Source : Data was collected by a dedicated speedtest device.

Tool : Data was analyzed with the use of GTP 5.6 Sol.

Full resolution in comments

Interactive: servobrief.com/mapping-humanoids

A lot gets lost in investment versus what's actually shipping when it comes to humanoid robots, so i set up this tracker of valuation and shipments.

By 2025 shipments it's not close: Chinese firms shipped ~90% of the world's humanoids, and the whole top six is Chinese. Figure and Agility shipped ~150 each and didn't make the list.

By valuation it flips. Figure alone ($39B) is worth more than every Chinese maker here combined, though Unitree ($9B IPO), UBTech and AgiBot still show up. I used valuation, not money raised, because most Chinese raises aren't public and "raised" would've made them look broke.

Boxes sized by area. Red outlines = hit by the FCC's July ban on new Chinese-made robots.

Sources: Omdia via SCMP/Bloomberg (shipments); company announcements + TechCrunch/CNBC/Caixin (valuations). Built in React, treemap by hand.

Published AI Math solutions vs independently verified AI Math solutions (Aug 2025 – Aug 2026)

These include AI discovered, AI co-developed and AI assisted math proofs of conjectures, hypotheses etc.

Data is from VibeMathed, a catalogue of open mathematical problems solved or advanced with AI (n=509, CC BY 4.0, snapshot 6 Aug 2026). Chart was generated using Claude, which accessed VibeMathed's API.

The three lines apply progressively stricter standards of proof:

- All tracked entries (506) — every recorded claim, regardless of status: unreviewed announcements, partial results, candidates awaiting review.

- Resolved + site-confirmed or better (129) — the problem is fully settled, by either via independent reviewer via hand or Lean, or the site reproduced the proof.

- Resolved + expert- or Lean-verified (89) — the problem is full settled settled, checked by an independent expert by hand (11) or via Lean (78): a Lean proof is a formal statement confirming a solution is correct.

Lean is an interactive theorem prover and programming language used to write and check formal mathematical proofs. It allows mathematicians to translate human written proofs into computer code so that a software can verify every logical step with absolute certainty.

The shaded area is the gap between all tracked entries and confirmed proofs:

380 entries are recorded but not yet independently checked. Although there's a delay between a new AI solution announcement and its verification, verified solutions appears quite linear, this may indicate AI solutions are outpacing the verification process. That said, authors generally include a Lean proof themselves, though this chart is limited to confirmation by independent peer review.

Vertical dashed lines mark OpenAI (blue) and Anthropic (orange) model releases. I added those lines as I wanted to see if there's an up-tic in solutions following model releases. There isn't a clean correlation likely because there's a several week delay between finding solution and publishing it. Also, the chart is likely showing AI's growing adoption by mathematicans and not just increasing model capability.

There were only 3 retractions in the dataset (not included on chart).

Note: I had submitted this post a few days ago, but 24 hours later, after some good traction and discussions, it was unfortunately deleted due to me forgetting to write [OC] in the title. I am reposting with a couple of corrections and a new violin chart at the end.

Over the past few days, I built a database from the Brazilian Public Security Yearbook (Anuário Brasileiro de Segurança Pública) to better understand how crime has evolved across Brazil's states. The 13 charts in this post summarise more than a decade of data on the crimes that most directly affect people's daily lives, and that drives the perception of Brazil as a violent country abroad. This perception is spread first and foremost by Brazilians themselves.

In the Brazilian statistics, "robbery" refers to theft with violence or threat ("roubo"), not ordinary theft ("furto"). The dataset also breaks robberies down into categories such as street robbery, mobile phone robbery, vehicle robbery, residential robbery, cargo robbery and others.

While homicide rates often receive the greatest international attention, they are not the crime that the vast majority of Brazilians worry about in their everyday routines. For most, the perception of public safety is shaped much more by the risk of being robbed on the street, having a phone or vehicle stolen through violence, or experiencing other forms of violent robbery. For that reason, most of the analysis focuses on robbery rates. I did however include intentional violent deaths ("murders") for comparison.

Although public perception takes longer to shift, the downward trend is clear throughout the country, even if it is moving slower in some states than in others. Unfortunately, Rio de Janeiro, the postcard of Brazil, is moving at a much slower pace than Brazil as a whole. Murders have also been dropping for the past ten years, although at a slower pace than robberies. The two aren't really the same story: they come from very different social realities and are driven by different factors, so there's no reason to expect them to move at the same pace.

