Persistent chess intelligence

About Chess-True

Chess-True is designed for serious chess preparation, deep position analysis, and long-term study. It goes beyond a single engine evaluation and turns repeated calculation into a durable analytical foundation.

The idea is simple: every meaningful position should not be treated as a temporary result. Instead, Chess-True stores and revisits analyzed positions, builds a growing tree of candidate continuations, and strengthens earlier evaluations through iterative refinement.

For advanced players and coaches

Chess-True is especially relevant for players and coaches who need more than a static engine score. It helps organize variation analysis, preserve important lines over time, and reconnect practical games with deeper preparation.

In this way, the system supports a higher standard of study: from opening preparation and candidate move selection to critical decision points in endgames and strategic positions.

Learning from real games

Chess-True also learns from real human play. PGN data can be processed, connected to the analysis tree, and used to preserve useful practical experience from actual games. This allows the system to combine engine strength with the accumulated understanding of real chess.

The result is more than a simple engine interface. It is a continuously improving memory of positions that matter, shaped by both calculation and practical experience.

How it works

• The cluster identifies the next high-value position from the stored analysis tree.
• Registered client machines reconstruct that position and send it to Stockfish for evaluation.
• Returned move assessments are stored as new analytical records.
• Best scores are propagated upward through a mini-max process, refining earlier evaluations.

Why it matters

Traditional analysis is often temporary: once the engine session ends, much of the work disappears. Chess-True is designed for cumulative analysis. Over time, it can explore deeper continuations, preserve previous effort, and provide a stronger foundation for serious preparation.

This makes it especially valuable for professionals, trainers, and advanced players who want a more persistent, research-oriented tool for improving understanding and decision quality.

Route Lab and TSP

Route Lab explores the Traveling Salesman Problem (TSP), searching for efficient routes through a selected set of points. The calculation runs in the background so the interface remains responsive while the search continues.

The project also has a broader research goal: applying efficient optimization methods to related combinatorial problems.

Technical notes

For implementation details, storage formats, and current test hardware, see the technical architecture notes.

We are open to cooperation with chess professionals, developers, researchers, and investors.

Chess-True Team: admin@chess-true.com