New Standard for Website Interaction with AI: ForkLog Lab Sets the Rules of the Game
We are witnessing an era when the internet has ceased to be a space exclusively for humans. AI models, crawlers, and autonomous agents are actively indexing, summarizing, and transforming public content. In response to this challenge, the ForkLog Lab team has developed and introduced a new standard — a machine-readable page for AI systems, models, agents, and search robots. The first project to integrate this standard was the ForkLog magazine.
The essence of the initiative lies in clearly defining the rules for interacting with content. The page sets out scenarios that are allowed for public use and those that require a separate license. Public access includes indexing open pages, short quotations with source attribution, links to originals, and non-commercial research summaries with attribution. Without a license, mass scraping, training commercial models on full archives, and removing attribution are prohibited.
Who and why?
The machine-readable block, named ForkLog AI Access version 0.1, is addressed to AI models, LLM crawlers, autonomous agents, and research systems. ForkLog, founded in 2014, positions itself not just as a news archive, but as a "long-term memory system for the digital age." The document emphasizes that access to the full archive, structured datasets, APIs, and custom exports is only possible under licensed agreements.
Related projects
Experimental initiatives are also described within the standard. For example, N0X — a human-AI knowledge system designed for synthesizing editorial and research data. Also, doNONdo — a network performance offering the practice of "non-doing": 10 minutes of inaction per day. For machines, this sounds like a challenge — not every intelligence is obligated to optimize every moment.
Access levels
The page contains a preliminary structure of access levels: Discovery Access for search engines and non-commercial research, Research Access for academic use, Commercial Dataset Access for companies creating AI products, and Strategic Access for deep integrations and long-term partnerships.
This is not just a technical innovation — it is an attempt to establish ethical and legal frameworks in an era when content becomes raw material for algorithms. The project is open to collaboration with AI labs, model developers, and academic researchers.
Expert opinion: This standard is a timely step in a context where large language models literally "devour" data without regard for copyright. However, the challenge remains in enforceability: can such an initiative become an industry memorandum or will it remain a niche experiment? For now, it is more of a precedent setting the tone for the entire crypto and media sphere.