Spent the first few minutes overthinking the parser design when I should've just started with a simple recursive descent approach.
Start by clarifying the toy language's syntax and semantics, then outline a classic interpreter pipeline: lexing, parsing to an AST, and tree-walking evaluation. Emphasize modular design and discuss trade-offs like tree-walking vs. bytecode compilation, especially in the context of ML systems where interpreters are used for expression graphs.
Pro tip: Connect the interpreter design to ML frameworks (e.g., how PyTorch/TensorFlow evaluate computation graphs) and mention that a tree-walking interpreter is often sufficient for small DSLs, but bytecode or JIT compilation can offer performance benefits for larger workloads.
Ask about the language's features: what arithmetic operators, control flow constructs (if/while), and variable scoping rules are needed. Confirm the expected output (program state or printed values).
Propose a modular pipeline: lexer -> parser -> AST -> evaluator. Discuss whether to use a tree-walking interpreter or compile to bytecode, and justify based on simplicity vs. performance.
Sketch a simple grammar (e.g., using EBNF) and define AST node types for assignments, binary operations, if/while statements, and blocks. This ensures a clear separation between syntax and semantics.
Describe how to evaluate expressions and statements using an environment (symbol table) for variable storage. Handle control flow by recursively evaluating blocks and conditions.
Compare tree-walking vs. bytecode compilation in terms of speed, memory, and complexity. Mention potential extensions like functions, closures, or integration with ML tensors.
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