Removed all inline for (0..total) for either builtin or for loop without inline

This is to prevent giant binary for Tensor with a lot of Scalar

src/Base.zig

@@ -3,34 +3,39 @@ const std = @import("std");
 // Adjust these imports to match your actual file names
 const Dimensions = @import("Dimensions.zig");
 const Scales = @import("Scales.zig");
-const Scalar = @import("Quantity.zig").Scalar;
+const Tensor = @import("Tensor.zig").Tensor;
 
 fn PhysicalConstant(comptime d: Dimensions.ArgOpts, comptime val: f64, comptime s: Scales.ArgOpts) type {
     return struct {
-        const dims = Dimensions.init(d);
+        pub const dims = Dimensions.init(d);
-        const scales = Scales.init(s);
+        pub const scales = Scales.init(s);
 
         /// Instantiates the constant into a specific numeric type.
-        pub fn Of(comptime T: type) Scalar(T, d, s) {
+        pub fn Of(comptime T: type) Tensor(T, d, s, &.{1}) {
-            return .{ .data = @splat(@as(T, @floatCast(val))) };
+            const casted_val: T = switch (@typeInfo(T)) {
+                .float => @floatCast(val),
+                .int => @intFromFloat(val),
+                else => @compileError("Unsupported type for PhysicalConstant"),
+            };
+            return Tensor(T, d, s, &.{1}).splat(casted_val);
         }
     };
 }
 
 fn BaseScalar(comptime d: Dimensions.ArgOpts) type {
     return struct {
-        const dims = Dimensions.init(d);
+        pub const dims = Dimensions.init(d);
 
         /// Creates a Scalar of this dimension using default scales.
-        /// Example: const V = Quantities.Velocity.Base(f32);
+        /// Example: const V = Quantities.Velocity.Of(f32);
         pub fn Of(comptime T: type) type {
-            return Scalar(T, d, .{});
+            return Tensor(T, d, .{}, &.{1});
         }
 
         /// Creates a Scalar of this dimension using custom scales.
-        /// Example: const Kmh = Quantities.Velocity.Scaled(f32, Scales.init(.{ .L = .k, .T = .hour }));
+        /// Example: const Kmh = Quantities.Velocity.Scaled(f32, .{ .L = .k, .T = .hour });
         pub fn Scaled(comptime T: type, comptime s: Scales.ArgOpts) type {
-            return Scalar(T, d, s);
+            return Tensor(T, d, s, &.{1});
         }
     };
 }
@@ -107,7 +112,7 @@ pub const ElectricCapacitance = BaseScalar(.{ .T = 4, .L = -2, .M = -1, .I = 2 }
 pub const ElectricImpedance = ElectricResistance;
 pub const MagneticFlux = BaseScalar(.{ .M = 1, .L = 2, .T = -2, .I = -1 });
 pub const MagneticDensity = BaseScalar(.{ .M = 1, .T = -2, .I = -1 });
-pub const MagneticStrength = BaseScalar(.{ .L = -1, .I = 1 }); // Fixed typo from MagneticStrengh
+pub const MagneticStrength = BaseScalar(.{ .L = -1, .I = 1 });
 pub const MagneticMoment = BaseScalar(.{ .L = 2, .I = 1 });
 
 // ==========================================
@@ -140,7 +145,7 @@ pub const ThermalHeat = Energy;
 pub const ThermalWork = Energy;
 pub const ThermalCapacity = BaseScalar(.{ .M = 1, .L = 2, .T = -2, .Tr = -1 });
 pub const ThermalCapacityPerMass = BaseScalar(.{ .L = 2, .T = -2, .Tr = -1 });
-pub const ThermalFluxDensity = BaseScalar(.{ .M = 1, .T = -3 }); // Fixed typo from ThermalluxDensity
+pub const ThermalFluxDensity = BaseScalar(.{ .M = 1, .T = -3 });
 pub const ThermalConductance = BaseScalar(.{ .M = 1, .L = 2, .T = -3, .Tr = -1 });
 pub const ThermalConductivity = BaseScalar(.{ .M = 1, .L = 1, .T = -3, .Tr = -1 });
 pub const ThermalResistance = BaseScalar(.{ .M = -1, .L = -2, .T = 3, .Tr = 1 });
@@ -152,20 +157,24 @@ pub const ThermalEntropy = BaseScalar(.{ .M = 1, .L = 2, .T = -2, .Tr = -1 });
 // ==========================================
 pub const Frequency = BaseScalar(.{ .T = -1 });
 pub const Viscosity = BaseScalar(.{ .M = 1, .L = -1, .T = -1 });
-pub const SurfaceTension = BaseScalar(.{ .M = 1, .T = -2 }); // Corrected from MT-2a
+pub const SurfaceTension = BaseScalar(.{ .M = 1, .T = -2 });
+
+// ==========================================
+// Tests
+// ==========================================
 
 test "BaseQuantities - Core dimensions instantiation" {
     // Basic types via generic wrappers
     const M = Meter.Of(f32);
     const distance = M.splat(100);
-    try std.testing.expectEqual(100.0, distance.value());
+    try std.testing.expectEqual(100.0, distance.data[0]);
     try std.testing.expectEqual(1, M.dims.get(.L));
     try std.testing.expectEqual(0, M.dims.get(.T));
 
     // Test specific scale variants
     const Kmh = Speed.Scaled(f32, .{ .L = .k, .T = .hour });
     const speed = Kmh.splat(120);
-    try std.testing.expectEqual(120.0, speed.value());
+    try std.testing.expectEqual(120.0, speed.data[0]);
     try std.testing.expectEqual(.k, @TypeOf(speed).scales.get(.L));
     try std.testing.expectEqual(.hour, @TypeOf(speed).scales.get(.T));
 }
@@ -176,12 +185,12 @@ test "BaseQuantities - Kinematics equations" {
 
     // Velocity = Distance / Time
     const v = d.div(t);
-    try std.testing.expectEqual(25.0, v.value());
+    try std.testing.expectEqual(25.0, v.data[0]);
     try std.testing.expect(Speed.dims.eql(@TypeOf(v).dims));
 
     // Acceleration = Velocity / Time
     const a = v.div(t);
-    try std.testing.expectEqual(12.5, a.value());
+    try std.testing.expectEqual(12.5, a.data[0]);
     try std.testing.expect(Acceleration.dims.eql(@TypeOf(a).dims));
 }
 
@@ -193,13 +202,13 @@ test "BaseQuantities - Dynamics (Force and Work)" {
 
     // Force = mass * acceleration
     const f = m.mul(a);
-    try std.testing.expectEqual(98, f.value());
+    try std.testing.expectEqual(98, f.data[0]);
     try std.testing.expect(Force.dims.eql(@TypeOf(f).dims));
 
     // Energy (Work) = Force * distance
     const distance = Meter.Of(f32).splat(5.0);
     const energy = f.mul(distance);
-    try std.testing.expectEqual(490, energy.value());
+    try std.testing.expectEqual(490, energy.data[0]);
     try std.testing.expect(Energy.dims.eql(@TypeOf(energy).dims));
 }
 
@@ -209,26 +218,26 @@ test "BaseQuantities - Electric combinations" {
 
     // Charge = Current * time
     const charge = current.mul(time);
-    try std.testing.expectEqual(6.0, charge.value());
+    try std.testing.expectEqual(6.0, charge.data[0]);
     try std.testing.expect(ElectricCharge.dims.eql(@TypeOf(charge).dims));
 }
 
 test "Constants - Initialization and dimension checks" {
     // Speed of Light
     const c = Constants.SpeedOfLight.Of(f64);
-    try std.testing.expectEqual(299792458.0, c.value());
+    try std.testing.expectEqual(299792458.0, c.data[0]);
     try std.testing.expectEqual(1, @TypeOf(c).dims.get(.L));
     try std.testing.expectEqual(-1, @TypeOf(c).dims.get(.T));
 
     // Electron Mass (verifying scale as well)
     const me = Constants.ElectronMass.Of(f64);
-    try std.testing.expectEqual(9.1093837139e-31, me.value());
+    try std.testing.expectEqual(9.1093837139e-31, me.data[0]);
     try std.testing.expectEqual(1, @TypeOf(me).dims.get(.M));
     try std.testing.expectEqual(.k, @TypeOf(me).scales.get(.M)); // Should be scaled to kg
 
     // Boltzmann Constant (Complex derived dimensions)
     const kb = Constants.Boltzmann.Of(f64);
-    try std.testing.expectEqual(1.380649e-23, kb.value());
+    try std.testing.expectEqual(1.380649e-23, kb.data[0]);
     try std.testing.expectEqual(1, @TypeOf(kb).dims.get(.M));
     try std.testing.expectEqual(2, @TypeOf(kb).dims.get(.L));
     try std.testing.expectEqual(-2, @TypeOf(kb).dims.get(.T));
@@ -237,7 +246,7 @@ test "Constants - Initialization and dimension checks" {
 
     // Vacuum Permittivity
     const eps0 = Constants.VacuumPermittivity.Of(f64);
-    try std.testing.expectEqual(8.8541878188e-12, eps0.value());
+    try std.testing.expectEqual(8.8541878188e-12, eps0.data[0]);
     try std.testing.expectEqual(-1, @TypeOf(eps0).dims.get(.M));
     try std.testing.expectEqual(-3, @TypeOf(eps0).dims.get(.L));
     try std.testing.expectEqual(4, @TypeOf(eps0).dims.get(.T));
@@ -245,7 +254,7 @@ test "Constants - Initialization and dimension checks" {
 
