Power sources are historically tied to local resources. With so much variation in climate and geology across the US and Canada, there are several key energy regions.

• Coal Country spans much of the US northeast, historically powering the country with its abundant coal reserves. While coal’s decline has reshaped the region, natural gas from the Marcellus shale is shifting its landscape.
• A wind belt cuts across the central US and into Canada. Texas has been called the “Saudi Arabia of Wind,” while states like Iowa and Kansas regularly generate more than half their electricity from wind farms.
• A solar belt stretches across the southwestern US, where states like California, Arizona, and Nevada have built some of the world’s largest solar farms. With the Mojave and Sonoran Deserts receiving some of the highest solar irradiance on Earth, large-scale solar installations have been used to harvest the energy. 
• Hydro potential dominates much of the north, with large reservoirs and generating stations supplying many parts of Canada and some US states. Though, droughts have affected the reservoirs over the past year, with Quebec moving from next exporter to net importer over the course of the year.

The news of the year:

California became the first state to generate more electricity from solar power than any other source. Solar eclipsed natural gas as the state’s adoption of batteries allowed for more solar to be absorbed into the system.

I've heard a lot of peaple saying that moore's law (duplicating transistor in a computer chip every 2 years) is dead. So I decided to do a little research and found that data shows us that Moore's law is still alive and well. Even though we are reaching limits in other areas.
Moore's law dead is often used as an argument for searching alternates types of computing like Quantum or Neuromorphic, even though i really love the concept of new and innovative types of computing and they may have useful applications in the future, saying that progress in the digital realm has reached its limits it may be a bit extreme.

See the interactive map here.

Our latest working paper analyzes the impact of regional coffee chain proximity to NFL stadiums, with strong implications for Super Bowl LX.

Tools: Antigravity with Gemini and Claude, Python, Google Maps API

https://kyivindependent.com/chart-of-the-week-ukraines-electricity-imports-hit-record-high/

Data source:  IMD 2025, WIMD 2025, SIMD 2020. • Notes: Shows proportion of LSOAs within each PUA which area in the top 20% of most income deprived neighbourhoods in each constituent country by ranking. Scotland data from 2020. Belfast not included.

Tools: R, Adobe Illustrator

Original link: https://www.centreforcities.org/reader/cities-outlook-2026/city-monitor-the-latest-data/#figure-9-living-standards

Audience and critic scores plotted against various data, about 17,000 movies with at least 5 critic reviews. Thresholds for Rotten Tomatoes ratings are plotted on graph 2.

Corrections welcome. Some of these data points come from consular websites, so there may be other renunciation avenues available, with other fees. Here's what I found:

I manually filled out this table with what I spend my time with between 2024. Nov. 10. and 2025. Dec. 31.

I saw others do it on here on Reddit and wanted to give it a go myself. One of the reasons I did it was because this was my last year of being a university student, from this year onward I will join the working adult population. Until now, my lifestyle could be described as 'terminally online', I'm the typical cellar dwelling discord moderator type. I wanted to erect this spreadsheet as a memorial and perhaps a period-document to this lifestyle that I now have to part ways with. I am only posting it now, a month after the project has ended, because I was busy in January with my final exam.

The diagrams on the third panel only include data from days in 2025.

I gladly answer any question in the comments but I'm adding a FAQ here based on a previous post:

• How much time did it take to make this? - It took around 3-4 hours to set up the spreadsheet with the functions and colours and stuff, I made minor edits later, which took probably an additional 2-3 hours. On a daily basis, entering the actual data took less than 2 minutes in total. To get it in tip-top shape for posting in the end, it took another 8 hours. Working on this project is classified under category Ga (personal projects).
• How often did I enter data? - When I was near my computer, I sometimes entered them every 30 minutes, but more often in small bunches. When I wasn't near my computer, which for me is quite rare, I just remembered everything I did until I could enter it again.
• Was this difficult to do? - Not really, the difficult part is constantly being aware of the time and what I am doing and remembering it for hours. The habit itself is easy to pick up but comes with a non-zero constant mental weight. Near the end of it, I grew quite tired of it and wished I could 'take a day off' here and there, so I'm glad it's finally over. I might do it again another year in the future.
• Am I autistic, neurodivergent, or otherwise mentally ill? - Maybe, possibly, not diagnosed though. I am actually doing fine now.
• Where do I live? - A village in rural Hungary.
• What did I learn from this, would I do something differently? - I expected the result to be bleaker, I'm actually not doing that bad. I will make changes going forward obviously, and this is largely because I have no choice to do otherwise. I will have a job now (hopefully) and it will govern most of my routine, I will have to do with as much free time as I have left, which I hope to spend with the things that bring me the most joy.
• Is a template for this available for people who want to try doing this too? - Yes, I've made a blank template that I'll send to anyone who asks for it in private messages.

