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When it's about purchasing land, one of the pivotal components is evaluating its value. Land valuation is the process of ascertaining the value of a

Land Report 100 Explore the 2025 Top 100 U.S. Landowners. View Top 100 Presented by: Land Report 100 Explore the 2025 Top 100 U.S. Landowners. View Top 100 Presented by: Land Report 100 Who is America's Largest Landowner? This question is the quest

Nationwide property data and mapping tools for everyone. Surf 159 million land parcels on our map or license them for yours.

Simon Couch demonstrates how the btw package provides context to LLMs through system prompts and tool calls.

Build LIVING software. Your guide to mastering prompts, ai coding, ai agents, and agentic workflows.

Deploy your first application to Azure Container Apps using the Azure CLI containerapp up command.

Learn to create self-hosted CI/CD runners and agents with jobs in Azure Container Apps

Nationwide property data and mapping tools for everyone. Surf 159 million land parcels on our map or license them for yours.

From finances to closing, learn how to buy land in 9 steps so you know what to expect as you start your search.

Look up property lines and ownership info nationwide.

The R package shinymgr provides a unifying framework that allows Shiny developers to create, manage, and deploy a master Shiny application comprised of one or more "apps", where an "app" is a tab-based workflow that guides end-users through a step-by-step analysis. Each tab in a given "app" consists of one or more Shiny modules. The shinymgr app builder allows developers to "stitch" Shiny modules together so that outputs from one module serve as inputs to the next, creating an analysis pipeline that is easy to implement and maintain. Apps developed using shinymgr can be incorporated into R packages or deployed on a server, where they are accessible to end-users. Users of shinymgr apps can save analyses as an RDS file that fully reproduces the analytic steps and can be ingested into an RMarkdown or Quarto report for rapid reporting. In short, developers use the shinymgr framework to write Shiny modules and seamlessly combine them into Shiny apps, and end-users of these apps can execute reproducible analyses that can be incorporated into reports for rapid dissemination. A comprehensive overview of the package is provided by 12 learnr tutorials.

A Unifying Parallelization Framework in R for Everyone

Build production-ready Copilots and Agents effortlessly.

THE Property Data Solution. Our revolutionary tech allows us to get you property and owner data (and lots of it!) faster and cheaper than you've ever been able to before. Slow or buggy applications due to unreliable third party data APIs are a problem of the past.

Unable to find practical examples of idempotent data pipelines? Then, this post is for you. In this post, we go over a technique that you can use to make your data pipelines professional and data reprocessing a breeze.

In this extensive lecture, I, Steven Bucher, a product manager on the PowerShell team, discuss the integration of AI into the shell environment. Over the pas...

This is a book that covers the standard shiny app workflow.

Other than location, what’s it take to run a successful real estate business? We examine a data model to help real estate agencies stay organized.

Most of us are familiar with the apartment rental process. But what does it take to run a leasing office? In this article, we look at a data model designed to do just that.

AI Database Generator is a sophisticated tool that utilizes artificial intelligence and machine learning algorithms to automate the design and creation of database schemas.

Get a quick rent estimate by address or zip code with Rentometer. Compare rental rates and comps to ensure you're pricing your property right.

Use modern Icon libraries to construct customisable leaflet marker icons.

Automatic normalisation of a data frame to third normal form, with the intention of easing the process of data cleaning. (Usage to design your actual database for you is not advised.) Originally inspired by the 'AutoNormalize' library for 'Python' by 'Alteryx' (<a href="https://github.com/alteryx/autonormalize" target="_top"https://github.com/alteryx/autonormalize/a>), with various changes and improvements. Automatic discovery of functional or approximate dependencies, normalisation based on those, and plotting of the resulting "database" via 'Graphviz', with options to exclude some attributes at discovery time, or remove discovered dependencies at normalisation time.