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how does a car hang upside down, let go, and still land on its wheels? watch Tesla’s Roadster on the curved ramp underneath, fans pull air out of the gap between the car and the surface. seals help keep that air from rushing straight back in the result works like a suction cup: the air outside presses the car against the ramp. Tesla says the grip is strong enough to hold it upside down even while standing still once it leaves the ramp, a different system takes over the car carries compressed gas and releases it through small nozzles. think of letting go of an inflated balloon: air shoots one way, the balloon goes the other with nozzles around the car, those pushes can also turn it. push one way to start rotating, then counter that turn to bring the wheels back underneath before landing, downward jets push upward on the car to slow its fall. Tesla says the system makes tiny, rapid adjustments to keep it stable there’s a catch: every burst uses up some of the stored gas. how long it can stay airborne depends on how much it carries and how quickly it spends it

how does a car hang upside down, let go, and still land on its wheels? watch Tesla’s Roadster on the curved ramp underneath, fans pull air out of the gap between the car and the surface. seals help keep that air from rushing straight back in the result works like a suction cup: the air outside presses the car against the ramp. Tesla says the grip is strong enough to hold it upside down even while standing still once it leaves the ramp, a different system takes over the car carries compressed gas and releases it through small nozzles. think of letting go of an inflated balloon: air shoots one way, the balloon goes the other with nozzles around the car, those pushes can also turn it. push one way to start rotating, then counter that turn to bring the wheels back underneath before landing, downward jets push upward on the car to slow its fall. Tesla says the system makes tiny, rapid adjustments to keep it stable there’s a catch: every burst uses up some of the stored gas. how long it can stay airborne depends on how much it carries and how quickly it spends it

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this creator just built a zero-delay auto-aim system on an $8 microcontroller he deployed a custom local AI algorithm on a cheap ESP-32 to track human movement with absolute 0-pixel accuracy. the system completely eliminates standard computation delay. it processes the bounding box and moves the sniper reticle instantly, locking onto the target in real time. the next step is hooking it up to a physical robotic arm. it proves you don't need a massive GPU rig to run complex computer vision. 1. runs locally on a microcontroller 2. local AI inference for zero latency 3. 100% targeting precision when an eight-dollar chip can track movement with zero latency, AI becomes dangerous local infrastructure. this article breaks down exactly how the ESP-32 is powering this physical hardware shift.

this creator just built a zero-delay auto-aim system on an $8 microcontroller he deployed a custom local AI algorithm on a cheap ESP-32 to track human movement with absolute 0-pixel accuracy. the system completely eliminates standard computation delay. it processes the bounding box and moves the sniper reticle instantly, locking onto the target in real time. the next step is hooking it up to a physical robotic arm. it proves you don't need a massive GPU rig to run complex computer vision. 1. runs locally on a microcontroller 2. local AI inference for zero latency 3. 100% targeting precision when an eight-dollar chip can track movement with zero latency, AI becomes dangerous local infrastructure. this article breaks down exactly how the ESP-32 is powering this physical hardware shift.

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forget the $699 AI pins. this $8 chip just shattered the barrier for local AI hardware. a developer just forced a 28.9 million-parameter LLM onto a standard ESP32-S3 microcontroller. it costs roughly 8 dollars, runs completely offline, and draws the power of a single LED. conventional wisdom said a model of this size simply would not fit. the chip only has 512 KB of fast SRAM and 16 MB of flash. the breakthrough is architectural. the developer moved the bulk of the embedding table into flash memory and memory-mapped it. the chip only needs to pull about 450 bytes per token, keeping the active working memory inside the fast SRAM. this means you can now embed a capable language model into a physical node for the price of two coffees. and we are already seeing the beginnings of this custom physical hardware. in the video, a creator built a minimalist voice-controlled universal remote using an ESP32. it captures voice and remotely controls the computer over bluetooth LE. he simply says "open chrome and open 20 new tabs", and the custom hardware executes it instantly. we have spent years watching model sizes explode upward. but the true frontier is the opposite direction. when an eight-dollar chip can power offline intelligence and custom physical interfaces, AI becomes local infrastructure rather than a cloud service.

