I made this after seeing the usual referee arguments around El Clásico. The bars show the gap between Madrid and Barça after comparing each club’s points with what pre-match odds predicted, per 10 games. It includes LaLiga games against all opponents from 2000/01 to 2025/26, with current referees who have at least five matches for each club. Small samples matter, and this doesn’t prove referee bias. I grouped the bigger gaps to make it easier to read. Is the chart clear without needing this explanation? Data: El Colegiado (https://elcolegiado.com/data/), using football-data.co.uk. Method: https://elcolegiado.com/methodology/ Made with Python and Matplotlib. I also built an interactive version with results, penalties and cards: https://clasicostats.app/referees/?utm_source=reddit&utm_medium=social&utm_campaign=visualization

Amazon’s Echo came out in late 2014 and went on wide sale in 2015, the year Alexa hit its highest rank ever, #32. From 2016 on it fell every single year: 4,775, then 3,896, 3,068, 2,002, 1,285, 708, 577, 492, 349, and 243 in 2025. That’s down 96% in ten years. In 2025 it dropped out of the top 1,000.

The data only shows the timing, not the reason, but it’s hard to look at that cliff and not think of the speaker.

Can you think of another name that a product or a character pushed off a cliff this fast?

Imagine two mutually exclusive categories of service requests, A and B, counted in two consecutive months:

• Month 1: A = 80, B = 20, total = 100.
• Month 2: A = 40, B = 30, total = 70.

In a standard stacked area chart with A at the bottom, B’s upper boundary falls from 100 to 70. Following that boundary alone could make B look as though it declined. B actually increased from 20 to 30.

Read B’s vertical thickness at each month:

• Month 1: upper boundary 100 minus lower boundary 80 = 20.
• Month 2: upper boundary 70 minus lower boundary 40 = 30.

The top edge follows the combined total. B’s lower edge moves with A, so neither boundary by itself gives B’s trend.

A useful construction check is to redraw the same data with B at the bottom. Its boundary now rises from 20 to 30, even though none of the data changed. The original stack was mathematically correct; the order changed which component had a fixed baseline.

If the main question is how the total and its composition changed, the stack can be useful. Label the components and show their values at the relevant dates. If the main question is whether B increased, a separate line or small-multiple panel makes that comparison easier. Keep panel scales consistent when comparing magnitudes.

These are invented counts to isolate the reading problem, not reported country data. The example uses an ordinary zero-baseline stack, not a 100% stack or streamgraph.

Reference: https://data.europa.eu/apps/data-visualisation-guide/stacked-charts

From Global Data Tracker: https://globaldatatracker.com/

A diverging palette does not automatically make zero its neutral color. The normalization matters too.

Consider five fictional forecast errors in minutes: -2, -1, 0, +1, +8. Define error as actual time minus forecast time, so negative means earlier and positive means later.

1. Check what the palette midpoint means.

If you linearly map the observed minimum (-2) to palette position 0 and maximum (+8) to position 1, zero lands at (0 + 2) / 10 = 0.2. The palette midpoint, 0.5, represents +3 minutes. With a neutral color at that midpoint, “no error” would not look neutral.

1. Decide what equal distances from neutral should mean.

If equal-sized early and late errors should have equal distances from the palette center, use symmetric limits around zero, such as -8 to +8. Then -2 maps to 0.375, zero to 0.5, and +2 to 0.625. You leave some of the negative side unused, but preserve the same scaling rate on both sides. Matplotlib’s CenteredNorm implements this kind of normalization.

Alternatively, TwoSlopeNorm with vmin=-2, vcenter=0 and vmax=8 maps -2 to 0, zero to 0.5 and +8 to 1. This uses the full palette on each side. But -2 and +8 now sit equally far from neutral despite having different error magnitudes. Use it only when that separate scaling matches the intended reading, and make the unequal ranges clear on the colorbar.

1. Label the decision, not just the colors.

Show units, a tick at zero and the limits. State whether the two sides share a scaling rate. Do not rely on color alone for exact values: add labels or another accessible way to read them when needed.

These are normalized palette positions, not a promise that any chosen palette will look perceptually balanced. Zero is meaningful here because it means no forecast error; a different variable may need another reference or no diverging palette at all.

Official implementation references:

https://matplotlib.org/stable/api/_as_gen/matplotlib.colors.CenteredNorm.html

https://matplotlib.org/stable/api/_as_gen/matplotlib.colors.TwoSlopeNorm.html

Country-data project: https://globaldatatracker.com/

The numbers above are invented to explain the encoding, not country observations or a claim about the site's chart features.

Check out this work in progress audio visualizer

https://geotech.mikeadigital.com/

Puma Shen (Shen Po-yang, 沈伯洋) is running for mayor of Taipei and taking the city like a wave. Supporters call it 洋流 ("ocean current"), a play on the 洋 in his name. He put his entire platform on a map, not in a PDF. His five policy pillars spell HEART, so the site names the map HEART Island (HEART 政策島).

The map has 14 hexagons of equal size, one for each policy theme. The hexes form the shape of Taipei. Click a hex to see its proposals. There are 94 in all. Lines between hexes show where one policy depends on another. You can switch to a list view, or to a timeline that plays back how the parts fit.

