LLMs are trained to interpret language, not data. Bridging the gap between AI and data means obsessing over context, semantics, and performance.
1Why LLMs struggle with analytics
Learn how to use Tinybird’s built-in MCP server to create LLM based analytics agents that autonomously explore and report on your data
110 Analytics Agents examples you can copy
1How to build an analytics agent with Agno and Tinybird: Step-by-step
1Chat with your data using the Birdwatcher Slack App
1Building an autonomous analytics agent with Agno and Tinybird
1MCP vs APIs: When to Use Which for AI Agent Development
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1Using LLMs to generate user-defined real-time data visualizations
1Build natural language filters for real-time analytics dashboards
1Hype v. Reality: 5 AI features that actually work in production
1Instrument your LLM calls to analyze AI costs and usage
1Hey Claude, help me analyze Bluesky data.
1Real-Time Anomaly Detection: Use Cases and Code Examples

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