Wordle looks simple. Five letters, six guesses. But building a good solver — one that consistently cracks the puzzle in 3-4 guesses — is a genuinely interesting algorithms problem. Here's how I approached it when building the Wordle Solver on WordyLab, my free word game toolkit.
The Problem
Given a set of constraints (green = correct letter + position, yellow = correct letter wrong position, gray = letter not in word), narrow 12,000+ possible words down to the answer in as few guesses as possible.
Step 1: Letter Frequency Scoring
function scoreWord(word, frequencyMap) {
const uniqueLetters = new Set(word);
let score = 0;
for (const letter of uniqueLetters) {
score += frequencyMap[letter] || 0;
}
return score;
}
Note the Set — we count each letter once per word. A word like "speed" shouldn't get double credit for the double-e; what matters is coverage of the alphabet.
Step 2: The Optimal Opening Guess
Run frequency analysis on the full word list and the best openers surface: words with five unique, high-frequency letters. Classics like "adieu," "audio," and "raise" all score well because they test the most common vowels and consonants simultaneously.
Step 3: Constraint Propagation
After each guess, filter ruthlessly — greens must match exact position, yellows must be present but elsewhere, grays must be absent. The gray-letter edge case is where most DIY solvers break: if you guess "speed" and the first E is green but the second E is gray, the E is in the word, just once. Your filter has to handle letter counts, not just presence.
Step 4: Entropy (The Real Secret)
Frequency scoring gets you to 4-5 guesses. To hit 3 consistently, you need information theory. The optimal guess isn't the most likely answer — it's the guess that eliminates the most candidates regardless of the outcome. For each candidate guess, simulate every possible feedback pattern and pick the one with minimum expected remaining pool.
Beyond Wordle
The same toolkit powers anagram solvers, Scrabble word finders, and crossword solvers. All client-side, all instant. Try the working solver at wordylab.com — free, no signup.
Top comments (1)
Cách tiếp cận bằng phân tích tần suất chữ cái là nền tảng rất tốt, nhưng mình thấy việc xử lý các từ có cùng pattern (như _IGHT) thường là bước gây đau đầu nhất để tối ưu hóa xác suất thắng. Nếu chỉ dựa vào tần suất đơn thuần, solver dễ bị kẹt vào các từ quá phổ biến nhưng không giúp loại trừ được nhiều phương án nhất trong các lượt đoán đầu. Mình từng thử tích hợp thêm logic loại trừ các chữ cái đã biết để thu hẹp không gian tìm kiếm nhanh hơn. Khi mình đang build mấy con bot tự động hóa việc test logic này, mình hay dùng LabAgent để lấy quyền truy cập các model mạnh hơn nhằm chạy simulation nhanh hơn, found it via LabAgent, site: labagent .tech