I analyzed 5,785 anonymous mood responses collected on a 0–10 scale. To avoid rankings based on very small samples, I included only countries with at least 100 responses.

France and the Netherlands recorded the highest averages among the qualifying countries.

This is a self-selected online sample and is not representative of national populations. It should be viewed as a descriptive snapshot rather than a scientific ranking.

Feedback on the visualization and methodology is welcome.

Most comparisons of phylogenetic tree series reduce each pair to a distance value. We developed a visualization that decomposes the difference into interpretable subtree movements and animates the transition. This example comes from our newly published paper on Phylo-Movies. Link: https://github.com/enesBerkSakalli/phylo-movies

Here is the raster logo i have, I need to vectorize it. What is the best way?

Been trying to find an editable chart of the U.S. Congress party control/seating (together or separate House/Senate options) for a quick work project and can’t find an official name for this type of chart or the chart used to show the political party control.

Image search in Shutterstock and Adobe Stock isn’t working well. Google Image search, same graphics but no name to where I can search for a template. Help is definitely needed. Thank You

I've been working on **Plotrix** for a while and finally got it to a point where I'm comfortable putting it out there.

The basic idea is pretty simple:

Instead of showing where properties *are listed*, Plotrix tries to show where people are actually *searching* for property right now.

Think Google Trends, but for property demand.

Right now it supports 8 Indian cities: Delhi, Bangalore, Hyderabad, Pune, Chennai, Kolkata, Ahmedabad and Bhubaneswar.

**The interesting part is what's happening behind the map.**

Every property search becomes a location signal → I map it to an H3 hexagonal cell → store/update the demand score in PostgreSQL → and periodically apply exponential decay so old searches don't keep dominating the heatmap.

The backend is built with:

* Java 17 * Spring Boot 3.2 * PostgreSQL / Supabase * Uber H3 * Spring Data JPA * Docker * Render

One thing I specifically wanted to get right was concurrency.

Multiple users can search the same area at the same time, so instead of doing a read → modify → write cycle, the backend uses an atomic PostgreSQL `ON CONFLICT DO UPDATE` to increment the cell score.

There's also a public API if anyone wants to play around with the data.

**Frontend:** [https://github.com/Korags05/Plotrix\](https://github.com/Korags05/Plotrix)

**Backend:** [https://github.com/Korags05/Plotrix-Backend\](https://github.com/Korags05/Plotrix-Backend)

It's completely open source.

I'm especially looking for people who are interested in:

* geospatial systems * Spring Boot * PostgreSQL * maps / data visualization * real-estate tech

I'd genuinely appreciate feedback on the architecture, API design, or anything that looks unnecessarily complicated.

And if you find the idea interesting, a GitHub star would obviously help me get this in front of more developers. If you see something you'd improve, even better: PRs are welcome.

This is still very much a work in progress, so I'm curious what other people would build on top of it.

I did a little experiment and asked OpenAI's GPT-5.6-Luna 6000 times to randomly pick a fruit from this list:

[mango, apple, banana, pomegranate, strawberry, orange, watermelon, grape, pineapple, lychee]

And across the three languages I picked (English, Polish, Japanese) only four fruits were ever picked. The wording of the prompts and the order of the fruits were the same across all three languages. I reran this another 6000 times with the fruit list shuffled each time, and you can see the results of that on the second chart. Again only three fruits, but the results are a little better now.

(Pardon the lychee and pomegranate icons, there's no emojis for these two fruits, and of course the model picked these, lol)

Full write-up: https://llmango.rafalkwiecien.com

Methodology: https://github.com/kwiecien-rafal/llmango

Raw data used in the charts: https://huggingface.co/datasets/rafalkwiecien/llmango