FRITZ 20 and the Personalised Training Problem Facing Vietnamese Chess Players

02:14 in the morning, a small flat on Cach Mang Thang Tam Street, District 10, Ho Chi Minh City. A fifteen-year-old player is still sitting in front of a screen, rewinding the game he lost in round seven of the national youth championship. He does not cry. He highlights move twenty-three, pastes it into the analysis box, and mutters to himself: 'Why does the machine prefer this move to mine?' In forty-one years of watching chess, I have heard that question in many training rooms, many countries, many generations.

In 2026, in a training room in Chengdu, I asked exactly the same question while installing Fritz 5 on a desktop computer. The boundary between the person learning chess and the tool used to learn chess has shifted many times since then, and each shift arrived with a promise of a training revolution. This time the name is FRITZ 20, introduced in language that reads almost like a manifesto: a personal training tool, your own chess coach, your toughest opponent and your strongest ally.

To understand why a piece of software deserves space on a sports page, we need to go back to the beginning. Fritz 1 appeared in 2026, developed by Frans Morsch and Mathias Feist and tied to the German publisher ChessBase. Four years later, Fritz 3 won the 2026 World Computer Chess Championship in Hong Kong, the first time this commercial line stood level with far more expensive academic projects. In 2026, Garry Kasparov played Deep Fritz in New York under the X3D format and drew 2-2. Three years later, in Bonn, Vladimir Kramnik lost 2-4 to Deep Fritz; game six entered history through an almost unbelievable error, when the reigning world champion missed a mate in one.

The strongest engines of the 2000s relied on brute-force search and hand-written evaluation. In December 2026, AlphaZero appeared and overturned every assumption about how a machine learns chess. Leela Chess Zero followed in 2026 in open-source spirit, and Stockfish moved to NNUE neural networks from 2026. The gap between top engines compressed, while absolute strength moved far beyond any human player. When every programme evaluates well, a fifteen-move evaluation bar becomes meaningless to a learner. Competition moved to a different layer: the layer of teaching.

FRITZ 20 places its bet on that layer. The introduction emphasises three adjectives: more efficient, more intelligent, more individual. For a player taking serious first steps, that is a promise of a pathway. For a tournament player, it is a promise of hours saved.

To judge that promise, I usually separate the role of a chess engine into three functions. First, it is a sparring partner, where a human tests ideas without fear of judgement. Second, it is a referee, where every claim is confirmed or denied by objective fact. Third, it is a teacher, where a game is broken into lessons and handed back in a sequence suited to the student. The first two functions have been solved almost perfectly for more than a decade. The third remains an open problem, and it is where every marketing claim must prove itself. The value of a modern training tool lies not in computational strength, but in its ability to select the right problem for the right person at the right moment.

Personalisation is easy to say and hard to do. A strong engine can point to the best move within seconds, but a learner needs something else: an exercise hard enough to avoid boredom and easy enough to avoid despair. Educational psychology calls that zone desirable difficulty. A tool reaches that zone when it knows how to lower the bar, give material odds, limit search depth, or deliberately present distorted positions that force the learner to find the road alone. If FRITZ 20 does this automatically from an individual profile, it genuinely changes how a young Vietnamese player trains every evening.

The opening phase is where this shows most clearly. A decent training system must manage a user's game database, detect forgotten lines, and resurface them right before memory fades. Spaced repetition has long been used in medicine and language learning; bringing it to chess makes sense, because openings are essentially structured memorisation. The important detail is that the software must separate two kinds of forgetting: forgetting a line, and forgetting the idea behind the line. The second kind is what makes a player collapse at move twenty.

Error classification sits deeper still. A lost game usually contains several kinds of mistake: a missed tactic, a misjudged pawn structure, poor clock management, or simple fatigue. If a tool lumps all of them under one label called a serious blunder, the learner gains very little. A good system separates errors by motif and by game phase, then returns the matching exercise format. Here the language layer matters as much as the calculation layer: turning an evaluation bar into a sentence the learner understands.

Based on my experience following matches at domestic youth events, the biggest problem in Vietnamese chess has never been a shortage of software. Children have free Stockfish, enormous databases, recorded lectures. The problem lies in the number of hours a real coach sits beside them, points at the board and asks: 'What do you think here?' When Le Quang Liem won the World Blitz Championship in 2026 in Khanty-Mansiysk, that result came from a technical foundation built over years, not from a disc installed overnight. A tool only closes the gap if somebody knows how to use it properly.

The economics deserve attention too. Paid software must compete with free options already strong enough to analyse every game an amateur will ever play. So what exactly is the buyer paying for? An interface, a lesson structure, patience that has been programmed in advance. The feeling of a companion rather than an answering machine. The real investment is not in the engine but in pedagogic design, which costs far more than renting extra servers.

During the pandemic years, when every tournament moved onto screens, I noticed something: when the stands are empty, I hear the game whisper in a different language. That language is the click of a mouse, a sigh, and an electronic clock counting down in a room where nobody else is present.

Here the paradox I want to state plainly appears. The strongest engine is not necessarily the best teacher, and in many cases it is the worst. A tool that always supplies the right answer cancels the very exercise it intends to create. Learners need to wrestle with the position, to endure a few minutes of not knowing, to make a wrong judgement and then correct it themselves. If software cuts that wrestling phase to zero, it does not teach chess, it teaches dependency. The board never lies; we only lie to ourselves with applause.

The second trap is memorising instead of understanding. Enormous databases make it easy to recall the first twenty moves, and many young players leave the board with a memory full to the brim and no compass. When the opponent steps outside the book, they lose direction as early as move fifteen. Defeat at the board is not a wrong move; it is seeing the right idea clearly and still not daring to play it.

The final knot sits outside the software. It sits in the daily schedule of a twelve-year-old, in the number of properly trained coaches, in a tournament calendar dense enough for constant contact. No update patch can fix a week of training cancelled for school exams. From the keyboard to the board, the speed of words never matches the speed of a move, and the speed of a move never matches the speed at which a person matures.

What I expect from FRITZ 20 is not an engine a few dozen Elo stronger. What I expect is a generation of Vietnamese coaches who turn software into lesson plans, who ask questions instead of handing over answers, who allow their students to struggle at the right moment. The tool has been placed in our hands. The rest belongs to people.