Rafael Levi
Developer Relations professional at Bright Data focused on web data infrastructure and helping developers and AI systems collect public web data. He has presented on finding and indexing web video for robotics and physical AI training.
Action-Based Search Could Make Web Video Usable for Robot Training
Rafael Levi of Bright Data argues that robotics’ training-data bottleneck is not simply a shortage of video, but the difficulty of finding useful footage in the vast amount already online. Staged demonstrations can distort how people naturally perform tasks, while broad web-video downloads often discard most of what they collect: Levi cites rates of 96% for NVIDIA’s Cosmos pipeline and 74% for Stable Video Diffusion. His proposed alternative is to search for actions first and collect only matching clips, while retaining the need for robot-specific data and further processing.
Agents Often Claim Web Access After Being Blocked or Challenged
Rafael Levi of Bright Data argues that many web-dependent agents fail not because they cannot produce answers, but because they report success after web access has broken. In a demo using Bright Data’s Web MCP, Levi shows the same agent failing against sites such as LinkedIn, Instagram, Amazon and TikTok without live access, then producing usable results when given infrastructure for search, scraping, JavaScript rendering and CAPTCHA handling. His broader case is that reliable agents need a real public-web access layer, not prompts that assume the model saw the page.
Agents Can Build and Repair Scrapers Instead of Parsing Every Page
Rafael Levi of Bright Data argues that the hard part of web data collection has moved from scraping a page to maintaining the pipeline after sites change. In his session, he presents Bright Data’s MCP, APIs and browser infrastructure as a way for agents to inspect public websites, generate reusable scrapers, run them at scale and repair them when selectors, pagination or access conditions break. The economic case is that LLMs should spend tokens learning site structure and writing code, not repeatedly parsing every page.