---
title: "Liquid"
description: "2 posts about Liquid, summarised, each linking to the original."
---

# Liquid
> 2 posts about Liquid, summarised, each linking to the original.

## Articles

### [Transforming solar and wind maintenance reports with Genie and AI agents](https://yomu.fyi/post/transforming-solar-and-wind-maintenance-reports-with-genie-and-ai-agen.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Maria Vallarelli
- Published: Jun 8, 2026

Plenitude and Databricks built an agent-based system that turns solar and wind plant maintenance PDFs into structured data for cross-plant analysis. Event-driven ingestion uses Databricks Jobs and the ai\_parse\_document AI Function to extract text, tables, figures, and metadata, then stores page- and object-level JSON records in Delta Lake with coordinates, version history, and links to source reports. A Genie space uses Unity Catalog metadata, knowledge-store instructions, and SQL generation to answer natural-language questions, produce visualizations, and export results, while Agent Bricks can orchestrate multi-step workflows and downstream actions. The design also applies automatic liquid clustering to dynamic queries and row-level security to restrict results by country. The resulting data layer supports historical trends, plant comparisons, recurring-fault analysis, and a foundation for predictive maintenance, although the source frames predictive use as a future improvement.


### [How to Do an In-depth Liquid Render Analysis with Theme Inspector](https://yomu.fyi/post/how-to-do-an-in-depth-liquid-render-analysis-with-theme-inspector.md)
- Company: [Shopify](https://yomu.fyi/company/shopify.md)
- Author: 2023-10-18
- Published: Oct 20, 2020

Shopify theme code can create server-side rendering bottlenecks that leave customers waiting on a blank screen, so the article explains how to analyze Liquid performance with Shopify Theme Inspector. It describes flame graphs as representations of code paths and execution time, then compares clean Liquid with heavy loops, nested loops, and mixed global Liquid scopes. Accessing product attributes raised a 10-item example from 13 ms to 162 ms, while 50 products took 800 ms; two- and three-level nested examples rendered in 55 ms and 72 ms. The guidance recommends examining total iteration time, sawtooth or hair-comb patterns, unnecessary attributes, pagination size, and AJAX alternatives. It proposes aiming for 200 ms and keeping total page rendering under 500 ms, while noting that this alone does not guarantee a good LCP score.