schema
schema on Beyond Market Intelligence: a running collection of 5 stories we have gathered and hand-picked because they are worth your time. Every post here touches on schema in some way — the news, the analysis, the deep dives, and the occasional surprise find. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work with data. New stories are added to this page as we find them, so check back if you want to keep up with what is happening around schema, or subscribe to the RSS feed to get them as soon as they are published. Browse the collection below, or head back to the homepage to see everything Beyond Market Intelligence is covering right now.
The Types of Dimensions in a Star Schema, and How to Use Them
Dimensional modeling hinges on understanding dimensions—one of its two core object types. But dimensions aren't monolithic; they encompass several distinct varieties, each serving a specific purpose in structuring data for analysis. This post explores these types, detailing how to effectively leverage them within a star schema to unlock deeper insights. We’ll clarify their roles in providing context and enabling powerful data exploration. For a related perspective on optimizing data retrieval, see "Retrieve One Row from a Table, Not the Whole Table: Row-Level Chunks for RAG."
Loop Engineering for RAG: The Small Loops Inside Each Step, the Big Loops Across the Pipeline
Retrieval-Augmented Generation (RAG) systems rely on core components delivering consistent results, but what happens when those components falter? Loop Engineering addresses precisely that—the often-overlooked work performed *between* those core steps. This first installment of Enterprise Document Intelligence explores the critical control surfaces—trigger, termination, and recovery—that ensure a RAG pipeline remains productive, even when faced with retrieval misses or API timeouts. Discover how these “small loops” safeguard against common failures, building on insights from articles like "How to Perform Effective Project Management with AI."
New Fable5/Opus4.8 harness called "Schema" claims 99% on ARC-3 [R]
Introducing Schema, a new Fable5/Opus4.8 harness achieving impressive results on the ARC-AGI-3 benchmark. Schema attains 99% accuracy with Claude Opus 4.8 and 95.35% with GPT-5.6 Sol—all without modifying model weights. This innovative harness refines the interaction process, optimizing how observations inform models, predictions are tested, and plans are executed. A fixed fallback rule prioritizes Opus 4.8 and Sol, ensuring robust performance across all games, as noted by ARC Prize. Explore the technical details and methodology at [https://schema-harness.github.io/](https
Pydantic + OpenAI: The Cleanest Way to Get Structured Outputs from LLMs
Stop wrestling with manual JSON parsing and embrace a more reliable approach to leveraging Large Language Models (LLMs). Pydantic, a powerful data validation library, combined with OpenAI’s models, provides the cleanest path to structured outputs. This integration empowers you to trust your model’s responses, streamlining workflows and boosting productivity. Discover how this pairing eliminates parsing headaches and unlocks the true potential of LLMs—a significant advancement for data-driven applications.
Evolutionary Data Through Schemaboi: Achieving Forward, Backwards, and Sideways Compatibility
Data management faces a critical challenge: evolving formats without sacrificing accessibility. Drawing inspiration from the robust compatibility of HTML and HTTP, Olimpiu Pop’s work with Schemaboi introduces a novel solution. By embedding self-contained schemas directly within file headers, Schemaboi ensures enduring readability, prioritizing forward, backward, and sideways compatibility. This innovative approach allows for format evolution *without* centralized coordination or data loss, empowering users to confidently navigate the future of data.