search-indexing

2 posts

cloudflare

Your site, your rules: new AI traffic options for all customers (opens in new tab)

Cloudflare is replacing its broad “Block AI Bots” approach with finer controls based on what automated systems do: Search, Agent, or Training. The goal is to let website owners preserve discoverability and useful automation while blocking uncompensated model training and other unwanted access. These controls will be available to all Cloudflare customers, including Free-tier users. ## Why AI traffic needs more nuance - The traditional crawler exchange—content in return for referrals—has weakened as AI systems increasingly consume content without sending traffic back. - Website owners previously faced a binary choice: - Allow AI access to remain discoverable. - Block automation and protect content at the risk of losing visibility. - This tradeoff particularly harms small sites and can favor established search providers that use the same crawlers for search and training. ## A behavior-based AI taxonomy Cloudflare will classify automated traffic by its purpose rather than simply labeling bots as “AI”: - **Search** - Collects or indexes content to answer future queries. - Builds a database proactively. - Should generally provide referrals or other fair compensation. - **Agent** - Acts in real time on behalf of a person. - Includes chat-fetch bots such as ChatGPT-User and browser-use agents driven by Gemini or Claude. - Visits a site to complete a specific task for a human. - **Training** - Collects content to train or fine-tune a model. - Permanently incorporates data into the model’s underlying architecture. Bots may have multiple classifications. Cloudflare encourages operators to separate Search, Agent, and Training crawlers so site owners can understand and control their access more effectively. ## New controls for AI traffic - Cloudflare is adding separate controls for Search, Agent, and Training traffic. - These options replace the need for a single all-or-nothing AI blocking decision. - The controls will be available to all customers, including those on the Free plan. - Cloudflare will continue tracking other automated behaviors, such as ad verification, feed fetching, and agentic transactions. ## New default rules Starting September 15, 2026: - For new domains, **Training** and **Agent** crawlers will be blocked by default on pages displaying ads. - **Search** crawlers will remain allowed by default because they are more likely to send visitors back. - The policy treats ads as an indication that human attention—and therefore monetizable traffic—is the intended outcome. - Multi-purpose crawlers will be governed by all of their classifications, using the most restrictive applicable rule. - As a result, crawlers such as Googlebot, Applebot, and BingBot may be blocked when customers choose to block Training traffic. - Website owners can opt out of the new defaults through Cloudflare Security settings before September 15. Cloudflare’s recommendation is to manage AI access by behavior: allow Search when referrals matter, permit Agents when real-time user tasks are valuable, and block Training where content reuse is not adequately compensated.

figma

A deep dive on deep search | Figma Blog (opens in new tab)

Figma’s deep search lets users find files by searching text inside them rather than relying on file names or metadata. Building it required extending infrastructure originally created for Design System Analytics to process `.fig` files stored in Amazon S3. Because analyzing large file trees is expensive, Figma accepted briefly stale results and processed deduplicated changes hourly. ## Deep Search in a Browser-Based Product - Figma’s web-based architecture provides detailed access to files and usage data. - This enables features such as: - Component usage analytics - File-view frequency - Inspection of file structure - Searching content inside files - Deep search builds on the browser’s collaboration and discoverability advantages. ## Reusing Design System Analytics Infrastructure - Design System Analytics already opened recently edited files, retrieved them from storage, and traversed their contents. - Analytics extracted shared-library usage information. - Deep search applies the same general workflow to extract text from Figma files. - The existing file-analyzer worker platform provided support for computationally intensive, periodic processing. ## Regular Search vs. Deep Search - Regular search indexes database metadata, including: - File name - Creator - Folder ID - Team ID - Its pipeline: - Database changes are streamed into a messaging system. - Search indexers retrieve current records. - The metadata is indexed in Elasticsearch. - Deep search cannot rely on database metadata because the actual file contents are stored as `.fig` documents in Amazon S3. - A `.fig` file is represented as a tree of nodes, such as frames, rectangles, vectors, ellipses, and text objects, each with its own properties. ## Managing the Cost of File Analysis - Retrieving and traversing a complete Figma file is significantly more expensive than reading database records. - Files may contain thousands of nodes, and users can trigger saves approximately every 30 seconds. - Re-indexing every save would produce substantial duplicated computation. - Figma therefore: - Deduplicates file changes over one-hour windows. - Sends changed files to file-analyzer workers. - Allows deep-search results to be temporarily stale. - This tradeoff reduces server workload while maintaining useful search functionality. Deep search demonstrates how content-aware features require different infrastructure from conventional metadata search. Periodic, deduplicated processing offers a practical balance between timely results and the high computational cost of analyzing complete design files.