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Steven Chao’s Sudoku100 project combines a Rust puzzle generator compiled to WebAssembly with API routes for puzzle images, puzzle data, and an interactive embed. Its central design is to generate a complete solution grid, remove clues to target a selected difficulty, and reject puzzles that do not have a unique solution. The service’s current availability and access terms have not been independently verified, so treat the endpoint examples below as those described in the author’s tutorial.
How the puzzle generator works
The tutorial describes a three-stage generator for classic 9×9 Sudoku. Randomized candidate ordering makes the generated solution vary, while backtracking lets the algorithm recover when a placement leads to a dead end.
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1. Fill a complete solution grid
The generator begins with an empty grid. It tries candidate digits in randomized order, places a digit only when it satisfies the row, column, and box constraints, and recurses to fill the next cell. If no candidate works, it backs up and tries another branch.
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2. Remove clues toward a difficulty target
Once the grid is complete, the implementation removes clues to reach the selected tier’s target range:
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| Tier | Target clues in the tutorial |
|---|---|
| Beginner | 40–45 |
| Easy | 35–39 |
| Medium | 30–34 |
| Hard | 25–29 |
| Expert | 20–24 |
| Extreme | 17–19 |
These are Sudoku100’s stated clue-count targets, not a universal difficulty scale. Clue count by itself does not determine how challenging a puzzle will be for a person; solving techniques and the placement of the clues also matter. The tutorial identifies 17 clues as the theoretical minimum for a uniquely solvable classic Sudoku and uses that figure as the lower end of its Extreme range.
3. Check that the puzzle has one solution
After clues are removed, the generator checks whether the puzzle has a unique solution. According to the tutorial, a puzzle that has multiple solutions is rejected or retried. This check is important because a visually plausible grid is not necessarily a well-formed puzzle with one intended answer.
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Why use Rust compiled to WebAssembly?
Chao describes writing the core logic in Rust and compiling it with wasm-pack and wasm-bindgen. The stated aim is to reuse the same puzzle engine in browser-side play and in a server API, rather than maintaining separate implementations of the generation logic.
WebAssembly also suits the backtracking loop because it runs compiled code in browser environments. The tutorial characterizes performance as “near-native,” but does not report timings, workload details, hardware, or a controlled comparison with JavaScript. That phrase is therefore the author’s rationale, not a measured performance result.
JavaScript-facing build artifacts
The article lists browser ESM and Node.js CommonJS and ESM build artifacts, with JavaScript functions named generateSudokuData and generateSudokuImage. The image helper is described as using canvas to render PNG, WebP, or JPG output. This lets a consumer work with puzzle data or request a rendered image without implementing the generation algorithm itself.
What the API examples expose
The tutorial describes several ways to request or display a puzzle. These are examples from the article, not confirmation that the routes currently respond:
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/sudoku-imgrequests a random puzzle image; routes such as/sudoku-img/easyand/sudoku-img/hardselect a named difficulty.- Image requests can include width and format options, with PNG and WebP among the examples.
/img-id/238is shown as a fixed-puzzle image route./api/sudoku-imageis described as accepting options for difficulty, size, format, grid size, and whether to return data or a base64 representation./embed/interactiveprovides an iframe example for embedding a playable Sudoku.
The article names Beginner, Easy, Medium, Hard, Expert, and Extreme as difficulty labels, and PNG, WebP, and JPG as image formats. It does not establish that every format, grid size, or response option is available through every route.
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The author presents Sudoku100 as free to use without an API key or a rate-limit process. Those are author-reported access claims, not independently confirmed service commitments. Before building a production feature around the API, check the current official documentation and terms for route behavior, availability, usage limits, and permitted use. The tutorial also does not provide measured latency, throughput, operating cost, or evidence that every generated puzzle passes uniqueness checks in a live deployment.
For an experiment or prototype, the route examples illustrate a useful separation of concerns: request an image when a static puzzle is enough, use puzzle data when the application needs its own interface or solver, and use the interactive embed when a hosted playable experience is appropriate. For a dependable integration, verify the exact current parameters and response format rather than assuming every tutorial example remains supported.
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