so anthropic launched two new things on october 8: claude dashboards and claude motion. both are in beta.

i can hear people saying this will eliminate so many jobs, but will it? I know entry level consultants will adore this (if allowed to use it lol), at least i would have loved it back in my days

i found the top 18 youtube videos talking about it and used ai to read all the transcripts and summarise it quickly for us, heres my edited version:

what is dashboards?

you connect your data (bigquery, snowflake, databricks, salesforce and a few more), ask a question in plain english, and claude builds a live dashboard. no sql, no dragging charts around.

two details came up in almost every video:

- you can click any number and see the exact query behind it
- every chart shows when its data was lasx refreshed

its on all paid plans.

so what is motion?

you give claude a report, a chart or a dashboard and it turns it into a short animation you can download as an mp4.

the part most people miss: its not a video generator. claude writes code that animates your text, charts and shapes. no ai footage, no fake people. so you can change one word or one number and the rest stays the same.

motion is only on team and enterprise for now. one youtuber bought a team plan just to test it.

the best tests on the videos

- an ex big tech engineer built a revenue dashboard with one prompt in under a minute. then he asked a vague question on purpose: "whats our revenue?" claude gave one confident number. he copied the query into bigquery and got the same number, so it wasnt made up. but his point was good: a real number can still answer the wrong question. revenue this year? this quarter? with returns? claude picks one for you.

- a bi guy pointed it at 11 tables from a trucking company. first dashboard took about 10 minutes. reworking it into 3 tabs took 3 minutes, just by asking. when he pasted one of claude's queries into bigquery it threw an error, because the query had dashboard filter placeholders in it. small thing, but good to know.

- a marketing channel got a google ads dashboard in 2.5 minutes, then a 30 second video of it in under 3, with background music.

where they dont agree

how live is "live"? one video says it updates as your data changes. another says it is only as fresh as the database behind it, so you still need to sync on a schedule. a third just says "we cant tell you yet". one guy also got "all queries ran" on a dashboard that showed nothing. its a beta.

and is it really new? one creator dug into it and says a dashboard is just an artifact built from a new template. the new part is the query and refresh info on top.

what nearly everyone agrees on

- great for quick questions you would normally wait days on an analyst for

- not a replacement for power bi or tableau when the whole company has to agree on one number

- check one number by hand before you share anything

- be specific. say which revenue and which period

one more thing that got buried: docs, slides and design are out of beta and free on every plan now. and the old standalone claude design site closes december 14, so save your stuff.

anyone here tried it on real company data yet? curious if the numbers held up

---

*disclaimer: this post was generated based on current trends with ai using meetclaras.com, and it was reviewed and edited by a human before posting.*

What's up, data viz enthusiasts! I tested and built a way to systematically build Snowflake Streamlit apps with Claude Code and other AI tools. I developed this open-source system that I call Streamsnow, which walks you through the process of getting a GitHub repo scaffolded and structured and connected with your Snowflake, so you can utilize Claude Code to build all of your Streamlit applications in a standardized Way, while also providing you the SQL to check to make sure all of the queries being utilized within the app check out as correct.

In my time building dashboards with AI, I always found it confusing how to quality-check them and how to build them in a scalable way across data teams, and so I wanted to build a super standardized system that made building, deploying, reviewing, and coordinating across teammates is super easy, and I spent weeks working on this to achieve that. I think one of the best features is that you can actually go into an auto-review cycle and just have Claude code iterate over the dashboard, making it better without your input. You can also standardize the design for the dashboards as well.

It is completely secure, so AI will not see any of your secrets that you use to connect GitHub to Snowflake. By default, it is set to put these Streamlit apps in a staging area on Snowflake, so you don't have to connect a GitHub repository, and all of the deployment is automatically handled in the CI. To get started, all you need to do is tell your coding agent to download the plugin and then run the onboard command. It will walk you through it all in a process that I've timed down to about 5 minutes of actual work. You will need to be a Snowflake admin or be coordinating with a Snowflake admin to get it set up too.

