Partially done work: b. Extra processes: c. Extra features: d. Task switching: e. Waiting: f. Motion: g. Defects: h. Nonstandard or manual work:i. Heroics: d. The Second Way: The Principles of

advantage: they can roll out AI as a feature, not a product, and monetize it without changing the buying motion. The next chapter of AI monetization may not be a winner-take-all battle, but a convergence. Horizontal working out today, and definitely not tomorrow. Stagnation is almost certain, and stagnation is slow-motion failure. If you're not climbing, you're sliding… Shopify Co-Founder & CEO Tobias Lütke in Internal

27. CALLING C AND FORTRAN CODE 348 As the example shows, the original Julia array A has now been sorted: [-2.7, 1.3, 3.1, 4.4]. Note that Julia takes care of converting the array to a Ptr{Cdouble}), computing false. Alter- natively, in isolation m1 and m2 might be ordered, but if a third method cannot be sorted with them, they may cause an ambiguity together. For parametric types, the ambiguous_bottom keyword then calling (sort!(A); unique!(A)) will be much more efficient as long as the elements of A can be sorted. Examples julia> unique!([1, 1, 1]) 1-element Vector{Int64}: 1 julia> A = [7, 3, 2, 3, 7, 5];

27. CALLING C AND FORTRAN CODE 348 As the example shows, the original Julia array A has now been sorted: [-2.7, 1.3, 3.1, 4.4]. Note that Julia takes care of converting the array to a Ptr{Cdouble}), computing false. Alter- natively, in isolation m1 and m2 might be ordered, but if a third method cannot be sorted with them, they may cause an ambiguity together. For parametric types, the ambiguous_bottom keyword then calling (sort!(A); unique!(A)) will be much more efficient as long as the elements of A can be sorted. Examples julia> unique!([1, 1, 1]) 1-element Vector{Int64}: 1 julia> A = [7, 3, 2, 3, 7, 5];

Vector{Float64}: -2.7 1.3 3.1 4.4 As the example shows, the original Julia array A has now been sorted: [-2.7, 1.3, 3.1, 4.4]. Note that Julia takes care of converting the array to a Ptr{Cdouble}), computing false. Alter- natively, in isolation m1 and m2 might be ordered, but if a third method cannot be sorted with them, they may cause an ambiguity together. For parametric types, the ambiguous_bottom keyword true, then names explicitly imported from other modules are also included. Names are returned in sorted order. As a special case, all names defined in Main are considered "public", since it is not idiomatic

Vector{Float64}: -2.7 1.3 3.1 4.4 As the example shows, the original Julia array A has now been sorted: [-2.7, 1.3, 3.1, 4.4]. Note that Julia takes care of converting the array to a Ptr{Cdouble}), computing false. Alter- natively, in isolation m1 and m2 might be ordered, but if a third method cannot be sorted with them, they may cause an ambiguity together. For parametric types, the ambiguous_bottom keyword true, then names explicitly imported from other modules are also included. Names are returned in sorted order. As a special case, all names defined in Main are considered "public", since it is not idiomatic

Vector{Float64}: -2.7 1.3 3.1 4.4 As the example shows, the original Julia array A has now been sorted: [-2.7, 1.3, 3.1, 4.4]. Note that Julia takes care of converting the array to a Ptr{Cdouble}), computing false. Alter- natively, in isolation m1 and m2 might be ordered, but if a third method cannot be sorted with them, they may cause an ambiguity together. For parametric types, the ambiguous_bottom keyword true, then names explicitly imported from other modules are also included. Names are returned in sorted order. As a special case, all names defined in Main are considered "public", since it is not idiomatic

Vector{Float64}: -2.7 1.3 3.1 4.4 As the example shows, the original Julia array A has now been sorted: [-2.7, 1.3, 3.1, 4.4]. Note that Julia takes care of converting the array to a Ptr{Cdouble}), computing false. Alter- natively, in isolation m1 and m2 might be ordered, but if a third method cannot be sorted with them, they may cause an ambiguity together. For parametric types, the ambiguous_bottom keyword usings is true, then names explicitly imported via using are also included. Names are returned in sorted order. As a special case, all names defined in Main are considered "public", since it is not idiomatic

Vector{Float64}: -2.7 1.3 3.1 4.4 As the example shows, the original Julia array A has now been sorted: [-2.7, 1.3, 3.1, 4.4]. Note that Julia takes care of converting the array to a Ptr{Cdouble}), computing false. Alter- natively, in isolation m1 and m2 might be ordered, but if a third method cannot be sorted with them, they may cause an ambiguity together. For parametric types, the ambiguous_bottom keyword usings is true, then names explicitly imported via using are also included. Names are returned in sorted order. As a special case, all names defined in Main are considered "public", since it is not idiomatic

Vector{Float64}: -2.7 1.3 3.1 4.4 As the example shows, the original Julia array A has now been sorted: [-2.7, 1.3, 3.1, 4.4]. Note that Julia takes care of converting the array to a Ptr{Cdouble}), computing false. Alter- natively, in isolation m1 and m2 might be ordered, but if a third method cannot be sorted with them, they may cause an ambiguity together. For parametric types, the ambiguous_bottom keyword usings is true, then names explicitly imported via using are also included. Names are returned in sorted order. As a special case, all names defined in Main are considered "public", since it is not idiomatic