Too heavy, clumsy to handle and open, worse camera than pro max, buttons in the wrong places etc.

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.

It’s easy to spot bot responses on this post

I have been running my computer vision and AI consulting company for 14 years and my investor, Mr. Mallick, has never asked send him a blurb.

You can use agents for turning in the assignments.

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

It's actually worse from one year back. It used to be bad even back then, but it was quick. Now it is bad and slow.

I am not sure if you are being sarcastic here, but I did talk to a computer vision professor a couple of days back. I was saying that maybe students will be redundant because professors can do their own research using agents. His take was that maybe professors will be redundant because students can do their own research (and write grants) using agents.

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.

Watch the full Muse Wonder football test here: youtube.com/watch?v=gZHAZtzs…

The word “Academic” is unnecessary in this sentence

We tested Meta’s Muse Wonder on football—and checked what it actually saw. On 40 goal/shot clips, its best guess was correct on 26 (65%); our teammate got 34 (85%). A red marker hidden in one frame went unnoticed. Held for one second, it was spotted. Then we asked it to cut highlights from a 92-minute match. All 12 tool calls worked, but only 2 of the 5 goals landed in the selected clips. A useful coordinator needs reliable eyes. This is one controlled case study, not an overall benchmark. Watch on LearnOpenCV: “We Tested Meta's Muse Wonder on Football. It Missed Goals a Person Could See.” youtube.com/@LearnOpenCV Data: SoccerTrack v2 (atomscott/soccertrack-v2, CC BY 4.0). #ComputerVision #MultimodalAI #ModelEvaluation #OpenCV

Nobody knows

That’s how they say no without saying no.

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.