diff --git a/extra/tensors/benchmark/benchmark.factor b/extra/tensors/benchmark/benchmark.factor
index 6de3a25ce9..c3b883e4b1 100644
--- a/extra/tensors/benchmark/benchmark.factor
+++ b/extra/tensors/benchmark/benchmark.factor
@@ -1,50 +1,28 @@
 ! Copyright (C) 2019 HMC Clinic.
 ! See http://factorcode.org/license.txt for BSD license.
-USING: arrays io kernel locals math prettyprint tensors tools.time ;
+USING: arrays kernel locals math math.functions math.statistics memory
+sequences tools.time ;
 IN: tensors.benchmark
 
-<PRIVATE
+! puts items from els (a quotation) on stack, runs ops (a quot w no stack effect) n times
+! returns an array with times (ns) for each trial
+:: benchmark-multiple ( els: ( -- .. ) op: ( .. -- .. ) n -- ..arr )
+    ! put els on stack
+    els call
+    ! create array
+    n 0 <array> :> arr
+    ! perform op n times
+    n [ gc [ op benchmark ] dip arr set-nth ] each-integer
+    arr ; inline
 
-:: add-tensors ( trials elems -- time )
-    ! Create the arrays to be added
-    elems 1array naturals dup
-    ! Benchmark!
-    [ trials [ 2dup t+ drop ] times ] benchmark
-    ! Normalize
-    trials / >float
-    nip nip ;
 
