I’ve been playing with tiny domain-specific languages (DSLs) in Python + Ruby. Building these “mini-languages” is like grabbing a laser-focused toolbox—fewer gadgets, clearer thinking. Here’s what I’ve bumped into:

Constraints ≠ limitations. A grammar with ~15 rules is weirdly freeing. Less bickering over braces vs. commas, more brain-power on the real problem.

Need more than those 15 rules? You’ll end up fighting the language. For many targets—build files, test specs, text transforms—the trade-off works great.

Program synthesis loves small languages: shrink the syntax, shrink the search space. Same math that speeds up the solver also helps my attention span—fewer knobs to fiddle with.

Shrink too far and key constructs vanish. Then you hack around with ugly patterns (e.g., looping only via recursion) and the “semantic” search space explodes.

Naming is brutal. No giant stdlib to lean on, so every verb I coin sticks forever. Choose badly and it haunts you. Versioning or aliases can soften the pain, but many DSLs never get that luxury.

Natural language is fuzzier than you think. My toy commands—move, turn, wait—felt obvious until I had to nail down what “around” means. Suddenly I respect game-dev folks a lot more.

Error messages are 50 % of the UX. If the parser can’t say why it bailed, either the DSL is too loose or the logs are garbage. When the grammar’s strict but details are missing, that’s on implementation, not design.

README-first design: I write the docs before the parser. If the usage blurb reads clunky, the primitives are wrong. Works almost every time—great litmus test for DSL ergonomics.

Tiny program synthesizer 🤗🦖: an enumerator + three I/O examples and—boom—solutions I’d never type by hand. Feels like math magic 🪄

Tiny-synth caveats: • Over-fitting—three examples can pass but miss edge cases. • Readability—enumerators optimise for existence, not elegance.

Constraints move the work. Too many and you’re writing programs for the user; too few and they’re filing bug reports at you. Find the sweet spot.

Tooling helps: linters, interactive editors, and versioned extensions can relax strictness or tighten looseness, but the trade-off never vanishes.

Net takeaway: crafting DSLs is a crash course in translating messy human intent into crisp, checkable rules—and back again. After that, “regular” code looks very different.