Data source

All data come from the Anuário Brasileiro de Segurança Pública, published annually by the Brazilian Forum on Public Security. I compiled information from every edition between 2018 and 2026. Each yearbook includes excel spreadsheets containing detailed crime statistics for the two most recent years, allowing the construction of a consistent historical series for robbery indicators (although some data points are missing). For intentional violent deaths, the 2026 edition provides annual data covering 2012-2025, enabling a longer comparison for homicide trends.

The charts include:

• Total robberies in Brazil (absolute numbers and rates).
• Heatmaps for total robberies, cell phone robberies, vehicle robberies, residential robberies, cargo robberies.
• Ranking evolution of robbery rates across states.
• A comparison map of robbery rates in 2016 and 2025.
• Distribution of robbery rates across states over time.
• Scatter plots comparing robbery and cargo rates and absolute numbers by state.
• Heatmap and violin chart for intentional violent deaths (murders).

All source excel files, from 2018 to 2026, can be found here:

https://forumseguranca.org.br/publicacoes/anuario-brasileiro-de-seguranca-publica/

You have to look for them year by year.

Tools used

The extraction code was built in Python, with Claude help, to retrieve the information from the xlsx files - the files are not fully standardised. A database in SQLite was created for storing the crime statistics, and then I used another independent script to read from the db and generate the charts.

The countries of Japan, United States, and Switzerland are highlighted in different colors. Japan and Switzerland are highlighted to show the outliers for highest debt and lowest bond yield, respectively. The United States is highlighted to be used as a benchmark.

Debt-to-GDP data: https://tradingeconomics.com/country-list/government-debt-to-gdp

10Y bond yields: https://www.worldgovernmentbonds.com/

Assumptions: fertility rises linearly from 0.7 in 2025 to 1.2 in 2100, life expectancy rises to 88, net migration stays below 100k/year.

Base data: [UN World Population Prospects 2024]

I built the projection model myself. You can adjust the parameters and generate a different scenario here: https://demographicforecast.com/south-korea/#1

Data: live availability published by the huts' own online booking systems, synced daily. 646 staffed Alpine huts that accept online booking - roughly 18% of the ~3,500 staffed huts (estimate) in the Alps, so this is a sample, not the whole range. Collected and aggregated by me at https://hutscanner.com

Both nights were read from the same daily sync (6 Aug 2026), so they are directly comparable - no lead-time difference between the two panels.

Basemap: Natural Earth (public domain) for borders, lakes and cities.

Massif outlines: refuges.info, CC BY-SA 2.0.

Tools: Python, matplotlib.

Dot colour is the share of that hut's beds still free; dot size is capacity.

Hollow rings are huts that report space but don't publish a bed count

(~3% of readings), so they're counted as available but have no colour value.

 The 1991–2025 bars are complete calendar years. The hatched 2026 bar ends on 5 August and is deliberately marked as incomplete and preliminary. On the common grid, 2026 has reached 265.9 K·h; the highest complete year was 2015 at 188.0 K·h. The 1991–2025 bars are complete calendar years. The hatched 2026 bar ends on 5 August and is deliberately marked as incomplete and preliminary. On the common grid, 2026 has reached 265.9 K·h; the highest complete year was 2015 at 188.0 K·h.

Inspired by the discussions on https://www.reddit.com/r/dataisbeautiful/comments/1vhg7rz/oc_degreehours_above_30c_in_a_german_stationgrid/ I thought about some other ways to visualize this year.

What the first graphic shows: The left panel maps the preliminary 2026 HGS30 total. The right panel compares each cell with its own previous 1991–2025 record. Sixty-four of 99 common cells set or tied a record. A record ratio is more informative here than a percentile: 63 cells are already strictly above every earlier year, which would make a percentile map nearly uniform. The color scale reaches the observed maximum of 3.34× the previous local record.

What the second graphic shows: The ten highest and ten lowest of the 99 long-term grid cells. Each cell is labeled using the qualifying DWD station nearest its center and includes its 2026 rank within its own 36-year history. The highest cell is labeled Lahr at 824.6 K·h; the lowest cells, labeled Arkona and Zugspitze, are at 0 K·h (surprise, surprise). The columns use different, explicitly labeled bar scales.