forget the $699 AI pins. this $8 chip just shattered the barrier for local AI hardware. a developer just forced a 28.9 million-parameter LLM onto a standard ESP32-S3 microcontroller. it costs roughly 8 dollars, runs completely offline, and draws the power of a single LED. conventional wisdom said a model of this size simply would not fit. the chip only has 512 KB of fast SRAM and 16 MB of flash. the breakthrough is architectural. the developer moved the bulk of the embedding table into flash memory and memory-mapped it. the chip only needs to pull about 450 bytes per token, keeping the active working memory inside the fast SRAM. this means you can now embed a capable language model into a physical node for the price of two coffees. and we are already seeing the beginnings of this custom physical hardware. in the video, a creator built a minimalist voice-controlled universal remote using an ESP32. it captures voice and remotely controls the computer over bluetooth LE. he simply says "open chrome and open 20 new tabs", and the custom hardware executes it instantly. we have spent years watching model sizes explode upward. but the true frontier is the opposite direction. when an eight-dollar chip can power offline intelligence and custom physical interfaces, AI becomes local infrastructure rather than a cloud service.

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for everyone who still doesn’t believe you can run local AI on an ESP32: here’s the same class of chip booting an entire operating system. Windows XP boot screen. welcome screen. the Bliss wallpaper. and, of course, an error dialog😁 we went from fitting a 28.9M-parameter language model into an ESP32-S3... to recreating the full Windows XP experience on a tiny 240×240 display. the era of "microcontrollers are only good for blinking LEDs" is over

for everyone who still doesn’t believe you can run local AI on an ESP32: here’s the same class of chip booting an entire operating system. Windows XP boot screen. welcome screen. the Bliss wallpaper. and, of course, an error dialog😁 we went from fitting a 28.9M-parameter language model into an ESP32-S3... to recreating the full Windows XP experience on a tiny 240×240 display. the era of "microcontrollers are only good for blinking LEDs" is over

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found this DIY ESP32 object scanner online and it looks like pure magic. the creator claims this simple copper wire loop can instantly count the exact number of matchsticks inside a closed box, and even 4,000 individual grains of salt. physically, a capacitive or inductive sensor can only measure total mass or dielectric changes. it cannot count thousands of tiny non-metallic crystals. are there any hardware engineers here? someone explain to me how he actually pulled off this trick.

found this DIY ESP32 object scanner online and it looks like pure magic. the creator claims this simple copper wire loop can instantly count the exact number of matchsticks inside a closed box, and even 4,000 individual grains of salt. physically, a capacitive or inductive sensor can only measure total mass or dielectric changes. it cannot count thousands of tiny non-metallic crystals. are there any hardware engineers here? someone explain to me how he actually pulled off this trick.

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yesterday it was Windows XP. today, it’s DOOM on an ESP32. a tiny microcontroller, a microSD card, and a round LCD wired up on a breadboard - and the classic game is running on the display. the funny part isn’t just that DOOM keeps appearing on unexpected hardware. it’s that every new platform eventually gets asked the same question: “can it run DOOM?” even an ESP32.

yesterday it was Windows XP. today, it’s DOOM on an ESP32. a tiny microcontroller, a microSD card, and a round LCD wired up on a breadboard - and the classic game is running on the display. the funny part isn’t just that DOOM keeps appearing on unexpected hardware. it’s that every new platform eventually gets asked the same question: “can it run DOOM?” even an ESP32.

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when you start building projects with AI before learning how electricity works

when you start building projects with AI before learning how electricity works

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a camera rig tracks a flying drone watch the crosshair: the drone moves, two servos turn the camera mount, and the target comes back to centre that is the whole control loop in physical AI: camera frame → target position → servo correction my ESP32 vision guide starts with the same loop. the full guide below

a camera rig tracks a flying drone watch the crosshair: the drone moves, two servos turn the camera mount, and the target comes back to centre that is the whole control loop in physical AI: camera frame → target position → servo correction my ESP32 vision guide starts with the same loop. the full guide below

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the smallest useful physical AI project has two jobs: 1 notice one thing 2 do one thing about it this small demo puts a flame inside a test box water comes through the tube and the flame goes out that is the first loop: physical world → sensor reading → decision → action i put together a practical ESP32 guide to building that layer! distance, motion, heat, sound, touch, and a safe first project to start with👇

the smallest useful physical AI project has two jobs: 1 notice one thing 2 do one thing about it this small demo puts a flame inside a test box water comes through the tube and the flame goes out that is the first loop: physical world → sensor reading → decision → action i put together a practical ESP32 guide to building that layer! distance, motion, heat, sound, touch, and a safe first project to start with👇