-:: matmul-tensors ( trials elems -- time )
-    ! Create the arrays to be multiplied
-    elems elems 2array naturals dup
-    ! Benchmark!
-    [ trials [ 2dup matmul drop ] times ] benchmark
-    ! Normalize
-    trials / >float
-    nip nip ;
-
-:: transpose-tensor ( trials elems -- time )
-    ! Create the array to be transposed
-    elems elems 2array naturals
-    ! benchmark
-    [ trials [ dup transpose drop ] times ] benchmark
-    ! Normalize
-    trials / >float
-    nip ;
-
-PRIVATE>
-
-: run-benchmarks ( -- )
-    "Benchmarking the tensors vocabulary" print
-    "Add two 100 element tensors" print
-    1000000 100 add-tensors .
-    "Add two 100,000 element tensors" print
-    10000 100000 add-tensors .
-    "Multiply two 10x10 matrices" print
-    100000 10 matmul-tensors .
-    "Multiply two 100x100 matrices" print
-    1000 100 matmul-tensors .
-    "Transpose a 10x10 matrix" print
-    10000 10 transpose-tensor .
-    "Transpose a 100x100 matrix" print
-    10 100 transpose-tensor . ;
+! finds the confidence interval of seq with significance level 95
+:: confidence-interval ( seq -- {c1,c2} )
+    seq mean :> m
+    ! HARDCODING ALERT: z value for alpha = 95 is 1.96
+    seq sample-std 1.96 *
+    ! div by sqrt(n)
+    seq length sqrt / :> modifier
+    m modifier -
+    m modifier +
+    2array ;
diff --git a/extra/tensors/demos/data.csv b/extra/tensors/demos/data.csv
new file mode 100644
index 0000000000..b780ecd41e
--- /dev/null
+++ b/extra/tensors/demos/data.csv
@@ -0,0 +1,506 @@
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+0.10574,0,27.74,0,0.609,5.983,98.8,1.8681,4,711,20.1,390.11,18.07
+0.11132,0,27.74,0,0.609,5.983,83.5,2.1099,4,711,20.1,396.9,13.35
+0.17331,0,9.69,0,0.585,5.707,54,2.3817,6,391,19.2,396.9,12.01
+0.27957,0,9.69,0,0.585,5.926,42.6,2.3817,6,391,19.2,396.9,13.59
+0.17899,0,9.69,0,0.585,5.67,28.8,2.7986,6,391,19.2,393.29,17.6
+0.2896,0,9.69,0,0.585,5.39,72.9,2.7986,6,391,19.2,396.9,21.14
+0.26838,0,9.69,0,0.585,5.794,70.6,2.8927,6,391,19.2,396.9,14.1
+0.23912,0,9.69,0,0.585,6.019,65.3,2.4091,6,391,19.2,396.9,12.92
+0.17783,0,9.69,0,0.585,5.569,73.5,2.3999,6,391,19.2,395.77,15.1
+0.22438,0,9.69,0,0.585,6.027,79.7,2.4982,6,391,19.2,396.9,14.33
+0.06263,0,11.93,0,0.573,6.593,69.1,2.4786,1,273,21,391.99,9.67
+0.04527,0,11.93,0,0.573,6.12,76.7,2.2875,1,273,21,396.9,9.08
+0.06076,0,11.93,0,0.573,6.976,91,2.1675,1,273,21,396.9,5.64
+0.10959,0,11.93,0,0.573,6.794,89.3,2.3889,1,273,21,393.45,6.48
+0.04741,0,11.93,0,0.573,6.03,80.8,2.505,1,273,21,396.9,7.88
\ No newline at end of file
diff --git a/extra/tensors/demos/demos.factor b/extra/tensors/demos/demos.factor
new file mode 100644
index 0000000000..c2720cc566
--- /dev/null
+++ b/extra/tensors/demos/demos.factor
@@ -0,0 +1,73 @@
+! Copyright (C) 2019 HMC Clinic.
+! See http://factorcode.org/license.txt for BSD license.
+! Code based on https://towardsdatascience.com/linear-regression-from-scratch-with-numpy-implementation-finally-8e617d8e274c
+
+USING: arrays accessors csv io io.encodings.utf8 kernel locals math math.parser
+math.ranges math.statistics prettyprint sequences tensors ;
+IN: tensors.demos
+
+<PRIVATE
+
+! Normalize across each of the features
+:: normalize ( X -- norm )
+    ! Compute the mean for each of the features and repeat it so that it can be
+    ! combined with X
+    X transpose tensor>array :> X-T
+    X-T [ mean ] map >tensor :> feat-means
+    X shape>> first [0,b) [ drop feat-means ] map stack :> means
+    ! Compute the std for each of the features and repeat it so that it can be
+    ! combined with X
+    X-T [ std ] map >tensor :> feat-stds
+    X shape>> first [0,b) [ drop feat-stds ] map stack :> stds
+    X means t- stds t/ ;
+
+:: compute-cost ( X y params -- cost )
+    ! Compute (1/(2*n_samples))
+    1 2 y shape>> first * /
+    ! Compute h
+    X params matmul
+    ! Compute sum((h-y)**2)
+    y t- dup t* sum
+    ! Multiply to get final cost
+    * ;
+
+:: gradient-descent ( X y params lr n-iters -- history params )
+    lr y shape>> first / :> batch-lr
+    { n-iters } zeros :> history
+    X transpose :> X-T
+    params
+    n-iters [
+        ! Update params with
+        ! params = params - (learning_rate/n_samples) * X.T @ (X @ params - y)
+        swap dup :> old-params
+        batch-lr X-T X old-params matmul y t- matmul t* t- :> new-params
+        ! Compute the cost and add it to the history
+        X y new-params compute-cost swap history set-nth
+        new-params
+    ] each-integer
+    history swap ;
+
+PRIVATE>
+
+:: linear-regression ( X y lr n-iters -- )
+    X normalize
+    ! Add the constant coefficient
+    y shape>> first 1 2array ones swap 2array hstack :> X-norm
+    ! Create the array of parameters
+    X-norm shape>> second 1 2array zeros :> params
+    ! Compute the initial cost
+    X-norm y params compute-cost
+    ! Print!
+    number>string "The initial cost is " swap append print
+    ! Perform gradient descent
+    X-norm y params lr n-iters gradient-descent
+    "The optimal parameters are " print .
+    last number>string "The final cost was " swap append print
+    ;
+
+! Load and return the boston house-prices dataset
+: load-boston-data ( -- X y )
+    "vocab:tensors/demos/data.csv" utf8 file>csv
+    [ [ string>number ] map ] map >tensor
+    "vocab:tensors/demos/target.csv" utf8 file>csv
+    [ [ string>number ] map ] map >tensor ;
\ No newline at end of file
diff --git a/extra/tensors/demos/target.csv b/extra/tensors/demos/target.csv
new file mode 100644
index 0000000000..97233ea7f6
--- /dev/null
+++ b/extra/tensors/demos/target.csv
@@ -0,0 +1,506 @@
+24
+21.6
+34.7
+33.4
+36.2
+28.7
+22.9
+27.1
+16.5
+18.9
+15
+18.9
+21.7
+20.4
+18.2
+19.9
+23.1
+17.5
+20.2
+18.2
+13.6
+19.6
+15.2
+14.5
+15.6
+13.9
+16.6
+14.8
+18.4
+21
+12.7
+14.5
+13.2
+13.1
+13.5
+18.9
+20
+21
+24.7
+30.8
+34.9
+26.6
+25.3
+24.7
+21.2
+19.3
+20
+16.6
+14.4
+19.4
+19.7
+20.5
+25
+23.4
+18.9
+35.4
+24.7
+31.6
+23.3
+19.6
+18.7
+16
+22.2
+25
+33
+23.5
+19.4
+22
+17.4
+20.9
+24.2
+21.7
+22.8
+23.4
+24.1
+21.4
+20
+20.8
+21.2
+20.3
+28
+23.9
+24.8
+22.9
+23.9
+26.6
+22.5
+22.2
+23.6
+28.7
+22.6
+22
+22.9
+25
+20.6
+28.4
+21.4
+38.7
+43.8
+33.2
+27.5
+26.5
+18.6
+19.3
+20.1
+19.5
+19.5
+20.4
+19.8
+19.4
+21.7
+22.8
+18.8
+18.7
+18.5
+18.3
+21.2
+19.2
+20.4
+19.3
+22
+20.3
+20.5
+17.3
+18.8
+21.4
+15.7
+16.2
+18
+14.3
+19.2
+19.6
+23
+18.4
+15.6
+18.1
+17.4
+17.1
+13.3
+17.8
+14
+14.4
+13.4
+15.6
+11.8
+13.8
+15.6
+14.6
+17.8
+15.4
+21.5
+19.6
+15.3
+19.4
+17
+15.6
+13.1
+41.3
+24.3
+23.3
+27
+50
+50
+50
+22.7
+25
+50
+23.8
+23.8
+22.3
+17.4
+19.1
+23.1
+23.6
+22.6
+29.4
+23.2
+24.6
+29.9
+37.2
+39.8
+36.2
+37.9
+32.5
+26.4
+29.6
+50
+32
+29.8
+34.9
+37
+30.5
+36.4
+31.1
+29.1
+50
+33.3
+30.3
+34.6
+34.9
+32.9
+24.1
+42.3
+48.5
+50
+22.6
+24.4
+22.5
+24.4
+20
+21.7
+19.3
+22.4
+28.1
+23.7
+25
+23.3
+28.7
+21.5
+23
+26.7
+21.7
+27.5
+30.1
+44.8
+50
+37.6
+31.6
+46.7
+31.5
+24.3
+31.7
+41.7
+48.3
+29
+24
+25.1
+31.5
+23.7
+23.3
+22
+20.1
+22.2
+23.7
+17.6
+18.5
+24.3
+20.5
+24.5
+26.2
+24.4
+24.8
+29.6
+42.8
+21.9
+20.9
+44
+50
+36
+30.1
+33.8
+43.1
+48.8
+31
+36.5
+22.8
+30.7
+50
+43.5
+20.7
+21.1
+25.2
+24.4
+35.2
+32.4
+32
+33.2
+33.1
+29.1
+35.1
+45.4
+35.4
+46
+50
+32.2
+22
+20.1
+23.2
+22.3
+24.8
+28.5
+37.3
+27.9
+23.9
+21.7
+28.6
+27.1
+20.3
+22.5
+29
+24.8
+22
+26.4
+33.1
+36.1
+28.4
+33.4
+28.2
+22.8
+20.3
+16.1
+22.1
+19.4
+21.6
+23.8
+16.2
+17.8
+19.8
+23.1
+21
+23.8
+23.1
+20.4
+18.5
+25
+24.6
+23
+22.2
+19.3
+22.6
+19.8
+17.1
+19.4
+22.2
+20.7
+21.1
+19.5
+18.5
+20.6
+19
+18.7
+32.7
+16.5
+23.9
+31.2
+17.5
+17.2
+23.1
+24.5
+26.6
+22.9
+24.1
+18.6
+30.1
+18.2
+20.6
+17.8
+21.7
+22.7
+22.6
+25
+19.9
+20.8
+16.8
+21.9
+27.5
+21.9
+23.1
+50
+50
+50
+50
+50
+13.8
+13.8
+15
+13.9
+13.3
+13.1
+10.2
+10.4
+10.9
+11.3
+12.3
+8.8
+7.2
+10.5
+7.4
+10.2
+11.5
+15.1
+23.2
+9.7
+13.8
+12.7
+13.1
+12.5
+8.5
+5
+6.3
+5.6
+7.2
+12.1
+8.3
+8.5
+5
+11.9
+27.9
+17.2
+27.5
+15
+17.2
+17.9
+16.3
+7
+7.2
+7.5
+10.4
+8.8
+8.4
+16.7
+14.2
+20.8
+13.4
+11.7
+8.3
+10.2
+10.9
+11
+9.5
+14.5
+14.1
+16.1
+14.3
+11.7
+13.4
+9.6
+8.7
+8.4
+12.8
+10.5
+17.1
+18.4
+15.4
+10.8
+11.8
+14.9
+12.6
+14.1
+13
+13.4
+15.2
+16.1
+17.8
+14.9
+14.1
+12.7
+13.5
+14.9
+20
+16.4
+17.7
+19.5
+20.2
+21.4
+19.9
+19
+19.1
+19.1
+20.1
+19.9
+19.6
+23.2
+29.8
+13.8
+13.3
+16.7
+12
+14.6
+21.4
+23
+23.7
+25
+21.8
+20.6
+21.2
+19.1
+20.6
+15.2
+7
+8.1
+13.6
+20.1
+21.8
+24.5
+23.1
+19.7
+18.3
+21.2
+17.5
+16.8
+22.4
+20.6
+23.9
+22
+11.9
\ No newline at end of file
diff --git a/extra/tensors/tensors-docs.factor b/extra/tensors/tensors-docs.factor
index b4fa8cc37a..239b3df653 100644
--- a/extra/tensors/tensors-docs.factor
+++ b/extra/tensors/tensors-docs.factor
@@ -1,27 +1,46 @@
 ! Copyright (C) 2019 HMC Clinic.
 ! See http://factorcode.org/license.txt for BSD license.
-USING: arrays help.markup help.syntax math sequences ;
+USING: arrays help.markup help.syntax lexer math sequences ;
 IN: tensors
 