     // Fine Structure Constant (Dimensionless)
     const alpha = Constants.FineStructure.Of(f64);
-    try std.testing.expectEqual(0.0072973525643, alpha.value());
+    try std.testing.expectEqual(0.0072973525643, alpha.data[0]);
     try std.testing.expectEqual(0, @TypeOf(alpha).dims.get(.M));
     try std.testing.expectEqual(0, @TypeOf(alpha).dims.get(.L));
 }

src/Dimensions.zig

@@ -51,17 +51,17 @@ data: std.EnumArray(Dimension, comptime_int),
 /// Unspecified dimensions default to 0.
 pub fn init(comptime init_val: ArgOpts) Self {
     var s = Self{ .data = std.EnumArray(Dimension, comptime_int).initFill(0) };
-    inline for (std.meta.fields(@TypeOf(init_val))) |f|
+    for (std.meta.fields(@TypeOf(init_val))) |f|
         s.data.set(@field(Dimension, f.name), @field(init_val, f.name));
     return s;
 }
 
 pub fn initFill(comptime val: comptime_int) Self {
-    return .{ .data = std.EnumArray(Dimension, comptime_int).initFill(val) };
+    comptime return .{ .data = std.EnumArray(Dimension, comptime_int).initFill(val) };
 }
 
 pub fn get(comptime self: Self, comptime key: Dimension) comptime_int {
-    return self.data.get(key);
+    comptime return self.data.get(key);
 }
 
 pub fn set(comptime self: *Self, comptime key: Dimension, comptime val: i8) void {
@@ -70,40 +70,40 @@ pub fn set(comptime self: *Self, comptime key: Dimension, comptime val: i8) void
 
 pub fn argsOpt(self: Self) ArgOpts {
     var args: ArgOpts = undefined;
-    inline for (std.enums.values(Dimension)) |d|
+    for (std.enums.values(Dimension)) |d|
         @field(args, @tagName(d)) = self.get(d);
-    return args;
+    comptime return args;
 }
 
 /// Add exponents component-wise. Used internally by `mul`.
 pub fn add(comptime a: Self, comptime b: Self) Self {
     var result = Self.initFill(0);
-    inline for (std.enums.values(Dimension)) |d|
+    for (std.enums.values(Dimension)) |d|
         result.set(d, a.get(d) + b.get(d));
-    return result;
+    comptime return result;
 }
 
 /// Subtract exponents component-wise. Used internally by `div`.
 pub fn sub(comptime a: Self, comptime b: Self) Self {
     var result = Self.initFill(0);
-    inline for (std.enums.values(Dimension)) |d|
+    for (std.enums.values(Dimension)) |d|
         result.set(d, a.get(d) - b.get(d));
-    return result;
+    comptime return result;
 }
 
 /// Multiply exponents by a scalar integer. Used internally by `pow` in Scalar.
 pub fn scale(comptime a: Self, comptime exp: comptime_int) Self {
     var result = Self.initFill(0);
-    inline for (std.enums.values(Dimension)) |d|
+    for (std.enums.values(Dimension)) |d|
         result.set(d, a.get(d) * exp);
-    return result;
+    comptime return result;
 }
 
 pub fn div(comptime a: Self, comptime exp: comptime_int) Self {
     var result = Self.initFill(0);
     inline for (std.enums.values(Dimension)) |d|
         result.set(d, a.get(d) / exp);
-    return result;
+    comptime return result;
 }
 
 /// Returns true if every dimension exponent is equal. Used to enforce type compatibility in `add`, `sub`, `to`.

src/Scales.zig

@@ -1,5 +1,4 @@
 const std = @import("std");
-const hlp = @import("helper.zig");
 const Dimensions = @import("Dimensions.zig");
 const Dimension = @import("Dimensions.zig").Dimension;
 
@@ -66,7 +65,7 @@ pub const UnitScale = enum(isize) {
     }
 
     pub inline fn getFactor(self: @This()) comptime_float {
-        return comptime switch (self) {
+        comptime return switch (self) {
             // Standard SI Exponents
             inline .P, .T, .G, .M, .k, .h, .da, .none, .d, .c, .m, .u, .n, .p, .f => std.math.pow(f64, 10.0, @floatFromInt(@intFromEnum(self))),
 
@@ -84,7 +83,7 @@ pub const UnitScale = enum(isize) {
             inline .lb => 453.59237,
             inline .st => 6350.29318,
 
-            inline else => @floatFromInt(@intFromEnum(self)),
+            else => @floatFromInt(@intFromEnum(self)),
         };
     }
 };
@@ -99,16 +98,16 @@ data: std.EnumArray(Dimension, UnitScale),
 pub fn init(comptime init_val: ArgOpts) Self {
     comptime var s = Self{ .data = std.EnumArray(Dimension, UnitScale).initFill(.none) };
     inline for (std.meta.fields(@TypeOf(init_val))) |f| {
-        if (comptime hlp.isInt(@TypeOf(@field(init_val, f.name))))
+        if (comptime @typeInfo(@TypeOf(@field(init_val, f.name))) == .comptime_int)
             s.data.set(@field(Dimension, f.name), @enumFromInt(@field(init_val, f.name)))
         else
             s.data.set(@field(Dimension, f.name), @field(init_val, f.name));
     }
-    return s;
+    return comptime s;
 }
 
 pub fn initFill(comptime val: UnitScale) Self {
-    return comptime .{ .data = std.EnumArray(Dimension, UnitScale).initFill(val) };
+    comptime return .{ .data = std.EnumArray(Dimension, UnitScale).initFill(val) };
 }
 
 pub fn get(comptime self: Self, comptime key: Dimension) UnitScale {
@@ -116,7 +115,7 @@ pub fn get(comptime self: Self, comptime key: Dimension) UnitScale {
 }
 
 pub fn set(comptime self: *Self, comptime key: Dimension, comptime val: UnitScale) void {
-    comptime self.data.set(key, val);
+    self.data.set(key, val);
 }
 
 pub fn argsOpt(self: Self) ArgOpts {
@@ -145,5 +144,5 @@ pub inline fn getFactor(comptime s: Self, comptime d: Dimensions) comptime_float
                 factor /= base;
         }
     }
-    return comptime factor;
+    comptime return factor;
 }

src/Tensor.zig

@@ -1,47 +1,57 @@
 const std = @import("std");
-const hlp = @import("helper.zig");
 const Scales = @import("Scales.zig");
 const UnitScale = Scales.UnitScale;
 const Dimensions = @import("Dimensions.zig");
 const Dimension = Dimensions.Dimension;
 
 // ─────────────────────────────────────────────────────────────────────────────
-// Comptime shape utilities
+// Comptime utilities
 // ─────────────────────────────────────────────────────────────────────────────
 
-pub fn shapeTotal(comptime shape: []const usize) usize {
+pub fn shapeTotal(comptime shape: []const comptime_int) usize {
-    var t: usize = 1;
+    var t: comptime_int = 1;
     for (shape) |s| t *= s;
     return t;
 }
 
+/// Check if two shapes are strictly identical.
+pub fn shapeEql(comptime a: []const comptime_int, comptime b: []const comptime_int) bool {
+    if (a.len != b.len) return false;
+    for (a, 0..) |v, i|
+        if (v != b[i]) return false;
+    return true;
+}
+
 /// Row-major (C-order) strides: strides[i] = product(shape[i+1..]).
 ///   e.g. shape {3, 4} → strides {4, 1}
 ///        shape {2, 3, 4} → strides {12, 4, 1}
-pub fn shapeStrides(comptime shape: []const usize) [shape.len]usize {
+pub fn shapeStrides(comptime shape: []const comptime_int) [shape.len]comptime_int {
-    var st: [shape.len]usize = undefined;
+    var st: [shape.len]comptime_int = undefined;
     if (shape.len == 0) return st;
     st[shape.len - 1] = 1;
     if (shape.len > 1) {
-        var i: usize = shape.len - 1;
+        var i: comptime_int = shape.len - 1;
         while (i > 0) : (i -= 1) st[i - 1] = st[i] * shape[i];
     }
     return st;
 }
 
 /// Return a copy of `shape` with the element at `axis` removed.
-pub fn shapeRemoveAxis(comptime shape: []const usize, comptime axis: usize) [shape.len - 1]usize {
+pub fn shapeRemoveAxis(comptime shape: []const comptime_int, comptime axis: comptime_int) [shape.len - 1]comptime_int {
-    var out: [shape.len - 1]usize = undefined;
+    var out: [shape.len - 1]comptime_int = undefined;
-    var j: usize = 0;
+    var j: comptime_int = 0;
     for (shape, 0..) |v, i| {
-        if (i != axis) { out[j] = v; j += 1; }
+        if (i != axis) {
+            out[j] = v;
+            j += 1;
+        }
     }
     return out;
 }
 
 /// Concatenate two compile-time slices.
-pub fn shapeCat(comptime a: []const usize, comptime b: []const usize) [a.len + b.len]usize {
+pub fn shapeCat(comptime a: []const comptime_int, comptime b: []const comptime_int) [a.len + b.len]comptime_int {
-    var out: [a.len + b.len]usize = undefined;
+    var out: [a.len + b.len]comptime_int = undefined;
     for (a, 0..) |v, i| out[i] = v;
     for (b, 0..) |v, i| out[a.len + i] = v;
     return out;
@@ -50,11 +60,11 @@ pub fn shapeCat(comptime a: []const usize, comptime b: []const usize) [a.len + b
 /// Decode a flat row-major index into N-D coordinates.
 /// Called only in comptime contexts (all arguments are comptime).
 pub fn decodeFlatCoords(
-    comptime flat: usize,
+    comptime flat: comptime_int,
-    comptime n: usize,
+    comptime n: comptime_int,
-    comptime strd: [n]usize,
+    comptime strd: [n]comptime_int,
 ) [n]usize {
-    var coords: [n]usize = undefined;
+    var coords: [n]comptime_int = undefined;
     var tmp = flat;
     for (0..n) |i| {
         coords[i] = if (strd[i] == 0) 0 else tmp / strd[i];
@@ -96,22 +106,48 @@ pub fn insertAxis(
     return out;
 }
 