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THIS GUY IS BUILDING INSANE CUSTOM SITES FOR $0.23 IN API COSTS WITH THE NEW KIMI K3 currently #1 on the coding arena. the video attached shows a complex, highly detailed website. it was coded entirely by a new model called Kimi K3. early testers are calling it scarily good because it quietly removes the need for complex agent swarms. here is the instant breakdown of what makes it terrifying. 1. native vision in the loop it iterates code while analyzing live screenshots of its own output. it literally looks at the site it builds and corrects the styling autonomously. 2. massive sparse architecture it has 2.8 trillion parameters but only activates 50b per token. this makes it insanely fast and allows for a native 1,000,000 token context window. 3. recursive self-improvement it spends a massive amount of compute on self-verification. it runs unit tests and simulates environments before giving you the final frontend code. 4. brutal economics it costs exactly $3 per million input tokens. the entire custom site in the video cost around $0.23 to generate. the era of orchestrating 12 dumb agents to build a simple web app is over. one smart instance is all you need.

THIS GUY IS BUILDING INSANE CUSTOM SITES FOR $0.23 IN API COSTS WITH THE NEW KIMI K3 currently #1 on the coding arena. the video attached shows a complex, highly detailed website. it was coded entirely by a new model called Kimi K3. early testers are calling it scarily good because it quietly removes the need for complex agent swarms. here is the instant breakdown of what makes it terrifying. 1. native vision in the loop it iterates code while analyzing live screenshots of its own output. it literally looks at the site it builds and corrects the styling autonomously. 2. massive sparse architecture it has 2.8 trillion parameters but only activates 50b per token. this makes it insanely fast and allows for a native 1,000,000 token context window. 3. recursive self-improvement it spends a massive amount of compute on self-verification. it runs unit tests and simulates environments before giving you the final frontend code. 4. brutal economics it costs exactly $3 per million input tokens. the entire custom site in the video cost around $0.23 to generate. the era of orchestrating 12 dumb agents to build a simple web app is over. one smart instance is all you need.

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what if the robot could test its next move before the real arm moves? this setup already shows the first step: a 3D model follows the physical arm on screen right now, the model shows what the robot is doing the next step would be letting it test a movement in software before sending it to the real arm the ESP32 guide covers the practical side: sensors, filtering and turning movement into actions follow for more builds where small boards make physical things move

what if the robot could test its next move before the real arm moves? this setup already shows the first step: a 3D model follows the physical arm on screen right now, the model shows what the robot is doing the next step would be letting it test a movement in software before sending it to the real arm the ESP32 guide covers the practical side: sensors, filtering and turning movement into actions follow for more builds where small boards make physical things move

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this DIY camera stabilizer costs roughly $30–45 to build. not because it beats DJI. because it doesn’t try to. it uses a 3D-printed frame, an ESP32, an IMU, and two $4 micro servos to keep a lightweight action camera roughly level. for comparison: DJI’s RS 4 Mini starts at $309. the $260+ difference is not just branding. DJI gives you three brushless axes, smooth motion, better mechanics, calibration, battery management, and a product that works out of the box. this build gives you something else: a working feedback-control system you can hold in your hand. $40 buys the lesson. $309 buys the polished result.

this DIY camera stabilizer costs roughly $30–45 to build. not because it beats DJI. because it doesn’t try to. it uses a 3D-printed frame, an ESP32, an IMU, and two $4 micro servos to keep a lightweight action camera roughly level. for comparison: DJI’s RS 4 Mini starts at $309. the $260+ difference is not just branding. DJI gives you three brushless axes, smooth motion, better mechanics, calibration, battery management, and a product that works out of the box. this build gives you something else: a working feedback-control system you can hold in your hand. $40 buys the lesson. $309 buys the polished result.

