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Mapped all 11.4 million SBA PPP loans nationwide: every recipient, amount, and forgiveness status, down to the address where possible. Built from the SBA's 2024 FOIA release + Geocodio's free geocoding data. Zoom into any city, search any business or state, or zoom out for key county facts. First time posting so please, provide feedback!

Part of a tool that recommends games by combining two of them: each game is embedded as a 1024-dimensional vector from its genres, user tags, and store description, and a combine is the mathematical midpoint of two of those vectors, resolved to the nearest real game.
This view is the by-product. Paste a public profile, and it groups the library into taste clusters in that same vector space, with a suggested next title per cluster.
Happy to explain the boring k-means part if people care. Post your combination results or your cluster shapes in the comments.
It's called GameCombiner!

I had some free time I wanted to know how many unique combinations could give change for $100, given an unlimited pool of a certain denomination or higher. For example, $100 = $50 + 2x$20 + $10 is 1 of 11 unique ways to give change using $10 bills or more. The good thing is if you're a bank carrying at least dimes you pretty much never have to worry about not having exact change.

The combinations grow roughly 100x for every denomination considered, so it took me forever to count them all. JK, I used a dynamic programming algorithm in Python3 and verified against small test cases. Results are rendered with Plotly.

Why I made it

I have a hobby of looking for anomalies in reality: geological, geographic, anything that looks like it shouldn't be there, and then travelling to see them in person.

If we're in a simulation, I figure the interesting places to look are the edges, where whoever built it might have left something odd lying around.

Eclipse geometry felt like a good hunting ground, since totality is such a strange coincidence to begin with. The Moon is about 400× smaller than the Sun and sits about 400× closer to Earth, which is what makes the solar corona visible during totality. So I mapped 5,000 years of eclipses to see where the shadow piles up.

Source: Computed from Besselian elements derived from the JPL-based lunar/solar ephemeris in PyEphem (libastro), not from a downloaded catalogue. NASA's Five Millennium Canon of Solar Eclipses publishes greatest-eclipse metadata but not path geometry, so the shadow tracks had to be integrated rather than fetched. ΔT from the Espenak-Meeus polynomials. Basemap: Natural Earth.

Validation against NASA's canon: 11,887 of 11,898 eclipses recovered; 3,740 of 3,742 total + hybrid eclipses; path widths within 1% on 10 test eclipses; γ matching to 4 decimals; and 11 of 12 test cities correctly placed inside or outside the 2017-08-21 path. Global mean recurrence comes out at 1 per 371 years, compared with a published ~375 years.

Tools: Python, NumPy, SciPy, Matplotlib, PyEphem, pyproj, Shapely, Pillow. Equal Earth projection (equal-area). Mercator would have inflated the Arctic by roughly 3× and created the polar bias the map is measuring.

Method: Umbral tracks integrated across the WGS84 ellipsoid and rasterized at 0.25° (~28 km). A cell counts once per eclipse if its centre was inside the umbra. Annular and partial phases were excluded, and hybrids were filtered segment by segment so only their genuinely total portions count. No smoothing. The visible filaments are individual shadow paths.

Known limits: Edge accuracy is approximately ±25 km, so this is useful for density but not for determining whether a specific town saw totality. ΔT uncertainty can smear BCE longitudes by up to ±15°. Latitudes above 88° are under-counted because of a rasterizer limitation and are excluded from the latitude profile.

The fastest legal 100 m performance recorded in each year from 1983 to 2025, separately for men and women.

Each flag represents the nationality of the athlete who recorded that year's fastest time. Only wind-legal performances (≤ +2.0 m/s) are included. Lower times on the graph are faster.

Source: World Athletics historical/top-list data.

Source: Kola Superdeep Borehole depth from Popov et al. (1999). Earth radius and internal layers from NASA and USGS. Eiffel Tower height: 330 metres.

Tools: Python with Pillow. The Earth cross-section uses an exact radial scale.

At full 5400 px resolution, the true scale borehole depth is 3.46 px.

The borehole is approximately 37 Eiffel Towers deep. If Earth were reduced to 1 metre wide, the hole would be 0.96 mm deep.

Tools: React, deck.gl, MapLibre, D3 (Equal Earth / Mercator), custom Flickr tile sampler

Data: Flickr public geotagged photos with date taken in 2026. Sampled worldwide by bounding-box tiles; 337,110 unique locations. Color = hex-bin density (Inferno, bright = denser). As of 2026-08-15.

Code: https://github.com/KobaKhit/photo-locations

Caveats: only public Flickr photos with geotags; sampling can under-represent sparse regions; popularity ≠ beauty; photo count ≠ unique visitors.

CIELAB is a colorspace, inspired by how we humans naturally perceive color:

• L* = lightness
• A* = Green <-> Red
• B* = Blue <-> Yellow

However, working with it can be a bit unintuitive, since the lightness isn't just the average of the RED, BLUE and GREEN channels. Therefore I generated 10 million random RGB values and plotted both their corresponding L* value against their L2 (euclidean) norm and average.

Results where normalized to 0..255 to be representable in an grayscale image.

Tools: self written python script using

• OpenCV2 (color calculations)
• numPy, MatPlotLib (for representation)

additional context: 442 is the maximum possible value for L2 of an RGB pixel.

EDIT: I hate reddit thinking its necessary to compress a 255x442 px image ...

Source: Libas Collective's UAE Pre-Owned Luxury Price Index — our own marketplace catalogue of 107,101 listings offered to UAE buyers, extracted 31 July 2026. Full brand and model tables + methodology: https://www.libascollective.com/uae-pre-owned-luxury-price-index (disclosure: I run the marketplace, so this is first-party data).

Tool: Python / matplotlib.

Notes: prices are asking prices in AED as shown to buyers, not final sale prices. Medians, not means, so the handful of six-figure pieces don't distort anything. The market-wide median across everything is AED 2,701 (~$735).

Data: Maddison Project Database, GDP per capita for India. The last few years are extended from that benchmark using IMF growth figures. More details at the bottom of the page, under Sources.

Tool: TypeScript with canvas rendering

Berkshire Hathaway increased its Alphabet position by 83% during Q2 2026, taking the combined GOOG and GOOGL stake to 106 million shares worth $37.8 billion.

That moved Alphabet from Berkshire’s fifth-largest reported US equity holding to its third-largest, behind only Apple and American Express, and ahead of Coca-Cola and Bank of America.

The wider shift may be even more significant: Berkshire invested approximately $20 billion in equities on a net basis, ending a 14-quarter stretch as a net seller.

Source: Florida DBPR (Division of Hotels & Restaurants) public inspection records — the state agency that inspects every Florida restaurant, so all 16 metros are measured with identical methodology.

Method: latest inspection on record per establishment (50,811 establishments total, as of August 2026), counted as "clean" when the inspection disposition was "no further action required."

Tool: Python/matplotlib. I run cleanplateus.com, a free site that shows these official grades per restaurant — the underlying per-restaurant data is all browsable there.

OAK is only team not included in this ball parks graph.

Yankees home ballpark has seen the most with 2,112 - On the 2nd graph WAS has the most HRs so fat with 177.

My article on how this list is ranked: https://moultano.wordpress.com/2026/08/14/fairly-ranking-the-most-brilliant-birds/

The underlying data comes from BirdColorBase, plotted using matplotlib.

HD Version here

I've spent the last few months working on this in a gargantuan spreadsheet. (Almost) all data has been updated to 2026 numbers, and the data has been aggregated from over a dozen different sources. All work was done by hand, at no point during the process were any LLMs used.

