diff --git a/docs/developer.md b/docs/developer.md
index f5b131816f..821e9b3bd2 100644
--- a/docs/developer.md
+++ b/docs/developer.md
@@ -9,7 +9,7 @@ There is a good [bunch of tutorials](https://mesozoic-egg.github.io/tinygrad-not
 
 ## Frontend
 
-Everything in [Tensor](tensor.md) is syntactic sugar around [function.py](function.md), where the forwards and backwards passes are implemented for the different functions. There's about 25 of them, implemented using about 20 basic ops. Those basic ops go on to construct a graph of:
+Everything in [Tensor](tensor/index.md) is syntactic sugar around [function.py](function.md), where the forwards and backwards passes are implemented for the different functions. There's about 25 of them, implemented using about 20 basic ops. Those basic ops go on to construct a graph of:
 
 ::: tinygrad.lazy.LazyBuffer
     options:
diff --git a/docs/index.md b/docs/index.md
index d83658903b..f096262197 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -16,7 +16,7 @@ We also have [developer docs](developer.md), and Di Zhu has created a [bunch of
 
 ## tinygrad Usage
 
-The main class you will interact with is [Tensor](tensor.md). It functions very similarly to PyTorch, but has a bit more of a functional style. tinygrad supports [many datatypes](dtypes.md).  All operations in tinygrad are lazy, meaning they won't do anything until you realize.
+The main class you will interact with is [Tensor](tensor/index.md). It functions very similarly to PyTorch, but has a bit more of a functional style. tinygrad supports [many datatypes](dtypes.md).  All operations in tinygrad are lazy, meaning they won't do anything until you realize.
 
 * tinygrad has a built in [neural network library](nn.md) with some classes, optimizers, and load/save state management.
 * tinygrad has a JIT to make things fast. Decorate your pure function with `TinyJit`
@@ -36,11 +36,11 @@ There's nothing special about a "Module" class in tinygrad, it's just a normal c
 
 ### tinygrad is functional
 
-In tinygrad, you can do [`x.conv2d(w, b)`](tensor.md/#tinygrad.Tensor.conv2d) or [`x.sparse_categorical_cross_entropy(y)`](tensor.md/#tinygrad.Tensor.sparse_categorical_crossentropy). We do also have a [`Conv2D`](nn.md/#tinygrad.nn.Conv2d) class like PyTorch if you want a place to keep the state, but all stateless operations don't have classes.
+In tinygrad, you can do [`x.conv2d(w, b)`](tensor/ops.md/#tinygrad.Tensor.conv2d) or [`x.sparse_categorical_cross_entropy(y)`](tensor/ops.md/#tinygrad.Tensor.sparse_categorical_crossentropy). We do also have a [`Conv2D`](nn.md/#tinygrad.nn.Conv2d) class like PyTorch if you want a place to keep the state, but all stateless operations don't have classes.
 
 ### tinygrad is lazy
 
-When you do `a+b` in tinygrad, nothing happens. It's not until you [`realize`](tensor.md/#tinygrad.Tensor.realize) the Tensor that the computation actually runs.
+When you do `a+b` in tinygrad, nothing happens. It's not until you [`realize`](tensor/index.md/#tinygrad.Tensor.realize) the Tensor that the computation actually runs.
 
 ### tinygrad requires @TinyJit to be fast
 
diff --git a/docs/quickstart.md b/docs/quickstart.md
index 61bcd3373a..e02c3ed499 100644
--- a/docs/quickstart.md
+++ b/docs/quickstart.md
@@ -50,7 +50,7 @@ randn = Tensor.randn(2, 3) # create a tensor of shape (2, 3) filled with random
 uniform = Tensor.uniform(2, 3, low=0, high=10) # create a tensor of shape (2, 3) filled with random values from a uniform distribution between 0 and 10
 ```
 
-There are even more of these factory methods, you can find them in the [Tensor](tensor.md) file.
+There are even more of these factory methods, you can find them in the [Tensor Creation](tensor/creation.md) file.
 
 All the tensors creation methods can take a `dtype` argument to specify the data type of the tensor, find the supported `dtype` in [dtypes](dtypes.md).
 
@@ -75,7 +75,7 @@ print(t6.numpy())
 # [-56. -48. -36. -20.   0.]
 ```
 
-There are a lot more operations that can be performed on tensors, you can find them in the [Tensor](tensor.md) file.
+There are a lot more operations that can be performed on tensors, you can find them in the [Tensor Ops](tensor/ops.md) file.
 Additionally reading through [abstractions2.py](https://github.com/tinygrad/tinygrad/blob/master/docs/abstractions2.py) will help you understand how operations on these tensors make their way down to your hardware.
 
 ## Models
diff --git a/docs/tensor.md b/docs/tensor.md
deleted file mode 100644
index 8b13789179..0000000000
--- a/docs/tensor.md
+++ /dev/null
@@ -1 +0,0 @@
-
diff --git a/docs/tensor/index.md b/docs/tensor/index.md
index 28cdf3e38e..825faf538a 100644
--- a/docs/tensor/index.md
+++ b/docs/tensor/index.md
@@ -1,3 +1,5 @@
+# Tensor
+
 ::: tinygrad.Tensor
     options:
         heading_level: 2