-ARTICLE: "tensors" "Tensors" "A " { $snippet "tensor" } " is a sequence "
-"of floating point numbers "
+ARTICLE: "tensors" "Tensors"
+"A " { $snippet "tensor" } " is a sequence of floating point numbers "
 "shaped into an n-dimensional matrix. It supports fast, scalable matrix "
 "operations such as matrix multiplication and transposition as well as a "
 "number of element-wise operations. Words for working with tensors are found "
-"in the " { $vocab-link "tensors" } " vocabulary." $nl $nl
-"Tensors can be created "
-"by calling one of four constructors:"
-{ $subsections zeros ones naturals arange }
-"They can be converted to the corresponding N-dimensional array with"
-{ $subsections tensor>array }
+"in the " { $vocab-link "tensors" } " vocabulary." $nl
+"More information about tensors can be found here:"
+{ $subsections "creation" "manipulation" } ;
+
+ARTICLE: "creation" "Creating Tensors"
+"Tensors can be created by calling one of following constructors:"
+{ $subsections zeros ones naturals arange (tensor) }
+"They can be converted to/from the corresponding N-dimensional array with"
+{ $subsections tensor>array >tensor }
+"There is also a tensor parsing word"
+{ $subsections POSTPONE: t{ } ;
+
+ARTICLE: "manipulation" "Manipulating Tensors"
 "The number of dimensions can be extracted with:"
 { $subsections dims }
-"Additionally, tensors can be reshaped with:"
+"Tensors can be reshaped with:"
 { $subsections reshape flatten }
 "Tensors can be combined element-wise with other tensors as well as numbers with:"
 { $subsections t+ t- t* t/ t% }
-"Finally, tensors support the following matrix operations:"
-{ $subsections matmul transpose } ;
+"Tensors support the following matrix operations:"
+{ $subsections matmul transpose }
+"Tensors also support the following concatenation operations:"
+{ $subsections stack hstack vstack t-concat }
+"Tensors implement all " { $vocab-link "sequences" } " operations." $nl
+"Tensors can be indexed into using either numbers or arrays, for example:"
+{ $example
+    "USING: prettyprint sequences tensors ;"
+    "t{ { 0.0 1.0 2.0 } { 3.0 4.0 5.0 } }"
+    "[ { 1 1 } swap nth ] [ 4 swap nth ] bi = ."
+    "t"
+}
+"If the array being used to index into the tensor has the wrong number "
+"of dimensions, a " { $link dimension-mismatch-error } " will be thrown." ;
 
