ABMarks is live!

This day has finally come.

The product I’ve been working on for quite a while is now live.

I’ll share more about how I realized there was a real need for this product. The A/B testing and causal inference domain is unique when it comes to the learning curve.

The foundations are everywhere: t-tests, z-tests, what A/B testing is, statistics basics.

But when it comes to truly understanding experimentation and working on real projects, so many questions start to appear — and most answers are scattered across years of practice, discussions with other practitioners, standalone blog posts, or academic papers that are very hard to find.

Questions like:

• How does randomization actually work in A/B testing?

• How can we run many experiments in parallel?

• How do we measure long-term impact?

• Is there a way to bundle multiple experiments for one feature and get a more precise answer?

• What actually happens when randomization and analysis units are different?

• Why do we need geo testing or cluster testing?

And there’s an endless number of questions like these.

I couldn’t find one place where I could learn all of this, practice it, and start applying it confidently.

Now there is one: ABmarks.

If you go through every lesson and every exercise, you’ll be ready for experimentation interviews, ready to deliver real insights from experiments, ready to work with complex experimental designs, and — what I think is most important — ready to validate assumptions, results, and experiments properly.

For subscribers of my newsletter, I’m sharing a discount for the first two weeks:

LAUNCH2026

Meanwhile, I’ve already started working on a fully practical module for the platform — where people with zero experience can get everything they need to become successful in experimentation, and people with strong exposure can sharpen their skills by working on a variety of real-world designs.