diff --git a/nn/functional/nn_math.h b/nn/nn_math.h
similarity index 89%
rename from nn/functional/nn_math.h
rename to nn/nn_math.h
index b11ab5e..0e138b0 100644
--- a/nn/functional/nn_math.h
+++ b/nn/nn_math.h
@@ -128,67 +128,6 @@
 // #endif
 // }
 
-// inline static void NN__scale_F32(const int n, float *y, const float v) {
-// #if defined(GGML_USE_ACCELERATE)
-//     vDSP_vsmul(y, 1, &v, y, 1, n);
-// #elif defined(GGML_SIMD)
-//     const int np = (n & ~(GGML_F32_STEP - 1));
-
-//     GGML_F32_VEC vx = GGML_F32_VEC_SET1(v);
-
-//     GGML_F32_VEC ay[GGML_F32_ARR];
-
-//     for (int i = 0; i < np; i += GGML_F32_STEP) {
-//         for (int j = 0; j < GGML_F32_ARR; j++) {
-//             ay[j] = GGML_F32_VEC_LOAD(y + i + j*GGML_F32_EPR);
-//             ay[j] = GGML_F32_VEC_MUL(ay[j], vx);
-
-//             GGML_F32_VEC_STORE(y + i + j*GGML_F32_EPR, ay[j]);
-//         }
-//     }
-
-//     // leftovers
-//     for (int i = np; i < n; i += 1) {
-//         y[i] *= v;
-//     }
-// #else
-//     // scalar
-//     for (int i = 0; i < n; i += 1) {
-//         y[i] *= v;
-//     }
-// #endif
-// }
-
-// inline static void NN__scale_f16(const int n, float16_t * y, const float v) {
-// #if defined(GGML_SIMD)
-//     const int np = (n & ~(GGML_F16_STEP - 1));
-
-//     GGML_F16_VEC vx = GGML_F16_VEC_SET1(v);
-
-//     GGML_F16_VEC ay[GGML_F16_ARR];
-
-//     for (int i = 0; i < np; i += GGML_F16_STEP) {
-//         for (int j = 0; j < GGML_F16_ARR; j++) {
-//             ay[j] = GGML_F16_VEC_LOAD(y + i + j*GGML_F16_EPR, j);
-//             ay[j] = GGML_F16_VEC_MUL(ay[j], vx);
-
-//             GGML_F16_VEC_STORE(y + i + j*GGML_F16_EPR, ay, j);
-//         }
-//     }
-
-//     // leftovers
-//     for (int i = np; i < n; i += 1) {
-//         y[i] = GGML_FP32_TO_FP16(GGML_FP16_TO_FP32(y[i])*v);
-//     }
-// #else
-//     // scalar
-//     for (int i = 0; i < n; i += 1) {
-//         y[i] = GGML_FP32_TO_FP16(GGML_FP16_TO_FP32(y[i])*v);
-//     }
-// #endif
-// }
-
-
 // inline static void NN__step_F32 (const int n, float *y, const float *x) { for (int i = 0; i < n; i += 1) y[i] = (x[i] > 0.f) ? 1.f : 0.f; }
 // inline static void NN__tanh_F32 (const int n, float *y, const float *x) { for (int i = 0; i < n; i += 1) y[i] = tanhf(x[i]);  }
 // inline static void NN__elu_F32  (const int n, float *y, const float *x) { for (int i = 0; i < n; i += 1) y[i] = (x[i] > 0.f) ? x[i] : expf(x[i])-1; }
@@ -503,32 +442,6 @@
 //         dx[i] = ggml_silu_backward_F32(x[i], dy[i]);
 //     }
 // }
-
-
-// inline static void NN__sum_f32_ggf(const int n, float *s, const float *x) {
-//     float sum = 0.0;
-//     for (int i = 0; i < n; i += 1) {
-//         sum += (float)x[i];
-//     }
-//     *s = sum;
-// }
-
-// inline static void NN__sum_f16_ggf(const int n, float *s, const float16_t * x) {
-//     float sum = 0.0f;
-//     for (int i = 0; i < n; i += 1) {
-//         sum += GGML_FP16_TO_FP32(x[i]);
-//     }
-//     *s = sum;
-// }
-
-// inline static void NN__sum_bf16_ggf(const int n, float *s, const bfloat16_t * x) {
-//     float sum = 0.0f;
-//     for (int i = 0; i < n; i += 1) {
-//         sum += GGML_BF16_TO_FP32(x[i]);
-//     }
-//     *s = sum;
-// }
-
 // inline static void NN__argmax_F32(const int n, int * s, const float *x) {
 //     float max = -INFINITY;
 //     int idx = 0;
diff --git a/nn/nn_todo b/nn/nn_todo
index 10b9f76..fa6514d 100644
--- a/nn/nn_todo
+++ b/nn/nn_todo
@@ -1,15 +1,3 @@
-#ifndef __NN_H
-#define __NN_H
-
-#include <math.h>
-#include <float.h>
-#include <stddef.h>
-
-typedef struct {
-  size_t rows;
-  size_t cols;
-  float *data;
-} Matrix;
 
 /*
  * ====== Math Functions ======
@@ -20,39 +8,6 @@ void NN_initMatrix(Matrix *m, size_t rows, size_t cols) {
   m->data = malloc(rows * cols * sizeof(float));
 }
 
-void NN_matmul(Matrix *out, Matrix *a, Matrix *b) {
-  NN_assert(a->cols == b->rows, "matmul: dimension mismatch");
-  NN_assert(out->rows == a->rows, "matmul: dimension mismatch");
-  NN_assert(out->cols == b->cols, "matmul: dimension mismatch");
-  for (size_t i = 0; i < a->rows; i += 1) {
-    for (size_t j = 0; j < b->cols; j += 1) {
-      float sum = 0;
-      for (size_t k = 0; k < a->cols; k += 1) {
-        sum += a->data[i * a->cols + k] * b->data[k * b->cols + j];
-      }
-      out->data[i * out->cols + j] = sum;
-    }
-  }
-}
-
-void NN_matadd(Matrix *out, Matrix *a, Matrix *b) {
-  NN_assert(a->rows == b->rows, "matadd: dimension mismatch");
-  NN_assert(a->cols == b->cols, "matadd: dimension mismatch");
-  for (size_t i = 0; i < a->rows; i += 1) {
-    for (size_t j = 0; j < a->cols; j += 1) {
-      out->data[i * out->cols + j] = a->data[i * a->cols + j] + b->data[i * b->cols + j];
-    }
-  }
-}
-
-void NN_transpose(Matrix *out, Matrix *a) {
-  for (size_t i = 0; i < a->rows; i += 1) {
-    for (size_t j = 0; j < a->cols; j += 1) {
-      out->data[j * out->cols + i] = a->data[i * a->cols + j];
-    }
-  }
-}
-
 void NN_concatenate(Matrix *out, Matrix *a, Matrix *b) {
   for (size_t i = 0; i < a->cols; i += 1) {
     out->data[i] = a->data[i];
@@ -78,11 +33,6 @@ size_t NN_argmax(Matrix *a) {
  * ====== Operators ======
  */
 
-void NN_linear(Matrix *out, Matrix *weight, Matrix *bias, Matrix *input) {
-  NN_matmul(out, input, weight);
-  NN_matadd(out, out, bias);
-}
-
 void NN_logSoftmax(Matrix *out, Matrix *a) {
   float sum = 0;
   for (size_t i = 0; i < a->cols; i += 1) {
@@ -93,5 +43,3 @@ void NN_logSoftmax(Matrix *out, Matrix *a) {
   }
 }
 
-
-#endif  // __NN_H