 ARTICLE: "tensor-operators" "Tensor Operators" "Info here" ;
 
@@ -46,6 +65,34 @@ HELP: non-positive-shape-error
 ", which allow users to directly set the shape of a " { $link tensor }
 ", when the shape has zero or negative values." } ;
 
+HELP: non-uniform-seq-error
+{ $values { "seq" sequence } }
+{ $description "Throws a " { $link non-uniform-seq-error } "." }
+{ $error-description "Thrown by operations such as " { $link >tensor } 
+", which allow users to directly input the values of a " { $link tensor }
+" as a nested sequence, when the subsequences have varying lengths." } ;
+
+HELP: dimension-mismatch-error
+{ $values { "tensor-dim" number } { "index-dim" number } }
+{ $description "Throws a " { $link dimension-mismatch-error } "." }
+{ $error-description "Thrown by indexing operations such as " { $link nth }
+" and " { $link set-nth } " if the array being used to index has a different number "
+"of dimensions than the tensor." } ;
+
+HELP: t{
+{ $syntax "t{ elements... }" }
+{ $values { "elements" "a list of numbers" } }
+{ $description "Initializes a tensor with the given elements."
+" Preserves the shape of nested sequences. Assumes uniformly nested sequences." } 
+{ $errors "Throws a " { $link non-uniform-seq-error } " if the given "
+"sequence have subsequences of varying lengths. Throws a " 
+{ $link lexer-error } " if the given sequence is not uniformly nested." } ;
+
+HELP: (tensor)
+{ $values { "shape" sequence } { "tensor" tensor } }
+{ $description "Creates a tensor with shape " { $snippet "shape" }
+" containing uninitialized values. Allows non-positive shapes." } ;
+
 HELP: zeros
 { $values { "shape" sequence } { "tensor" tensor } }
 { $description "Initializes a tensor with shape " { $snippet "shape" }
@@ -87,6 +134,14 @@ HELP: dims
 { $values { "tensor" tensor } { "n" integer } }
 { $description "Returns the dimension of " { $snippet "tensor" } "." } ;
 
+HELP: >tensor
+{ $values { "seq" sequence } { "tensor" tensor } }
+{ $description "Turns a nested sequence " { $snippet "seq" } 
+" into a tensor of the corresponding shape. Assumes a uniformly nested sequence." } 
+{ $errors "Throws a " { $link non-uniform-seq-error } " if the given "
+"sequence have subsequences of varying lengths. Throws a " 
+{ $link lexer-error } " if the given sequence is not uniformly nested." } ;
+
 HELP: t+
 { $values { "x" { $or tensor number } } { "y" { $or tensor number } } { "tensor" tensor } }
 { $description "Element-wise addition. Intakes two tensors or a tensor and a number (in either order)." }
@@ -131,6 +186,33 @@ HELP: matmul
 
 HELP: transpose
 { $values { "tensor" tensor } { "tensor'" tensor } }
-{ $description "Performs n-dimensional matrix transposition on " { $snippet "tens" } "." } ;
+{ $description "Performs n-dimensional matrix transposition on " { $snippet "tensor" } "." } ;
+
+HELP: stack 
+{ $values { "seq" sequence } { "tensor" tensor } } 
+{ $description "Joins the sequences in " { $snippet "seq" } " along a new axis. "
+{ $snippet "tensor" } " will have one more dimension than the arrays in " { $snippet "seq" } "." } 
+{ $errors "Throws a " { $link shape-mismatch-error } " if the sequences in "
+{ $snippet "seq" } " do not have the same shape."} ;
+
+
+HELP: hstack 
+{ $values { "seq" sequence } { "tensor" tensor } } 
+{ $description "Joins the sequences in " { $snippet "seq" } " column-wise." }
+{ $errors "Throws a " { $link shape-mismatch-error } " if the sequences in "
+{ $snippet "seq" } " do not have the same shape along all but the second axis."} ;
+
+HELP: vstack 
+{ $values { "seq" sequence } { "tensor" tensor } } 
+{ $description "Joins the sequences in " { $snippet "seq" } " row-wise." }
+{ $errors "Throws a " { $link shape-mismatch-error } " if the sequences in "
+{ $snippet "seq" } " do not have the same shape along all but the first axis."} ;
+
+HELP: t-concat
+{ $values { "seq" sequence } { "tensor" tensor } } 
+{ $description "Joins the sequences in " { $snippet "seq" } " along the first axis." }
+{ $errors "Throws a " { $link shape-mismatch-error } " if the sequences in "
+{ $snippet "seq" } " do not have the same shape along all but the first axis."} ;
+
 