+fn isInt(comptime T: type) bool {
+    return @typeInfo(T) == .int or @typeInfo(T) == .comptime_int;
+}
+
+fn finerScales(comptime T1: type, comptime T2: type) Scales {
+    const d1: Dimensions = T1.dims;
+    const d2: Dimensions = T2.dims;
+    const s1: Scales = T1.scales;
+    const s2: Scales = T2.scales;
+    comptime var out = Scales.initFill(.none);
+    for (std.enums.values(Dimension)) |dim| {
+        const scale1 = comptime s1.get(dim);
+        const scale2 = comptime s2.get(dim);
+        out.set(dim, if (comptime d1.get(dim) == 0 and d2.get(dim) == 0)
+            .none
+        else if (comptime d1.get(dim) == 0)
+            scale2
+        else if (comptime d2.get(dim) == 0)
+            scale1
+        else if (comptime scale1.getFactor() > scale2.getFactor())
+            scale2
+        else
+            scale1);
+    }
+    comptime return out;
+}
 // ─────────────────────────────────────────────────────────────────────────────
 // File-scope RHS normalisation helpers
-//
-// Any bare comptime_int / comptime_float / runtime T used as an arithmetic
-// or comparison RHS is wrapped into a dimensionless Tensor of shape {1}.
-// Actual Tensor types are passed through unchanged.
 // ─────────────────────────────────────────────────────────────────────────────
 
+fn isTensor(comptime Rhs: type) bool {
+    return comptime @typeInfo(Rhs) == .@"struct" and @hasDecl(Rhs, "ISTENSOR");
+}
+
 fn RhsTensorType(comptime T: type, comptime Rhs: type) type {
-    if (@hasDecl(Rhs, "ISTENSOR")) return Rhs;
+    if (comptime isTensor(Rhs)) return Rhs;
     return Tensor(T, .{}, .{}, &.{1});
 }
 
 fn toRhsTensor(comptime T: type, r: anytype) RhsTensorType(T, @TypeOf(r)) {
     const Rhs = @TypeOf(r);
-    if (comptime @hasDecl(Rhs, "ISTENSOR")) return r;
+    if (comptime isTensor(Rhs)) return r;
     const scalar: T = switch (comptime @typeInfo(Rhs)) {
         .comptime_int => switch (comptime @typeInfo(T)) {
             .float => @as(T, @floatFromInt(r)),
@@ -134,55 +170,44 @@ fn toRhsTensor(comptime T: type, r: anytype) RhsTensorType(T, @TypeOf(r)) {
     return Tensor(T, .{}, .{}, &.{1}){ .data = .{scalar} };
 }
 
-// ─────────────────────────────────────────────────────────────────────────────
+pub fn printSuperscript(writer: *std.Io.Writer, n: i32) !void {
-// Tensor — unified dimensioned ND type.
+    if (n == 0) return;
-//
+    var val = n;
-//   T      : element numeric type  (f32, f64, i32, i128, …)
+    if (val < 0) {
-//   d_opt  : SI dimension exponents
+        try writer.writeAll("\u{207B}");
-//   s_opt  : unit scales
+        val = -val;
-//   shape_ : compile-time shape
+    }
-//              &.{1}       → scalar
+    var buf: [12]u8 = undefined;
-//              &.{3}       → 3-vector
+    const str = std.fmt.bufPrint(&buf, "{d}", .{val}) catch return;
-//              &.{4, 4}    → 4×4 matrix
+    for (str) |c| {
-//              &.{3, 3, 3} → 3D field
+        const s = switch (c) {
-//
+            '0' => "\u{2070}",
-// Storage: flat @Vector(total, T) where total = product(shape_).
+            '1' => "\u{00B9}",
-// All arithmetic operates on the flat vector directly → SIMD wherever possible.
+            '2' => "\u{00B2}",
-//
+            '3' => "\u{00B3}",
-// Shape-related comptime constants exposed on every Tensor type:
+            '4' => "\u{2074}",
-//   dims        : Dimensions  — SI exponent struct
+            '5' => "\u{2075}",
-//   scales      : Scales      — unit scale struct
+            '6' => "\u{2076}",
-//   shape       : []const usize
+            '7' => "\u{2077}",
-//   rank        : usize        = shape.len
+            '8' => "\u{2078}",
-//   total       : usize        = product(shape)
+            '9' => "\u{2079}",
-//   strides_arr : [rank]usize  — row-major strides
+            else => unreachable,
-//
+        };
-// Index helper:
+        try writer.writeAll(s);
-//   Tensor.idx(.{row, col}) → flat index  (comptime, no runtime cost)
+    }
-//
+}
-// GPU readiness:
-//   tensor.asSlice() → []T  (zero-copy pointer to the flat @Vector storage)
-//
-// Contraction (replaces dot / cross / matmul):
-//   a.contract(b, axis_a, axis_b)
-//   For rank-1 × rank-1 this is the dot product.
-//   For rank-2 × rank-2 with axis_a=1, axis_b=0 this is matrix multiply.
-//
-// Removed from Quantity:
-//   Scalar / Vector aliases, Vec3 / ScalarType, .value(), .vec(), .vec3(),
-//   dot(), cross(), mulScalar(), divScalar(), eqScalar() and friends.
-//   Use Tensor(..., &.{1}), .data[0], mul(), div(), eq() respectively.
-// ─────────────────────────────────────────────────────────────────────────────
 
 pub fn Tensor(
     comptime T: type,
     comptime d_opt: Dimensions.ArgOpts,
     comptime s_opt: Scales.ArgOpts,
-    comptime shape_: []const usize,
+    comptime shape_: []const comptime_int,
 ) type {
     comptime {
-        std.debug.assert(shape_.len >= 1);
+        if (shape_.len == 0) @compileError("Tensor shape must have at least 1 dimension (rank >= 1).");
-        for (shape_) |s| std.debug.assert(s >= 1);
+        for (shape_) |s| {
+            if (s == 0) @compileError("Tensor shape dimensions must be strictly >= 1.");
+        }
     }
     @setEvalBranchQuota(10_000_000);
 
@@ -191,41 +216,32 @@ pub fn Tensor(
     const Vec = @Vector(_total, T);
 
     return struct {
-        /// Flat SIMD storage.  All arithmetic operates here directly.
         data: Vec,
 
         const Self = @This();
 
-        pub const ValueType : type               = T;
+        pub const ValueType: type = T;
-        pub const dims      : Dimensions         = Dimensions.init(d_opt);
+        pub const dims: Dimensions = Dimensions.init(d_opt);
-        pub const scales    : Scales             = Scales.init(s_opt);
+        pub const scales: Scales = Scales.init(s_opt);
-        pub const shape     : []const usize      = shape_;
+        pub const shape: []const comptime_int = shape_;
-        pub const rank      : usize              = shape_.len;
+        pub const rank: comptime_int = shape_.len;
-        pub const total     : usize              = _total;
+        pub const total: comptime_int = _total;
-        pub const strides_arr: [shape_.len]usize = _strides;
+        pub const strides_arr: [shape_.len]comptime_int = _strides;
         pub const ISTENSOR = true;
 
-        // ───────────────────────────────────────────────────────────────
-        // Index helper
-        // ───────────────────────────────────────────────────────────────
-
         /// Convert N-D coords (row-major) to flat index — fully comptime.
         /// Usage: Tensor.idx(.{row, col})
-        pub fn idx(comptime coords: [rank]usize) usize {
+        pub inline fn idx(comptime coords: [rank]usize) usize {
             comptime {
                 var flat: usize = 0;
                 for (0..rank) |i| {
-                    std.debug.assert(coords[i] < shape[i]);
+                    if (coords[i] >= shape[i]) @compileError("idx: Coordinate out of bounds");
                     flat += coords[i] * strides_arr[i];
                 }
                 return flat;
             }
         }
 
-        // ───────────────────────────────────────────────────────────────
-        // Constructors
-        // ───────────────────────────────────────────────────────────────
-
         /// Broadcast a single value across all elements.
         pub inline fn splat(v: T) Self {
             return .{ .data = @splat(v) };
@@ -234,25 +250,17 @@ pub fn Tensor(
         pub const zero: Self = splat(0);
         pub const one: Self = splat(1);
 
-        // ───────────────────────────────────────────────────────────────
-        // GPU readiness
-        // ───────────────────────────────────────────────────────────────
-
         /// Return a mutable slice to the flat storage — zero-copy WebGPU buffer mapping.
         pub inline fn asSlice(self: *Self) []T {
             return @as([*]T, @ptrCast(&self.data))[0..total];
         }
 
-        // ───────────────────────────────────────────────────────────────
+        inline fn RhsT(comptime Rhs: type) type {
-        // Internal: RHS normalisation
+            return RhsTensorType(T, Rhs);
-        // ───────────────────────────────────────────────────────────────
+        }
-
+        inline fn rhs(r: anytype) RhsT(@TypeOf(r)) {
-        inline fn RhsT(comptime Rhs: type) type { return RhsTensorType(T, Rhs); }
+            return toRhsTensor(T, r);
-        inline fn rhs(r: anytype) RhsT(@TypeOf(r)) { return toRhsTensor(T, r); }
+        }
-
-        // ───────────────────────────────────────────────────────────────
-        // Internal: scalar broadcast  (shape {1} → full Vec)
-        // ───────────────────────────────────────────────────────────────
 
         inline fn broadcastToVec(comptime RhsType: type, r: RhsType) Vec {
             return if (comptime RhsType.total == 1 and total > 1)
@@ -261,57 +269,54 @@ pub fn Tensor(
                 r.data;
         }
 