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Wi-Fi is overrated sometimes your device only needs to send a few bytes. making it depend on a nearby router can be a bigger limitation than the amount of data it needs to send pair an ESP32 with a LoRa radio, and it can send small messages directly to another node beyond typical Wi-Fi range no router, SIM card, or internet connection needed between them a remote sensor could report rising water. a weather station could send a reading. a rover could report its battery level the tradeoff is speed: think short updates, with range depending on antennas, terrain, and radio settings i made this 3D animation to show the path from sensor to receiver a first experiment: two nodes, one button, one message appearing on the other end starter code in the reply

Wi-Fi is overrated sometimes your device only needs to send a few bytes. making it depend on a nearby router can be a bigger limitation than the amount of data it needs to send pair an ESP32 with a LoRa radio, and it can send small messages directly to another node beyond typical Wi-Fi range no router, SIM card, or internet connection needed between them a remote sensor could report rising water. a weather station could send a reading. a rover could report its battery level the tradeoff is speed: think short updates, with range depending on antennas, terrain, and radio settings i made this 3D animation to show the path from sensor to receiver a first experiment: two nodes, one button, one message appearing on the other end starter code in the reply

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this tiny solar tracker tilts its panel toward a moving light source two LDRs sense the imbalance. a small geared motor does the rest it’s a rough DIY build, but the final flashlight test is a lovely proof of the idea want a guide to build the same prototype?

this tiny solar tracker tilts its panel toward a moving light source two LDRs sense the imbalance. a small geared motor does the rest it’s a rough DIY build, but the final flashlight test is a lovely proof of the idea want a guide to build the same prototype?

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three GitHub projects show how much vision can fit inside an ESP32-S3 esp32-camera handles the first layer: sensor data, JPEG frames, PSRAM, and frame buffers 🖇️ once the camera pipeline works, esp-who adds ready-made vision tasks such as face detection, pedestrian detection, and QR recognition 🖇️ the third project explores a smaller but more ambitious setup: training and deploying a custom vision model directly on the microcontroller 🖇️ put the pieces together and the loop becomes straightforward: capture an image run the model turn the result into a decision trigger something in the physical world a light, a servo, an MQTT event, or a local log the useful part is the short path from something the camera sees to something the hardware does

three GitHub projects show how much vision can fit inside an ESP32-S3 esp32-camera handles the first layer: sensor data, JPEG frames, PSRAM, and frame buffers 🖇️ once the camera pipeline works, esp-who adds ready-made vision tasks such as face detection, pedestrian detection, and QR recognition 🖇️ the third project explores a smaller but more ambitious setup: training and deploying a custom vision model directly on the microcontroller 🖇️ put the pieces together and the loop becomes straightforward: capture an image run the model turn the result into a decision trigger something in the physical world a light, a servo, an MQTT event, or a local log the useful part is the short path from something the camera sees to something the hardware does

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if i were building an AI career today, i would put more weight on physical AI the usual AI stack is becoming everybody’s baseline (same models, frameworks, agents, and weekend demos) that gives you speed. it also makes the purely digital layer easier to copy physical AI creates a different set of problems: sensors lie motors overshoot power runs out latency turns into a crash the physical world refuses to behave like a clean dataset this clip is the smallest version of that world: gravity changes → IMU measures tilt → code renders pitch and roll before a robot balances, avoids a wall, or follows a person, it has to sense what is happening around it if i were building the skill from scratch, i would spend a few hours each week on one small loop: sensor → data → decision → action i put together a practical ESP32 guide to that first layer, including a safe build and the path to a small obstacle-avoiding rover

if i were building an AI career today, i would put more weight on physical AI the usual AI stack is becoming everybody’s baseline (same models, frameworks, agents, and weekend demos) that gives you speed. it also makes the purely digital layer easier to copy physical AI creates a different set of problems: sensors lie motors overshoot power runs out latency turns into a crash the physical world refuses to behave like a clean dataset this clip is the smallest version of that world: gravity changes → IMU measures tilt → code renders pitch and roll before a robot balances, avoids a wall, or follows a person, it has to sense what is happening around it if i were building the skill from scratch, i would spend a few hours each week on one small loop: sensor → data → decision → action i put together a practical ESP32 guide to that first layer, including a safe build and the path to a small obstacle-avoiding rover

