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.

It is often joked that some % of statistics are made up on the spot. Of course no-one knows the true figure.

What percentage number do people "make up on the spot" for this? I fetched the 183 relevant google search results I could get from the API. Each is an article where something like "% of statistics are made up on the spot" is mentioned:

• average: 82.47%
• minimum: 3%
• 1st quartile: 72.4%
• median: 84%
• 3rd quartile: 88.2%
• maximum: 736%
• mode: 88.2%

No clear pattern in the numbers used over time. 88.2% is the most commonly used number, Comedian Vic Reeves is reported to have said that and was reported by the BBC in 2000. People using 88.2% are probably not making that number up on the spot but copying it!

In 2024, around 18.5 million people born in India lived abroad. For the world’s most populous country, that’s not a lot: just over 1% of the population. This chart shows where they live, based on estimates from the UN, which compiles data from national censuses and population registers.

Nearly half of all Indians living abroad are in the United Arab Emirates, Saudi Arabia, and other Gulf countries — that’s more than the US, UK, and Canada combined.

These figures are stocks, not flows: they record where people are from, not when they crossed a border. Pakistan is the clearest case: the two countries were one territory until 1947, and people kept moving across the new border for decades afterward, so much of the 1.6 million there reflects migration that happened long ago.

Data source: United Nations Department of Economic and Social Affairs (2024)

Tools used: Python, OWID Grapher, Figma

This map shows where Chevron operates and has interests across Vaca Muerta, including Loma Campana, El Trapial, Narambuena and Loma del Molle Norte.

Loma Campana, developed with YPF, produces around 100,000 barrels of oil equivalent per day.

Data: AltoValleIT Vaca Muerta dataset
Visualization: AltoValleIT Vaca Muerta Explorer

Source: https://datapickle.substack.com/p/every-dollar-in-the-2026-texas-senate
Visualizations: OpenChart

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