-        // ───────────────────────────────────────────────────────────────
-        // Arithmetic
-        // ───────────────────────────────────────────────────────────────
-
         /// Element-wise add.  Dimensions must match; scales resolve to finer.
-        /// RHS must have the same element count as self, or total == 1 (broadcast).
+        /// RHS must have the same shape as self, or total == 1 (broadcast).
         pub inline fn add(self: Self, r: anytype) Tensor(
             T,
             dims.argsOpt(),
-            hlp.finerScales(Self, RhsT(@TypeOf(r))).argsOpt(),
+            finerScales(Self, RhsT(@TypeOf(r))).argsOpt(),
             shape_,
         ) {
             const rhs_q = rhs(r);
             const RhsType = @TypeOf(rhs_q);
             if (comptime !dims.eql(RhsType.dims))
                 @compileError("Dimension mismatch in add: " ++ dims.str() ++ " vs " ++ RhsType.dims.str());
-            if (comptime RhsType.total != total and RhsType.total != 1)
+            if (comptime RhsType.total != 1 and !shapeEql(shape_, RhsType.shape))
-                @compileError("Shape mismatch in add: element counts must match or RHS must be scalar (total=1).");
+                @compileError("Shape mismatch in add: element-wise operations require identical shapes, or a scalar RHS.");
 
-            const TargetType = Tensor(T, dims.argsOpt(), hlp.finerScales(Self, RhsType).argsOpt(), shape_);
+            const TargetType = Tensor(T, dims.argsOpt(), finerScales(Self, RhsType).argsOpt(), shape_);
             const l: Vec = if (comptime Self == TargetType) self.data else self.to(TargetType).data;
             const rr: Vec = blk: {
-                const RhsNorm = Tensor(T, RhsType.dims.argsOpt(), hlp.finerScales(Self, RhsType).argsOpt(), RhsType.shape);
+                const RhsNorm = Tensor(T, RhsType.dims.argsOpt(), finerScales(Self, RhsType).argsOpt(), RhsType.shape);
                 const rn = if (comptime RhsType == RhsNorm) rhs_q else rhs_q.to(RhsNorm);
                 break :blk broadcastToVec(RhsNorm, rn);
             };
-            return .{ .data = if (comptime hlp.isInt(T)) l +| rr else l + rr };
+            return .{ .data = if (comptime isInt(T)) l +| rr else l + rr };
         }
 
-        /// Element-wise subtract.  Dimensions must match; scales resolve to finer.
+        /// Element-wise sub.  Dimensions must match; scales resolve to finer.
+        /// RHS must have the same shape as self, or total == 1 (broadcast).
         pub inline fn sub(self: Self, r: anytype) Tensor(
             T,
             dims.argsOpt(),
-            hlp.finerScales(Self, RhsT(@TypeOf(r))).argsOpt(),
+            finerScales(Self, RhsT(@TypeOf(r))).argsOpt(),
             shape_,
         ) {
             const rhs_q = rhs(r);
             const RhsType = @TypeOf(rhs_q);
             if (comptime !dims.eql(RhsType.dims))
                 @compileError("Dimension mismatch in sub: " ++ dims.str() ++ " vs " ++ RhsType.dims.str());
-            if (comptime RhsType.total != total and RhsType.total != 1)
+            if (comptime RhsType.total != 1 and !shapeEql(shape_, RhsType.shape))
-                @compileError("Shape mismatch in sub.");
+                @compileError("Shape mismatch in sub: element-wise operations require identical shapes, or a scalar RHS.");
 
-            const TargetType = Tensor(T, dims.argsOpt(), hlp.finerScales(Self, RhsType).argsOpt(), shape_);
+            const TargetType = Tensor(T, dims.argsOpt(), finerScales(Self, RhsType).argsOpt(), shape_);
             const l: Vec = if (comptime Self == TargetType) self.data else self.to(TargetType).data;
             const rr: Vec = blk: {
-                const RhsNorm = Tensor(T, RhsType.dims.argsOpt(), hlp.finerScales(Self, RhsType).argsOpt(), RhsType.shape);
+                const RhsNorm = Tensor(T, RhsType.dims.argsOpt(), finerScales(Self, RhsType).argsOpt(), RhsType.shape);
                 const rn = if (comptime RhsType == RhsNorm) rhs_q else rhs_q.to(RhsNorm);
                 break :blk broadcastToVec(RhsNorm, rn);
             };
-            return .{ .data = if (comptime hlp.isInt(T)) l -| rr else l - rr };
+            return .{ .data = if (comptime isInt(T)) l -| rr else l - rr };
         }
 
         /// Element-wise multiply.  Dimension exponents summed.
@@ -319,20 +324,20 @@ pub fn Tensor(
         pub inline fn mul(self: Self, r: anytype) Tensor(
             T,
             dims.add(RhsT(@TypeOf(r)).dims).argsOpt(),
-            hlp.finerScales(Self, RhsT(@TypeOf(r))).argsOpt(),
+            finerScales(Self, RhsT(@TypeOf(r))).argsOpt(),
             shape_,
         ) {
             const rhs_q = rhs(r);
             const RhsType = @TypeOf(rhs_q);
-            if (comptime RhsType.total != total and RhsType.total != 1)
+            if (comptime RhsType.total != 1 and !shapeEql(shape_, RhsType.shape))
-                @compileError("Shape mismatch in mul.");
+                @compileError("Shape mismatch in mul: element-wise operations require identical shapes, or a scalar RHS.");
 
-            const SelfNorm = Tensor(T, dims.argsOpt(), hlp.finerScales(Self, RhsType).argsOpt(), shape_);
+            const SelfNorm = Tensor(T, dims.argsOpt(), finerScales(Self, RhsType).argsOpt(), shape_);
-            const RhsNorm  = Tensor(T, RhsType.dims.argsOpt(), hlp.finerScales(Self, RhsType).argsOpt(), RhsType.shape);
+            const RhsNorm = Tensor(T, RhsType.dims.argsOpt(), finerScales(Self, RhsType).argsOpt(), RhsType.shape);
             const l: Vec = if (comptime Self == SelfNorm) self.data else self.to(SelfNorm).data;
             const rr_base = if (comptime RhsType == RhsNorm) rhs_q else rhs_q.to(RhsNorm);
             const rr: Vec = broadcastToVec(RhsNorm, rr_base);
-            return .{ .data = if (comptime hlp.isInt(T)) l *| rr else l * rr };
+            return .{ .data = if (comptime isInt(T)) l *| rr else l * rr };
         }
 
         /// Element-wise divide.  Dimension exponents subtracted.
@@ -340,32 +345,26 @@ pub fn Tensor(
         pub inline fn div(self: Self, r: anytype) Tensor(
             T,
             dims.sub(RhsT(@TypeOf(r)).dims).argsOpt(),
-            hlp.finerScales(Self, RhsT(@TypeOf(r))).argsOpt(),
+            finerScales(Self, RhsT(@TypeOf(r))).argsOpt(),
             shape_,
         ) {
             const rhs_q = rhs(r);
             const RhsType = @TypeOf(rhs_q);
-            if (comptime RhsType.total != total and RhsType.total != 1)
+            if (comptime RhsType.total != 1 and !shapeEql(shape_, RhsType.shape))
-                @compileError("Shape mismatch in div.");
+                @compileError("Shape mismatch in div: element-wise operations require identical shapes, or a scalar RHS.");
 
-            const SelfNorm = Tensor(T, dims.argsOpt(), hlp.finerScales(Self, RhsType).argsOpt(), shape_);
+            const SelfNorm = Tensor(T, dims.argsOpt(), finerScales(Self, RhsType).argsOpt(), shape_);
-            const RhsNorm  = Tensor(T, RhsType.dims.argsOpt(), hlp.finerScales(Self, RhsType).argsOpt(), RhsType.shape);
+            const RhsNorm = Tensor(T, RhsType.dims.argsOpt(), finerScales(Self, RhsType).argsOpt(), RhsType.shape);
             const l: Vec = if (comptime Self == SelfNorm) self.data else self.to(SelfNorm).data;
             const rr_base = if (comptime RhsType == RhsNorm) rhs_q else rhs_q.to(RhsNorm);
             const rr: Vec = broadcastToVec(RhsNorm, rr_base);
-            if (comptime hlp.isInt(T)) {
+            if (comptime isInt(T)) {
-                var result: Vec = undefined;
+                return .{ .data = @divTrunc(l, rr) };
-                inline for (0..total) |i| result[i] = @divTrunc(l[i], rr[i]);
-                return .{ .data = result };
             } else {
                 return .{ .data = l / rr };
             }
         }
 
-        // ───────────────────────────────────────────────────────────────
-        // Unary
-        // ───────────────────────────────────────────────────────────────
-
         /// Absolute value of every element.
         pub inline fn abs(self: Self) Self {
             return .{ .data = @bitCast(@abs(self.data)) };
@@ -378,19 +377,27 @@ pub fn Tensor(
             scales.argsOpt(),
             shape_,
         ) {
-            if (comptime hlp.isInt(T)) {
+            if (comptime exp == 0) return .{ .data = @splat(1) };
-                var result: Vec = undefined;
+            if (comptime exp == 1) return self;
-                inline for (0..total) |i|
+
-                    result[i] = std.math.powi(T, self.data[i], exp) catch std.math.maxInt(T);
+            var base = self.data;
-                return .{ .data = result };
-            } else {
-                const abs_exp = comptime @abs(exp);
             var result: Vec = @splat(1);
-                comptime var i = 0;
+            comptime var e = @abs(exp);
-                inline while (i < abs_exp) : (i += 1) result *= self.data;
+
-                if (comptime exp < 0) result = @as(Vec, @splat(1)) / result;
+            // $O(\log n)$ Exponentiation by squaring applied to the entire vector
-                return .{ .data = result };
+            inline while (e > 0) {
+                if (e % 2 == 1) {
+                    result = if (comptime isInt(T)) result *| base else result * base;
                 }
+                e /= 2;
+                if (e > 0) {
+                    base = if (comptime isInt(T)) base *| base else base * base;
+                }
+            }
+            if (comptime !isInt(T) and exp < 0) {
+                result = @as(Vec, @splat(1)) / result;
+            }
+            return .{ .data = result };
         }
 
