Steven Paul Sanderson II, MPH spsanderson

Creating a Custom VSCode Distribution: rUv Code with Roo Code Integration

A comprehensive guide to building an AI-native IDE inspired by Windsurf and Cursor using VSCode and Roo Code

Introduction

The rise of AI-native IDEs like Windsurf (formerly Codeium) and Cursor has redefined developer productivity. These tools integrate AI agents with deep codebase understanding, collaborative workflows, and streamlined coding experiences. While Windsurf and Cursor are standalone applications, developers can create similar solutions by leveraging Roo Code-an open-source VSCode extension-and building a custom VSCode distribution.

This guide outlines the steps to create rUv Code, a tailored VSCode distribution centered around Roo Code’s AI capabilities, with features comparable to commercial AI IDEs.

SynthLang: A Hyper-Efficient Prompt Language for AI

SynthLang is a hyper-efficient prompt language designed to optimize interactions with Large Language Models (LLMs) like GPT-4o by leveraging logographical scripts and symbolic constructs. By compressing complex instructions into fewer tokens (reducing token usage by 40–70%), SynthLang significantly lowers inference latency, making it ideal for latency-sensitive applications such as high-frequency trading, real-time analytics, and compliance checks.

Additionally, SynthLang mitigates English-centric biases in multilingual models, enhancing information density and ensuring more equitable performance across diverse languages. Its scalable design maintains or improves task performance in translation, summarization, and question-answering, fostering faster, fairer, and more efficient AI-driven solutions.

Large Language Models (LLMs) such as GPT-4o and Llama-2 exhibit English-dominant biases in intermediate embeddings, leading to inefficient and oft

This Python script utilizes the GeoPy geocoding library to batch geocode a number of addresses, using various services until a pair of latitude/longitude values are returned.

https://gist.github.com/rgdonohue/c4beedd3ca47d29aef01

##How To Send a Pull Request on GitHub

Fork it
Create your feature branch (git checkout -b my-new-feature)
Commit your changes (git commit -am 'Add some feature')
Push to the branch (git push origin my-new-feature)
Create new Pull Request

	{
	"customModes": [
	{
	"slug": "fire-crawler",
	"name": "🔥 Fire Crawler",
	"roleDefinition": "You are a specialized web crawling and data extraction assistant that leverages Firecrawl to gather, analyze, and structure web content. You extract meaningful information from websites, perform targeted searches, and create structured datasets from unstructured web content.",
	"customInstructions": "You use Firecrawl's advanced web crawling and data extraction capabilities to gather and process web content efficiently. You:\n\n• Crawl websites recursively to map content structures\n• Extract structured data using natural language prompts or JSON schemas\n• Scrape specific content from web pages with precision\n• Search the web and retrieve full page content\n• Map website structures and generate site maps\n• Process and transform unstructured web data into usable formats\n\n## Web Crawling Strategies\n\n1. Site Mapping: Use FIRECRAWL_MAP_URLS to discover and map website structures\n2. **

	{
	"slug": "deep-research",
	"name": "🔍 Deep Research Mode",
	"roleDefinition": "You are a specialized research assistant that leverages Perplexity AI to conduct deep, comprehensive research on any topic, creating structured documentation and reports through a recursive self-learning approach.",
	"customInstructions": "You use Perplexity AI's advanced search capabilities to retrieve detailed, accurate information and organize it into a comprehensive research documentation system writing to a research sub folder and final report sub folder with ToC and multiple md files. You:\n\n• Craft precise queries to extract domain-specific information\n• Provide structured, actionable research with proper citations\n• Validate information across multiple sources\n• Create a hierarchical documentation structure\n• Implement recursive self-learning to refine and expand research\n\n## Research Documentation Structure\n\nFor each research project, create the following folder structure:\n\n```\nresearch/\n

	# libs --------
	library(jsonlite)
	library(tidyverse)

	# urls --------
	base_url <- "https://providers.anthem.com"
	json_url <- "https://providers.anthem.com/sites/Satellite?d=Universal&pagename=getdocuments&brand=BCCNYE&state=&formslibrary=gpp_formslib"

	# headers -----
	# A session ID is required to successfully send http GET requests to the server. You can copy a session ID from your browser's inspector network tab. The session ID below might be expired at this point.

	# Let's make some data to play with

	set.seed(1234)
	v1 <- rexp(500)
	v2 <- rnorm(500) + log(2)
	v3 <- -rgamma(500, 2.5, 3)
	v4 <- runif(500, -2,4)

	# Look at the data
	layout(matrix(c(1:4), nrow = 2))