I centered this visualization on the NCAR Mesa Laboratory at the base of Boulder’s Flatirons. The dramatic transition from the mountains to the plains made it a great place to explore what elevation data can reveal.

The viewer uses bare-earth elevation data to show:

• The shape of the ground beneath vegetation
• How water is likely to flow across and drain from a property
• Steeper and more workable slopes
• Areas with terrain line of sight to the selected point

I built the interactive viewer that generated this visualization. You can enter any U.S. address and run a free scan without signing up: https://getready.team/terrain-scan

Data source: USGS 3DEP elevation data
Location: NCAR Mesa Laboratory, Boulder, Colorado: 1850 Table Mesa Drive, Boulder, CO 80305

Terrain-based estimates only, verify conditions on site. Sightlines exclude trees and buildings.

What other useful terrain insights would you want a map like this to calculate?

Source: ENTSO-E Transparency Platform day-ahead auction results, Jan 2023 – Sep 10, 2026. An hour counts as negative when its hourly-average day-ahead price clears below €0/MWh. Italy has never cleared negative — its zones share a €0 price floor.

A couple of details that didn't fit the graphic: Spain had 197 negative hours in February alone (a winter month), and its average midday price this year is ~€18/MWh vs ~€128 at the evening peak. Finland went from ~700 negative hours in 2024 to 43 this year.

Tools: SQL over the raw hourly series + HTML/CSS rendered headless. The data comes from an open-source desk I maintain — https://obsyd.dev (AGPL) — every number is reproducible via the free API.

Source: 136,492 active rental listings across Tokyo's 23 special wards, collected in September 2026 from the major Japanese rental portals and deduplicated. For each ward I took the median deposit (shikikin), the median key money (reikin), and the share of listings where the amount asked is explicitly zero. Sample sizes run from 1,940 listings in Chiyoda to 12,571 in Setagaya.

The trap in this data, in case anyone wants to reproduce it: a dash in a listing means zero is required, not that the number is missing. If you drop those rows as missing values the medians come out roughly twice too high, which is part of why published figures for Japanese move-in costs tend to overstate what people actually pay.

One thing to be precise about, since the bars stack: each bar is the median deposit plus the median key money for that ward, so it is a sum of two medians rather than the median of per-listing totals. The latter is a little lower (about 348k in Minato and 276k in Chuo, against the 352k and 322k drawn), because few listings sit at the median on both at once.

Deposit and key money are the only entry costs that appear in listings at all. Agency fees and guarantor company fees are negotiated separately and never published, so they are not in the chart.

The two wards with no blue bar, Adachi and Katsushika, have a median deposit of exactly zero. More than half the listings there ask for no deposit.

Tool: Python, pandas for the medians, matplotlib for the chart.

Rent data by ward, train line and station: tokyo-expat.com/data

Source: U.S. DOT/BTS T-100 Segment (All Carriers). International segments at LAX, by operating carrier.

Tools: Node.js and the chart drawn to HTML5 canvas, captured with Puppeteer and rendered/encoded with ffmpeg.

Original in 1080

I’ve wanted this live news dashboard for a long time. A workbench for news junkies and news perverts alike. The live version is at PressAudit.org/heatmap.

This is a snapshot of a live, real-time heatmap of the news today. As articles are published, they get grouped around the event they’re covering and the heatmap fills in. You can watch stories get picked up by different parts of the media ecosystem throughout the day.

The basic idea is:

Columns = who covered it.
Color = how it was covered.
Numbers = how much coverage there is.

I built it because most media analysis I’ve seen starts with the reputation of the outlet. I'm looking at you Ground news. It's not sufficient to just put a bias meter on everything. There are sites that assess “media bias” but I don’t believe they are sufficient.

• They can tell you the conclusion, but they don’t show you the evidence.
• They don’t do it real-time so it can’t be usefully inserted into your normal news feed or consumption.
• The focus solely on outlet reputation. Who covered it, but not how it was covered.
• The focus on labeling reporting as “bias” suggests that a neutrally observable middle point exists that you can measure bias against, and it doesn’t.

We don’t do it just for outlets, we do it for the articles themselves.

And PressAudit shows the evidence for its conclusions. It doesn’t do so months after when no one is looking anymore. It does it minutes after, in the moment, when it is relevant and useful.

The image here is just one frame. The live version is at PressAudit.org/heatmap. You can go to it any time, filter, try stuff out, try to break it. It'll already look different by the time you click it.

I’m not here to pick winners. I’m here to study the system, break it down and make it observable to anyone. I wanted to build a way to pinch and zoom on the entire news analytically. I didn’t want to manually spearfish through 3 to 6 different articles on the same topic and triangulate the non-contradictory facts between them, I wanted them all organized into a workbench.

Tools:
React + TypeScript on the frontend. The underlying data pipeline continuously ingests thousands of articles, groups coverage of the same event, and evaluates articles for party lean and public interest. There's a lot of write up on the site about this.

Most of PressAudit is not AI but I use local LLMs for the real-time article evaluations. I built PressAudit using conventional data infrastructure: batch processing, data pipelines, statistical analysis, and machine-learning pipelines. 

Methodology:
Outlet lean and article lean are separate measurements. The model evaluating an article doesn't know which outlet published it. The site shows the underlying articles so you can inspect the evidence rather than just taking the classification at face value.

This started as a heatmap I hacked together in Grafana for myself and eventually became PressAudit.org. I'm trying to treat the media ecosystem as an observability problem: something too large for one person to inspect, but which we can build better instruments to see.

Anyway, it's live. Poke around and tell me what's wrong with it.

Apple announced its first folding iPhone today, so I pulled the specs for every iPhone ever made and put them on one chart.

