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Ilir Aliu

@IlirAliu_ • 53,938 subscribers

If it matters in European AI and Robotics, you'll see it here first. Building @22astronauts_

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You don’t need expensive hardware to build something that feels like real robotics. An Arduino, a servo, and an ultrasonic sensor can scan space and turn echoes into a simple radar view. It sweeps, measures distance, and visualizes it in real time. Cheap, simple, and a solid way to understand how machines sense the world. Limits show up fast, which is where better sensing and smarter software come in. Original build by SunFounder —— Weekly robotics and AI insights. Subscribe free:

You don’t need expensive hardware to build something that feels like real robotics. An Arduino, a servo, and an ultrasonic sensor can scan space and turn echoes into a simple radar view. It sweeps, measures distance, and visualizes it in real time. Cheap, simple, and a solid way to understand how machines sense the world. Limits show up fast, which is where better sensing and smarter software come in. Original build by SunFounder —— Weekly robotics and AI insights. Subscribe free:

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AI in robotics gets all the attention right now, but sometimes the most interesting work is very practical. Viet built a small vision system that counts potatoes on a conveyor belt. No giant dataset. No huge model. Just a clear problem and a smart setup. He used Ultralytics’ ObjectCounter, trained a tiny YOLO11 nano model, and because there was no potato dataset, he annotated a single frame with SAM 2 and trained from that. One frame. Still works across the whole video. It is a good reminder that useful AI in industry often looks like this. Focused. Lightweight. Solves a real task. If you work in manufacturing or robotics, these small systems are usually the fastest wins. They save time, reduce errors, and do not need massive infrastructure. Nice work, Viet. His projects: —- Weekly robotics and AI insights. Subscribe free:

AI in robotics gets all the attention right now, but sometimes the most interesting work is very practical. Viet built a small vision system that counts potatoes on a conveyor belt. No giant dataset. No huge model. Just a clear problem and a smart setup. He used Ultralytics’ ObjectCounter, trained a tiny YOLO11 nano model, and because there was no potato dataset, he annotated a single frame with SAM 2 and trained from that. One frame. Still works across the whole video. It is a good reminder that useful AI in industry often looks like this. Focused. Lightweight. Solves a real task. If you work in manufacturing or robotics, these small systems are usually the fastest wins. They save time, reduce errors, and do not need massive infrastructure. Nice work, Viet. His projects: —- Weekly robotics and AI insights. Subscribe free:

1,674,988 görüntüleme

A student built a real anti-gravity machine… using an Arduino. How to Make an Acoustic Levitator: Arduino Nano + motor driver + about 60 ultrasonic transducers. They all emit ~40 kHz sound. The sound waves meet and form fixed pockets in the air. Tiny bits of styrofoam get stuck in those pockets and just hang there. If you put your hand in, the pattern breaks and they fall. Same principle labs use to move droplets or samples without touching them. Credit: u/williamlk5341 on r/arduino Based on an “Acoustic Levitator” Instructable guide: ---- Weekly robotics and AI insights. Subscribe free:

A student built a real anti-gravity machine… using an Arduino. How to Make an Acoustic Levitator: Arduino Nano + motor driver + about 60 ultrasonic transducers. They all emit ~40 kHz sound. The sound waves meet and form fixed pockets in the air. Tiny bits of styrofoam get stuck in those pockets and just hang there. If you put your hand in, the pattern breaks and they fall. Same principle labs use to move droplets or samples without touching them. Credit: u/williamlk5341 on r/arduino Based on an “Acoustic Levitator” Instructable guide: ---- Weekly robotics and AI insights. Subscribe free:

817,094 görüntüleme

Why add sensors and complex systems when physics can do the job? This production line sorts products using only weight and controlled bursts of air. ✅ No cameras or vision models ✅ No expensive integration ✅ Just reliable, repeatable separation at scale It’s a reminder that not every automation breakthrough is about AI or advanced robotics... sometimes simple mechanical principles are the most efficient solution. —- Weekly robotics and AI insights. Subscribe free:

Why add sensors and complex systems when physics can do the job? This production line sorts products using only weight and controlled bursts of air. ✅ No cameras or vision models ✅ No expensive integration ✅ Just reliable, repeatable separation at scale It’s a reminder that not every automation breakthrough is about AI or advanced robotics... sometimes simple mechanical principles are the most efficient solution. —- Weekly robotics and AI insights. Subscribe free:

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Hardware Production 🤝 Robotics But these motors... are CRAZY synced: CNC motor synchronization helps make sure that different motors in a machine work together smoothly so that the machine can cut and shape materials with perfect accuracy. This is really important for making sure the machine does its job correctly. ✅ Helps the machine's parts move together perfectly, reducing mistakes and making things more accurate. ✅ Prevents the machine from getting damaged by keeping everything aligned. ✅ Allows the machine to work faster by making sure the motors are in sync. Synchronized CNC motors make sure machines work better, last longer, and create more precise products. —- Weekly robotics and AI insights. Subscribe free:

Hardware Production 🤝 Robotics But these motors... are CRAZY synced: CNC motor synchronization helps make sure that different motors in a machine work together smoothly so that the machine can cut and shape materials with perfect accuracy. This is really important for making sure the machine does its job correctly. ✅ Helps the machine's parts move together perfectly, reducing mistakes and making things more accurate. ✅ Prevents the machine from getting damaged by keeping everything aligned. ✅ Allows the machine to work faster by making sure the motors are in sync. Synchronized CNC motors make sure machines work better, last longer, and create more precise products. —- Weekly robotics and AI insights. Subscribe free:

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$1,000,000,000+ raised... How can a guy from a tiny town in Illinois, with no engineering degree, be the one building the most powerful humanoid company in the world? The story makes no sense. Until it does. [Long Thread 🧵]

$1,000,000,000+ raised... How can a guy from a tiny town in Illinois, with no engineering degree, be the one building the most powerful humanoid company in the world? The story makes no sense. Until it does. [Long Thread 🧵]

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XYZ: Let’s build a humanoid company to save humanity from the labour shortage Meanwhile:

XYZ: Let’s build a humanoid company to save humanity from the labour shortage Meanwhile:

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America and China build robots. Europe builds committees. Tesla just showed Optimus in pilot production. Humanoids being assembled like cars. Meanwhile, in Europe, we’re still arguing over regulation, ethics boards, and frameworks that nobody in the real world reads. This isn’t about copying the US. It’s about realizing that if we keep optimizing for safety over progress, we’ll end up safe... but irrelevant. I work in robotics companies every day. We have world-class talent here... But talent without permission to execute is just potential wasted. —- Weekly robotics and AI insights. Subscribe free:

America and China build robots. Europe builds committees. Tesla just showed Optimus in pilot production. Humanoids being assembled like cars. Meanwhile, in Europe, we’re still arguing over regulation, ethics boards, and frameworks that nobody in the real world reads. This isn’t about copying the US. It’s about realizing that if we keep optimizing for safety over progress, we’ll end up safe... but irrelevant. I work in robotics companies every day. We have world-class talent here... But talent without permission to execute is just potential wasted. —- Weekly robotics and AI insights. Subscribe free:

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The uncomfortable truth in robotics: Better tech does not win. Execution does. A small French team raised $20M and deployed in 100+ factories with ZERO hardware or manufacturing background. Here is how they did it 👇 [If you build robotics, save this thread. 🧵]

The uncomfortable truth in robotics: Better tech does not win. Execution does. A small French team raised $20M and deployed in 100+ factories with ZERO hardware or manufacturing background. Here is how they did it 👇 [If you build robotics, save this thread. 🧵]

153,085 görüntüleme

For 41 years, to find shortest paths in a graph, the Dijkstra’s algorithm, was seen as the best possible way. Not any longer: Now a team from Tsinghua University has beaten it. They created the first faster algorithm for directed shortest paths since 1984. • Faster than Dijkstra on large sparse graphs • Works with real, non-negative edge weights • Proves that sorting is not the main bottleneck anymore Shortest-path algorithms power maps, GPS, logistics, networking, and robotics. This result shows that even the most “finished” algorithms can still be improved. Thanks for sharing, Md Ismail Šojal 🕷️! 📍 Paper: —— Weekly robotics and AI insights. Subscribe free:

For 41 years, to find shortest paths in a graph, the Dijkstra’s algorithm, was seen as the best possible way. Not any longer: Now a team from Tsinghua University has beaten it. They created the first faster algorithm for directed shortest paths since 1984. • Faster than Dijkstra on large sparse graphs • Works with real, non-negative edge weights • Proves that sorting is not the main bottleneck anymore Shortest-path algorithms power maps, GPS, logistics, networking, and robotics. This result shows that even the most “finished” algorithms can still be improved. Thanks for sharing, Md Ismail Šojal 🕷️! 📍 Paper: —— Weekly robotics and AI insights. Subscribe free:

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Robots can now reconstruct 3D scenes in real time from a single RGB camera. [📍 Projects page + paper] No depth sensor. No retraining. 30 FPS. Researchers at the Imperial College London introduced KV-Tracker, a training-free method that makes heavy models like π³ and Depth Anything 3 fast enough for real-time tracking. The idea is simple. These models use global self-attention, which is powerful but computationally expensive. KV-Tracker caches the key and value pairs from selected keyframes and reuses them for new frames. That cache becomes an implicit scene representation. Result: • Up to 30 FPS • 10 to 15x speedup • Accurate 6-DoF tracking on benchmarks like TUM RGB-D and 7-Scenes • Works with monocular RGB only It also supports object-level tracking with masks and allows saving the KV-cache for later reuse. For robotics, this reduces hardware constraints and moves real-time 3D perception closer to practical deployment. Credit to Marwan Taher (Marwan Taher) at Imperial’s Dyson Robotics Lab and many others who contributed to this! 📍 Save projects page + paper for later: Video: ——- if it matters in AI or Robotics you'll read it here first:

Robots can now reconstruct 3D scenes in real time from a single RGB camera. [📍 Projects page + paper] No depth sensor. No retraining. 30 FPS. Researchers at the Imperial College London introduced KV-Tracker, a training-free method that makes heavy models like π³ and Depth Anything 3 fast enough for real-time tracking. The idea is simple. These models use global self-attention, which is powerful but computationally expensive. KV-Tracker caches the key and value pairs from selected keyframes and reuses them for new frames. That cache becomes an implicit scene representation. Result: • Up to 30 FPS • 10 to 15x speedup • Accurate 6-DoF tracking on benchmarks like TUM RGB-D and 7-Scenes • Works with monocular RGB only It also supports object-level tracking with masks and allows saving the KV-cache for later reuse. For robotics, this reduces hardware constraints and moves real-time 3D perception closer to practical deployment. Credit to Marwan Taher (Marwan Taher) at Imperial’s Dyson Robotics Lab and many others who contributed to this! 📍 Save projects page + paper for later: Video: ——- if it matters in AI or Robotics you'll read it here first:

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It doesn’t look like much. But this small robot from Stanford might do for robotics what the Apple II did for computing... make it personal. 🧵 [📍 Save this... you’ll want to remember his name.]

