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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 doesshow more

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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

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You pretrained a robot policy on millions of camera frames. Now you want to add a force sensor. Do you really have to retrain on everything from scratch? MuSe adds a force-torque sensor to a frozen vision-only policy using a tiny amount of contact data. - lifts contact-rich task success (peg insertion 60% to 87%, vase wiping 33% to 77%) - fuse the new sensor both early (shared token space) and late (cross-attention), train the policy as a world model that predicts future video, future force, and actions together, and replay old vision-only data with the force input masked to prevent forgetting. - adding new sensor modalities to pre-trained World Model can be cheap & improve performance significantly

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Simplifying AI

24,059 Aufrufe • vor 7 Tagen

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Blaze

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ard

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I'll always root for a team that open-sources its best work, and Robbyant just did it properly. Robbyant, Ant Group's embodied-AI company, released LingBot-Vision, a vision foundation model for robots, and the part I love is the data. They trained it on 161M images, filtered down from 2B raw ones and mostly pulled straight from the open web, with no human labels, no edge detectors, no depth sensors anywhere in the loop. It learns the exact edges of objects from raw pixels. That's roughly a tenth of the data DINOv3 saw, and under a third of the training. And it shows in the results. On depth, working out how far away things are, the 1B model edges out a 7B on NYU-Depth. It also powers LingBot-Depth 2.0, which reads the surfaces cameras usually choke on, glass and mirrors, and halves indoor depth error. LingBot-Vision is fully open. Weights from the 1.1B flagship down to a tiny 21M version, code, and the paper. This is the timeline I want more of. Robbyant

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48,249 Aufrufe • vor 1 Monat

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Blender just became a prompt box. With Kimi K3 connected through Blender MCP, you can describe a scene like this in plain English and let the model handle the ugly part: terrain, buildings, lighting, materials, camera movement, animation, and the Python scripts holding everything together. The interesting part is not the first render. Kimi can inspect the result, notice that the camera clips through a tree or the city looks suspiciously like plastic, then edit the actual Blender scene and render it again instead of restarting from zero. You still need taste, because “make it cinematic” remains one of humanity’s least useful instructions. But the distance between an empty Blender file and a fully editable 3D world just got embarrassingly small. A few years ago, creating this meant weeks of modeling, scripting, lighting, and animation. Now you can describe the world, watch Kimi build it, and spend your time fixing the final 10% instead of manually constructing the first 90%. Kimi K3 + Blender MCP is basically text-to-3D without trapping the result inside a useless generated video. Every object, material, light, camera, and keyframe stays editable.

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Seedance 2.0 has a fix for character drift almost nobody uses. Every new text description is a fresh interpretation of appearance. That's where the drift comes from: a slightly different face, a slightly different outfit, proportions that shift from scene to scene. The @ Image tag solves this directly — attach a photo once, and it becomes the fixed source of truth for face, outfit, and proportions across the entire multi-shot sequence. The @ Video tag works the same way for motion and camera — tag a reference instead of describing it, and the model copies that exact style instead of an approximation. You're not making the model remember. You're just stopping yourself from reminding it differently every single time.

Zentrix⌚️

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It is believed that chanting this stotram three times a day, for six months, fulfils the wishes of devotees. The Yantrodharaka Hanumat Stotram is a hymn in the praise of Lord Hanuman written by Shri Vyasaraja (Vyasatirtha). One day, when sage Vyasaraja was meditating on the banks of River Tungabhadra, he obtained a vision of Lord Hanuman. In order to capture this vision, he etched an image of a monkey on a rock. To his great surprise, the monkey from the illustration came alive. So, he etched an image once again. But, a live monkey materialised from the image once more. So, he repeated this process twelve times. After the twelfth time, he drew a monkey and encased it within a geometric pattern (a yantra) so that it wouldn't incarnate. Hence, the idol came to be known as the 'Yantrodharaka Hanuman' or the 'Hanuman held in the yantra'. Today, one finds a temple around this idol, known as the Yantrodharaka Hanuman Temple, near Hampi.

It is believed that chanting this stotram three times a day, for six months, fulfils the wishes of devotees. The Yantrodharaka Hanumat Stotram is a hymn in the praise of Lord Hanuman written by Shri Vyasaraja (Vyasatirtha). One day, when sage Vyasaraja was meditating on the banks of River Tungabhadra, he obtained a vision of Lord Hanuman. In order to capture this vision, he etched an image of a monkey on a rock. To his great surprise, the monkey from the illustration came alive. So, he etched an image once again. But, a live monkey materialised from the image once more. So, he repeated this process twelve times. After the twelfth time, he drew a monkey and encased it within a geometric pattern (a yantra) so that it wouldn't incarnate. Hence, the idol came to be known as the 'Yantrodharaka Hanuman' or the 'Hanuman held in the yantra'. Today, one finds a temple around this idol, known as the Yantrodharaka Hanuman Temple, near Hampi.

