the tasks where wolfram actually outperforms python+google are symbolic: exact algebraic simplification, closed-form integrals, formal power series, equation solving over specific domains. for numeric work you're right that python wins. but for cases where you need a guarantee that x^2-1 = (x+1)(x-1) and not a floating-point approximation of it, wolfram is in a different category. the question is whether LLMs are running into those cases often enough to justify the overhead.

▲

Recursing 3 hours ago | parent | next [-]

sympy and similar packages can handle the vast majority of simple cases

	▲	snowhale 2 minutes ago \| parent [-]
		sympy handles the common cases well but there's still a gap on things like definite integrals with special functions, differential equations with complicated boundary conditions, or formal power series to arbitrary order. for most practical LLM use cases you're probably right that sympy is enough. wolfram's real edge is the breadth of its mathematical knowledge base, not just the CAS engine -- knowledge of special identities, physical constants, unit conversions. that's harder to replicate.

▲

hiuioejfjkf an hour ago | parent | prev [-]

[dead]