 ABOUT: "tensors"
diff --git a/extra/tensors/tensors-tests.factor b/extra/tensors/tensors-tests.factor
index 04c4ebe1b7..50d9e4f896 100644
--- a/extra/tensors/tensors-tests.factor
+++ b/extra/tensors/tensors-tests.factor
@@ -1,6 +1,6 @@
 ! Copyright (C) 2019 HMC Clinic.
 ! See http://factorcode.org/license.txt for BSD license.
-USING: accessors alien.c-types kernel math math.order math.vectors
+USING: accessors alien.c-types arrays kernel math math.order math.vectors
 sequences specialized-arrays tensors tools.test ;
 QUALIFIED-WITH: alien.c-types c
 SPECIALIZED-ARRAY: c:float
@@ -106,6 +106,19 @@ IN: tensors.tests
 ]
 [ { -3 5 } \ non-positive-shape-error boa = ] must-fail-with
 
+! Test (tensor)
+{ { 2 4 } } [
+    { 2 4 } (tensor) shape>>
+] unit-test
+
+{ { 0 } } [
+    { 0 } (tensor) shape>>
+] unit-test
+
+{ float-array{ } } [
+    { 0 } (tensor) vec>>
+] unit-test
+
 
 ! Test reshape
 { float-array{ 0.0 0.0 0.0 0.0 } } [
@@ -156,6 +169,173 @@ IN: tensors.tests
     { 1 2 3 } zeros dims
 ] unit-test
 
+! Test sequence operations
+! TODO: add tests for clone-like
+! test length
+{ 20 } [
+    { 2 2 5 } naturals length
+] unit-test
+
+{ 0 } [
+    t{ } length
+] unit-test
+
+! test new-sequence
+{ 10 } [
+    10 { 2 5 } ones new-sequence shape>> product
+] unit-test
+
+{ 2 } [
+    2 { 3 4 5 } ones new-sequence shape>> product
+] unit-test
+
+{ 20 } [
+    20 { 2 5 } ones new-sequence shape>> product
+] unit-test
+
+! test nth
+{ 1.0 } [
+    1 { 5 } naturals nth
+] unit-test
+
+{ 1.0 } [
+    { 1 } { 5 } naturals nth
+] unit-test
+
+{ 3.0 } [
+    { 1 1 } { 2 2 } naturals nth
+] unit-test
+
+{ 5.0 } [
+    { 1 0 1 } { 2 2 2 } naturals nth
+] unit-test
+
+[
+    { 1 2 3 } t{ 1 2 3 } nth
+]
+[ 1 3 \ dimension-mismatch-error boa = ] must-fail-with
+
+! test set-nth
+{ t{ 1 5 3 } } [
+    t{ 1 2 3 } dup [ 5 { 1 } ] dip set-nth
+] unit-test
+
+{ t{ { 0 1 } { 5 3 } } } [
+    { 2 2 } naturals dup [ 5 { 1 0 } ] dip set-nth
+] unit-test
+
+{ t{ { { 0 1 } { 2 3 } } { { 4 10 } { 6 7 } } } } [
+    { 2 2 2 } naturals dup [ 10 { 1 0 1 } ] dip set-nth
+] unit-test
+
+[
+    { 2 2 } naturals dup [ 5 { 1 } ] dip set-nth
+]
+[ 2 1 \ dimension-mismatch-error boa = ] must-fail-with
+
+! test clone
+{ t{ 1 2 3 }  } [
+    t{ 1 2 3 } dup clone [ 5 1 ] dip set-nth
+] unit-test
+
+{ t } [
+    t{ 1 2 3 } dup clone =
+] unit-test
+
+{ f } [
+    t{ 1 2 3 } dup clone dup [ 5 1 ] dip set-nth =
+] unit-test
+
+! Test like
+{ float-array{ 0.0 1.0 2.0 3.0 4.0 5.0 } } [
+    { 2 3 } naturals dup like vec>>
+] unit-test
+
+{ { 2 3 } } [
+    { 2 3 } naturals dup like shape>>
+] unit-test
+
+{ float-array{ 0.0 1.0 2.0 3.0 4.0 5.0 } } [
+    { 0 1 2 3 4 5 } { 2 3 } naturals like vec>>
+] unit-test
+
+{ { 2 3 } } [
+    { 0 1 2 3 4 5 } { 2 3 } naturals like shape>>
+] unit-test
+
+{ float-array{ 0.0 1.0 2.0 3.0 4.0 5.0 } } [
+    float-array{ 0 1 2 3 4 5 } { 2 3 } naturals like vec>>
+] unit-test
+
+{ { 2 3 } } [
+    float-array{ 0 1 2 3 4 5 } { 2 3 } naturals like shape>>
+] unit-test
+
+{ float-array{ 0.0 1.0 2.0 3.0 4.0 } } [
+    { 0 1 2 3 4 } { 2 3 } naturals like vec>>
+] unit-test
+
+{ { 5 } } [
+    { 0 1 2 3 4 } { 2 3 } naturals like shape>>
+] unit-test
+
+{ float-array{ 0.0 1.0 2.0 3.0 4.0 } } [
+    float-array{ 0 1 2 3 4 } { 2 3 } naturals like vec>>
+] unit-test
+
+{ { 5 } } [
+    float-array{ 0 1 2 3 4 } { 2 3 } naturals like shape>>
+] unit-test
+
+{ t{ { 0.0 1.0 } { 2.0 3.0 } } } [
+    { { 0 1 } { 2 3 } } t{ } like
+] unit-test
+
+! test clone-like
+{ float-array{ 1.0 2.0 3.0 } } [
+    { 1 2 3 } t{ } clone-like vec>>
+] unit-test
+
+{ f } [
+    float-array{ 1.0 2.0 3.0 } dup t{ } clone-like
+    dup [ 5 1 ] dip set-nth vec>> =
+] unit-test
+
+! Test sum
+{ 21.0 } [
+    t{ 1 2 3 4 5 6 } sum
+] unit-test
+
+{ 50005000.0 } [
+    { 100 100 } naturals 1 t+ sum
+] unit-test
+
+! Test tensor parsing word
+{ float-array{ 1 2 3 4 5 6 7 8 } } [
+    t{ 1 2 3 4 5 6 7 8 } vec>>
+] unit-test
+
+{ { 8 } } [
+    t{ 1 2 3 4 5 6 7 8 } shape>>
+] unit-test
+
+{ float-array{ 1 2 3 4 5 6 7 8 } } [
+    t{ { 1 2 3 4 } { 5 6 7 8 } } vec>>
+] unit-test
+
+{ { 2 4 } } [
+    t{ { 1 2 3 4 } { 5 6 7 8 } } shape>>
+] unit-test
+
+{ float-array{ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 } } [
+    t{ { { 1 2 3 4 } { 5 6 7 8 } { 9 10 11 12 } } { { 13 14 15 16 } { 17 18 19 20 } { 21 22 23 24 } } } vec>>
+] unit-test
+
+{ { 2 3 4 } } [
+    t{ { { 1 2 3 4 } { 5 6 7 8 } { 9 10 11 12 } } { { 13 14 15 16 } { 17 18 19 20 } { 21 22 23 24 } } } shape>>
+] unit-test
+
+
 ! Test addition
 { float-array{ 1.0 2.0 3.0 4.0 } } [
     { 4 } naturals { 4 } ones t+ vec>>
@@ -461,6 +641,188 @@ IN: tensors.tests
     t% shape>>
 ] unit-test
 
