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{ | |
"$schema": "https://raw.githubusercontent.com/jsonresume/resume-schema/v1.0.0/schema.json", | |
"basics": { | |
"name": "Wyatt Walsh", | |
"label": "Data Integrations Software Engineer | AI, Data, Software, & Optimization | JPMorgan Chase & Co.", | |
"image": "https://i.ibb.co/FLtKMt6p/avatar-min.webp", | |
"email": "[email protected]", | |
"phone": "(209) 602-2545", | |
"url": "https://www.w4w.dev", | |
"summary": "Accomplished Senior Software Engineer and data engineering specialist bridging advanced operations research with AI-driven optimization strategies. Drawing on extensive experience at JPMorgan Chase & Co., I architect compliance-centric, real-time data infrastructures that empower critical enterprise decision-making. Skilled in synthesizing theoretical constructs into robust, high-availability software pipelines, I have led cross-functional teams, implemented fault-tolerant frameworks, and championed leading-edge AI solutions. My enduring commitment to research-driven innovation and stringent |
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// Final Production Version: p5.js sketch for exporting a highly detailed SVG background | |
let svg; | |
function setup() { | |
svg = createGraphics(windowWidth, 300, SVG); | |
noLoop(); | |
} | |
function draw() { | |
// Gradient Background with Complex Color Layers |
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Crawl4AI (version 0.3.73) is a powerful, open-source Python library tailored for large-scale web crawling and data extraction. It simplifies integration with Large Language Models (LLMs) and AI applications through robust, efficient, and flexible extraction techniques.
This notebook is mainly inspired by Bo's tweet about using Jina Reader to follow a website's sitemap.xml
for check grounding.
: | |
""" | |
Merges all .txt files found in the specified input directory into a single output file. | |
Parameters: | |
- input_dir (str): The directory to search for .txt files. | |
- output_file (str): The path to the output file where the merged content will be stored. |
How would this best be enhanced, improved, optimized, and refined to produce the most advanced version possible?
What would the full implementation look like with these enhancement recommendations robustly integrated throughout the existing code?
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