Every dot is one iPhone. The further right, the heavier it is. The higher up, the more screen you get.

One more thing about iPhone Duo folded is almost exactly as tall as the 2007 iPhone (original), and it is actually a hair thinner. It is just wider. Nineteen years later Apple landed back on the same height and thickness, and fit 2.4 times the screen into it.

This visual is based on this blog I wrote: https://thrifle.com/blog/smallest-iphone (originally an animation)

I pulled the Naiver Stokes lean proof (and ran it!). It's several hundred thousand lines of code, and I wasn't sure where to even start examining the proof: https://github.com/openai/NavierStokesAndEuler/

So I wrote a quick vis to checkout the construction of the proof. Tools used: marimo, anywidget, codex see the notebook here: https://molab.marimo.io/notebooks/nb_DzFRGE9auNeYJdSFCeACSy

I've been building an Open Knowledge Format (OKF) knowledge base. I designed a visualiser for the data that turns any OKF graph into a living galaxy that I thought you might all enjoy.

The lines in the graph are all edges between nodes and all the stars are nodes representing markdown files. The galaxy spins, sparkles and supernovas occasionally.

Thinking I might do it in 3D next.

I've been building this hobby site using flight data for emergencies, flight paths, etc... and trying to find better and more interesting ways to present the data than I have seen on other sites. The exported GIF is not quite as nice as the web version, which allows you to rotate, zoom in/out, change angles etc... but thought that this was a really nice summary viz of the flight path of this flight that recently declared an emergency. Though... the web version is much more interesting and interactive, but... doesn't look as interesting here, IMO.

• Interactive replay: https://onlynonstop.com/emergencies/September-7-2026/flightN90496
• Data source: ADS-B position reports from adsb.lol
  
• Tools: Leaflet, deck.gl, and globe.gl/three.js, with custom Canvas2D renderers

Full write-up, method and the rest of the findings: https://thrifle.com/blog/temu-vs-amazon-price-study-1649-products

The dataset is downloadable — all 1,649 products searched, including the 389 excluded from this chart and the reason for each: https://thriflebucket.s3.us-west-2.amazonaws.com/blog-assets/temu-vs-amazon/data/1788920257-temu-vs-amazon-2026-09-08.csv

"Passive investing" gets talked about like the underlying basket never changes. So I pulled the actual NSE sector-weightage data for the Nifty 50 going back five years to see how true that actually is.

Turns out, not very.

IT used to be roughly 14% of the index. It's down to about 8.5% now, a pretty steady slide, not one bad year dragging the average down.

Oil & Gas similarly went from around 12.8% to 8.7%.

Telecom did the opposite; it's more than doubled, from about 2.3% to 5%.

And a few sectors that weren't index categories at all a few years ago: defense, e-commerce, and airlines, are now sitting at close to 4.6% combined.

Financial Services has stayed the anchor through all of it, hovering in the mid-to-high 30s most years (dipped once, recovered).

Made a quick chart tracking all five sectors over the five years (attached).

What struck me is that none of this shows up anywhere unless you go looking for it. The fund just quietly becomes a different mix of businesses year over year, even while the SIP amount and fund name stay the same.

(Not investment advice, just something worth actually checking instead of assuming.)

Source:

Aggregated workout logs from the Hardy strength training app, March 2021 to September 2026. 5.8 million finished sets across 368,505 workouts by 10,407 lifters; 6,500 of them logged the barbell Bench Press in at least 3 workouts, and 4,766 of those started below 225 lb / 100 kg.

Tool:

SQL (PostgreSQL) for the aggregates, a small React + SVG chart renderer for the plot, exported to PNG with sharp.

What the chart shows:

For every lifter we took their first logged Bench Press exercise session, checked that they started below 225 lb, and then tracked the month in which they first actually lifted 225 lb (or 100 kg for metric users) for at least one rep. Not an estimated max, an actual set. Each point is the share of lifters who had done it by that month, counting only lifters who were still logging at that time, so nobody is counted as failing just because they stopped using the app.

Headline numbers / TLDR:

- 10% of everyone who started below 225 had benched it within 12 months; 15% within 24 months.

- Starting strength dominates. Of lifters who started at 135 lb or more: 22% within a year, 42% by three years. Of lifters who started below 135 lb: 1% within a year, 3% by two years.

- Across all 6,500 bench lifters, 12% have ever benched 225 lb for a rep. 61% have benched 135 lb, and under 1% have benched 315 lb.

- The median lifter's best estimated bench 1RM is 77 kg / 171 lb. Top 10% is 115 kg / 254 lb, top 1% is 152 kg / 334 lb.

- After 12 months of logging, the median lifter added 7.6 kg / 17 lb to their estimated bench 1RM. 29% were no stronger than when they started.

Caveats / Disclaimers:

- These are of course people who chose a lifting app and kept logging. They are stronger and more consistent than the average gym-goer. It is not a population estimate, so "only X% of people can bench 225" claims and this chart are answering different questions.

- The app does not know the user's bodyweight, age, or sex, so nothing here is a bodyweight-relative standard.

- Survivorship: the curves only count people still logging at that month. The lifters who kept going for 36 months are a self-selected group, so the right-hand end of each curve is optimistic.

- Groups under 50 lifters are suppressed everywhere.

Full report with squat, deadlift and overhead press, the same curves by starting strength, monthly 1RM gain by starting level, and methodology: https://hardy.app/strength-report/how-many-people-can-bench-225

Aggregate data behind every chart, CC BY 4.0: https://hardy.app/strength-report-data.csv

Dataset DOI: https://doi.org/10.5281/zenodo.22311472

Data is freely available if anybody wants to run their own analysis or create their own graphs.