It doesn’t look like much. But this small robot from Stanford might do for robotics what the Apple II did for computing... make it personal. 🧵 [📍 Save this... you’ll want to remember his name.]

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This. Is. Open. Source.

This. Is. Open. Source.

54,783 görüntüleme

Mini 6 dof Arm. 3D printed planetary gearboxs & more… [📍GitHub link below ] A mini 6-axis arm driven by stepper motors with custom 3D printed split ring planetary gearboxs and an inverted belt differential wrist with custom bearings, driven by low-cost stepper motors and TMC5150 drivers. Custom firmware was written in C for the STM32 MCU on an BTT Octopus board, to allow for full closed loop PID control using AS5048a encoders daisy-chained over SPI. The controller takes joint targets and returns joint states to a Raspberry Pi 5 streamed over CAN bus. All credit to James Gullberg: 📍GitHub: —— Weekly robotics and AI insights. Subscribe free:

Mini 6 dof Arm. 3D printed planetary gearboxs & more… [📍GitHub link below ] A mini 6-axis arm driven by stepper motors with custom 3D printed split ring planetary gearboxs and an inverted belt differential wrist with custom bearings, driven by low-cost stepper motors and TMC5150 drivers. Custom firmware was written in C for the STM32 MCU on an BTT Octopus board, to allow for full closed loop PID control using AS5048a encoders daisy-chained over SPI. The controller takes joint targets and returns joint states to a Raspberry Pi 5 streamed over CAN bus. All credit to James Gullberg: 📍GitHub: —— Weekly robotics and AI insights. Subscribe free:

33,958 görüntüleme

Everyone: automation is boring. Me:

Everyone: automation is boring. Me:

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A drone that flies, drives, and switches modes in 0.1 seconds: [Build it yourself: CAD + parts ⬇️] No extra actuators, no deformation, just clever mechanics and full control. DUAWLFIN is a ground-aerial robot with unified actuation: flying like a quadcopter, rolling like a car, and transitioning seamlessly between modes. ✅ Climbs 30° slopes ✅ Hits 2 m/s on wheels with just 15W ✅ Only 3% added energy in flight mode ✅ Mode switch in 0.1s ✅ Fully open-source and 3D-printable Perfect for urban logistics, indoor nav, or just rethinking what drones can be. Paper: Website: Build it yourself: CAD + parts list in the paper 📍 BOOKMARK FOR LATER This is how you merge air and ground without compromise. —- Weekly robotics and AI insights. Subscribe free:

A drone that flies, drives, and switches modes in 0.1 seconds: [Build it yourself: CAD + parts ⬇️] No extra actuators, no deformation, just clever mechanics and full control. DUAWLFIN is a ground-aerial robot with unified actuation: flying like a quadcopter, rolling like a car, and transitioning seamlessly between modes. ✅ Climbs 30° slopes ✅ Hits 2 m/s on wheels with just 15W ✅ Only 3% added energy in flight mode ✅ Mode switch in 0.1s ✅ Fully open-source and 3D-printable Perfect for urban logistics, indoor nav, or just rethinking what drones can be. Paper: Website: Build it yourself: CAD + parts list in the paper 📍 BOOKMARK FOR LATER This is how you merge air and ground without compromise. —- Weekly robotics and AI insights. Subscribe free:

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Open-Source Multi-Sensor Data Platform for Neural 3D Reconstruction and Physical AI [📍github] It handles cameras, LiDAR, radar, poses, calibrations & labels in one clean format. No more messy custom parsers. • Super efficient (non-redundant storage) • New .itar single-file format with lightning-fast random access • Streams straight from S3/GCS/Azure – perfect for huge datasets • Built-in converters for Waymo, ScanNet++ & more • Already powers NVIDIA NuRec, 3DGRUT & gsplat Saw this at Janick Martinez Esturo, thanks for sharing! Easy to try: • pip install nvidia-ncore • GitHub: • Docs & project page: NCore slashes data wrangling time, cuts storage waste, and makes large-scale neural 3D training faster and simpler than ever. A real standard for physical AI. ——- Weekly robotics and AI insights. Subscribe free:

Open-Source Multi-Sensor Data Platform for Neural 3D Reconstruction and Physical AI [📍github] It handles cameras, LiDAR, radar, poses, calibrations & labels in one clean format. No more messy custom parsers. • Super efficient (non-redundant storage) • New .itar single-file format with lightning-fast random access • Streams straight from S3/GCS/Azure – perfect for huge datasets • Built-in converters for Waymo, ScanNet++ & more • Already powers NVIDIA NuRec, 3DGRUT & gsplat Saw this at Janick Martinez Esturo, thanks for sharing! Easy to try: • pip install nvidia-ncore • GitHub: • Docs & project page: NCore slashes data wrangling time, cuts storage waste, and makes large-scale neural 3D training faster and simpler than ever. A real standard for physical AI. ——- Weekly robotics and AI insights. Subscribe free:

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Robotics keeps hitting the same wall. Single task RL works, but... it does not scale to hundreds of tasks or new embodiments. This new paper looks like a real step toward fixing that. The team introduces MMBench, a benchmark with 200 tasks across many domains and robots, and Newt, a language conditioned world model trained online across all 200 tasks at once. The simple idea behind Newt: The model learns from demos to get the right priors It trains across many tasks through online interaction It uses language to ground the goal It adapts fast when a new task shows up What stood out to me: ✅ One model trained on 200 tasks at the same time ✅ Language conditioned control for both states and RGB ✅ Better data efficiency than strong baselines ✅ Strong open loop control ✅ Fast adaptation to new tasks and embodiments ✅ Full release of 200 checkpoints, 4000 demos, code, and benchmark This is a good push toward general control instead of one model per task. If you want the full paper: Project page: —- Weekly robotics and AI insights. Subscribe free:

Robotics keeps hitting the same wall. Single task RL works, but... it does not scale to hundreds of tasks or new embodiments. This new paper looks like a real step toward fixing that. The team introduces MMBench, a benchmark with 200 tasks across many domains and robots, and Newt, a language conditioned world model trained online across all 200 tasks at once. The simple idea behind Newt: The model learns from demos to get the right priors It trains across many tasks through online interaction It uses language to ground the goal It adapts fast when a new task shows up What stood out to me: ✅ One model trained on 200 tasks at the same time ✅ Language conditioned control for both states and RGB ✅ Better data efficiency than strong baselines ✅ Strong open loop control ✅ Fast adaptation to new tasks and embodiments ✅ Full release of 200 checkpoints, 4000 demos, code, and benchmark This is a good push toward general control instead of one model per task. If you want the full paper: Project page: —- Weekly robotics and AI insights. Subscribe free:

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Polymorphic moulding: a manufacturing method that forms parts using a grid of computer-controlled pins: Each pin can move up or down independently, so together they shape a surface that acts like a custom mould. Instead of building a fixed mould for every product, the machine quickly repositions the pins to match a new digital model. This means a mould can be created in minutes, used, and then reshaped again for the next design. Because the hardware stays the same and only its configuration changes: > no permanent tooling is needed > very little material is wasted > setup time between products is minimal The system essentially turns mould making into a programmable process. Engineers send a design file, the pins form the mould geometry, and the material is cast or formed inside it. Especially useful for rapid prototyping and customised manufacturing, where many different shapes must be produced in small quantities without rebuilding tools each time. Credit: ---- Weekly robotics and AI insights. Subscribe free:

Polymorphic moulding: a manufacturing method that forms parts using a grid of computer-controlled pins: Each pin can move up or down independently, so together they shape a surface that acts like a custom mould. Instead of building a fixed mould for every product, the machine quickly repositions the pins to match a new digital model. This means a mould can be created in minutes, used, and then reshaped again for the next design. Because the hardware stays the same and only its configuration changes: > no permanent tooling is needed > very little material is wasted > setup time between products is minimal The system essentially turns mould making into a programmable process. Engineers send a design file, the pins form the mould geometry, and the material is cast or formed inside it. Especially useful for rapid prototyping and customised manufacturing, where many different shapes must be produced in small quantities without rebuilding tools each time. Credit: ---- Weekly robotics and AI insights. Subscribe free:

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Robotics is obsessed with foundation models and humanoids. It’s missing the most critical piece. One founder just raised $3M to build the “AWS for robots.” Fixing the silent bottleneck for most robotics startup: Data Infrastructure: 🧵

Robotics is obsessed with foundation models and humanoids. It’s missing the most critical piece. One founder just raised $3M to build the “AWS for robots.” Fixing the silent bottleneck for most robotics startup: Data Infrastructure: 🧵

37,370 görüntüleme

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Robotics companies: let us solve repetitive tasks like folding shirts. Meanwhile, the factory floor:
2:59
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Robotics companies: let us solve repetitive tasks like folding shirts. Meanwhile, the factory floor:

Ilir Aliu

1,656,665 görüntüleme • 1 ay önce

Forget lidar. One single camera. Runs in real time & is open source: A streaming 3D model that reconstructs scenes live, at ~20 FPS, over long sequences. End-to-end. Optimization tricks, cleanup steps? Nope. And it beats both streaming and even some offline methods. Perception is becoming software-first. Closer to machines that see and understand the world as it unfolds. Thanks for sharing, Yinghao Xu 📍Models: Project page: Code: Paper: —— Weekly robotics and AI insights. Subscribe free:
3:30
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Forget lidar. One single camera. Runs in real time & is open source: A streaming 3D model that reconstructs scenes live, at ~20 FPS, over long sequences. End-to-end. Optimization tricks, cleanup steps? Nope. And it beats both streaming and even some offline methods. Perception is becoming software-first. Closer to machines that see and understand the world as it unfolds. Thanks for sharing, Yinghao Xu 📍Models: Project page: Code: Paper: —— Weekly robotics and AI insights. Subscribe free:

Ilir Aliu

589,215 görüntüleme • 23 gün önce

Everyone talks about humanoids. Meanwhile, Loki Robotics:
1:29
IlirAliu_'s profile picture

Everyone talks about humanoids. Meanwhile, Loki Robotics:

Ilir Aliu

3,279,872 görüntüleme • 5 ay önce

You need $100 Billion to solve self-driving? Wrong. A small team of engineers in San Diego is doing it with a smartphone chip and a few cameras: ... for $999.🧵
0:44
IlirAliu_'s profile picture

You need $100 Billion to solve self-driving? Wrong. A small team of engineers in San Diego is doing it with a smartphone chip and a few cameras: ... for $999.🧵

Ilir Aliu

1,524,907 görüntüleme • 7 ay önce

I spoke with Andrew Kang, Co-Founder and CEO of RoboStrategy. RoboStrategy already holds concentrated positions in industry-defining companies including Figure, Apptronik, Standard Bots, and Dyna Robotics. It is the first publicly listed vehicle purpose-built to give investors liquid exposure to private robotics and physical AI giants, applying the MicroStrategy capital-markets playbook to automation. The first episode of the third season! Full interview now live: Ep 101 | Investing $19M in Humanoids with Zero Robotics Experience 📽️ YouTube: 🎧 Spotify: And thanks for making this happen, intern & Humanoid Scott, Jack 🤖! Timestamps 0:00 Why we built a public fund for private robotics 0:35 Building the MicroStrategy for robotics 1:22 Escaping the tiger parent trap 2:11 Video games are elite training for game theory and critical thinking 6:30 Learning arbitrage in RuneScape and Neopets 7:59 Don't believe experts just because of their titles 10:24 Why investing is just a video game with real money 12:40 Harvard nutrition professors don't know what they're talking about 18:48 Recreating global finance from first principles 19:57 Why robotics is the early days of crypto all over again 21:50 Realizing humanoids are no longer sci-fi 22:58 Why I wrote a $19M check when every VC told me not to 27:24 How SPVs are democratizing venture investing 30:55 Bet on the founder and their execution speed, not the pitch deck 36:39 The math behind a $50 trillion physical labor market 41:44 Applying the MicroStrategy playbook to private markets 45:48 Humanoids are the GPUs of physical labor 47:48 Building a new SoftBank for the robotics era 50:51 Why robotics startups need engineers for attention 54:35 Don't be satisfied with surface level answers
55:49
IlirAliu_'s profile picture

I spoke with Andrew Kang, Co-Founder and CEO of RoboStrategy. RoboStrategy already holds concentrated positions in industry-defining companies including Figure, Apptronik, Standard Bots, and Dyna Robotics. It is the first publicly listed vehicle purpose-built to give investors liquid exposure to private robotics and physical AI giants, applying the MicroStrategy capital-markets playbook to automation. The first episode of the third season! Full interview now live: Ep 101 | Investing $19M in Humanoids with Zero Robotics Experience 📽️ YouTube: 🎧 Spotify: And thanks for making this happen, intern & Humanoid Scott, Jack 🤖! Timestamps 0:00 Why we built a public fund for private robotics 0:35 Building the MicroStrategy for robotics 1:22 Escaping the tiger parent trap 2:11 Video games are elite training for game theory and critical thinking 6:30 Learning arbitrage in RuneScape and Neopets 7:59 Don't believe experts just because of their titles 10:24 Why investing is just a video game with real money 12:40 Harvard nutrition professors don't know what they're talking about 18:48 Recreating global finance from first principles 19:57 Why robotics is the early days of crypto all over again 21:50 Realizing humanoids are no longer sci-fi 22:58 Why I wrote a $19M check when every VC told me not to 27:24 How SPVs are democratizing venture investing 30:55 Bet on the founder and their execution speed, not the pitch deck 36:39 The math behind a $50 trillion physical labor market 41:44 Applying the MicroStrategy playbook to private markets 45:48 Humanoids are the GPUs of physical labor 47:48 Building a new SoftBank for the robotics era 50:51 Why robotics startups need engineers for attention 54:35 Don't be satisfied with surface level answers

Ilir Aliu

14,914 görüntüleme • 3 gün önce

Every robot you see is a data firehose generating terabytes of chaos. This hidden crisis is the #1 reason robots fail, and it's costing the industry billions. You see hardware, but not the data swamp drowning engineers. In 2025, a quiet revolution is fixing it. Here’s how. 🧵
0:29
IlirAliu_'s profile picture

Every robot you see is a data firehose generating terabytes of chaos. This hidden crisis is the #1 reason robots fail, and it's costing the industry billions. You see hardware, but not the data swamp drowning engineers. In 2025, a quiet revolution is fixing it. Here’s how. 🧵

