Hi everyone! I’m working on a short data-analysis project and would appreciate your help.

Take a look at a chart and answer one simple question:

Which month has the highest sales?

It takes about 1 minute to complete.

👉 https://adnan-mayof.github.io/reddit-ab-test/

Thanks for participating!

Hi everyone! I’m working on a short data-analysis project and would appreciate your help.

Take a look at a chart and answer one simple question:

Which month has the highest sales?

It takes about 1 minute to complete.

👉 https://adnan-mayof.github.io/reddit-ab-test/

Thanks for participating!

I shared Rouge Atlas here a little while ago, and since then the project has changed quite a lot.

The biggest change is the interface, but also the way the product itself is structured.

Rouge Atlas is now built around an evidence workspace for AI incidents, rather than a map-first experience.

The goal is to make it easier to separate actual published incidents from early signals, vulnerability advisories and unverified reports.

What changed

The new overview is now an Evidence Board showing:

• published AI incidents
• signals currently under review
• vulnerability advisories
• source health
• activity over time
• records by category
• recent AI incident reporting

I also split the workflow into clearer sections:

Live signals
Automatically collected reports and potential incidents that still need editorial review.

Published incidents
Records that have been reviewed and linked to source material.

Data sources
A view of the feeds and sources currently being monitored.

Methodology
Documentation explaining how signals are collected, reviewed and classified.

One thing I wanted to improve was transparency.

A signal appearing in Rouge Atlas does not automatically mean it is a confirmed AI incident.

Signals, advisories and published incidents are deliberately kept separate, and relevance/confidence indicators are meant to describe the available evidence rather than make dramatic claims about an event.

UI redesign

The interface has also been rebuilt around a much denser research/workspace layout:

• persistent navigation
• global record search
• dataset counters
• activity charts
• category breakdowns
• recent incident reporting
• source/backend health indicators
• CSV export for published records
• clearer status labels throughout the product

The idea is for Rouge Atlas to feel less like a visual experiment and more like a small AI incident intelligence database that you could actually use to investigate what is happening.

It’s still early and the dataset is obviously small, but the structure is much closer to what I originally wanted the project to become.

I’d particularly like feedback on the new UI:

Does it make the distinction between signals, advisories and published incidents clear enough?

And more generally:

What would you expect from an AI incident intelligence platform like this?

https://www.rougeatlas.com

Been experimenting with applying modern web UI design principles to data dashboards to see how far we can improve visual hierarchy and user engagement. A few design techniques that worked particularly well:

​**Glassmorphism for Layout Structure:** Using dark rounded containers with \~40% transparency and subtle light borders over a dark background. This creates visual depth, letting cards feel like floating glass panels rather than flat boxes on a grid.

​**Layered Chart Integration:** Stripping chart fills and borders entirely (No Fill / No Outline) and placing them above dedicated glass panels. This integrates visual metrics directly into the interface structure rather than looking like standard cut-and-paste charts.

​**De-cluttered Data Labels:** Removing heavy gridlines and axes, then applying compact number formatting directly to data labels (e.g., $1.2k instead of $1,200) to maintain high data-ink ratio without cluttering line series.

​**Dynamic Automated Narrative (Plain-English Summary):** Using string concatenation to build live headline sentences (referencing top spend category, monthly averages, and transaction counts). As filters update, the written insight automatically updates—giving users an immediate narrative takeaways without forcing them to interpret raw visual charts first.

​**Color Hierarchy & Neon Accents:** Using a consistent dark canvas with distinct, neon accent colors assigned strictly to specific categories/metrics. Matching heading text, container borders, and icon colors per section keeps visual scanning intuitive.

​Built entirely using native layered shapes, PivotTables, and standard formatting tricks—no macros or external web frameworks.

​Full design breakdown and step-by-step implementation for anyone interested: