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Rachinsky's 1001 Problems in Mental Arithmetic

What if mathematics trained the mind instead of the hand? 1001 Problems for Mental Calculation is the first complete English translation of Sergey Rachinsky’s classic, a remarkable collection of problems designed to build number sense, mental discipline, and real mathematical thinking.

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🧠 1001 Problems for Mental Calculation
The Lost Russian System That Trained Mathematical Thinking

What if children could solve problems like this in their heads?

(102+112+122+132+142)/365=?(102+112+122+132+142)/365=?

No calculator.
No written steps.
Just thinking.

This is not a trick.
This is how mathematics used to be taught.

1001 Problems for Mental Calculation by Sergey Rachinsky is one of the most overlooked classics of Russian mathematical education.

Written by a former Moscow University professor who left academia to teach peasant children, this book embodies a radically different philosophy of learning:

Train the mind, not the hand.
Develop intuition, not procedures.
Solve, don’t memorize.

These are not ordinary exercises. They are designed to build pattern recognition, decomposition skills, number sense, and mental discipline. There is no hand-holding here. The problems are meant to make you think.

This book will appeal to parents who want their children to develop real mathematical strength, to students tired of mechanical school math, to adults who want to rebuild their numerical intuition, and to anyone interested in the traditions that shaped serious Russian mathematical training.

This English edition provides a faithful translation of Rachinsky’s original work, presented in a clean modern format while preserving the rigor, structure, and spirit of the source.

This is not a beginner workbook.
This is not step-by-step tutorial math.
This is training.

Modern education optimizes for memorization, procedure, and exam performance. Rachinsky optimized for something else: thinking, insight, and mental strength.

That difference matters.

If you care about mathematics not as button-pressing, but as a discipline of the mind, this book is for you.

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Author

About the Author

Valery Manokhin

Valery Manokhin, PhD, is an independent machine learning researcher, author, and educator specialising in conformal prediction, uncertainty quantification, and modern time series forecasting. He holds a PhD in Machine Learning from Royal Holloway, University of London, where he was supervised by Professor Vladimir Vovk — the creator of conformal prediction. He also holds an MBA from the University of Warwick, an MSc in Computational Statistics and Machine Learning from University College London, and a Certificate in Quantitative Finance (CQF).

His doctoral research produced the first multi-class Venn-Abers predictor (PKPD — Pairwise Kriging Probability Distribution), extending distribution-free calibration guarantees beyond binary classification. His 72-dataset empirical study remains one of the largest systematic evaluations of classifier calibration ever published.

Valery’s books have reached readers across 100+ countries. His titles include Mastering Modern Time Series ForecastingApplied Conformal PredictionMastering CatBoost, and the English translation of Kiselev’s classic Arithmetic — now available on Amazon KDP. He publishes through Gumroad, Amazon, and Leanpub.

He maintains the widely cited Awesome Conformal Prediction repository on GitHub and has built a technical community of 100K+ followers on LinkedIn and Twitter/X. His work has been read by practitioners and researchers at organisations including Amazon, Deloitte, Microsoft, EY, and JPMorgan.

He writes for people who already understand the fundamentals and demand rigour, mathematical precision, and production-grade thinking — not hype.

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