         /// Square root of every element.  All dimension exponents must be even.
@@ -403,15 +410,16 @@ pub fn Tensor(
             if (comptime !dims.isSquare())
                 @compileError("Cannot take sqrt of " ++ dims.str() ++ ": exponents must be even.");
             if (comptime @typeInfo(T) == .float) {
-                return .{ .data = @sqrt(self.data) };
+                return .{ .data = @sqrt(self.data) }; // Float is natively vectorized!
             } else {
-                var result: Vec = undefined;
+                const arr: [total]T = self.data; // Add this!
+                var res_arr: [total]T = undefined;
                 const UnsignedT = @Int(.unsigned, @typeInfo(T).int.bits);
-                inline for (0..total) |i| {
+                for (0..total) |i| {
-                    const v = self.data[i];
+                    const v = arr[i];
-                    result[i] = if (v < 0) 0 else @as(T, @intCast(std.math.sqrt(@as(UnsignedT, @intCast(v)))));
+                    res_arr[i] = if (v < 0) 0 else @as(T, @intCast(std.math.sqrt(@as(UnsignedT, @intCast(v)))));
                 }
-                return .{ .data = result };
+                return .{ .data = res_arr };
             }
         }
 
@@ -420,13 +428,9 @@ pub fn Tensor(
             return .{ .data = -self.data };
         }
 
-        // ───────────────────────────────────────────────────────────────
-        // Conversion
-        // ───────────────────────────────────────────────────────────────
-
         /// Convert to a compatible Tensor type.
         ///   • Dimension mismatch → compile error.
-        ///   • Dest.total must equal self.total, or Dest.total == 1 (scalar pattern).
+        ///   • Dest.shape must equal self.shape, or Dest.total == 1 (scalar pattern).
         ///   • Scale ratio is computed fully at comptime; only a SIMD multiply at runtime.
         pub inline fn to(
             self: Self,
@@ -434,82 +438,92 @@ pub fn Tensor(
         ) Tensor(Dest.ValueType, Dest.dims.argsOpt(), Dest.scales.argsOpt(), shape_) {
             const ActualDest = Tensor(Dest.ValueType, Dest.dims.argsOpt(), Dest.scales.argsOpt(), shape_);
 
-            if (comptime !dims.eql(ActualDest.dims))
-                @compileError("Dimension mismatch in to: " ++ dims.str() ++ " vs " ++ ActualDest.dims.str());
             if (comptime Self == ActualDest) return self;
 
-            comptime std.debug.assert(Dest.total == total or Dest.total == 1);
+            // Run validation checks FIRST before dealing with types
+            if (comptime !dims.eql(ActualDest.dims))
+                @compileError("Dimension mismatch in to: " ++ dims.str() ++ " vs " ++ ActualDest.dims.str());
+            if (comptime Dest.total != 1 and !shapeEql(shape_, Dest.shape))
+                @compileError("Shape mismatch in to: destination type must have the identical shape, or be a scalar.");
 
-            const DestT  = ActualDest.ValueType;
             const ratio = comptime (scales.getFactor(dims) / ActualDest.scales.getFactor(ActualDest.dims));
+            const DestT = ActualDest.ValueType;
             const DestVec = @Vector(total, DestT);
 
-            // ── Same numeric type ──────────────────────────────────────
+            // If ratio is 1, handle type conversion correctly based on BOTH source and dest types
+            if (comptime ratio == 1.0) {
+                const T_info = @typeInfo(T);
+                const Dest_info = @typeInfo(DestT);
+
+                return .{
+                    .data = if (comptime T_info == .int and Dest_info == .int)
+                        @as(DestVec, @intCast(self.data))
+                    else if (comptime T_info == .float and Dest_info == .float)
+                        @as(DestVec, @floatCast(self.data))
+                    else if (comptime T_info == .int and Dest_info == .float)
+                        @as(DestVec, @floatFromInt(self.data))
+                    else if (comptime T_info == .float and Dest_info == .int)
+                        @as(DestVec, @intFromFloat(self.data)) // Or @intFromFloat(@round(self.data)) if you want rounding
+                    else
+                        unreachable,
+                };
+            }
+
             if (comptime T == DestT) {
                 if (comptime @typeInfo(T) == .float)
                     return .{ .data = self.data * @as(DestVec, @splat(@as(T, @floatCast(ratio)))) };
 
-                // Integer — branch prevents division-by-zero
                 if (comptime ratio >= 1.0) {
                     const mult: T = comptime @intFromFloat(@round(ratio));
                     return .{ .data = self.data *| @as(Vec, @splat(mult)) };
                 } else {
                     const div_val: T = comptime @intFromFloat(@round(1.0 / ratio));
                     const half: T = comptime @divTrunc(div_val, 2);
-                    var result: DestVec = undefined;
+
-                    inline for (0..total) |i| {
+                    if (comptime @typeInfo(T).int.signedness == .unsigned) {
-                        const val = self.data[i];
+                        return .{ .data = @divTrunc(self.data + @as(Vec, @splat(half)), @as(Vec, @splat(div_val))) };
-                        result[i] = if (val >= 0)
+                    } else {
-                            @divTrunc(val + half, div_val)
+                        // Vectorized branchless negative handling
-                        else
+                        const is_pos = self.data >= @as(Vec, @splat(0));
-                            @divTrunc(val - half, div_val);
+                        const offsets = @select(T, is_pos, @as(Vec, @splat(half)), @as(Vec, @splat(-half)));
+                        return .{ .data = @divTrunc(self.data + offsets, @as(Vec, @splat(div_val))) };
                     }
-                    return .{ .data = result };
                 }
             }
 
-            // ── Cross numeric type ─────────────────────────────────────
+            // Cross-type fully vectorized casting with scales
-            var result: DestVec = undefined;
+            const FVec = @Vector(total, f64);
-            inline for (0..total) |i| {
+            const float_vec: FVec = switch (comptime @typeInfo(T)) {
-                const float_val: f64 = switch (comptime @typeInfo(T)) {
+                .float => @floatCast(self.data),
-                    .float => @floatCast(self.data[i]),
+                .int => @floatFromInt(self.data),
-                    .int   => @floatFromInt(self.data[i]),
                 else => unreachable,
             };
-                const scaled = float_val * ratio;
-                result[i] = switch (comptime @typeInfo(DestT)) {
-                    .float => @floatCast(scaled),
-                    .int   => @intFromFloat(@round(scaled)),
-                    else   => unreachable,
-                };
-            }
-            return .{ .data = result };
-        }
 
-        // ───────────────────────────────────────────────────────────────
+            const scaled = float_vec * @as(FVec, @splat(ratio));
-        // Comparisons
+
-        //
+            return switch (comptime @typeInfo(DestT)) {
-        // Return type:  bool       when total == 1  (scalar semantics)
+                .float => .{ .data = @floatCast(scaled) },
-        //               [total]bool when total >  1  (element-wise, flat-indexed)
+                .int => .{ .data = @intFromFloat(@round(scaled)) },
-        //
+                else => unreachable,
-        // Whole-tensor equality check → eqAll / neAll (always returns bool).
+            };
-        // A shape {1} RHS is broadcast automatically, unifying the old
+        }
-        // eqScalar / gtScalar / … family into the plain eq / gt / … methods.
-        // ───────────────────────────────────────────────────────────────
 
         const CmpResult = if (total == 1) bool else [total]bool;
 
         inline fn cmpResult(v: @Vector(total, bool)) CmpResult {
-            return if (comptime total == 1) v[0] else @as([total]bool, v);
+            return if (comptime total == 1) @reduce(.And, v) else @as([total]bool, v);
         }
 
         /// Resolve both sides to the finer scale, broadcasting shape {1} RHS if needed.
         inline fn resolveScalePair(self: Self, rhs_q: anytype) struct { l: Vec, r: Vec } {
             const RhsType = @TypeOf(rhs_q);
-            const TargetType = Tensor(T, dims.argsOpt(), hlp.finerScales(Self, RhsType).argsOpt(), shape_);
+            if (comptime RhsType.total != 1 and !shapeEql(shape_, RhsType.shape))
+                @compileError("Shape mismatch in comparison: element-wise operations require identical shapes, or a scalar RHS.");
+
+            const TargetType = Tensor(T, dims.argsOpt(), finerScales(Self, RhsType).argsOpt(), shape_);
             const l: Vec = if (comptime Self == TargetType) self.data else self.to(TargetType).data;
             const rr: Vec = blk: {
-                const RhsNorm = Tensor(T, RhsType.dims.argsOpt(), hlp.finerScales(Self, RhsType).argsOpt(), RhsType.shape);
+                const RhsNorm = Tensor(T, RhsType.dims.argsOpt(), finerScales(Self, RhsType).argsOpt(), RhsType.shape);
                 const rn = if (comptime RhsType == RhsNorm) rhs_q else rhs_q.to(RhsNorm);
                 break :blk broadcastToVec(RhsNorm, rn);
             };
@@ -577,26 +591,6 @@ pub fn Tensor(
             return !self.eqAll(other);
         }
 
