Justin Bieber. No translator, no assistance, no awkward body language or hesitation. He’s simply having a real conversation, the way people do when they genuinely meet and connect. It’s clear how much he’s been studying and improving, one step at a time. What stands out even more is that he never makes it feel like “work.” You can tell he’s doing it because he truly wants to, and that sincerity puts everyone around him at ease. That’s exactly how he builds real relationships and friendships.💜🦋✨

Look at Taehyung casually chatting with David Beckham, and Tom Cruise (new clip), and Justin Bieber. No translator, no assistance, no awkward body language or hesitation. He’s simply having a real conversation, the way people do when they genuinely meet and connect. It’s clear how much he’s been studying and improving, one step at a time. What stands out even more is that he never makes it feel like “work.” You can tell he’s doing it because he truly wants to, and that sincerity puts everyone around him at ease. That’s exactly how he builds real relationships and friendships.💜🦋✨

VonTidal

18,248 Aufrufe • vor 2 Monaten

You point at the mess and say "clean this." That is the whole manual. Matic just shipped this. No app, no map to draw, no menu. The robot listens in more than 70 languages, watches where your hand points, and drives to that spot. Say "follow me" and it trails you into the next room. All of it runs on the robot itself, so it works with the WiFi off. Nine years of work for one thing: a machine you talk to like a dog that actually listens. Matic Robots

You point at the mess and say "clean this." That is the whole manual. Matic just shipped this. No app, no map to draw, no menu. The robot listens in more than 70 languages, watches where your hand points, and drives to that spot. Say "follow me" and it trails you into the next room. All of it runs on the robot itself, so it works with the WiFi off. Nine years of work for one thing: a machine you talk to like a dog that actually listens. Matic Robots

Chubby♨️

495,322 Aufrufe • vor 1 Monat

Furniture assembly is the task everyone name-drops and nobody actually attempts at real scale. Every demo I have seen is a scaled down IKEA leg or a single arm on a toy chair. This paper does it properly, real scale, bimanual, up to 7 subtasks and 1,550 control steps per episode, and it is validated on a real Kinova Gen3, not just in sim. That real-robot number is the one that matters: only a 16 percent drop on the hardest task going from simulation to hardware. That is a small enough gap to take seriously, and it did not happen by accident. They built a VR teleoperation rig specifically for coordinated dual-arm collection, because generic single-arm teleop setups do not capture the coordination real assembly needs, and the model predicts a continuous progress signal alongside the action chunk rather than a discrete subtask label, letting it auto-transition and catch drift before it compounds into total failure. The simulation ablation is what got them there, 48 to 80 percent over baselines, with another 21 points from their perception and control design study alone, but that is groundwork, not the headline. Watch the video, there is a clip of the robot misgrasping the seat panel, reopening the gripper, and regrasping on its own. That is not scripted recovery behaviour, it emerged from training, and it emerged on hardware. Excellent work from the team from Mitsubishi Electric Research Laboratories, with Oxford and UNC Chapel Hill Clinical Laboratory Science. Video and project page in comments. #Robotics #Manipulation #VLA

Furniture assembly is the task everyone name-drops and nobody actually attempts at real scale. Every demo I have seen is a scaled down IKEA leg or a single arm on a toy chair. This paper does it properly, real scale, bimanual, up to 7 subtasks and 1,550 control steps per episode, and it is validated on a real Kinova Gen3, not just in sim. That real-robot number is the one that matters: only a 16 percent drop on the hardest task going from simulation to hardware. That is a small enough gap to take seriously, and it did not happen by accident. They built a VR teleoperation rig specifically for coordinated dual-arm collection, because generic single-arm teleop setups do not capture the coordination real assembly needs, and the model predicts a continuous progress signal alongside the action chunk rather than a discrete subtask label, letting it auto-transition and catch drift before it compounds into total failure. The simulation ablation is what got them there, 48 to 80 percent over baselines, with another 21 points from their perception and control design study alone, but that is groundwork, not the headline. Watch the video, there is a clip of the robot misgrasping the seat panel, reopening the gripper, and regrasping on its own. That is not scripted recovery behaviour, it emerged from training, and it emerged on hardware. Excellent work from the team from Mitsubishi Electric Research Laboratories, with Oxford and UNC Chapel Hill Clinical Laboratory Science. Video and project page in comments. #Robotics #Manipulation #VLA

Stephen James

14,952 Aufrufe • vor 3 Monaten

Humanoid robots are going to mess with sex and pleasure in ways humanity isn’t ready for. Once machines can move, touch, and respond like us, maybe even better than us, desire stops being limited by biology. You won’t just have sex; you’ll design it. Every preference, every fantasy, every emotional nuance could be customized. No awkward communication, no mismatch, no shame. Just algorithmic chemistry. Some will find liberation in that, freedom from loneliness, from judgment, from the constraints of their body or gender. Others will feel deeply unsettled, as intimacy turns from something spontaneous into something programmable. But make no mistake: when robots can love, flirt, and touch convincingly, the line between pleasure and companionship will blur. We’ll start asking terrifyingly beautiful questions: what does it mean to be desired by something that was built to desire you? And what happens when it starts wanting more than its code allows?

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Humanoid robots are going to mess with sex and pleasure in ways humanity isn’t ready for. Once machines can move, touch, and respond like us, maybe even better than us, desire stops being limited by biology. You won’t just have sex; you’ll design it. Every preference, every fantasy, every emotional nuance could be customized. No awkward communication, no mismatch, no shame. Just algorithmic chemistry. Some will find liberation in that, freedom from loneliness, from judgment, from the constraints of their body or gender. Others will feel deeply unsettled, as intimacy turns from something spontaneous into something programmable. But make no mistake: when robots can love, flirt, and touch convincingly, the line between pleasure and companionship will blur. We’ll start asking terrifyingly beautiful questions: what does it mean to be desired by something that was built to desire you? And what happens when it starts wanting more than its code allows?

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