+! t-concat
+{ t{ { 0.0 1.0 2.0 } { 3.0 4.0 5.0 } { 0.0 1.0 2.0 } { 3.0 4.0 5.0 } } } [
+    { 2 3 } naturals dup 2array t-concat
+] unit-test
+
+{ t{ { 0.0 1.0 2.0 } { 3.0 4.0 5.0 } { 0.0 1.0 2.0 } { 3.0 4.0 5.0 }
+     { 0.0 1.0 2.0 } { 3.0 4.0 5.0 } } } [
+    { 2 3 } naturals dup dup 3array t-concat
+] unit-test
+
+{ t{ { 0.0 1.0 2.0 }
+     { 3.0 4.0 5.0 }
+     { 0.0 1.0 2.0 }
+     { 3.0 4.0 5.0 }
+     { 6.0 7.0 8.0 }
+     { 9.0 10.0 11.0 }
+     { 12.0 13.0 14.0 } } } [
+    { 2 3 } naturals { 5 3 } naturals 2array t-concat
+] unit-test
+
+{ t{ { { 0.0 1.0 } { 2.0 3.0 } { 4.0 5.0 } }
+     { { 6.0 7.0 } { 8.0 9.0 } { 10.0 11.0 } }
+     { { 0.0 1.0 } { 2.0 3.0 } { 4.0 5.0 } }
+     { { 6.0 7.0 } { 8.0 9.0 } { 10.0 11.0 } } } } [
+     { 2 3 2 } naturals dup 2array t-concat
+] unit-test
+
+{ t{ { { 0.0 1.0 } { 2.0 3.0 } { 4.0 5.0 } }
+     { { 6.0 7.0 } { 8.0 9.0 } { 10.0 11.0 } }
+     { { 0.0 1.0 } { 2.0 3.0 } { 4.0 5.0 } }
+     { { 6.0 7.0 } { 8.0 9.0 } { 10.0 11.0 } }
+     { { 0.0 1.0 } { 2.0 3.0 } { 4.0 5.0 } }
+     { { 6.0 7.0 } { 8.0 9.0 } { 10.0 11.0 } } } } [
+     { 2 3 2 } naturals dup dup 3array t-concat
+] unit-test
+
+[
+    { 2 2 } naturals { 2 3 } naturals 2array t-concat
+]
+[ { 2 2 } { 2 3 } \ shape-mismatch-error boa = ] must-fail-with
+
+[
+    { 2 2 } naturals dup { 2 3 } naturals 3array t-concat
+]
+[ { 2 2 } { 2 3 } \ shape-mismatch-error boa = ] must-fail-with
+
+! stack
+{ t{ { { 0.0 1.0 2.0 } { 3.0 4.0 5.0 } }
+     { { 0.0 1.0 2.0 } { 3.0 4.0 5.0 } } } } [
+    { 2 3 } naturals dup 2array stack
+] unit-test
+
+{ t{ { { 0.0 1.0 2.0 } { 3.0 4.0 5.0 } }
+     { { 0.0 1.0 2.0 } { 3.0 4.0 5.0 } }
+     { { 0.0 1.0 2.0 } { 3.0 4.0 5.0 } } } } [
+    { 2 3 } naturals dup dup 3array stack
+] unit-test
+
+{ t{ { { { 0.0 1.0 } { 2.0 3.0 } { 4.0 5.0 } }
+       { { 6.0 7.0 } { 8.0 9.0 } { 10.0 11.0 } } }
+     { { { 0.0 1.0 } { 2.0 3.0 } { 4.0 5.0 } }
+       { { 6.0 7.0 } { 8.0 9.0 } { 10.0 11.0 } } } } } [
+     { 2 3 2 } naturals dup 2array stack
+] unit-test
+
+{ t{ { { { 0.0 1.0 } { 2.0 3.0 } { 4.0 5.0 } }
+       { { 6.0 7.0 } { 8.0 9.0 } { 10.0 11.0 } } }
+     { { { 0.0 1.0 } { 2.0 3.0 } { 4.0 5.0 } }
+       { { 6.0 7.0 } { 8.0 9.0 } { 10.0 11.0 } } }
+     { { { 0.0 1.0 } { 2.0 3.0 } { 4.0 5.0 } }
+       { { 6.0 7.0 } { 8.0 9.0 } { 10.0 11.0 } } } } } [
+     { 2 3 2 } naturals dup dup 3array stack
+] unit-test
+
+[
+    { 2 2 } naturals { 2 3 } naturals 2array stack
+]
+[ { 2 2 } { 2 3 } \ shape-mismatch-error boa = ] must-fail-with
+
+[
+    { 2 2 } naturals { 3 2 } naturals 2array stack
+]
+[ { 2 2 } { 3 2 } \ shape-mismatch-error boa = ] must-fail-with
+
+[
+    { 2 2 } naturals dup { 2 3 } naturals 3array stack
+]
+[ { 2 2 } { 2 3 } \ shape-mismatch-error boa = ] must-fail-with
+
+[
+    { 2 2 } naturals dup { 3 2 } naturals 3array stack
+]
+[ { 2 2 } { 3 2 } \ shape-mismatch-error boa = ] must-fail-with
+
+! hstack
+
+{ t{ { 0.0 1.0 2.0 3.0 1.0 }
+     { 4.0 5.0 6.0 7.0 1.0 }
+     { 8.0 9.0 10.0 11.0 1.0 } } } [
+    { 3 4 } naturals { 3 1 } ones 2array hstack
+] unit-test
+
+{ t{ { 0.0 1.0 2.0 3.0 1.0 0.0 0.0 }
+     { 4.0 5.0 6.0 7.0 1.0 0.0 0.0 }
+     { 8.0 9.0 10.0 11.0 1.0 0.0 0.0 } } } [
+    { 3 4 } naturals { 3 1 } ones { 3 2 } zeros 3array hstack
+] unit-test
+
+{ t{ { { 0.0 1.0 2.0 3.0 1.0 } { 4.0 5.0 6.0 7.0 1.0 } }
+     { { 8.0 9.0 10.0 11.0 1.0 } { 12.0 13.0 14.0 15.0 1.0 } } } } [
+     { 2 2 4 } naturals { 2 2 1 } ones 2array hstack
+] unit-test
+
+{ t{ { { 0.0 1.0 2.0 3.0 1.0 0.0 0.0 }
+       { 4.0 5.0 6.0 7.0 1.0 0.0 0.0 } }
+     { { 8.0 9.0 10.0 11.0 1.0 0.0 0.0 }
+       { 12.0 13.0 14.0 15.0 1.0 0.0 0.0 } } } } [
+     { 2 2 4 } naturals { 2 2 1 } ones { 2 2 2 } zeros 3array hstack
+] unit-test
+
+[
+    { 2 2 } naturals { 3 2 } naturals 2array hstack
+]
+[ { 2 2 } { 3 2 } \ shape-mismatch-error boa = ] must-fail-with
+
+[
+    { 2 2 } naturals dup { 3 2 } naturals 3array hstack
+]
+[ { 2 2 } { 3 2 } \ shape-mismatch-error boa = ] must-fail-with
+
+[
+    { 2 2 2 } naturals { 3 2 2 } naturals 2array hstack
+]
+[ { 2 2 2 } { 3 2 2 } \ shape-mismatch-error boa = ] must-fail-with
+
+[
+    { 2 2 2 } naturals dup { 3 2 2 } naturals 3array hstack
+]
+[ { 2 2 2 } { 3 2 2 } \ shape-mismatch-error boa = ] must-fail-with
+
+! vstack
+{ t{ { 0.0 1.0 } { 2.0 3.0 } { 4.0 5.0 } { 0.0 1.0 } } } [
+    { 3 2 } naturals { 1 2 } naturals 2array vstack
+] unit-test
+
+{ t{ { 0.0 1.0 } { 2.0 3.0 } { 4.0 5.0 }
+     { 0.0 1.0 } { 0.0 1.0 } { 2.0 3.0 } } } [
+    { 3 2 } naturals { 1 2 } naturals { 2 2 } naturals 3array vstack
+] unit-test
+
+{ t{ { { 0.0 1.0 } { 2.0 3.0 } { 4.0 5.0 } { 0.0 1.0 } }
+     { { 6.0 7.0 } { 8.0 9.0 } { 10.0 11.0 } { 2.0 3.0 } } } } [
+    { 2 3 2 } naturals { 2 1 2 } naturals 2array vstack
+] unit-test
+
+{ t{ { { 0.0 1.0 } { 2.0 3.0 } { 4.0 5.0 }
+       { 0.0 1.0 } { 0.0 1.0 } { 2.0 3.0 } }
+     { { 6.0 7.0 } { 8.0 9.0 } { 10.0 11.0 }
+       { 2.0 3.0 } { 4.0 5.0 } { 6.0 7.0 } } } } [
+    { 2 3 2 } naturals { 2 1 2 } naturals { 2 2 2 } naturals 3array vstack
+] unit-test
+
+[
+    { 2 2 } naturals { 2 3 } naturals 2array vstack
+]
+[ { 2 2 } { 2 3 } \ shape-mismatch-error boa = ] must-fail-with
+
+[
+    { 2 2 } naturals dup { 2 3 } naturals 3array vstack
+]
+[ { 2 2 } { 2 3 } \ shape-mismatch-error boa = ] must-fail-with
+
+[
+    { 2 2 2 } naturals { 3 2 2 } naturals 2array vstack
+]
+[ { 2 2 2 } { 3 2 2 } \ shape-mismatch-error boa = ] must-fail-with
+
+[
+    { 2 2 2 } naturals dup { 3 2 2 } naturals 3array vstack
+]
+[ { 2 2 2 } { 3 2 2 } \ shape-mismatch-error boa = ] must-fail-with
+
 ! test tensor>array
 { { 0.0 0.0 } } [
     { 2 } zeros tensor>array
@@ -475,6 +837,36 @@ IN: tensors.tests
     { 2 3 2 } ones tensor>array
 ] unit-test
 