-        // ───────────────────────────────────────────────────────────────
-        // Contraction — generalised dot product / matrix multiply / einsum
-        //
-        //   a.contract(b, axis_a, axis_b)
-        //
-        //   Sums over dimension `axis_a` of `a` and `axis_b` of `b`.
-        //   Requires a.shape[axis_a] == b.shape[axis_b]  (checked at comptime).
-        //
-        //   Result shape  = a.shape \ axis_a  ++  b.shape \ axis_b
-        //   Result dims   = a.dims + b.dims  (exponents summed, as in mul)
-        //   Result scales = finer of a, b
-        //
-        //   Special cases:
-        //     rank-1 × rank-1, axis 0 × 0  →  dot product (result shape {1})
-        //     rank-2 × rank-2, axis 1 × 0  →  matrix multiply
-        //     rank-1 × rank-2, axis 0 × 0  →  vector–matrix product
-        //
-        //   All index arithmetic is comptime; runtime cost is the multiply-add loop only.
-        // ───────────────────────────────────────────────────────────────
-
         pub inline fn contract(
             self: Self,
             other: anytype,
@@ -604,18 +598,18 @@ pub fn Tensor(
             comptime axis_b: usize,
         ) blk: {
             const OT = @TypeOf(other);
-            comptime std.debug.assert(axis_a < rank);
+            if (axis_a >= rank) @compileError("contract: axis_a out of bounds");
-            comptime std.debug.assert(axis_b < OT.rank);
+            if (axis_b >= OT.rank) @compileError("contract: axis_b out of bounds");
-            comptime std.debug.assert(shape_[axis_a] == OT.shape[axis_b]);
+            if (shape_[axis_a] != OT.shape[axis_b]) @compileError("contract: shape mismatch at contraction axes");
-            // Contracted-away free axes; empty joint → scalar shape {1}
+
             const sa = shapeRemoveAxis(shape_, axis_a);
             const sb = shapeRemoveAxis(OT.shape, axis_b);
             const rs_raw = shapeCat(&sa, &sb);
-            const rs: []const usize = if (rs_raw.len == 0) &.{1} else &rs_raw;
+            const rs: []const comptime_int = if (rs_raw.len == 0) &.{1} else &rs_raw;
             break :blk Tensor(
                 T,
                 dims.add(OT.dims).argsOpt(),
-                hlp.finerScales(Self, OT).argsOpt(),
+                finerScales(Self, OT).argsOpt(),
                 rs,
             );
         } {
@@ -625,55 +619,131 @@ pub fn Tensor(
             const sa = comptime shapeRemoveAxis(shape_, axis_a);
             const sb = comptime shapeRemoveAxis(OT.shape, axis_b);
             const rs_raw = comptime shapeCat(&sa, &sb);
-            const rs: []const usize = comptime if (rs_raw.len == 0) &.{1} else &rs_raw;
+            const rs: []const comptime_int = comptime if (rs_raw.len == 0) &.{1} else &rs_raw;
 
             const ResultType = Tensor(
                 T,
                 dims.add(OT.dims).argsOpt(),
-                hlp.finerScales(Self, OT).argsOpt(),
+                finerScales(Self, OT).argsOpt(),
                 rs,
             );
 
-            // Normalise scales before accumulation
+            const SelfNorm = Tensor(T, dims.argsOpt(), finerScales(Self, OT).argsOpt(), shape_);
-            const SelfNorm  = Tensor(T, dims.argsOpt(), hlp.finerScales(Self, OT).argsOpt(), shape_);
+            const OtherNorm = Tensor(T, OT.dims.argsOpt(), finerScales(Self, OT).argsOpt(), OT.shape);
-            const OtherNorm = Tensor(T, OT.dims.argsOpt(), hlp.finerScales(Self, OT).argsOpt(), OT.shape);
+
             const a_data = if (comptime Self == SelfNorm) self.data else self.to(SelfNorm).data;
             const b_data = if (comptime OT == OtherNorm) other.data else other.to(OtherNorm).data;
 
-            // Precompute result strides from rs_raw (for coord decoding)
+            // FAST PATH: Dot Product
+            if (comptime rank == 1 and OT.rank == 1 and axis_a == 0 and axis_b == 0) {
+                if (comptime !isInt(T)) {
+                    return .{ .data = @splat(@reduce(.Add, a_data * b_data)) };
+                } else {
+                    // For integers, we do a vectorized saturating multiply,
+                    // then convert to an array to do a saturating sum
+                    const mul_arr: [total]T = a_data *| b_data;
+                    var acc: T = 0;
+                    for (mul_arr) |val| acc +|= val;
+                    return .{ .data = @splat(acc) };
+                }
+            }
+
+            // --- ZERO-COST COERCION TO ARRAYS FOR RUNTIME INDEXING ---
+            const a_arr: [total]T = a_data;
+            const b_arr: [OT.total]T = b_data;
+
+            // FAST PATH: 2D Matrix Multiplication
+            if (comptime rank == 2 and OT.rank == 2 and axis_a == 1 and axis_b == 0) {
+                const rows = shape_[0];
+                const cols = OT.shape[1];
+                const inner = shape_[1];
+
+                // Create a mutable array for the result, NOT a Tensor struct
+                var res_arr: [ResultType.total]T = undefined;
+
+                for (0..rows) |i| {
+                    for (0..cols) |j| {
+                        var acc: T = 0;
+                        for (0..inner) |id| {
+                            const a_flat = i * _strides[0] + id * _strides[1];
+                            const b_flat = id * OT.strides_arr[0] + j * OT.strides_arr[1];
+
+                            // Use a_arr and b_arr here
+                            if (comptime isInt(T)) acc +|= a_arr[a_flat] *| b_arr[b_flat] else acc += a_arr[a_flat] * b_arr[b_flat];
+                        }
+                        // Write to the array
+                        res_arr[i * cols + j] = acc;
+                    }
+                }
+                // Return the initialized Tensor struct
+                return .{ .data = res_arr };
+            }
+
+            // FALLBACK PATH
             const rs_raw_strides = comptime shapeStrides(&rs_raw);
 
-            var result: ResultType = .{ .data = @splat(0) };
+            // Create a mutable array for the result
+            var result_arr: [ResultType.total]T = undefined;
 
-            inline for (0..ResultType.total) |res_flat| {
+            for (0..ResultType.total) |res_flat| {
-                // Decode result flat index into free coords using rs_raw layout.
+                const res_coords = decodeFlatCoords(res_flat, rs_raw.len, rs_raw_strides);
-                // When rs_raw.len == 0, decodeFlatCoords returns [0]usize{} — correct.
-                const res_coords = comptime decodeFlatCoords(res_flat, rs_raw.len, rs_raw_strides);
 
-                const a_free: [sa.len]usize = comptime res_coords[0..sa.len].*;
+                var a_free: [sa.len]usize = undefined;
-                const b_free: [sb.len]usize = comptime res_coords[sa.len..].*;
+                for (0..sa.len) |i| a_free[i] = res_coords[i];
+                var b_free: [sb.len]usize = undefined;
+                for (0..sb.len) |i| b_free[i] = res_coords[sa.len + i];
 
                 var acc: T = 0;
-                inline for (0..k) |ki| {
+                for (0..k) |ki| {
-                    // Reinsert the contracted index into free coords → full coord arrays
+                    const a_coords = insertAxis(rank, axis_a, ki, &a_free);
-                    const a_coords = comptime insertAxis(rank, axis_a, ki, &a_free);
+                    const b_coords = insertAxis(OT.rank, axis_b, ki, &b_free);
-                    const b_coords = comptime insertAxis(OT.rank, axis_b, ki, &b_free);
+                    const a_flat = encodeFlatCoords(&a_coords, rank, _strides);
-                    const a_flat   = comptime encodeFlatCoords(&a_coords, rank, _strides);
+                    const b_flat = encodeFlatCoords(&b_coords, OT.rank, OT.strides_arr);
-                    const b_flat   = comptime encodeFlatCoords(&b_coords, OT.rank, OT.strides_arr);
 
-                    if (comptime hlp.isInt(T))
+                    // Use a_arr and b_arr here
-                        acc +|= a_data[a_flat] *| b_data[b_flat]
+                    if (comptime isInt(T)) acc +|= a_arr[a_flat] *| b_arr[b_flat] else acc += a_arr[a_flat] * b_arr[b_flat];
-                    else
-                        acc += a_data[a_flat] * b_data[b_flat];
                 }
-                result.data[res_flat] = acc;
+                // Write to the array
-            }
+                result_arr[res_flat] = acc;
-            return result;
             }
 
-        // ───────────────────────────────────────────────────────────────
+            // Return the initialized Tensor struct
-        // Reduction helpers
+            return .{ .data = result_arr };
-        // ───────────────────────────────────────────────────────────────
+        }
+
+        /// 3D Cross Product. Only defined for Rank-1 tensors of length 3.
+        /// Result dimensions are the sum of input dimensions.
+        pub inline fn cross(self: Self, other: anytype) Tensor(
+            T,
+            dims.add(RhsT(@TypeOf(other)).dims).argsOpt(),
+            finerScales(Self, RhsT(@TypeOf(other))).argsOpt(),
+            &.{3},
+        ) {
+            const rhs_q = rhs(other);
+            const RhsType = @TypeOf(rhs_q);
+
+            if (comptime rank != 1 or shape[0] != 3 or RhsType.rank != 1 or RhsType.shape[0] != 3) {
+                @compileError("cross product is only defined for 3D vectors (rank-1, length 3)");
+            }
+
+            // Bring both to the same scale (e.g., mm vs m)
+            const p = self.resolveScalePair(rhs_q);
+            const l = p.l;
+            const r = p.r;
+
+            var res: [3]T = undefined;
+            if (comptime isInt(T)) {
+                res[0] = (l[1] *| r[2]) -| (l[2] *| r[1]);
+                res[1] = (l[2] *| r[0]) -| (l[0] *| r[2]);
+                res[2] = (l[0] *| r[1]) -| (l[1] *| r[0]);
+            } else {
+                res[0] = (l[1] * r[2]) - (l[2] * r[1]);
+                res[1] = (l[2] * r[0]) - (l[0] * r[2]);
+                res[2] = (l[0] * r[1]) - (l[1] * r[0]);
+            }
+
+            return .{ .data = res };
+        }
 