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an ordinary calculator can become a counter for the physical world it already has a display, arithmetic logic, and a running total add an infrared sensor, and changes in reflected light can become counting pulses the trick is the "=" key: on compatible calculators, repeated presses keep adding 1. let the sensor trigger those presses, and the calculator does the counting the same principle can count passing objects, shaft rotations, or winding turns that’s a useful starting point for sensor projects: 1 decide what physical change matters 2 turn it into a clear event 3make the device respond my ESP32 sensor guide below covers choosing sensors, filtering noisy readings, and building that loop yourself ↓

an ordinary calculator can become a counter for the physical world it already has a display, arithmetic logic, and a running total add an infrared sensor, and changes in reflected light can become counting pulses the trick is the "=" key: on compatible calculators, repeated presses keep adding 1. let the sensor trigger those presses, and the calculator does the counting the same principle can count passing objects, shaft rotations, or winding turns that’s a useful starting point for sensor projects: 1 decide what physical change matters 2 turn it into a clear event 3make the device respond my ESP32 sensor guide below covers choosing sensors, filtering noisy readings, and building that loop yourself ↓

50,967 görüntüleme • 3 gün önce

another example: a developer used the exact same $8 ESP32-S3 chip and local AI setup to build a real-time object recognition camera the fact that this is running entirely offline, on an eight-dollar microcontroller, without any cloud API calls, is wild... it captures the video feed, processes it locally through the model, and identifies objects in real-time. we are entering an era where you can just glue intelligence onto any physical object for less than $10. the hardware barrier is completely shattered.

another example: a developer used the exact same $8 ESP32-S3 chip and local AI setup to build a real-time object recognition camera the fact that this is running entirely offline, on an eight-dollar microcontroller, without any cloud API calls, is wild... it captures the video feed, processes it locally through the model, and identifies objects in real-time. we are entering an era where you can just glue intelligence onto any physical object for less than $10. the hardware barrier is completely shattered.

230,333 görüntüleme • 3 ay önce

before a robot can understand a room, it needs to notice one thing reliably this prototype is called a VAS DMSR human tracking radar a small handheld unit turns the space in front of it into a live sector view, with angle, avg m and diff m readouts changing as it moves that loop is the foundation of physical AI: physical world → sensor reading → filtering → decision → action before adding a language model or a camera to a robot, make one signal dependable i put together a practical ESP32 guide to distance, motion, heat, sound and touch (including how to filter noisy readings before they trigger an action) follow if you want to build the physical layer of AI, one useful sensor loop at a time

before a robot can understand a room, it needs to notice one thing reliably this prototype is called a VAS DMSR human tracking radar a small handheld unit turns the space in front of it into a live sector view, with angle, avg m and diff m readouts changing as it moves that loop is the foundation of physical AI: physical world → sensor reading → filtering → decision → action before adding a language model or a camera to a robot, make one signal dependable i put together a practical ESP32 guide to distance, motion, heat, sound and touch (including how to filter noisy readings before they trigger an action) follow if you want to build the physical layer of AI, one useful sensor loop at a time

55,029 görüntüleme • 1 ay önce

meet WALL-E no, not the one from the cartoon :) this is a fully 3D-printed, autonomous desk companion running on an ESP32. it uses a local AI voice assistant to process commands in real-time and control its own motors building intelligent robotics just dropped to zero cost...

meet WALL-E no, not the one from the cartoon :) this is a fully 3D-printed, autonomous desk companion running on an ESP32. it uses a local AI voice assistant to process commands in real-time and control its own motors building intelligent robotics just dropped to zero cost...

65,781 görüntüleme • 2 ay önce

for everyone wondering how zero-delay tracking is even possible on an $8 chip: it is all thanks to the Kalman filter. this 1-minute clip from Steve Brunton perfectly explains why it works (full lecture in reply) instead of just observing a target, it mathematically predicts where it will be in the next millisecond. here is what happens under the hood: - balances physics of motion against noisy camera data - corrects the trajectory instantly - moves the robotic arm before the next frame processes this is the exact same math used in rockets and autonomous driving, stripped down for a cheap microcontroller.

for everyone wondering how zero-delay tracking is even possible on an $8 chip: it is all thanks to the Kalman filter. this 1-minute clip from Steve Brunton perfectly explains why it works (full lecture in reply) instead of just observing a target, it mathematically predicts where it will be in the next millisecond. here is what happens under the hood: - balances physics of motion against noisy camera data - corrects the trajectory instantly - moves the robotic arm before the next frame processes this is the exact same math used in rockets and autonomous driving, stripped down for a cheap microcontroller.

