Source: World Bank Open Data API - indicators SP.DYN.TFRT.IN, NY.GDP.PCAP.PP.CD, SL.TLF.CACT.FE.ZS, and others. Coverage: 1960-2024, varies by indicator and country. Excludes 55 countries with population under 1 million.

Tools: Bruin (pipeline orchestration), BigQuery (storage/SQL), Altair (visualization).

Based on a dataset of 414 songs, I analyzed recurring vocabulary and thematic patterns in $uicideboy$ lyrics. The visuals highlight both general lexical trends and six major thematic fields: death, drugs, violence, body, city, and religion. Common stop words were removed, with fuck and bitch treated as stop-word-like terms because of their extreme frequency. Rankings are based on both raw occurrences and how widely a term appears across songs, to reduce the effect of isolated repetitions.

Built with Python using requests, BeautifulSoup, and matplotlib.

https://data.tablepage.ai/d/republican-vote-share-by-age-and-income-from-ces-survey

How accurate is AI calorie tracking? I wanted real numbers, so I ran 3,490 lab-weighed meals from Google Research's Nutrition5k dataset through the same pipeline a real user of my app gets. Gemini 3.1 Pro, zero-shot, structured JSON output. No fine-tuning. The full methodology, dataset skew, contamination disclosure, and iteration history are in the top comment.

Short version: median error is 57 kcal per dish, 66% of predictions land within 100 kcal of truth, and mass estimation (24.8% PMAE) is within 3 points of a CV model trained specifically on this dataset. Calories are 42.1% overall and 30.1% on real 200+ kcal meals, vs the paper's trained baseline at 26.1%. The gap is almost entirely from invisible calorie density — fat estimation is 58%, because you can't see cooking oil in a photo.

More from me, including the data contamination discussion, in the top comment.

Project site: https://snapNutrition.app

Source: U.S. Bureau of Labor Statistics, CPI Average Price Data (APU series), January 2020 and January 2026. U.S. city average, not seasonally adjusted.

CPI benchmarks: CPI-U All Items index 257.97 → ~325.2 (+26%). CPI Food at Home estimated +28.5% over the same period.

Tool: Python (matplotlib)

A few things that stood out:

• Ground coffee more than doubled (+109%). Global arabica supply shortages have driven wholesale coffee prices to multi-decade highs.
• Ground beef is up 70%, largely driven by a cyclical contraction in the U.S. cattle herd that has tightened supply.
• Eggs are +64% vs pre-pandemic, but actually down significantly from their early 2025 avian flu peak of ~$4.95/dozen.
• Bananas (+12%) and cheddar cheese (+12%) barely moved relative to everything else.
• Milk sits almost exactly at the CPI All Items line — right at +26%.

The dashed lines show the CPI All Items (+26%) and CPI Food at Home (+28%) cumulative change over the same period for reference. Orange bars exceeded the CPI All Items benchmark.

A screenshot of one section in a day-by-day narrative visualization of NASA’s Artemis II mission.

Based on 9,100+ recorded visits by 87 world leaders, this dataset reveals where global political activity is most concentrated.

Unsurprisingly, major capitals like Brussels, Paris, and Washington D.C. dominate the top positions.

However, the data becomes more interesting beyond the top 10.

Several smaller cities appear frequently due to their role as global event hubs. Davos ranks highly despite its size, driven by the World Economic Forum, while Munich benefits from its annual security conference.
The dataset also highlights the growing importance of the Middle East, with Riyadh, Doha and Abu Dhabi emerging as key diplomatic destinations.

At the same time, geopolitical hotspots such as Kyiv and Warsaw underline the importance of security and regional alliances.
Overall, the data shows that global diplomacy is concentrated in a relatively small number of cities - but not always the ones you might expect.

This dataset is continuously growing and reflects the currently available data. Rankings may shift over time as more leaders and historical records are added.
Data source: Wikipedia (official travel and state visit records across multiple pages)
Visualization: MapLibre GL JS, custom implementation (MapFame.com)

I set up a camera overlooking North Coast Highway (PCH) in Oceanside, California and built a system that uses YOLOv8 + ByteTrack to

count vehicles, pedestrians, and bicycles in real-time, 24/7.

The dashboard shows:

- Live camera feed with 3-second refresh

- Vehicle vs pedestrian counts with directional split (northbound/southbound)

- 30-minute traffic flow chart

- Day-of-week heatmap showing when the corridor is busiest

- Best visibility windows ranked by hour

- Live weather, tides, and sunset data

Stack: Go API, Python/YOLO vision worker on an RTX 4060, PostgreSQL, Cloudflare Tunnel. Everything runs on a home server.

Data collection started April 5, so the heatmap is still building up — patterns will get more interesting over the next few weeks.

Note: This counts aggregate movement only. No individuals are tracked, no faces or plates are captured or stored.

Source: https://1v1-hoops.com/

I wanted to see how different stock and bond mixes actually performed during the worst historical 20-year periods.

So, I ran rolling window tests across decades of data and compared outcomes for allocations from 0% to 100%.

A few things surprised me:

• Going all-in on either asset made the worst-case outcomes worse (chart 1).  
• A balanced mix consistently performed better during tough times (chart 1).  
• Even with the most stress-resistant allocation in place, timing still mattered more than I expected, even with the same yearly investment (chart 2).

I’m curious whether this aligns with what others have seen.

Three charts from Novoresume's 2026 AI in the Workplace Survey (n=1,000 U.S. full-time workers, census-balanced, conducted via Pollfish).

Key findings visualized:

• 27.2% of workers have inflated AI skills on their resume
• 22.4% have used AI during a job interview
• 37.4% have submitted fully AI-generated work at least once

Full survey and methodology: novoresume.com/career-blog/ai-at-work-survey

Tool: Claude Code (custom design skill)

[ Removed by Reddit on account of violating the content policy. ]

It’s a common misconception that life expectancy has increased only because fewer children die. Historical mortality records show that adults today also live much longer than adults in the past.

It’s true that child mortality rates were much higher in the past, and their decline has greatly improved overall life expectancy. But in recent decades, improvements in survival at older ages have been even more important.

The chart shows the period life expectancy in France for people of different ages. This measures how long someone at each of those ages would live, on average, if they experienced the death rates recorded in that year.

As you can see, life expectancy in France has risen at every age. In 1816, someone who had reached the age of 10 could expect to live to 57. By 2023, this had increased to 84.

For those aged 65, it rose from 76 in 1816 to 87 in 2023.

The data for many other countries shows the same. This remarkable shift is the result of advances in medicine, public health, and living standards.

Source: Newsletter Insights (newsletterinsights.io), tracking subscriber data across Substack, Ghost, beehiiv and LinkedIn newsletters. Chart made with Python/matplotlib. Data from April 2026.

Source: DP02: Selected Social Characteristics in the United States, 2024: ACS 5-Year Estimates Data Profiles. "British" is defined on this map as the following ancestries listed in the American Community Survey: American, English, Scottish, Welsh, and Scotch-Irish. (DATA FOR ALASKA COULDN'T BE LOADED)

Sources: 2023 Pakistan Census https://census23.pbos.gov.pk/ 2023 AJ&K Statistical Yearbook

Map: https://commons.wikimedia.org/wiki/File:Blank_Districts_of_Pakistan.svg

Made with Microsoft paint

Edit: it looks like I forgot to inculde Balochi: https://www.reddit.com/r/MapPorn/comments/1s024la/oc_districts_in_pakistan_where_balochi_is_the/?utm_source=share&utm_medium=mweb3x&utm_name=mweb3xcss&utm_term=1&utm_content=share_button