We believe empowering engineers drives innovation.
We believe empowering engineers drives innovation.
At Rearc Data, we build a wide variety of data pipelines from hard-to-process sources. These include PDF reports, web dashboards, and free text. Often, there is some pattern or visual guide that enables us to extract data cleanly. Other times, the data we want is entirely unstructured in that context, and there’s no “right” way to extract it. To illustrate this problem, let’s use a real-world use case. One of our Health and Life Sciences (HLS) datasets pulls weekly influenza data from a PDF report published by a Spanish agency.
Large language models (LLMs) have introduced a whole new world of opportunities. Suddenly, the interface between machines and human communication has broken down, and ideas that were recently untenable have become almost trivially simple. It’s an exciting time, for sure. Real success, however, depends on wrapping this new technology with software that makes it truly useful. One pattern that has arisen to make that happen is Retrieval Augmented Generation (RAG).
A picture is worth a thousand words - Fred R. Barnard In the rapidly changing world of LLMs, chatbots, and RAGs (Retrieval-Augmented Generation), most of the focus has been on plain text data. However, a wealth of information is available only in image form. What does something look like? How do elements of a diagram fit together? How is the food pyramid laid out? How do two algorithm outputs vary in visual quality?
My Summer Internship at Rearc Introduction & Background Hi, my name is Milan Patel. I am currently in 12th grade at Lenape Regional Highschool and I was a remote summer intern at Rearc from June to August 2023. AI Exploration Project General Problem Statement Given the abundance of datasets available to us, we always find it helpful to streamline idea generation. What does this dataset contain? What can I use this table for?
Introduction Tools to improve the development experience have existed for decades. From syntax checking to code formatting, refactoring to auto-completion, developers working within an IDE frequently make use of extensions to provide additional functionality which will improve their productivity. These development extensions have recently been enhanced by the possibilities driven by AI, to provide better recommendations to developers. As infrastructure engineers, let’s explore some of those new tools and how they interact with Infrastructure-as-Code (IaC) tasks.
