Devlog #32 — Wave 12 Content Sprint

Six deep-dive posts shipped in five weeks: special relativity, ecology, statistics, algorithms, cosmology and linear algebra. Here is what went into Wave 12, why we chose these topics, and where the series goes next.

6
Wave 12 posts
82
Total blog posts
345
Live simulations

All Six Wave 12 Posts

Wave 12 spans April–May 2028 and covers six distinct scientific domains. Each post pairs interactive simulations with the underlying mathematics — the format that has driven the majority of the blog's organic traffic.

Why These Topics?

Topic selection for content waves is guided by two signals. The first is simulation coverage: which categories have the most simulations but the fewest explanatory posts? Statistics, ecology and algorithms all scored high — they had 10+ simulations each but no dedicated Spotlight post. The second signal is search demand: queries like "special relativity explained" and "Big Bang nucleosynthesis interactive" consistently register in our analytics without landing on the right page.

Special relativity was overdue. We shipped the twin paradox, Lorentz contraction, Minkowski diagram and time dilation simulations in early 2026 but never wrote a full Learning post tying them together with the mathematics. Learning #21 fills that gap — six sections, four math boxes, eight algo-pills, and a misconceptions section that addresses the most common misunderstandings ("can I travel faster than light if I'm on a spaceship?").

The Cosmology Gap

We had five cosmology simulations live (Hubble, BBN, CMB, dark matter, gravitational lensing) but no Spotlight post connecting them. Cosmology is one of the richest areas for educational content: it spans 13.8 billion years of history, uses every branch of physics, and the observational evidence for ΛCDM is among the most powerful in all of science. Spotlight #25 now gives these simulations a home.

Linear Algebra as Infrastructure

Learning #22 on linear algebra was motivated by a different concern: many visitors arrive from machine learning tutorials wanting to understand gradient descent, K-Means or convolutional networks, but the blog had no post explaining the geometric foundations. Linear algebra is the connective tissue between our statistics, machine learning and visualisation simulations. This post makes that connection explicit — every ML algorithm reduced to its linear algebraic core.

Blog Series Status

After Wave 12, here is where each series stands:

Running total: 82 blog posts across 5 series, 25 Spotlight entries covering all major simulation categories, 22 Learning posts from WebGL basics to cosmology. The longest posts (Learning #21 and Learning #22) are each ~5 500 words with four math boxes — the format that drives the deepest engagement and the most inbound links from course sites and Wikipedia talk pages.

What Changed in the Writing Process

Starting with Wave 10, we changed one thing: every math box now includes a comment explaining why the formula matters, not just what it is. For example, the Voronoi diagram description in Learning #22 ends with "K-Means boundaries are always linear (Voronoi tessellation)" — connecting the algorithm to its geometric character in one sentence. This gives readers who skip equations something to take away.

The sim-card grid is now the most-clicked element in any post according to analytics. We moved it earlier in the post — before the last section — after data showed that readers often left without clicking through to simulations if the grid was only at the bottom. Spotlights #24 and #25 both include an inline sim-card pair after each major section so the path to the simulation is always within two scrolls.

Wave 13 Preview

Wave 13 is planned for June–July 2028. Candidate posts under discussion:

New simulations in the pipeline for Wave 13 include Faraday's Law / electromagnetic induction (🔴 highest priority in electromagnetism), the Klein bottle (completing the topology category), and Maxwell wave propagation. Each new simulation now ships with a corresponding Spotlight or Learning cross-link within 2–4 weeks.