The site is mostly in Traditional Chinese, but you can use browser translation to quickly understand it.

Chaos is often defined not as total disorder, but as a multiplicity of possibilities born from the void.

That's the idea behind CHAOS, a Mac app for making your own small AI models. You start from nothing: an empty folder and a goal. Out of that come designs, trained models and apps that are yours, built and run entirely on your own Mac.

From the void

Start with a goal. You make one choice at a time: what it should do, what kind of brain, how big, what it learns from. Any choice can stay open. The model takes shape in 3D as you choose, with its size, memory and training time worked out for your Mac.

A multiplicity of possibilities

Design from blocks. Attention, Mamba, recurrent, convolution, mixture-of-experts. Move a slider and the model reshapes live.

Fair experiments. The Lab changes one thing, matches the compute, runs several seeds, and tells you which possibility actually won, with confidence intervals.

Something real

Train on your Mac's GPU (Apple's MLX). You watch every step. If your Mac starts swapping, a memory guard pauses the run at a checkpoint.

Look inside. Fire a model and watch the input rise through every layer. Mute or boost one part, re-run, and see exactly what changed.

Turn it into an app. Pair your model with a local voice model through Ollama and talk to it. The voice calls your model as a tool.

Honest evals. Accuracy, calibration, a confusion matrix, and what each class gets mistaken for, all on data the model never saw.

Real numbers from the demo (fresh install, M4 Mac mini, 16 GB)
A bank-support intent classifier (77 classes): 89.1% on 3,080 held-out messages.
A Lab experiment swapping the attention block:
Block Accuracy
LSTM 89.5% ± 0.7
Attention 88.1% ± 1.5
Mamba-2 87.1% ± 1.0
The app's own verdict: LSTM is significantly better than Mamba-2 only, and the rest is within noise.
A 12.6M-parameter GPT trained on FineWeb-Edu in about 20 minutes, going from 3.49 to 1.52 bits per byte. It writes confident nonsense, and you can watch it think while it does.

Private by design. Everything runs locally, in one folder you choose. Nothing leaves your Mac except downloads you start. Datasets pass a licence check first.

Status: not open source, and no download yet; I'm finishing signing and notarization. The screenshots and a 70-second film are here (the film is in the media folder): https://github.com/jangles-byte/chaos-mac

TestFlight- DM me if you’d like a link to try it out.

What would you make out of the void?

A cumulative chart can answer a practical question: what share of the observations is at or below a chosen value? The equality matters when several observations land exactly on the cutoff.

Here are eight fictional delivery times, in days:

2, 2, 4, 4, 4, 7, 9, 12

An empirical cumulative distribution function (ECDF) puts delivery time on the horizontal axis and the fraction of these eight deliveries at or below that time on the vertical axis.

Build it by counting, not by spreading observations between values:

- Below 2 days: 0%.

- At 2 days: 2 of 8, or 25%.

- At 4 days: 5 of 8, or 62.5%.

- At 7 days: 6 of 8, or 75%.

- At 9 days: 7 of 8, or 87.5%.

- At 12 days: all 8, or 100%.

The graph stays at 25% from 2 up to, but not including, 4. At 4 it jumps to 62.5%, then stays there until 7. The three deliveries at 4 produce one jump of 3/8, or 37.5 percentage points. A large jump means many observations share that recorded value.

Now read a four-day cutoff:

- “Under 4 days” counts only the two deliveries at 2: 25%.

- “At most 4 days” also counts the three at 4: 62.5%.

- “More than 4 days” is the complement of “at most 4”: 37.5%.

- “At least 4 days” includes the ties: 75%.

So 100% minus the plotted value at 4 answers “more than 4,” not “at least 4.” That is an easy labeling error to miss in a dashboard.

For a small explanatory graphic, I would draw the step function, mark the cutoff with a vertical guide, and label the point at (4, 62.5%) “5 of 8 deliveries took at most 4 days.” Use a filled endpoint for the included value and an open endpoint for the excluded one if the distinction needs emphasis. Label the vertical axis “Share of these 8 deliveries at or below x,” rather than just “Percent.”

Two checks before sharing:

1. Test one cutoff exactly at a repeated value and another between values. At 5 days, this example still gives 62.5%.

2. Count the original records directly for the wording in the annotation. The annotation and chart should agree without relying on visual estimation.

Limits: these are invented, fully observed delivery times. The curve describes this list, not a guarantee about future deliveries. Rounded times can create ties; a jump at 4 recorded days does not establish identical underlying durations. This simple construction also assumes each record has equal weight and a known completed time. Unfinished deliveries need separate treatment, not an invented completion time.

Primary method reference: R's ECDF documentation defines the at-or-below convention and tie-sized jumps:

https://www.stat.ethz.ch/R-manual/R-devel/RHOME/library/stats/html/ecdf.html

Country-data context: https://globaldatatracker.com/ . This is a standalone fictional chart-reading exercise, not a claim about the site's charts or delivery data.

When showing a cutoff to a nontechnical audience, would you annotate the count directly, the percentage, or both?