Ilir Aliu

1,573,557 görüntüleme • 11 ay önce

Are you kidding me??? It grasps multiple objects with different ways, all at once with… a single hand??? No pauses. 1x speed. GENE-26.5 is Genesis AI’s robotics-native multimodal foundation model. It’s trained on 200,000+ hours of real human hand data (motion, force, touch) and runs on a 54-DoF bimanual system: Scaling that human data 4x lifted real-robot success rates from 16.6% to 65.6% on long-horizon dexterous tasks!! Same model weights, zero fine-tuning for this exact sequence. You know those tiny coordinated movements you do without thinking…? Robots couldn’t reliably do that before. Now they can. Today. This is the video you’ll send to my friends, outside of our bubble, when they say “robots are still just demos.” Congrats to the entire team around Zhou Xian! Credit: Seen at Zu Wang (Zu Wang) Genesis official announcement for the full story + longer demo: ——— Weekly robotics and AI insights. Subscribe free:
0:20
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Are you kidding me??? It grasps multiple objects with different ways, all at once with… a single hand??? No pauses. 1x speed. GENE-26.5 is Genesis AI’s robotics-native multimodal foundation model. It’s trained on 200,000+ hours of real human hand data (motion, force, touch) and runs on a 54-DoF bimanual system: Scaling that human data 4x lifted real-robot success rates from 16.6% to 65.6% on long-horizon dexterous tasks!! Same model weights, zero fine-tuning for this exact sequence. You know those tiny coordinated movements you do without thinking…? Robots couldn’t reliably do that before. Now they can. Today. This is the video you’ll send to my friends, outside of our bubble, when they say “robots are still just demos.” Congrats to the entire team around Zhou Xian! Credit: Seen at Zu Wang (Zu Wang) Genesis official announcement for the full story + longer demo: ——— Weekly robotics and AI insights. Subscribe free:

Ilir Aliu

203,868 görüntüleme • 2 ay önce

40 years... just imagine! How many ideas are sitting in someone's garage right now... waiting for the technology to finally bring them to life? An old patent collected dust since 1985. William Freeman sketched a three-sided zipper. A fastener that shifts objects from flexible to rigid at the push of a button. MIT rejected his proposal. He filed the patent anyway. Last month, MIT CSAIL finally built it... The Y-Zipper. 3D-printed, three arms, only one motion. A quadruped that hardens its legs on hard surfaces, softens them on rough terrain. The zipper is the only mechanism doing that work. 40 years in a garage... one month to change how robots move. Credit: Jiaji Li | Jiaji Li 📌Article: Paper: Video: ——- If it matters in AI or Robotics, you'll read it here first:
3:58
IlirAliu_'s profile picture

40 years... just imagine! How many ideas are sitting in someone's garage right now... waiting for the technology to finally bring them to life? An old patent collected dust since 1985. William Freeman sketched a three-sided zipper. A fastener that shifts objects from flexible to rigid at the push of a button. MIT rejected his proposal. He filed the patent anyway. Last month, MIT CSAIL finally built it... The Y-Zipper. 3D-printed, three arms, only one motion. A quadruped that hardens its legs on hard surfaces, softens them on rough terrain. The zipper is the only mechanism doing that work. 40 years in a garage... one month to change how robots move. Credit: Jiaji Li | Jiaji Li 📌Article: Paper: Video: ——- If it matters in AI or Robotics, you'll read it here first:

Ilir Aliu

96,007 görüntüleme • 1 ay önce

People who’ve never set foot in a factory will never understand. I watched this three times.
0:29
IlirAliu_'s profile picture

People who’ve never set foot in a factory will never understand. I watched this three times.

Ilir Aliu

429,520 görüntüleme • 8 ay önce

For all my lifters: computer vision app to measure back curvature during deadlift! main technical highlights: — RF-DETR (Roboflow) to segment the person (great performance out-the-box with no additional training!) — YOLO11n (Ultralytics) for bounding box prediction around the barbell weight plate (trained on my own small dataset). — Mediapipe (Google) for pose landmark detection to guide the bounds for the line fitting. — Custom logic to fit a line across the back. about the plots on the right side: The Back Roundness Map shows the deviation of the estimated back curve from a line of best fit. Credit: Jeremy Park —— if it matters in AI or Robotics you'll see it here first:
0:30
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For all my lifters: computer vision app to measure back curvature during deadlift! main technical highlights: — RF-DETR (Roboflow) to segment the person (great performance out-the-box with no additional training!) — YOLO11n (Ultralytics) for bounding box prediction around the barbell weight plate (trained on my own small dataset). — Mediapipe (Google) for pose landmark detection to guide the bounds for the line fitting. — Custom logic to fit a line across the back. about the plots on the right side: The Back Roundness Map shows the deviation of the estimated back curve from a line of best fit. Credit: Jeremy Park —— if it matters in AI or Robotics you'll see it here first:

Ilir Aliu

234,447 görüntüleme • 5 ay önce

This is real time. You can see how stable the motion is. A system placing droplets between 1 nanoliter and 1 microliter inside a 96 well plate with almost perfect repeatability. No shake. No drift. No overshoot. The robot keeps the tip on track even at this scale where tiny vibrations normally ruin the result. Why this matters: •In wet lab automation, volume accuracy is everything •Nano scale tasks fail fast if the robot is even slightly off •Reliable deposition unlocks fast screening and repeatable experiments •You can run full workflows without manual checking or correction This is the kind of precision that turns lab robots from “helpful tool” into a full automation pipeline. Credit: M2-Automation GmbH —- Weekly robotics and AI insights. Subscribe free:
1:06
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This is real time. You can see how stable the motion is. A system placing droplets between 1 nanoliter and 1 microliter inside a 96 well plate with almost perfect repeatability. No shake. No drift. No overshoot. The robot keeps the tip on track even at this scale where tiny vibrations normally ruin the result. Why this matters: •In wet lab automation, volume accuracy is everything •Nano scale tasks fail fast if the robot is even slightly off •Reliable deposition unlocks fast screening and repeatable experiments •You can run full workflows without manual checking or correction This is the kind of precision that turns lab robots from “helpful tool” into a full automation pipeline. Credit: M2-Automation GmbH —- Weekly robotics and AI insights. Subscribe free:

Ilir Aliu

286,948 görüntüleme • 8 ay önce

Everyone talks about humanoids. Meanwhile, this KUKA…
0:39
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Everyone talks about humanoids. Meanwhile, this KUKA…

Ilir Aliu

200,200 görüntüleme • 6 ay önce

Almost no one talks about height control. This clip shows why it matters. A capacitive sensor on the cutting head keeps the nozzle only fractions of a millimeter above the metal sheet, even when the sheet is warped or vibrating. That tiny distance is everything for a clean cut. The setup here: ✅ Kimla laser system ✅ Precitec cutting head ✅ Capacitive sensing for real-time height control ✅ Stable gap over uneven or bent sheets If the nozzle drifts even slightly, you lose cut quality, speed, and consistency. This is the part of laser systems that most people never think about, but it is what makes the difference between “it works” and “this is production ready.” Have you ever had to keep the nozzle as close as possible without risking a crash? Credit: Daltons Wadkin —- Weekly robotics and AI insights. Subscribe free:
0:20
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Almost no one talks about height control. This clip shows why it matters. A capacitive sensor on the cutting head keeps the nozzle only fractions of a millimeter above the metal sheet, even when the sheet is warped or vibrating. That tiny distance is everything for a clean cut. The setup here: ✅ Kimla laser system ✅ Precitec cutting head ✅ Capacitive sensing for real-time height control ✅ Stable gap over uneven or bent sheets If the nozzle drifts even slightly, you lose cut quality, speed, and consistency. This is the part of laser systems that most people never think about, but it is what makes the difference between “it works” and “this is production ready.” Have you ever had to keep the nozzle as close as possible without risking a crash? Credit: Daltons Wadkin —- Weekly robotics and AI insights. Subscribe free:

Ilir Aliu

224,579 görüntüleme • 8 ay önce

Triple inverted pendulum in transition control A classic control problem, done in real time. This setup moves smoothly between all eight equilibrium points of a triple inverted pendulum. The system reacts every 1 millisecond, which shows how fast modern control loops can be. • Real-time control at 1 ms sampling • Stable transitions between multiple balance points • Built with Simulink and LW-RCP02 hardware This is a beautiful example of how theory meets practice in advanced control engineering. Video: Credit: Embedded Control Lab, Inha University —- Weekly robotics and AI insights. Subscribe free:
1:09
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Triple inverted pendulum in transition control A classic control problem, done in real time. This setup moves smoothly between all eight equilibrium points of a triple inverted pendulum. The system reacts every 1 millisecond, which shows how fast modern control loops can be. • Real-time control at 1 ms sampling • Stable transitions between multiple balance points • Built with Simulink and LW-RCP02 hardware This is a beautiful example of how theory meets practice in advanced control engineering. Video: Credit: Embedded Control Lab, Inha University —- Weekly robotics and AI insights. Subscribe free:

Ilir Aliu

167,876 görüntüleme • 6 ay önce

Now all you need to make tactile sensors is a 3D printer, magnets, and magnetometers! [📍It’s open source!] A new tactile sensor, called e-Flesh, with a simple working principle… measure deformations in 3D printable microstructures. Since e-Flesh is 3D printable, you can make it in all shapes and sizes for applications ranging from foot fall sensing to multifingered hands. This is critical in getting touch not just on fingertips, but all around robots. eFlesh can democratize touch sensing with open-sourced❗️ Make your own: Paper:
0:57
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Now all you need to make tactile sensors is a 3D printer, magnets, and magnetometers! [📍It’s open source!] A new tactile sensor, called e-Flesh, with a simple working principle… measure deformations in 3D printable microstructures. Since e-Flesh is 3D printable, you can make it in all shapes and sizes for applications ranging from foot fall sensing to multifingered hands. This is critical in getting touch not just on fingertips, but all around robots. eFlesh can democratize touch sensing with open-sourced❗️ Make your own: Paper:

