Q2 2026Live
The Laboratory — Volume I
Root-finding and convergence algorithms. Five complete interactive pages: Babylonian, Bisection, Newton-Raphson, Secant, Fixed Point. Python cells embedded throughout.
Editorial Agenda
EDI. 4B51AUG 2026
Roadmap
Our editorial agenda for building the infrastructure of insight.
Q2 2026Live
The Laboratory — Volume II
Optimization and learning. Gradient Descent and Stochastic Gradient Descent and AdaGrad live. Adam in progress.
Q2 2026Live
Case Studies
Statistical Surgery on VGG19 and Premier League Predictions. Two complete interactive research documents.
Q3 2026WIP
Laboratory — Algorithms Vol. II Complete
Adam with momentum, scheduling, and failure modes.
Q3 2026WIP
Laboratory — Distributions
A new laboratory separate from algorithms. Bernoulli through the probabilistic foundations of modern AI. Launching after algorithms is complete.
Q3 2026WIP
Expanded Case Studies
More real-world applications. Sports analytics, medical imaging, econometrics. The platform's research voice growing.
Q3 2026WIP
Laboratory — Systems
Randomness and exploration. Monte Carlo, MCMC, Simulated Annealing. After distributions.
PlannedPlanned
Statistical Intuition
The interactive book. Honest timeline — this is a large project and it's being built properly. 14 chapters, electron app, cinematic, Python and R throughout.
PlannedPlanned
YouTube Channel
Probably earlier than this. Statistical thinking as cinematic content. No fixed date — it starts when the first video is ready.
What We Won't Build
Axiom 01
No AI Tutors.
The platform does not explain things to you. It puts you in front of the mathematics directly. Struggle is not a failure of design — it is the design. Comprehension that arrives without resistance does not last.
Axiom 02
No Subscriptions.
The platform is free. When the interactive book ships, you buy it once and own it permanently. No recurring fees, no paywalls on existing content.
Axiom 03
No Forums.
Community features built around volume produce noise. We do not host discussions, comment sections, or peer forums. If something needs to be said, it belongs in the content itself.
Axiom 04
No Artificial Divide.
Statistics and machine learning are taught here as one discipline. The separation between them is historical and terminological, not mathematical. We do not accept it.
Axiom 05
No Static Explanations.
Every concept on this platform has a visual proof. Every model has a mechanism you can manipulate. The mathematics is the visualization — not an illustration of it.