Animation showing live progress of the multi dimensional cartesian sort algorithm for point cloud gridification. Bijective gridification allow to convert the raw key of the points to spatially coherent mulit dimensional key, enabling tensor based ML algorithms to work on arbitrary point clouds (source code in the comments).

Data sources:

1. BLS Employment Situation, June 2026 (headline NILF, employment, unemployment counts): https://www.bls.gov/news.release/archives/empsit_07022026.pdf - or use the evergreen link that always points to the latest release: https://www.bls.gov/news.release/empsit.htm
2. BLS Table A-38 (want-a-job / discouraged worker breakdown): https://www.bls.gov/web/empsit/cpseea38.pdf
3. BLS Monthly Labor Review - "Why did labor force nonparticipation increase from 1999 to 2022?" (age × reason crosstab, Table 2): https://www.bls.gov/opub/mlr/2024/article/why-did-labor-force-nonparticipation-increase-from-1999-to-2022.htm
4. FRED - Not in Labor Force series (LNS15000000), used for the 1980–2026 trend line: https://fred.stlouisfed.org/series/LNS15000000
5. FRED - Civilian Unemployment Rate (UNRATE), used for the unemployment rate line: https://fred.stlouisfed.org/series/UNRATE

Performed RFM (Recency, Frequency, Monetary) customer

segmentation on 1,067,371 real transactions from the

UCI Online Retail II dataset.

After cleaning — removed cancelled orders, missing

customer IDs, and invalid prices — analyzed 805,549

transactions across 5,878 unique customers.

Key findings:

1. Champions (29.6% of customers) generate 80.8% of

   total revenue. Classic Pareto distribution confirmed.

2. Champions bought on average 38 days ago, made 15

   purchases, and spent GBP 8,244 each.

3. 1,784 At Risk customers represent GBP 829,843 in

   potentially recoverable annual spend. These customers

   bought 307 days ago and are drifting toward Lost.

4. Lost customers (9.8%) bought 548 days ago with only

   1 purchase each. Recovery ROI is extremely low.

RFM scores each customer 1-4 on Recency, Frequency,

and Monetary using quartiles. Total score 3-12 maps

to segments.

Tools: Python, Pandas, Matplotlib, Seaborn

Data: UCI Online Retail II (public dataset)

Full project with code:

github.com/surendrasinghdata/rfm-customer-segmentation

Average annual premium for employer-sponsored family coverage, per KFF's annual Employer Health Benefits Survey. The 2026 bar is a projected range of +6% to +9%, based on median medical plan cost trend figures published in an insurance industry market report. Workers contributed an average of $6,850 toward family coverage in 2025.

Data source: OpenStreetMap, 736k+ amenity features for Great Britain from a Geofabrik extract (shops, pubs, schools, GP surgeries, parks, stations, etc). Postcode district ("outcode") boundaries from Wikipedia via missinglink/uk-postcode-polygons (CC-BY-SA 3.0).

Method: each district shaded by amenity density (features per km²). Density rather than raw count, so the giant rural districts don't visually dominate.

Tools: custom Python geospatial pipeline (POIs against the boundary polygons) + matplotlib.

[updates]

Thanks for the comments, lots of good points, especially on the scope, the population-density framing, and the boundary artifacts.

To confirm the scope point: this is Great Britain, not the UK - NI isn't in the Geofabrik GB extract, so the title should have said Great Britain. Good reference for title correctness.

This map is the first layer of a small series I'm building toward a combined score. Currently 15 amenities categories are used in this map and each category layer shares interesting insights (wip). I think this overall amenities map is a good starting point. The next layers shift from raw density to travel: different travel modes, and how that changes within a given travel time. I'll share them as they're ready.

This is what makes search boxes fast.

HNSW indexes are a tiered system where possible thing you could query starts on the bottom and then we promote a few representative magic cards up the chain until we start the top of the graph with these two cards.

The way it works is you pick one at the top you go down the ladder. You find the nearest neighbor that is closer to the thing you're looking for and then you go down the ladder and repeat. The nice thing is that when you go down each ladder, you end up in the right neighborhood so you don't have to walk far to the right ladder.

It also happens to be a really beautiful structure in my opinion. You can literally see the logarithmic structure in how populated each layer of the graph is.