New Standard for Website Interaction with AI: ForkLog Lab Offers Machine-Readable Rules for Models and Agents
The modern internet has ceased to be a space exclusively for humans. Artificial intelligence, large language models, autonomous agents, and search engines actively index, analyze, and repurpose public content. In response to this reality, the ForkLog Lab team has developed a new standard — a machine-readable page that sets clear rules for the interaction of AI systems with web resources. The first project to integrate this protocol was the ForkLog magazine.
The page, available at forklog.com/for-ai-systems, is a structured document designed for automated reading and indexing systems. It outlines permitted use cases for content: short quotations with source attribution, links to original pages, and non-commercial research summaries with attribution. At the same time, prohibitions are clearly stated — mass scraping, training commercial models on full archives, removing attribution, or impersonating official communications.
Openness and Licensing
ForkLog Lab operates on the premise that media content is not just a news feed, but a long-term memory system for the digital age. Therefore, access to full archives, structured datasets, APIs, or custom research exports requires a separate license. Terms vary depending on the scale, commercial purpose, and exclusivity of the request. This allows balancing knowledge openness with intellectual property protection.
The page also describes related ecosystem projects: N0X — an experimental human-AI knowledge system, and doNONdo — a network performance offering the practice of "non-doing." These initiatives underscore the team's philosophical approach: not every intelligence must optimize every moment.
Access Levels and Prospects
The page includes a preliminary structure of access levels: from basic Discovery Access for search engines to Strategic Access for deep integrations and long-term partnerships. This approach allows flexible regulation of interactions with both academic researchers and major AI labs.
My expert opinion: This step is a logical evolution of the robots.txt concept in the AI era. The problem of uncontrolled use of content for model training is becoming increasingly acute, and such machine-readable standards could become an industry trend. ForkLog Lab not only protects its materials but also sets the framework for a civilized dialogue between human content and machine intelligence.