diff --git a/nac3core/src/codegen/expr.rs b/nac3core/src/codegen/expr.rs
index f6b7b541..486c833a 100644
--- a/nac3core/src/codegen/expr.rs
+++ b/nac3core/src/codegen/expr.rs
@@ -12,6 +12,7 @@ use crate::{
             call_int_umin, call_memcpy_generic,
         },
         need_sret, numpy,
+        object::ndarray::{NDArrayOut, ScalarOrNDArray},
         stmt::{
             gen_for_callback_incrementing, gen_if_callback, gen_if_else_expr_callback, gen_raise,
             gen_var,
@@ -28,7 +29,10 @@ use crate::{
 use inkwell::{
     attributes::{Attribute, AttributeLoc},
     types::{AnyType, BasicType, BasicTypeEnum},
-    values::{BasicValueEnum, CallSiteValue, FunctionValue, IntValue, PointerValue, StructValue},
+    values::{
+        BasicValue, BasicValueEnum, CallSiteValue, FunctionValue, IntValue, PointerValue,
+        StructValue,
+    },
     AddressSpace, IntPredicate, OptimizationLevel,
 };
 use itertools::{chain, izip, Either, Itertools};
@@ -1544,99 +1548,71 @@ pub fn gen_binop_expr_with_values<'ctx, G: CodeGenerator>(
     } else if ty1.obj_id(&ctx.unifier).is_some_and(|id| id == PrimDef::NDArray.id())
         || ty2.obj_id(&ctx.unifier).is_some_and(|id| id == PrimDef::NDArray.id())
     {
-        let llvm_usize = generator.get_size_type(ctx.ctx);
+        let left =
+            ScalarOrNDArray::split_object(generator, ctx, AnyObject { ty: ty1, value: left_val });
+        let right =
+            ScalarOrNDArray::split_object(generator, ctx, AnyObject { ty: ty2, value: right_val });
 
-        let is_ndarray1 = ty1.obj_id(&ctx.unifier).is_some_and(|id| id == PrimDef::NDArray.id());
-        let is_ndarray2 = ty2.obj_id(&ctx.unifier).is_some_and(|id| id == PrimDef::NDArray.id());
+        // Inhomogeneous binary operations are not supported.
+        assert!(ctx.unifier.unioned(left.get_dtype(), right.get_dtype()));
 
-        if is_ndarray1 && is_ndarray2 {
-            let (ndarray_dtype1, _) = unpack_ndarray_var_tys(&mut ctx.unifier, ty1);
-            let (ndarray_dtype2, _) = unpack_ndarray_var_tys(&mut ctx.unifier, ty2);
+        let common_dtype = left.get_dtype();
 
-            assert!(ctx.unifier.unioned(ndarray_dtype1, ndarray_dtype2));
+        let out = match op.variant {
+            BinopVariant::Normal => NDArrayOut::NewNDArray { dtype: common_dtype },
+            BinopVariant::AugAssign => {
+                // If this is an augmented assignment.
+                // `left` has to be an ndarray. If it were a scalar then NAC3 simply doesn't support it.
+                if let ScalarOrNDArray::NDArray(out_ndarray) = left {
+                    NDArrayOut::WriteToNDArray { ndarray: out_ndarray }
+                } else {
+                    panic!("left must be an ndarray")
+                }
+            }
+        };
 
-            let left_val =
-                NDArrayValue::from_ptr_val(left_val.into_pointer_value(), llvm_usize, None);
-            let right_val =
-                NDArrayValue::from_ptr_val(right_val.into_pointer_value(), llvm_usize, None);
-
-            let res = if op.base == Operator::MatMult {
-                // MatMult is the only binop which is not an elementwise op
-                numpy::ndarray_matmul_2d(
-                    generator,
-                    ctx,
-                    ndarray_dtype1,
-                    match op.variant {
-                        BinopVariant::Normal => None,
-                        BinopVariant::AugAssign => Some(left_val),
-                    },
-                    left_val,
-                    right_val,
-                )?
-            } else {
-                numpy::ndarray_elementwise_binop_impl(
-                    generator,
-                    ctx,
-                    ndarray_dtype1,
-                    match op.variant {
-                        BinopVariant::Normal => None,
-                        BinopVariant::AugAssign => Some(left_val),
-                    },
-                    (left_val.as_base_value().into(), false),
-                    (right_val.as_base_value().into(), false),
-                    |generator, ctx, (lhs, rhs)| {
-                        gen_binop_expr_with_values(
-                            generator,
-                            ctx,
-                            (&Some(ndarray_dtype1), lhs),
-                            op,
-                            (&Some(ndarray_dtype2), rhs),
-                            ctx.current_loc,
-                        )?
-                        .unwrap()
-                        .to_basic_value_enum(
-                            ctx,
-                            generator,
-                            ndarray_dtype1,
-                        )
-                    },
-                )?
-            };
-
-            Ok(Some(res.as_base_value().into()))
+        if op.base == Operator::MatMult {
+            // Handle matrix multiplication.
+            todo!()
         } else {
-            let (ndarray_dtype, _) =
-                unpack_ndarray_var_tys(&mut ctx.unifier, if is_ndarray1 { ty1 } else { ty2 });
-            let ndarray_val = NDArrayValue::from_ptr_val(
-                if is_ndarray1 { left_val } else { right_val }.into_pointer_value(),
-                llvm_usize,
-                None,
-            );
-            let res = numpy::ndarray_elementwise_binop_impl(
+            // For other operations, they are all elementwise operations.
+
+            // There are only three cases:
+            // - LHS is a scalar, RHS is an ndarray.
+            // - LHS is an ndarray, RHS is a scalar.
+            // - LHS is an ndarray, RHS is an ndarray.
+            //
+            // For all cases, the scalar operand is promoted to an ndarray,
+            // the two are then broadcasted, and starmapped through.
+
+            let left = left.to_ndarray(generator, ctx);
+            let right = right.to_ndarray(generator, ctx);
+
+            let result = NDArrayObject::broadcast_starmap(
                 generator,
                 ctx,
-                ndarray_dtype,
-                match op.variant {
-                    BinopVariant::Normal => None,
-                    BinopVariant::AugAssign => Some(ndarray_val),
-                },
-                (left_val, !is_ndarray1),
-                (right_val, !is_ndarray2),
-                |generator, ctx, (lhs, rhs)| {
-                    gen_binop_expr_with_values(
+                &[left, right],
+                out,
+                |generator, ctx, scalars| {
+                    let left_value = scalars[0];
+                    let right_value = scalars[1];
+
+                    let result = gen_binop_expr_with_values(
                         generator,
                         ctx,
-                        (&Some(ndarray_dtype), lhs),
+                        (&Some(left.dtype), left_value),
                         op,
-                        (&Some(ndarray_dtype), rhs),
+                        (&Some(right.dtype), right_value),
                         ctx.current_loc,
                     )?
                     .unwrap()
-                    .to_basic_value_enum(ctx, generator, ndarray_dtype)
-                },
-            )?;
+                    .to_basic_value_enum(ctx, generator, common_dtype)?;
 
-            Ok(Some(res.as_base_value().into()))
+                    Ok(result)
+                },
+            )
+            .unwrap();
+            Ok(Some(ValueEnum::Dynamic(result.instance.value.as_basic_value_enum())))
         }
     } else {
         let left_ty_enum = ctx.unifier.get_ty_immutable(left_ty.unwrap());