The article discusses how AI agents are transforming the way data is organized and utilized, moving away from rigid tabular structures to more flexible, interconnected data models. This shift is driven by the need for applications to be more intelligent and context-aware, requiring vast and complex datasets. The database layer is becoming increasingly critical, with AI agents necessitating richly structured data to enable near-human levels of logic and intuition.
LlamaExtract is a powerful, easy-to-use tool that allows users to extract structured data from unstructured documents with minimal effort, available through LlamaCloud’s web UI and Python SDK.
This post explores using GPT-4o's structured output feature for web scraping, highlighting its strengths, limitations, and cost considerations.
This article explains the differences between observability, telemetry, and monitoring, and how they work together to help teams understand and improve their software systems. It also discusses the benefits of using OpenTelemetry, a standard for creating and collecting telemetry for software systems, and Honeycomb's observability platform.
