@AIatMeta's SAM 3 is not an incremental release. It’s a whole new vision engine. Didn’t have time to read the paper? Here’s the breakdown. Thread ↓

Merry Christmas from all of us at Labellerr AI Grateful for the builders, researchers, and teams pushing computer vision forward this year. From open datasets to real world deployments, the community effort is what makes progress possible. Thank you for building with us. 🎄

Watch the complete quality inspection pipeline here: https://t.co/uwC4MdOO1Y

What if quality inspection could run 24/7 with fewer errors? With AI-Powered Camera, defect detection becomes faster and more precise. We fine-tuned @ultralytics 's YOLO model, implemented real-time inspection logic, and tested the full pipeline end to end.

Watch here:

Can common face-blurring techniques really keep people anonymous today? With vision models improving fast, privacy in video is becoming harder than it looks This video compares blur, pixelation, and black-box, and explains where each method works best and where it falls short.

We break down the full pipeline using YOLO and live video feeds here: https://t.co/PAHcFb7lpz

A true perimeter sensing system understands when to alert. Using @ultralytics's YOLO, @opencvlive and @matplotlib, the system detects people, evaluates their actions relative to defined perimeters, and raises alerts in real time.

Our blog explains how RoboTurk works, what problems it solves, and its impact on training smarter robots. Read here:

Training robots isn’t limited by models anymore, it’s limited by data. RoboTurk shows how large-scale, high-quality human demonstrations can unlock smarter, more reliable robot learning.

We explain how this system is built using YOLO and live video processing in the full tutorial: https://t.co/zrnJ7BhKfD

Real-time security with computer vision means more than just detection. This inference demonstrates how people detection + zone logic can accurately identify trespassing events as they happen.

Everyone is yelling “data labeling is dead.” Cool. But who owns the data you upload? Where do your frames go? Can you reproduce the pipeline a month later? Can you run it offline on sensitive datasets? That’s why we at @Labellerr1 open-sourced a local first SAM3 reference

Full analysis with visuals and structured tests: https://t.co/p34d3K08Pg

Curious what improved photorealism actually looks like? We tested @Google’s Nano Banana vs Nano Banana Pro across different scenarios, and the difference is unmistakable.

AI Data Labelling

Labellerr AI Ambassador Program is now open! Come join us: > Work on hands-on CV and robotics projects > Learn directly from our core team > Get early access to new Labellerr releases > Build your profile for future roles Apply now: https://t.co/gAP8h21mZS

LLMs got the internet. 2D vision got ImageNet. what about robotics ?? it is still waiting for its ImageNet moment. Collecting data for hands that tie laces, unzip bags, flip pages, plug chargers is still > tiny > expensive > hard to label > stuck in the lab Dexterous

We tested the model’s upgrades and why they matter for production use. Blog:

Gemini 3 feels like Google’s most practical multimodal model yet. Less hype, more real improvements in reasoning, coding, and visual understanding.

In robotics data collection, which source do you find easiest to collect (or scale) for model training/deployment?