         /// Sum of squared elements.  Cheaper than length(); use for ordering.
         pub inline fn lengthSqr(self: Self) T {
@@ -700,10 +770,6 @@ pub fn Tensor(
             return .{ .data = .{@reduce(.Mul, self.data)} };
         }
 
-        // ───────────────────────────────────────────────────────────────
-        // Formatting
-        // ───────────────────────────────────────────────────────────────
-
         pub fn formatNumber(
             self: Self,
             writer: *std.Io.Writer,
@@ -722,7 +788,8 @@ pub fn Tensor(
                 }
             } else {
                 try writer.writeAll("(");
-                inline for (0..total) |i| {
+                const max_to_print = 6;
+                inline for (0..@min(total, max_to_print)) |i| {
                     if (i > 0) try writer.writeAll(", ");
                     switch (@typeInfo(T)) {
                         .float, .comptime_float => try writer.printFloat(self.data[i], options),
@@ -734,6 +801,8 @@ pub fn Tensor(
                         }),
                         else => unreachable,
                     }
+                    if (comptime i == max_to_print - 1 and total != max_to_print - 1)
+                        try writer.writeAll(", ...");
                 }
                 try writer.writeAll(")");
             }
@@ -751,7 +820,7 @@ pub fn Tensor(
                 else
                     try writer.print("{s}{s}", .{ uscale.str(), bu.unit() });
 
-                if (v != 1) try hlp.printSuperscript(writer, v);
+                if (v != 1) try printSuperscript(writer, v);
             }
         }
     };
@@ -760,15 +829,6 @@ pub fn Tensor(
 // ═════════════════════════════════════════════════════════════════════════════
 // Tests
 // ─────────────────────────────────────────────────────────────────────────────
-// Naming convention used throughout:
-//   Tensor(T, d, s, &.{1})  →  former Scalar
-//   Tensor(T, d, s, &.{N})  →  former Vector of length N
-//   .data[0]                →  former .value()
-//   .mul(x)                 →  former .mulScalar(x)  (x may be scalar Tensor or bare number)
-//   .div(x)                 →  former .divScalar(x)
-//   .eq(x)                  →  former .eqScalar(x)   (broadcasts when x.total==1)
-//   .contract(other, 0, 0)  →  former .dot(other)    (for rank-1 tensors)
-// ═════════════════════════════════════════════════════════════════════════════
 
 // ─── Scalar tests ─────────────────────────────────────────────────────────
 
@@ -1185,7 +1245,6 @@ test "Vector Comparisons" {
 }
 
 test "Vector vs Scalar broadcast comparison" {
-    // Replaces the old eqScalar / gtScalar — now just eq / gt with a shape-{1} rhs.
     const Meter3 = Tensor(f32, .{ .L = 1 }, .{}, &.{3});
     const KiloMeter1 = Tensor(f32, .{ .L = 1 }, .{ .L = .k }, &.{1});
 
@@ -1210,7 +1269,6 @@ test "Vector contract — dot product (rank-1 × rank-1)" {
     const pos = Meter3{ .data = .{ 10.0, 0.0, 0.0 } };
     const force = Newton3{ .data = .{ 5.0, 5.0, 0.0 } };
 
-    // work = force · pos
     const work = force.contract(pos, 0, 0);
     try std.testing.expectEqual(50.0, work.data[0]);
     try std.testing.expectEqual(1, @TypeOf(work).dims.get(.M));
@@ -1219,28 +1277,17 @@ test "Vector contract — dot product (rank-1 × rank-1)" {
 }
 
 test "Vector contract — matrix multiply (rank-2 × rank-2)" {
-    // 2×3 matrix multiplied by 3×2 matrix → 2×2 result
+    const A = Tensor(f32, .{}, .{}, &.{ 2, 3 });
-    const A = Tensor(f32, .{}, .{}, &.{2, 3});
+    const B = Tensor(f32, .{}, .{}, &.{ 3, 2 });
-    const B = Tensor(f32, .{}, .{}, &.{3, 2});
 
-    // A = [[1, 2, 3],
-    //      [4, 5, 6]]
     const a = A{ .data = .{ 1, 2, 3, 4, 5, 6 } };
-    // B = [[7, 8],
-    //      [9, 10],
-    //      [11, 12]]
     const b = B{ .data = .{ 7, 8, 9, 10, 11, 12 } };
 
-    // C = A @ B  (contract over axis 1 of A × axis 0 of B)
-    // C[0][0] = 1*7 + 2*9  + 3*11 = 7  + 18 + 33 = 58
-    // C[0][1] = 1*8 + 2*10 + 3*12 = 8  + 20 + 36 = 64
-    // C[1][0] = 4*7 + 5*9  + 6*11 = 28 + 45 + 66 = 139
-    // C[1][1] = 4*8 + 5*10 + 6*12 = 32 + 50 + 72 = 154
     const c = a.contract(b, 1, 0);
-    try std.testing.expectEqual(58,  c.data[Tensor(f32, .{}, .{}, &.{2,2}).idx(.{0, 0})]);
+    try std.testing.expectEqual(58, c.data[Tensor(f32, .{}, .{}, &.{ 2, 2 }).idx(.{ 0, 0 })]);
-    try std.testing.expectEqual(64,  c.data[Tensor(f32, .{}, .{}, &.{2,2}).idx(.{0, 1})]);
+    try std.testing.expectEqual(64, c.data[Tensor(f32, .{}, .{}, &.{ 2, 2 }).idx(.{ 0, 1 })]);
-    try std.testing.expectEqual(139, c.data[Tensor(f32, .{}, .{}, &.{2,2}).idx(.{1, 0})]);
+    try std.testing.expectEqual(139, c.data[Tensor(f32, .{}, .{}, &.{ 2, 2 }).idx(.{ 1, 0 })]);
-    try std.testing.expectEqual(154, c.data[Tensor(f32, .{}, .{}, &.{2,2}).idx(.{1, 1})]);
+    try std.testing.expectEqual(154, c.data[Tensor(f32, .{}, .{}, &.{ 2, 2 }).idx(.{ 1, 1 })]);
 }
 
 test "Vector Abs, Pow, Sqrt and Product" {
@@ -1321,23 +1368,22 @@ test "Vector eq broadcast on dimensionless" {
 }
 
 test "Tensor idx helper and matrix access" {
-    const Mat3x3 = Tensor(f32, .{}, .{}, &.{3, 3});
+    const Mat3x3 = Tensor(f32, .{}, .{}, &.{ 3, 3 });
-    // Identity-like: set [0][0]=1, [1][1]=2, [2][2]=3
     var m: Mat3x3 = Mat3x3.zero;
-    m.data[Mat3x3.idx(.{0, 0})] = 1.0;
+    m.data[Mat3x3.idx(.{ 0, 0 })] = 1.0;
-    m.data[Mat3x3.idx(.{1, 1})] = 2.0;
+    m.data[Mat3x3.idx(.{ 1, 1 })] = 2.0;
-    m.data[Mat3x3.idx(.{2, 2})] = 3.0;
+    m.data[Mat3x3.idx(.{ 2, 2 })] = 3.0;
 
-    try std.testing.expectEqual(1.0, m.data[0]); // [0][0]
+    try std.testing.expectEqual(1.0, m.data[0]);
-    try std.testing.expectEqual(2.0, m.data[4]); // [1][1]  (stride 3 → 1*3+1=4)
+    try std.testing.expectEqual(2.0, m.data[4]);
-    try std.testing.expectEqual(3.0, m.data[8]); // [2][2]  (2*3+2=8)
+    try std.testing.expectEqual(3.0, m.data[8]);
-    try std.testing.expectEqual(0.0, m.data[1]); // [0][1]
+    try std.testing.expectEqual(0.0, m.data[1]);
 }
 
 test "Tensor strides_arr correctness" {
     const T1 = Tensor(f32, .{}, .{}, &.{3});
-    const T2 = Tensor(f32, .{}, .{}, &.{3, 4});
+    const T2 = Tensor(f32, .{}, .{}, &.{ 3, 4 });
-    const T3 = Tensor(f32, .{}, .{}, &.{2, 3, 4});
+    const T3 = Tensor(f32, .{}, .{}, &.{ 2, 3, 4 });
 
     try std.testing.expectEqual(1, T1.strides_arr[0]);
     try std.testing.expectEqual(4, T2.strides_arr[0]);

src/benchmark.zig

@@ -1,7 +1,6 @@
 const std = @import("std");
 const Io = std.Io;
-const Scalar = @import("Quantity.zig").Scalar;
+const Tensor = @import("Tensor.zig").Tensor;
-const Vector = @import("Quantity.zig").Vector;
 
 var io: Io = undefined;
 pub fn main(init: std.process.Init) !void {
@@ -11,16 +10,16 @@ pub fn main(init: std.process.Init) !void {
 
     io = init.io;
 
