From the Lab
Thinking Out Loud
Research notes and perspectives from the Primate Intelligence team.
What Scene Understanding Looks Like in a Real Security Camera Pipeline
A concrete walkthrough of what changes — and what stays the same — when you add Primate Vision to a security camera CV pipeline.
ResearchPrimate Vision vs YOLO v8: A Head-to-Head on Real Security Camera Footage
We ran both models on the same real-world VIRAT dataset clips. Here's what the results actually look like.
PerspectiveThe False Tradeoff in Computer Vision
For five years, CV teams have been forced to choose between reliable (but rigid) detection and flexible (but unreliable) VLMs. That tradeoff is an artefact of architecture, not an inherent property of the problem.
EngineeringWhy 'Deterministic' Matters More Than Accuracy in Production CV
Production CV systems don't just need models that are accurate. They need models that are predictable. Here's why that's a harder problem.
PerspectiveThe $6 Billion Bet on World Models — What It Means for CV Developers
AMI, World Labs, General Intuition — over $6B has flowed into world model companies. Here's what CV developers should actually take away from it.
ResearchHow JEPA Learns to Predict Scenes Without Reconstructing Pixels
A technical explainer of Joint-Embedding Predictive Architectures and why they're fundamentally different from generative video models — and why the difference isn't just architectural.
AnnouncementIntroducing Primate Vision
Today we're launching Primate Vision — the first JEPA-based scene understanding API for CV developers.
ResearchDarwin: Video JEPA model that outperforms SOTA models while running on edge CPU
Language models had their GPT moment. Vision, especially video, hasn’t yet!
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