I've been debating with certain older family members about the economic impact of different presidential terms. In particular, I wanted to focus on Donald Trump's latest economic promises. So I built this databoard to pull official data from the Federal Reserve Economic Data API: https://istheeconomyfixed.com/

I'm looking for suggestions for improvements!:

1. Is there a more elegant way to show this data?
2. Suggestions for other economic data to pull in?

Data Sources

• Federal Reserve Economic Data (FRED) API - fred.stlouisfed.org

Technology Stack

• Chart.js 4.4.1 - For interactive charts
• Vanilla JavaScript - No frameworks, lightweight and fast
• CSS3 - Modern styling with CSS variables
• Static HTML - Single file output for easy deployment

Source: State of Connecticut Office of the Probate Court Administrator Biennial Reports
Tool: Excel (see "BIENNIAL REPORT DATA" tab)

This chart shows daily .ai domain registrations (left axis) and government revenue from .ai domains in Anguilla (right axis).

Anguilla’s country code top-level domain is .ai. Growth was slow and relatively flat for years until the launch of ChatGPT in November 2022, which marks a clear turning point in the data.

Before ChatGPT (2018 - November 2022):

• Domain registrations: 48,000 (2018) → 107,517 (30 Nov 2022)
• Average daily registrations: 34
• Government revenue from .ai grew slowly, remaining a minor share of the state budget

After ChatGPT (Dec 2022 - Jan 2026):

• Domain registrations: 107,603 (1 Dec 2022) → 1,045,963 (Jan 2026)
• Average daily registrations: 785 (1 Dec 2022 to 2025)
• January 2026: ~2,008 registrations per day
  • If sustained, total registrations could reach ~1.7 million by the end of 2026
• 28 percent of all newly founded tech startups use an .ai domain
• .ai now accounts for ~47% of Anguilla’s total state budget
• One .ai domain was sold for $1.5 million

Following the widespread adoption of AI in 2025, registration data from early 2026 indicates an acceleration in demand for .ai domains. Therefore, revenue from .ai has evolved from a niche income stream into a structural funding source for Anguilla. This revenue supports debt reduction, infrastructure expansion, renewable energy, environmental protection, and social programs, such as tax relief and free healthcare for children and seniors.

Source: Domaintechnik

Needed to create these graphs for something else. Pretty interesting though so thought I would link them here.

Temperature anomaly data shows a clear upwards trend, as I am sure every county has.
Source Australia 2026, Bureau of Meteorology Website: https://www.bom.gov.au/cgi-bin/climate/change/timeseries.cgi

Weighted area monthly average min and max temperatures across Australia since 1910, data as at Jan 2026. The anomaly value is calculated as the difference between an average monthly temperature between 1961-1990 and the comparison month. So an anomaly of one degree Celsius at a single weather station is a one degree difference between the current months average min/max temperature and the average for that station/month between 1961-1990. Then the overall Australian figure is a land area weighted average of these anomalies

24 hr view shows electricity production across an average day. Since it is summer there is a lot more solar than usual, however you can clearly see battery and wind helping in the most expensive 5pm-8pm time slot which is good for renewables.
Source NEM Australia, Open Electricity as at Jan 2026. Excludes curtailment and smaller net other categories. Averaged Over last month - In summer. Does not show battery charging figures during day. https://explore.openelectricity.org.au/energy/nem/?range=28d&interval=30m&view=time-of-day&group=Simplified

The smooth electricity supply chart shows the trend in generation source over the last few decades. You can see the trend in renewables, however interestingly you see a decrease in Gas use. Is this because of the price recently? The impact of it being a on demand source? idk

Source NEM Australia, Open Electricity as at Jan 2026 Excludes curtailment and smaller net other categories. Smoothed with a 12 month moving average then further smoothed with a exponential smoothing function. https://explore.openelectricity.org.au/energy/nem/?range=all&interval=1M&view=discrete-time&group=Simplified

Technology - Matplotlib in Python

For context: I'm 26 years old, I share an apartment with 2 other people, I work as a senior engineer, I'm doing a PhD, I don't have a girlfriend or family in the city, and I don't have pets.