The text (and podcast from NotebookLLM) presents a design for a self-improving recursive web crawler. The crawler uses a recursive algorithm to navigate the internet, combining this with a machine learning component that allows it to learn from the data it gathers. This learning process enables the crawler to adapt its search strategy based on its findings, essentially making it "self-asking" by identifying knowledge gaps and prioritizing relevant links. The design includes considerations for efficiency, ethical practices, and potential future enhancements, such as integrating more sophisticated AI models. A Python code example illustrates the core functionality.
Self-Improving Recursive Web Crawler
Concept, Promise and Challenges
Jan 26, 2025

whitehatStoic
Exploring evolutionary psychology and archetypes, and leveraging gathered insights to create a safety-centric reinforcement learning (RL) method for LLMs
Exploring evolutionary psychology and archetypes, and leveraging gathered insights to create a safety-centric reinforcement learning (RL) method for LLMsListen on
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