-    // try vectorSIMDvsNative(f64, &stdout_writer.interface);
+    try vectorSIMDvsNative(f64, &stdout_writer.interface);
-    // try stdout_writer.flush();
+    try stdout_writer.flush();
-    // try vectorSIMDvsNative(f32, &stdout_writer.interface);
+    try vectorSIMDvsNative(f32, &stdout_writer.interface);
-    // try stdout_writer.flush();
+    try stdout_writer.flush();
-    // try vectorSIMDvsNative(i32, &stdout_writer.interface);
+    try vectorSIMDvsNative(i32, &stdout_writer.interface);
-    // try stdout_writer.flush();
+    try stdout_writer.flush();
-    // try vectorSIMDvsNative(i64, &stdout_writer.interface);
+    try vectorSIMDvsNative(i64, &stdout_writer.interface);
-    // try stdout_writer.flush();
+    try stdout_writer.flush();
-    // try vectorSIMDvsNative(i128, &stdout_writer.interface);
+    try vectorSIMDvsNative(i128, &stdout_writer.interface);
-    // try stdout_writer.flush();
+    try stdout_writer.flush();
 
     try bench_Scalar(&stdout_writer.interface);
     try stdout_writer.flush();
@@ -97,9 +96,9 @@ fn bench_Scalar(writer: *std.Io.Writer) !void {
 
     comptime var tidx: usize = 0;
     inline for (Types, TNames) |T, tname| {
-        const M = Scalar(T, .{ .L = 1 }, .{});
+        const M = Tensor(T, .{ .L = 1 }, .{}, &.{1});
-        const KM = Scalar(T, .{ .L = 1 }, .{ .L = .k });
+        const KM = Tensor(T, .{ .L = 1 }, .{ .L = .k }, &.{1});
-        const S = Scalar(T, .{ .T = 1 }, .{});
+        const S = Tensor(T, .{ .T = 1 }, .{}, &.{1});
 
         inline for (Ops, 0..) |op_name, oidx| {
             var samples: [SAMPLES]f64 = undefined;
@@ -199,8 +198,8 @@ fn bench_vsNative(writer: *std.Io.Writer) !void {
             var native_total_ns: f64 = 0;
             var quantity_total_ns: f64 = 0;
 
-            const M = Scalar(T, .{ .L = 1 }, .{});
+            const M = Tensor(T, .{ .L = 1 }, .{}, &.{1});
-            const S = Scalar(T, .{ .T = 1 }, .{});
+            const S = Tensor(T, .{ .T = 1 }, .{}, &.{1});
 
             std.mem.doNotOptimizeAway({
                 for (0..SAMPLES) |_| {
@@ -321,9 +320,9 @@ fn bench_crossTypeVsNative(writer: *std.Io.Writer) !void {
                 var native_total_ns: f64 = 0;
                 var quantity_total_ns: f64 = 0;
 
-                const M1 = Scalar(T1, .{ .L = 1 }, .{});
+                const M1 = Tensor(T1, .{ .L = 1 }, .{}, &.{1});
-                const M2 = Scalar(T2, .{ .L = 1 }, .{});
+                const M2 = Tensor(T2, .{ .L = 1 }, .{}, &.{1});
-                const S2 = Scalar(T2, .{ .T = 1 }, .{});
+                const S2 = Tensor(T2, .{ .T = 1 }, .{}, &.{1});
 
                 std.mem.doNotOptimizeAway({
                     for (0..SAMPLES) |_| {
@@ -429,9 +428,8 @@ fn bench_Vector(writer: *std.Io.Writer) !void {
             try writer.print("│ {s:<16} │ {s:<4} │", .{ op_name, tname });
 
             inline for (Lengths) |len| {
-                const Q_base = Scalar(T, .{ .L = 1 }, .{});
+                const Q_time = Tensor(T, .{ .T = 1 }, .{}, &.{1});
-                const Q_time = Scalar(T, .{ .T = 1 }, .{});
+                const V = Tensor(T, .{ .L = 1 }, .{}, &.{len});
-                const V = Vector(len, Q_base);
 
                 // cross product is only defined for len == 3
                 const is_cross = comptime std.mem.eql(u8, op_name, "cross");
@@ -455,10 +453,10 @@ fn bench_Vector(writer: *std.Io.Writer) !void {
                                 _ = v1.div(V.splat(getVal(T, i +% 2, 63)));
                             } else if (comptime std.mem.eql(u8, op_name, "mulScalar")) {
                                 const s_val = Q_time.splat(getVal(T, i +% 2, 63));
-                                _ = v1.mulScalar(s_val);
+                                _ = v1.mul(s_val);
                             } else if (comptime std.mem.eql(u8, op_name, "dot")) {
                                 const v2 = V.splat(getVal(T, i +% 5, 63));
-                                _ = v1.dot(v2);
+                                _ = v1.contract(v2, 0, 0);
                             } else if (comptime std.mem.eql(u8, op_name, "cross")) {
                                 // len == 3 guaranteed by the guard above
                                 const v2 = V.splat(getVal(T, i +% 5, 63));

src/helper.zig

@@ -1,97 +0,0 @@
-const std = @import("std");
-
-pub fn isInt(comptime T: type) bool {
-    return @typeInfo(T) == .int or @typeInfo(T) == .comptime_int;
-}
-
-pub fn printSuperscript(writer: *std.Io.Writer, n: i32) !void {
-    if (n == 0) return;
-    var val = n;
-    if (val < 0) {
-        try writer.writeAll("\u{207B}");
-        val = -val;
-    }
-    var buf: [12]u8 = undefined;
-    const str = std.fmt.bufPrint(&buf, "{d}", .{val}) catch return;
-    for (str) |c| {
-        const s = switch (c) {
-            '0' => "\u{2070}",
-            '1' => "\u{00B9}",
-            '2' => "\u{00B2}",
-            '3' => "\u{00B3}",
-            '4' => "\u{2074}",
-            '5' => "\u{2075}",
-            '6' => "\u{2076}",
-            '7' => "\u{2077}",
-            '8' => "\u{2078}",
-            '9' => "\u{2079}",
-            else => unreachable,
-        };
-        try writer.writeAll(s);
-    }
-}
-
-const Scales = @import("Scales.zig");
-const Dimensions = @import("Dimensions.zig");
-const Dimension = @import("Dimensions.zig").Dimension;
-
-pub fn finerScales(comptime T1: type, comptime T2: type) Scales {
-    const d1: Dimensions = T1.dims;
-    const d2: Dimensions = T2.dims;
-    const s1: Scales = T1.scales;
-    const s2: Scales = T2.scales;
-    comptime var out = Scales.initFill(.none);
-    inline for (std.enums.values(Dimension)) |dim| {
-        const scale1 = comptime s1.get(dim);
-        const scale2 = comptime s2.get(dim);
-        out.set(dim, if (comptime d1.get(dim) == 0 and d2.get(dim) == 0)
-            .none
-        else if (comptime d1.get(dim) == 0)
-            scale2
-        else if (comptime d2.get(dim) == 0)
-            scale1
-        else if (comptime scale1.getFactor() > scale2.getFactor())
-            scale2
-        else
-            scale1);
-    }
-    comptime return out;
-}
-
-// ---------------------------------------------------------------------------
-// RHS normalisation helpers
-// ---------------------------------------------------------------------------
-
-const Quantity = @import("Quantity.zig").Quantity;
-
-/// Returns true if `T` is a `Scalar_` type (has `dims`, `scales`, and `value`).
-pub fn isScalarType(comptime T: type) bool {
-    return @typeInfo(T) == .@"struct" and
-        @hasDecl(T, "ISQUANTITY") and
-        @field(T, "ISQUANTITY");
-}
-
-/// Resolve the Scalar type that `rhs` will be treated as.
-///
-/// Accepted rhs types:
-///   - Any `Scalar_` type               → returned as-is
-///   - `comptime_int` / `comptime_float` → dimensionless `Scalar_(BaseT, {}, {})`
-///   - `BaseT` (the scalar's value type) → dimensionless `Scalar_(BaseT, {}, {})`
-///
-/// Everything else is a compile error, including other int/float types.
-pub fn rhsQuantityType(comptime ValueType: type, N: usize, comptime RhsT: type) type {
-    if (comptime isScalarType(RhsT)) return RhsT;
-    if (comptime RhsT == comptime_int or RhsT == comptime_float or RhsT == ValueType)
-        return Quantity(ValueType, N, .{}, .{});
-    @compileError(
-        "rhs must be a Scalar, " ++ @typeName(ValueType) ++
-            ", comptime_int, or comptime_float; got " ++ @typeName(RhsT),
-    );
-}
-
-/// Convert `rhs` to its normalised Scalar form (see `rhsScalarType`).
-pub inline fn toRhsQuantity(comptime BaseT: type, N: usize, rhs: anytype) rhsQuantityType(BaseT, N, @TypeOf(rhs)) {
-    if (comptime isScalarType(@TypeOf(rhs))) return rhs;
-    const DimLess = Quantity(BaseT, N, .{}, .{});
-    return DimLess{ .data = @splat(@as(BaseT, rhs)) };
-}

src/main.zig

@@ -1,15 +1,13 @@
 const std = @import("std");
 
-pub const Vector = @import("Quantity.zig").Vector;
+pub const Tensor = @import("Tensor.zig").Tensor;
-pub const Scalar = @import("Quantity.zig").Scalar;
 pub const Dimensions = @import("Dimensions.zig");
 pub const Scales = @import("Scales.zig");
 pub const Base = @import("Base.zig");
 
 test {
-    _ = @import("Quantity.zig");
+    _ = @import("Tensor.zig");
     _ = @import("Dimensions.zig");
     _ = @import("Scales.zig");
     _ = @import("Base.zig");
-    _ = @import("helper.zig");
 }