New Tool Categorizes Web Traffic by Entity Type
The tracker uses cookie-free scripts and traps to distinguish between human users and automated agents.
Updated on Oct. 22, 2026 in Artificial Intelligence
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13 Labs, a research unit of Novel Cognition, has launched a tracking website to categorize web traffic into human, machine, hybrid, and unresolved entities. The project, live as of 21 October 2026, uses server logs and a cookie-free script to distinguish automated agents from people.
Why it matters
The site provides a method for studying entity architecture on the live web by separating organic traffic from AI-driven automation. It offers a standardized, transparent way to monitor how non-human agents interact with public infrastructure.
The tracker classifies visitors based on server logs, a 1x1 image, and a hidden path forbidden by robots.txt, while human identification requires active pointer movement or keyboard input. As of 02:40 UTC on 22 October 2026, 69% of the 94 total visits were identified as machine traffic.
The players
13 Labs
A research unit of Novel Cognition focused on testing AI strategy and entity architecture on the open web.
Novel Cognition
The parent organization overseeing 13 Labs and its research into web traffic characterization.
The details
The platform distinguishes human visitors by detecting real-time physical inputs, including pointer movement, scrolling, and key taps that lack automation signals. For non-human traffic, it utilizes a hidden path or 'trap'—a URL restricted in the robots.txt file—to identify crawlers that ignore standard access protocols. Hybrid visitors, defined as AI agents operating on behalf of a human user, are cataloged separately through script-based heuristics.
Timeline
The 13 Labs website went live on 21 October 2026.
Data for the first 94 classified visits were recorded by 02:40 UTC on 22 October 2026.
The Tech Race
13 Labs leverages the established robots.txt exclusion protocol to build a baseline for traffic analysis. This project represents a shift toward active, live-web experimentation in AI strategy rather than relying on static or simulated datasets.
Users can observe real-time traffic transparency via the public JSON file provided by the lab. The service requires no cookies, ensuring that human visitors do not need to provide personal data to be classified correctly.
The takeaway
The platform establishes a new transparent benchmark for monitoring AI activity on public domains. Interested parties can monitor the lab ledger to see if the proportion of hybrid or human traffic shifts as the project matures.
Further reading
For more on the development of AI entities, visit our Artificial Intelligence section.
More information
View the live classification results and experiment ledger on the 13 Labs website and experiment tracker.
Source note: This article includes information reported by The Cherry Creek News.
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