We understand the world through connections. When you learn something new, your brain doesn't file it away in isolation, it integrates it into existing knowledge, creating a web of relationships that gives meaning to individual facts. A data infrastructure provider recently shared with me that their metadata now signifacatlly outweighs their actual data, indicating that even in our information systems, relationships matter more than raw content. The internet is rich with these connections, but they're not always accessible or relevant. Now, as AI agents become primary consumers of web content, are we structuring our sites so these systems can build the same rich understanding that humans naturally create?

EVALUATING LLM PERFORMANCE ON SPECIALIZED TASKS¶

METRIC EVALUATION BEST PRACTICES

Building an Agentic-SRE: Future of Incident Response¶

In Site Reliability Engineering (SRE), every second counts. When an alert fires, its a race to identify the root cause and implement a fix. This process is often a manual, time-consuming journey through logs, metrics, and documentation.

Intelligence is what one can do with a little bit of information. Stupidity is what one can't do with a lot of information. Today's LLMs and AI fall into the latter category; however, we can push them in the direction of the former by providing them with the context they require to make decisions. The most effective way of doing that is with Retrieval-Augmented Generation (RAG).[1]

Introduction¶

The site Reliability Engineering (SRE) position was created out of necessity by Google to maintian its gigital infrastructure and is now a critical discipline across the technology industry. Today, SRE teams face mounting pressure to maintain system reliability while managing unprecedented scale and complexity. I believe The emergence of AI-driven assistants represents a paradigm shift in how we approach reliability engineering in all fields, promising to augment human expertise with intelligent automation, and its starting with SREs.

• Try the Investment Research Agent on Hugging Face
• See implementation on Github

Pydantic-AI Investment Research System¶

This document details a multi-agent investment research system designed to autonomously gather and analyze financial data. The system performs comprehensive financial analysis and generates actionable investment insights. Its adaptive architecture, featuring dynamic plan adjustment and intelligent memory management, allows it to evolve its research strategy based on real-time findings.