Bhagavad Gita

23,406 Aufrufe • vor 4 Monaten

Train a TensorFlow object detection model – then deploy it on a robot 🤖 Iulia Feroli (Iulia Feroli) shows how to turn a notebook into a real-time object detection app. This tutorial works for any project – though we demonstrate the deployment on (and assisted by!) the #ReachyMini, an open-source robot from Pollen Robotics. Built with PyCharm + Claude Code. 👉 Watch it in action:

Train a TensorFlow object detection model – then deploy it on a robot 🤖 Iulia Feroli (Iulia Feroli) shows how to turn a notebook into a real-time object detection app. This tutorial works for any project – though we demonstrate the deployment on (and assisted by!) the #ReachyMini, an open-source robot from Pollen Robotics. Built with PyCharm + Claude Code. 👉 Watch it in action:

PyCharm, a JetBrains IDE

34,908 Aufrufe • vor 4 Monaten

Only one Adobe Firefly video prompt from our research hit production-ready on the first generation. Prompt in comments 👀 It looks you have to direct the camera as much as the model to get the most out of Firefly. Every other video session in our research needed a revision. Revising the first frame is where Firefly broke: >motion failures (temporal flicker, motion continuity, physics in motion) carried 60% of video's issue mix once designers re-prompted. TLDR: in Adobe Firefly, the camera does a lot of the work. Bake it into the first prompt and the video lands. Its by far the best time to be a creative.

Only one Adobe Firefly video prompt from our research hit production-ready on the first generation. Prompt in comments 👀 It looks you have to direct the camera as much as the model to get the most out of Firefly. Every other video session in our research needed a revision. Revising the first frame is where Firefly broke: >motion failures (temporal flicker, motion continuity, physics in motion) carried 60% of video's issue mix once designers re-prompted. TLDR: in Adobe Firefly, the camera does a lot of the work. Bake it into the first prompt and the video lands. Its by far the best time to be a creative.

ben @ CVPR

12,847 Aufrufe • vor 3 Monaten

SOMEONE BUILT AN IPHONE APP THAT ONLY REVEALS WHATS UNDER YOUR HAND the whole screen stays hidden you rest the side of your hand on it, the way you shield a keypad at an atm, and only the part your hand covers becomes readable lift your hand and it goes back to hidden > uses a multi touch area to detect your hand, no camera or permissions needed > he tried the proximity sensor first but it just turned the screen off > skipped the camera on purpose since asking for camera access in a privacy app felt wrong > fully native ios, with app clip and imessage support its insane how many simple but useful ideas you can vibe code in a weekend

SOMEONE BUILT AN IPHONE APP THAT ONLY REVEALS WHATS UNDER YOUR HAND the whole screen stays hidden you rest the side of your hand on it, the way you shield a keypad at an atm, and only the part your hand covers becomes readable lift your hand and it goes back to hidden > uses a multi touch area to detect your hand, no camera or permissions needed > he tried the proximity sensor first but it just turned the screen off > skipped the camera on purpose since asking for camera access in a privacy app felt wrong > fully native ios, with app clip and imessage support its insane how many simple but useful ideas you can vibe code in a weekend

Om Patel

870,007 Aufrufe • vor 2 Monaten

The first smartphone I plan to buy in 2026 is the Honor Magic 8 Pro Air. There’s only one reason: despite being absurdly thin and light, it delivers truly uncompromised flagship-level hardware. The Honor Magic 8 Pro Air measures just 6.1 mm in thickness and weighs only 155 g, yet it packs a 5,500 mAh battery. This combination alone is extremely rare. It doesn’t sacrifice experience for thinness. Instead, it force-fits everything a flagship should have into an Air-class body. The camera system is where this phone becomes genuinely outrageous. The main camera features a 50 MP sensor with a 1/1.3-inch large sensor, an f/1.6 aperture, and a 23 mm equivalent focal length, along with OIS optical image stabilization. Through pixel binning, it delivers a 2.4 μm effective pixel size, which is squarely in Pro-grade territory. The ultra-wide camera is also 50 MP, with an f/2.2 aperture, a 16 mm equivalent focal length, and a 112° field of view. It even supports 2.5 cm high-resolution macro photography, making it far from a throwaway lens. For telephoto, the Magic 8 Pro Air uses a 64 MP periscope camera with 3.2× optical zoom, an f/2.6 aperture, and a 74 mm equivalent focal length, once again with OIS. It supports up to 100× digital zoom. In addition, the camera system includes a color temperature sensor and a flicker sensor, completing a full flagship-level imaging setup. The front-facing camera is a 50 MP sensor with an f/2.0 aperture, clearly not an afterthought.

The first smartphone I plan to buy in 2026 is the Honor Magic 8 Pro Air. There’s only one reason: despite being absurdly thin and light, it delivers truly uncompromised flagship-level hardware. The Honor Magic 8 Pro Air measures just 6.1 mm in thickness and weighs only 155 g, yet it packs a 5,500 mAh battery. This combination alone is extremely rare. It doesn’t sacrifice experience for thinness. Instead, it force-fits everything a flagship should have into an Air-class body. The camera system is where this phone becomes genuinely outrageous. The main camera features a 50 MP sensor with a 1/1.3-inch large sensor, an f/1.6 aperture, and a 23 mm equivalent focal length, along with OIS optical image stabilization. Through pixel binning, it delivers a 2.4 μm effective pixel size, which is squarely in Pro-grade territory. The ultra-wide camera is also 50 MP, with an f/2.2 aperture, a 16 mm equivalent focal length, and a 112° field of view. It even supports 2.5 cm high-resolution macro photography, making it far from a throwaway lens. For telephoto, the Magic 8 Pro Air uses a 64 MP periscope camera with 3.2× optical zoom, an f/2.6 aperture, and a 74 mm equivalent focal length, once again with OIS. It supports up to 100× digital zoom. In addition, the camera system includes a color temperature sensor and a flicker sensor, completing a full flagship-level imaging setup. The front-facing camera is a 50 MP sensor with an f/2.0 aperture, clearly not an afterthought.

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90,075 Aufrufe • vor 7 Monaten