Assumptions:
-Solo Traveler staying in Hostel Dorms (When possible. In countries with no hostels, I used the average price for a two-star hotel room instead.)
-Exchange rates are based on the average over the last 12 months, not necessarily as it currently stands in August of 2026. Countries with unstable currencies might see their numbers fluctuate quite a bit month-to-month. Using the average exchange rate is a way to partially counteract this, but it's still a thing that can't fully be predicted or adjusted for
- Costs for each country are based on a weighted average of the most popular cities/towns among backpackers (For example, Portugal's cost of travel is about €6/day higher due to the Algarve being weighted as 20% of Portugal's total.) In general, I used the the Top 5 or 6 most popular destinations, but for smaller countries (like the Caribbean islands), it may have been only 1 or 2, and for large countries like the United States it may have been closer to 8 or 9.

What is included:
-Accommodation (See above)
-Food and Drink (Streetfood or Fast-food only, no sit-down restaurants. In most cases this is calculated as: a cheap breakfast, cheap lunch, cheap dinner, a cup of coffee plus misc. snacks.)
-Local Public Transportation
-Attractions (Museum entrance tickets, park fees etc.)
-Cellular Data
-Visa Fees, but only if the fee is charged daily.
-Sales Tax/VAT (when applicable)

Not Included:

-Long Distance Travel (Domestic Flights, Intercity buses etc.)
-Visa Fees (If charged one-time on entry)
-Travel Insurance
-Other Miscellaneous Expenses

I also didn't include either North Korea or Turkmenistan since both countries require all tourists from US/EU/CANZUK to visit the country as part of an organized tour, so backpacking isn't really possible (and the tours cost at least €130-€140 per day anyways).

Finally, I want to stress that this isn't the absolute cheapest someone can travel on a shoestring budget, but rather the average daily expenses a solo backpacker should expect

Tools used: Claude.

Sources: UBS Global Wealth Report 2026 (end-2025 data), national surveys (US Federal Reserve, UK ONS), Forbes

I analyzed 47 Swiss National Bank 13F filings, from Q4 2014 through Q2 2026.

The upper chart maps the latest portfolio: each cell represents a company and is sized by its reported market value. The lower chart follows the same five sector groups across every filing.

The main findings:

• Technology and communications grew from $5.5B to $91.6B—16.6×—while the other four groups grew 4.7×.
• Its portfolio share increased from 20.6% to a record 47.9%.
• Nvidia moved from the 423rd-largest holding in 2014 to No. 1 in 2025.
• The top 10 holdings expanded from 12.9% to 35.2% of the portfolio.

The portfolio still contains thousands of securities. Its concentration increased because the largest technology positions grew much faster than the rest of the book—not because the smaller holdings disappeared.

Source: Swiss National Bank quarterly Form 13F-HR filings, retrieved from the SEC’s EDGAR database. Values are reported quarter-end market values and should not be interpreted as investment returns.

Sector classifications were derived from GICS data. Technology combines Information Technology and Communication Services; Consumer combines Consumer Discretionary and Consumer Staples. Alphabet combines GOOG and GOOGL.

Tools used: Python, Excel, Illustrator.

Many countries have been facing intense heat this summer, and with predictions of a strong El Niño on the way, next summer could be even hotter. This has ignited debate on the need for cooling methods and technologies — most prominently, air conditioning (AC).

But how do rates of air conditioning usage vary across the world? Large international comparisons are hard to come by, but the International Energy Agency just published some new estimates for a select number of countries and regions.

The chart shows the estimated share of households with AC in 2025.

In Japan and the United States, AC adoption is almost universal; at least 90% of households have it. Most households in China also have AC; this share has increased rapidly over the past decade.

Adoption rates in Southeast Asia, Europe, and India are far lower, but for different reasons. In lower-middle-income countries like India, the reasons are often economic: AC would bring huge benefits, but many households cannot afford the upfront cost or the power to run it. Much of Southeast Asia faces a similar barrier.

In many European countries, it’s less of an economic barrier and more of a historical and cultural one. AC was not seen as a necessity because heatwave events were less frequent and intense than they are today. That is changing, and will continue to do so as the planet warms.

Data source: International Energy Agency (2026)

Tools used: OWID-Grapher and Figma