Browserbase provides a programmable browser platform that allows developers to automate complex online tasks using code. It offers features like advanced debugging, session recording, a proxy supernetwork, and bot detection avoidance to streamline web automation for compatibility with popular tools like Puppeteer, Playwright, and Selenium. The startup aims to serve as a key building block in an emerging AI software stack.
“It’s not going to be just developers writing this code, it’s going to be large language models generating this code longer term,” Klein explains. “So it’s both developers and LLMs controlling these web browsers to go out and automate the daily tasks we do online.
The paper proposes a two-phase framework called TnT-LLM to automate the process of end-to-end label generation and assignment for text mining using large language models, where LLMs produce and refine a label taxonomy iteratively using a zero-shot, multi-stage reasoning approach, and are used as data labelers to yield training samples for lightweight supervised classifiers. The framework is applied to the analysis of user intent and conversational domain for Bing Copilot, achieving accurate and relevant label taxonomies and a favorable balance between accuracy and efficiency for classification at scale.
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