I'M A
THAT BUILDS FOR
IMPACT
ABOUT ME
Not your typical junior designer.
At 42 years old I'm considered a dinosaur amongst my peers. However, I use my 18 years of work experience and 14 years of advanced art education to my advantage. I discovered UXUI only two years ago and learn something new everyday.
This industry is difficult to get into, but I have never backed down from something when I put my mind to it. They say you need relevant design experience so I took my skills from Figma and learned how to build live sites with Framer. With two websites under my belt I'm positioning myself to work at a company as a designer full-time.
The best way to learn is by doing. So I challenge myself to get better everyday. I design something new, learn a new tool, or refine my previous work to get better. I have learned so much so far and I can't wait to see where this journey takes me from here.
SKILLS
AI TOOL INTIGRATION
Predictive Research Synthesis
Instead of spending weeks conducting interviews, non-linear designers use AI to process raw data instantly.
The Skill: Prompting AI engines to extract UX pain points from thousands of customer reviews simultaneously.
The Tools: Polymer for structuring quantitative user metadata, Kraftful for synthesizing app store reviews, and Notion AI to generate immediate user personas.
2. Prompt-to-Layout Generation
Rather than spending days drawing simple rectangles, designers skip straight to medium or high-fidelity mockups to test layouts instantly.
The Skill: Writing functional structural descriptions that generate UI component arrays across various screen breakpoints.
The Tools: Galileo AI and v0 by Vercel for generating editable Figma designs and clean UI code instantly, alongside native Figma AI plugins for rapid asset scaling.
3. Synthetic User Testing & Predictive Mapping
Non-linear workflows allow you to test your layout before a single real user ever sees it, cutting out traditional research bottlenecks.
The Skill: Analyzing mathematical visual patterns to optimize visual hierarchy instantly.
The Tools: Attention Insight and VisualEyes to simulate predictive eye-tracking heatmaps based on historic user data studies.
4. Designing Dynamic AI Interfaces
Modern UX requires knowing how to build interfaces that change layouts dynamically based on what a user types.
The Skill: Designing conversational interfaces, fluid data modules, and flexible states that change layout in real time.
The Tools: Leveraging Claude or the OpenAI API directly to test how your layout morphs dynamically depending on user prompting variables.
Modern non-linear UX relies on rapid data discovery.
Method 01
App Store & Marketplace Scrapers
Function:
They pull direct feedback from the Apple App Store or Google Play Store using free web scrapers.
TOOLS
Rivioo or FeaturePulse:
Allow you to paste any public app URL and download up to 1,000 recent customer reviews as a clean CSV dataset instantly.
USE CASE
Scraping a direct competitor's app to find out what features their users complain about most frequently.
Modern non-linear UX relies on rapid data discovery.
Method 01
App Store & Marketplace Scrapers
Building or improving a mobile app, they pull direct feedback from the Apple App Store or Google Play Store using free web scrapers.
TOOLS
Rivioo or FeaturePulse:
Allow you to paste any public app URL and download up to 1,000 recent customer reviews as a clean CSV dataset instantly.
The Use Case:
Scraping a direct competitor's app to find out what features their users complain about most frequently.
Method 02
🌐 Browser Extensions & Review Audits
For web-based SaaS products, e-commerce stores, or local business platforms, designers scrape browser-rendered pages directly from Google Maps, Amazon, or Yelp.
The Tools:
Extensions like Phantom or dedicated review scrapers allow designers to run a single "Review Audit" command directly on a Google Business profile or e-commerce listing, saving the entire text index as a spreadsheet file in under five minutes.
Method 03
📊 Open-Source Data Repositories
When designing for a broad industry segment (like standard e-commerce carts or generic fitness tracking), designers bypass scraping altogether and download massive, pre-packaged historical datasets.
The Platforms:
Websites like Kaggle house huge, verified open-source datasets, such as the McAuley Lab Amazon Reviews Dataset, which contains millions of categorized customer review text fields ready for prompt processing.
Method 04
🖥️ Internal API & Help Desk Systems
If a designer is working on improving a mature internal product, they pull raw, unedited text data directly from their company's customer relationship systems.
The Pipelines:
Exporting historical logs from tools like Zendesk, Intercom, or App Store Connect. This yields thousands of direct feature requests, bugs, and real user quotes straight from the company database.
The AI Processing Layer
🤖 How Non-Linear Designers Process the Data
Once the designer has the CSV containing 1,000 rows of raw review text, they do not read them individually. They drop the raw file into tools like Claude or Kraftful and prompt:
"Categorize these 1,000 reviews into visual layout issues, functional navigation bugs, and feature requests. Provide a breakdown table of the top 3 friction areas."
Claude
Kraftful
ChatGPT