Building Languages to Solve Problems
When a problem is complex enough, the right move is to build a language for that problem. SICP's most powerful idea.
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When a problem is complex enough, the right move is to build a language for that problem. SICP's most powerful idea.
An R package where optimization solvers are first-class functions that compose through chaining, racing, and restarts.
A Python library for symbolic computation with a readable DSL, pattern matching, and a security model that separates rules from computation.
27 image commands, one constraint: read JSON, write JSON. The closure property as a generative design principle.
When the problem is coordinating computation across parties who can't share data, the SICP move is to build a language for it. Apertures adds one primitive — holes — to a Lisp, and gets pausable, resumable evaluation for free.