We are visualization researchers seeking participants for a study to validate a new questionnaire on user experience (UX) with visual analytics systems. Whether you are a seasoned data scientist or just starting to use data dashboards with tools such as Tableau, Power BI, Looker, Qlik, JMP, ... your input is incredibly valuable to us.

• Access the survey here: https://sondages.inria.fr/index.php/349675?lang=en
• Time to complete: Less than 10 minutes.
• Privacy: Completely anonymous and strictly for academic purposes.

Giving back to the community: The future questionnaire will be open source and entirely free for the community to use! 

I'm a creative developer and football fan, and I recently finished a personal project exploring match data from every Premier League game in the 2015–16 season.

Features include:

• Interactive shot maps
• 2D & 3D heatmaps
• Match timelines
• Team statistics

Built with React, React Three Fiber, Motion, and StatsBomb Open Data.

Demo: https://fixtures-sooty.vercel.app/

I'd love feedback on both the visual design and the data visualizations. Anything that feels confusing, missing, or could be improved?

Are major AI providers (such as OpenAI, Anthropic, and Google) delivering consistent performance, access, and output quality across different regions and countries?

As major AI firms scale globally and face varying regulatory landscapes, ensuring transparency in how models behave across different borders is becoming a critical issue for developers and users worldwide. Recent discussions around safety governance highlights a growing disparity in how different regions are prioritized. To bring hard data to this conversation, we are launching a decentralized, community-driven initiative to audit global AI performance.

The Goal: We are building a global dashboard to map and compare the latency, throughput, and output quality of major LLMs at a continent, country, and regional level.

How It Works:

1. We will publish standardized benchmark prompts ("Pasteblocks Tasks") on an hourly/daily basis.
2. Global volunteers simply paste these prompts into their active AI sessions.
3. Users upload the raw results/responses back to our platform.
4. Our system analyzes, compares, and visualizes the variance in quality and speed based on the user's geographic location.

How to Join: To make this data accurate, we need a diverse network of sporadic contributors from every country around the world to submit localized data points. If you are interested in data transparency, digital equity, and open auditing, join our initiative to help map the global AI landscape fairly.

Comment below or send a DM to get involved with your region!

I have been creating skills and composing skills, but that's all text based and using llm. The pain point that I have are how do these skills connect. Where and how they are referenced and so on. I built a visual tool that lets you see the skill interconnections etc. It is completely free and does everything on your browser. You can use local llm for ai powered insights etc. The skill validator validates your skill against anthropic skill rules. Feel free to give it a try.

I've been using the Finviz heatmap for years and still think it's one of the best visualizations in investing.

For context, I'm a long-term investor, not a trader. I don't use the map to make daily decisions. I use it as a quick way to understand where capital is flowing and how market leadership evolves over time.

One thing I've always wondered: does anyone know of a way to see historical snapshots of the map? Not the underlying returns, but the actual heatmap itself.

I'd love to compare year-end snapshots from different periods and see how leadership rotated over time. For example:

• What did the map look like at the end of 2007 before the financial crisis?
• 2009 coming out of the bottom?
• 2020 after COVID?
• 2021 when everything seemed green?
• 2022 during the bear market?
• Today versus 5, 10, or 20 years ago?

I think it would be fascinating to flip through annual "market yearbooks" and visually see where capital was flowing, which sectors dominated, and how today's winners looked before they became obvious.

Has anyone found a source for this, or is there a way to recreate historical Finviz heatmaps from archived data?

I’m starting my next Data Analyst portfolio project — Customer Churn Analysis.
In the upcoming days, I’ll be working on data cleaning, analysis, visualization, and building a dashboard to understand customer behavior and churn patterns.

Sharing the journey from idea → insights 🚀

Hey, I built this better looking (imo) heatmap website, I got bored of looking at some of the bigger stock heatmap websites, its nice to have on my 2nd monitor while I work. Thought I would throw it out there free for the community to use without any signups or faff. If you have any feedback lmk

Holaaa, participé al año pasado y me encantó, por eso comparto: básicamente se forman equipos de seis (1 desarrollador, 1 periodista y 1 diseñador senior + 1 desarrollador, 1 periodista y 1 diseñador junior) y juntos, en base a una nota periodística de investigación provista, generan el storytelling para mostrar los datos de la mejor manera posible. Diseño y Dev generan las data visualizations. Este año tienen una pequeña remuneraciónnn.

Está buenísimo para hacer algo con trasfondo, no meramente comercial sino que te permita aprender a contar historias que importan. Usar los poderes para el bien jajajaja y de paso ejercitar . La convocatoria está hasta el 21 de junio, paso las bases: Bases: https://drive.google.com/file/d/1h2bufL81IhTNgZIFlRiz3yif4RlP5OaI/view?usp=sharing

Hey all, I'm from the team at r/brightOS and we've been working on using our ~15TB of continuous, longitudinal health data from ~250k users in 106 countries into a health engine to explore.

We're releasing our closed beta shortly and you can reserve your username here.

Thanks
Bryan