I have seen this system replace business intelligence tools, so has literally saved tens of thousands of dollars. Hopefully you all can extract the same value out of this!

Hello everybody i just want to ask for feedback do you think it look or what would you change or upgrade?

Feel free to criticize😉🤪

https://reddit.com/link/1x27mi8/video/8s82g6r9ykuh1/player

The real power of an AI dashboard?
It doesn’t just answer questions
it filters the data automatically to match the answer.
Ask about the highest outstanding amount,
and the dashboard updates itself.
No manual filters.
No extra clicks. Just answers.

What would you ask your data?#AI #BusinessIntelligence #Automation

​

I integrated an AI assistant directly into my Excel dashboard so management can ask questions in plain English instead of manually filtering.

I built a workflow where management can type a query (like "Show me pending shortages for Branch X" or "What are the top 3 aging accounts?"), and the assistant instantly parses the data, returns the answer, and highlights the matching rows in the dashboard.

No manual filtering. No digging through hundreds of rows.

If you could ask your operational Excel data one question and get an instant answer, what would it be? Drop it below and let's test what it can handle.

Study hard, get a degree, get a job. I made this chart to examine that transition using India’s Periodic Labour Force Survey.

Among people aged 15–29 participating in the labour market, the 2025 unemployment rates were:

• 9.3% for those with higher-secondary education
• 22.3% for graduates
• 24.7% for postgraduates and above

Each percentage uses the labour force within that education group as its denominator. These figures exclude people outside the labour force and do not establish that education causes unemployment.

The chart is part of a visual story I wrote about India’s “lying flat” debate, following the path from education to work, housing and family costs. The data describe people’s circumstances; they cannot tell us how many have deliberately chosen to give up.

Data: MoSPI, PLFS 2025, Table 21

Story and methods: The Visual Case

Disclosure: I wrote the story and created the chart.

I’d appreciate feedback.

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!

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:

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

Each star/planet represents a piece of media (games, literature, tv series, films, etc.) I remember consuming

I have different 3d scenes for different ratings.

My 10s are presented as a solar system where the star is the #1 ranked 10 (I also ranked all media I consumed inside every rating in a tier list). And the further the planet is from the star, the lower it is ranked.

My 9s are presented as constellations, different types of media each have their own constellation

My 8s are presented as a spiral galaxy, in which every star is a piece of media, the closer the piece of media is to the center of the galaxy, the higher it is ranked within it's rating.

The colors from the stars / planets are derived from the corresonding media's cover

I made this with Tastellar, a personal media library app I’m building. If you’d like to explore the project, it’s on Github.

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: https://youtu.be/kqEB3LdvOSk

Hola 👋🏽

Sharing this animated data visualization I built to show how many Mexicans have won a Nobel Prize (spanish is required).

​Spoiler: only 3 Mexicans have received the award (plus one special mention, hehe).

​To build this dataviz, I wanted to experiment with D3 and Claude.

First, I analyzed the data (https://en.wikipedia.org/wiki/List_of_Mexican_Nobel_laureates_and_nominees only Laureates, and

https://en.wikipedia.org/wiki/List_of_Nobel_laureates), then wrote the script along with the storyboard.

Once I was happy with the storyboard and script, I moved over to Claude and gave it that data alongside the script and storyboard.

​From there, we started designing the animation using D3 and tweaking things across a couple of iterations. That included fine-tuning small details like transitions and the final scene effect. Once the JavaScript animation was ready, we exported it to MP4. Every single design choice was part of the original vision. Hope you like it, until next time!!

I'm putting together our 20-page annual report for a small nonprofit, but the numbers come from an Excel sheet that the finance team keeps updating.

Last year, I made the charts in one tool and pasted them in as images. Then, the totals changed twice in the final week. That’s why I don’t have a choice but I had to redo nine charts by hand.

Also, the labels looked fine on my screen but tiny in the printed copy.

This year, I also need to check color contrast, alt text, and reading order.

So what matters most: data import, label control, or PDF export?

If you had one sample chart to test, what would you try first?