I was not sure if I wanted an iPhone Duo, but after experiencing it in real life for just 5 minutes, I’m 100% sure I don’t want it.

You can use agents for turning in the assignments.

Same floor plan. Two 3D models. Which workflow was faster? We gave GPT-6.1 Terra and Claude Opus 5.5 the same dimensioned apartment drawing, then checked their exported meshes against the original CAD file that neither model saw. Both passed all 28 checks: 8 dimensions, 7 room connections and 13 openings. Neither needed a human fix. Small opening errors of 5–10 mm remained, within our preset 100 mm allowance. Terra's successful first run: 54.3 seconds. Opus's first run: 96.9 seconds. The catch: a 44-second Terra launch was blocked by our own tool-approval settings before it saw the drawing. Include it and Terra's initial total is 98.3 seconds, excluding setup-fix time. These are whole-agent workflow times, not pure model speed. One drawing and one run per model make this a case study, not a general ranking. Watch the full comparison: youtube.com/watch?v=BNjaA9K-… What would you count when comparing agent workflows: successful runs, or every attempt? Floor plan: Innerscene, "Three-bedroom apartment, 105 m²", CC0 1.0; rasterized from the source DXF. No endorsement by OpenAI, Anthropic or Innerscene implied. #AIAgents #ComputerVision #GPT61 #ClaudeAI

Nemotron 3.5 Super VL couldn't confirm step one. It still said "agrees." We showed NVIDIA's video model a robot assembling a lamp. It confirmed the bulb and shade, marked the base step as unobserved or uncertain, and wrote that the base never moved. Its verdict still agreed with the checklist. The robot actually completed all three steps. The verdict was right, but the model's own evidence did not support it. Then it counted 3 squats where our reference count is 2. Count them yourself. Ask for evidence. Check that the verdict follows from it. Then check the evidence against the video. Full test: youtube.com/watch?v=BbRxAsp6… Three case studies, not a benchmark. Reference answers were AI-assisted; human review is pending. Footage: FurnitureBench — Heo, Lee, Lee and Lim, CLVR Lab/KAIST (RSS 2023): clvrai.github.io/furniture-b… Squats: "Squat - exercise demonstration video" by FitnessScape, CC BY 3.0, via Wikimedia Commons: commons.wikimedia.org/wiki/F… License: creativecommons.org/licenses… — replay, half-speed playback, cropping and overlays added. Road-sign sample and model: NVIDIA. No endorsement implied. Cover is an illustration. #Nemotron #NVIDIA #ComputerVision #OpenCV

I was talking to Prof. Ramamohan Paturi of UC San Diego yesterday and knew something massive was coming. Phew!

Every automated check passed. The evidence box still missed the bus. We gave Roboflow Agent one minute of drone footage and asked whether an intersection was blocked. The workflow ran, the output parsed, and the structural checks passed—but the box offered as evidence for the blue bus did not contain it. Three checks for your next vision workflow: Does it run? Does it parse? Does the evidence support the answer? Watch the full tutorial: youtu.be/27apodnar2M One feasibility test, not a benchmark or a measure of production accuracy. Footage: Songdo Traffic v2, Robert Fonod, Haechan Cho, Hwasoo Yeo and Nikolas Geroliminis. zenodo.org/records/17924857 — CC BY 4.0: creativecommons.org/licenses…. Resized and compressed; frames extracted and annotated. The dataset authors and Roboflow do not endorse this video. #ComputerVision #Roboflow #AIagents #OpenCV

Same face, different conditions: 0.95 in the studio, 0.78 against a car-video frame. Day, camera and setting changed together—we cannot blame lighting alone. Enroll across days and cameras. Similarity is not accuracy. #OpenCV #ComputerVision Lighting data: Collin Gros (2019), Facial Recognition Dataset VIDEO (part 1 of 2), Mendeley Data V1. doi.org/10.17632/xgg8xcscr5.… — CC BY 4.0 (creativecommons.org/licenses…). Used to derive scores and graphics; no dataset faces shown.

Nobody knows

The astonishing rise of China and the decline of Europe has been a work in progress for years. Yet, it’s unbelievable when you experience it first hand.

Datalab’s Indra OCR runs on CPUs, with no GPU. Its recognizer has about one million parameters. Datalab reports 49 pages/sec on a 32-core server; on 4 cores, about 7 pages/sec. Hardware matters when reading benchmark headlines. In our upside-down scan demo, it rotated the page, detected lines, determined reading order and wrote Markdown in 344 ms on 2 vCPUs. Built for clean printed documents; handwriting, faded scans and complex tables remain limitations. What document type should we test against Tesseract and PaddleOCR next? Sources: datalab.to/blog/indra-ocr github.com/datalab-to/indra Demo page: Attention Is All You Need — Vaswani et al. (2017). #OCR #ComputerVision #DocumentAI #OpenCV #Python

Would I let a video model sign off on completed work? Only after checking how it reached the verdict. Our Nemotron 3.5 Super VL test is a useful example: the robot completed the checklist, but the model could not support one step and still agreed. A correct verdict alone would hide that failure. We also tested a road sign and squat counting. Three case studies—not a benchmark. Human review of references is pending. Full video, “Nemotron 3.5 Super VL Tested: A Correct Answer for the Wrong Reason,” on LearnOpenCV: youtube.com/watch?v=BbRxAsp6… Video sources: NVIDIA; FurnitureBench (CLVR Lab/KAIST); FitnessScape (CC BY 3.0). Full credits in the description. Thumbnail is an illustration. What would you check beyond the final answer? #AI #ComputerVision

I finally watched the movie. The staring is nothing compared to the head-nod that messes your brain. xitter.cf/LearnOpenCV/status/209…

How to create this blue text string: #̢̛ͥ̀̽̽ͨ̌̓͆̇̀̽ͦͨͫͯ̏̎̇̈̚͜҉̕͢͏͡҉̸̶̛͢͡҉̵̨̭̳̘̹̫͔͖̜̠͖̫̲̖̱̤̻̖͉ It’s a # followed by a pile of Unicode combining marks, normally used for accents. They stack above and below the character, spilling into nearby lines. X makes it blue because it recognizes a hashtag. Copy the string to try it.