53,268 görüntüleme • 3 ay önce

ZERO-LATENCY AIMBOT? WORSE. A ZERO-LATENCY PHYSICAL TURRET someone built a rig in their garage that shoots exactly where you click on the live video feed no cloud APIs, no server lag. just raw local execution. a simple python tkinter GUI captures mouse coordinates and streams them over websockets. - the local $8 ESP32 chip processes the spatial data - the robotic arm pivots instantly to match the digital vector - the green laser locks onto the exact physical coordinate with zero delay you know where this is going? if the guy who built the head-tracking software ever meets the guy who built this physical turret... WE ARE SO COOKED.

ZERO-LATENCY AIMBOT? WORSE. A ZERO-LATENCY PHYSICAL TURRET someone built a rig in their garage that shoots exactly where you click on the live video feed no cloud APIs, no server lag. just raw local execution. a simple python tkinter GUI captures mouse coordinates and streams them over websockets. - the local $8 ESP32 chip processes the spatial data - the robotic arm pivots instantly to match the digital vector - the green laser locks onto the exact physical coordinate with zero delay you know where this is going? if the guy who built the head-tracking software ever meets the guy who built this physical turret... WE ARE SO COOKED.

49,616 görüntüleme • 2 ay önce

btw this robot runs on a fully open source AI framework called Xiaozhi it turns a $8 ESP32 chip into a fully autonomous voice assistant NO expensive cloud servers or high latency wake-word detection and audio streaming happen locally on the chip: > requires only an ESP32-S3, a microphone, and a basic speaker > supports real-time conversational voice interaction > completely open-source and customizable for any DIY robotics project anyone can now build a personal AI companion for the price of a cup of coffee. (github link in the replies)

btw this robot runs on a fully open source AI framework called Xiaozhi it turns a $8 ESP32 chip into a fully autonomous voice assistant NO expensive cloud servers or high latency wake-word detection and audio streaming happen locally on the chip: > requires only an ESP32-S3, a microphone, and a basic speaker > supports real-time conversational voice interaction > completely open-source and customizable for any DIY robotics project anyone can now build a personal AI companion for the price of a cup of coffee. (github link in the replies)

42,210 görüntüleme • 2 ay önce

$72/day for a voice that never sleeps or build a local AI device around an $8 class ESP32-S3 honestly, i prefer the second option you can teach the robot to fit your habits and needs, and that makes it feel more alive – even though building one is far from simple this little robot already shows the appeal: it catches a capacitor polarity mistake, warns about reverse polarity, and reacts with a face i have an article in my profile about running local AI on this kind of chip 😉

25,476 görüntüleme • 1 ay önce

remember when we thought the future would bring flying cars? someone may have started with a more practical prototype: a four-wheeled drone that drives across the floor, then takes off and flies maybe flying cars won’t begin as cars with wings maybe they’ll begin as robots built to do both: drive when they can, fly when they have to…

remember when we thought the future would bring flying cars? someone may have started with a more practical prototype: a four-wheeled drone that drives across the floor, then takes off and flies maybe flying cars won’t begin as cars with wings maybe they’ll begin as robots built to do both: drive when they can, fly when they have to…

24,357 görüntüleme • 2 ay önce

look at this robot, it's so cute! this builder created something amazing with just an ESP32 and a few sensors he wired up real-time proximity and touch so it actively tracks your hand. when you reach out, its eyes shift into pure affection 🥹 usually, hardware projects like this feel cold and mechanical, just running rigid pre-programmed loops, but this one actually feels it when you pet it! i could watch this little guy all day... would you want a companion like this?

look at this robot, it's so cute! this builder created something amazing with just an ESP32 and a few sensors he wired up real-time proximity and touch so it actively tracks your hand. when you reach out, its eyes shift into pure affection 🥹 usually, hardware projects like this feel cold and mechanical, just running rigid pre-programmed loops, but this one actually feels it when you pet it! i could watch this little guy all day... would you want a companion like this?

16,401 görüntüleme • 2 ay önce

Daha fazla içerik yok.