Ilir Aliu

154,765 görüntüleme • 5 ay önce

Robotics has transformed welding speed. Precision is great, but speed now matters just as much. And conventional TIG still lags behind... Balancing quality with speed, especially on pipe, pressure vessel, and roll welding projects. This solution helps change that. It delivers TIG-level quality with: ✅ Up to 300% faster travel speeds ✅ 4 lb/hr deposition rates ✅ A compact footprint that fits right into tight production environments It seems fast enough without compromising on quality and works across carbon steel, stainless, duplex, Inconel, etc. Aaaaaand the welds still pass X-ray, hardness, and corrosion tests. Found this via Novarc Technologies —- Weekly robotics and AI insights. Subscribe free:
0:20
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Robotics has transformed welding speed. Precision is great, but speed now matters just as much. And conventional TIG still lags behind... Balancing quality with speed, especially on pipe, pressure vessel, and roll welding projects. This solution helps change that. It delivers TIG-level quality with: ✅ Up to 300% faster travel speeds ✅ 4 lb/hr deposition rates ✅ A compact footprint that fits right into tight production environments It seems fast enough without compromising on quality and works across carbon steel, stainless, duplex, Inconel, etc. Aaaaaand the welds still pass X-ray, hardness, and corrosion tests. Found this via Novarc Technologies —- Weekly robotics and AI insights. Subscribe free:

Ilir Aliu

170,119 görüntüleme • 6 ay önce

Modern drone production. An assembly line in China shows how far drone manufacturing has been industrialized. Conveyor systems move the airframes between stations. Each worker performs a narrowly defined step, closer to poka-yoke than to classic workshop assembly. • Highly standardized components • Tight process segmentation • Visual quality control at each station • Flow-line production, not batch builds This looks less like hobby robotics and more like automotive manufacturing from ten years ago. The open question is not how fast this scales, but where it plateaus. At what point does product complexity collide with this level of standardization. Credit: Chinalifenews —— Weekly robotics and AI insights. Subscribe free:
0:25
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Modern drone production. An assembly line in China shows how far drone manufacturing has been industrialized. Conveyor systems move the airframes between stations. Each worker performs a narrowly defined step, closer to poka-yoke than to classic workshop assembly. • Highly standardized components • Tight process segmentation • Visual quality control at each station • Flow-line production, not batch builds This looks less like hobby robotics and more like automotive manufacturing from ten years ago. The open question is not how fast this scales, but where it plateaus. At what point does product complexity collide with this level of standardization. Credit: Chinalifenews —— Weekly robotics and AI insights. Subscribe free:

Ilir Aliu

139,698 görüntüleme • 6 ay önce

A simple CNC machine. Wood. A rotating tool. And yet the pattern shifts from circles to perfect hexagons with a level of accuracy most factories still struggle to hit. I worked five years in manufacturing at Siemens, and this is the part people often overlook. The real magic is not new or loud. It is stability. Repeatability. Tool paths that were tuned forever. Precision that just keeps working. Robotics is in a hype cycle right now, but manufacturing has delivered quiet perfection long before anyone talked about foundation models. Sometimes the most interesting engineering is the part that does not ask for attention. —- Weekly robotics and AI insights. Subscribe free:
0:26
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A simple CNC machine. Wood. A rotating tool. And yet the pattern shifts from circles to perfect hexagons with a level of accuracy most factories still struggle to hit. I worked five years in manufacturing at Siemens, and this is the part people often overlook. The real magic is not new or loud. It is stability. Repeatability. Tool paths that were tuned forever. Precision that just keeps working. Robotics is in a hype cycle right now, but manufacturing has delivered quiet perfection long before anyone talked about foundation models. Sometimes the most interesting engineering is the part that does not ask for attention. —- Weekly robotics and AI insights. Subscribe free:

Ilir Aliu

164,829 görüntüleme • 7 ay önce

One smartphone at the core of a robot. The idea is simple but interesting: use a used smartphone as the main controller for a hobby robot, in this case a hexapod. Instead of adding separate boards and sensors, the phone already brings a lot of what robots need: - IMU and environmental sensors - WiFi, Bluetooth, and cellular connectivity - Multiple cameras and GPS - Battery, display, GPU, and a fairly powerful processor All of this is already calibrated and supported by mature software tools and frameworks. What makes this approach worth looking at is not just convenience. It also reuses hardware that would otherwise end up as e-waste. Billions of smartphones are discarded every year. Nice example of leveraging existing consumer hardware for robotics. Project by Mehdi Alizadeh from YouTube: —- Weekly robotics and AI insights. Subscribe free:
0:26
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One smartphone at the core of a robot. The idea is simple but interesting: use a used smartphone as the main controller for a hobby robot, in this case a hexapod. Instead of adding separate boards and sensors, the phone already brings a lot of what robots need: - IMU and environmental sensors - WiFi, Bluetooth, and cellular connectivity - Multiple cameras and GPS - Battery, display, GPU, and a fairly powerful processor All of this is already calibrated and supported by mature software tools and frameworks. What makes this approach worth looking at is not just convenience. It also reuses hardware that would otherwise end up as e-waste. Billions of smartphones are discarded every year. Nice example of leveraging existing consumer hardware for robotics. Project by Mehdi Alizadeh from YouTube: —- Weekly robotics and AI insights. Subscribe free:

Ilir Aliu

150,850 görüntüleme • 7 ay önce

A mini factory in a box – Runs 24/7 – Costs $5,000 – Tools can be swapped – Builds electronics by itself Credit: MicroFactory
1:14
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A mini factory in a box – Runs 24/7 – Costs $5,000 – Tools can be swapped – Builds electronics by itself Credit: MicroFactory

Ilir Aliu

212,379 görüntüleme • 10 ay önce