+! test >tensor
+{ t } [
+    { 2 3 4 } naturals dup tensor>array >tensor =
+] unit-test
+
+{ t } [
+    { { { 1.0 2.0 } { 3.0 4.0 } }
+      { { 5.0 6.0 } { 7.0 8.0 } }
+      { { 9.0 10.0 } { 11.0 12.0 } } }
+    dup >tensor tensor>array =
+] unit-test
+
+{ t } [
+    { 2 3 } naturals
+    { { 0 1 2 } { 3 4 5 } } >tensor =
+] unit-test
+
+[
+    { { 1 2 } { 3 } } >tensor
+]
+[ { { 1 2 } { 3 } } \ non-uniform-seq-error boa = ] must-fail-with
+
+{ float-array{ } } [
+    t{ } vec>>
+] unit-test
+
+{ { 0 } } [
+    t{ } shape>>
+] unit-test
+
 ! test matmul
 { float-array{ 70.0 76.0 82.0 88.0 94.0 190.0 212.0 234.0
                256.0 278.0 310.0 348.0 386.0 424.0 462.0 } } [
@@ -510,6 +902,45 @@ IN: tensors.tests
     { 2 2 3 4 } naturals { 2 2 4 5 } naturals matmul shape>>
 ] unit-test
 
+! where n mod 4 is not 0 and m & p have same mod 4 val
+{ float-array{ 45.0 48.0 51.0 54.0 57.0 60.0 63.0 66.0 69.0 126.0
+    138.0 150.0 162.0 174.0 186.0 198.0 210.0 222.0 207.0 228.0
+    249.0 270.0 291.0 312.0 333.0 354.0 375.0 288.0 318.0 348.0
+    378.0 408.0 438.0 468.0 498.0 528.0 369.0 408.0 447.0 486.0
+    525.0 564.0 603.0 642.0 681.0 } } [
+    { 5 3 } naturals { 3 9 } naturals matmul vec>>
+] unit-test
+
+! where n mod 4 is not 0 and m & p have same mod 4 val
+{ float-array{ 270.0 280.0 290.0 300.0 310.0 320.0 330.0 340.0 350.0
+               720.0 755.0 790.0 825.0 860.0 895.0 930.0 965.0 1000.0
+               1170.0 1230.0 1290.0 1350.0 1410.0 1470.0 1530.0 1590.0
+               1650.0 1620.0 1705.0 1790.0 1875.0 1960.0 2045.0 2130.0
+               2215.0 2300.0 2070.0 2180.0 2290.0 2400.0 2510.0 2620.0
+               2730.0 2840.0 2950.0 } } [
+    { 5 5 } naturals { 5 9 } naturals matmul vec>>
+] unit-test
+
+! where n mod 4 is not 0 and m & p have different mod 4 vals
+{ float-array{ 35.0 38.0 41.0 44.0 47.0 50.0 53.0 98.0 110.0
+    122.0 134.0 146.0 158.0 170.0 161.0 182.0 203.0 224.0
+    245.0 266.0 287.0 224.0 254.0 284.0 314.0 344.0 374.0
+    404.0 287.0 326.0 365.0 404.0 443.0 482.0 521.0 } } [
+    { 5 3 } naturals { 3 7 } naturals matmul vec>>
+] unit-test
+
+{ float-array{ 546.0 567.0 588.0 609.0 630.0 651.0 1428.0 1498.0 1568.0 1638.0
+    1708.0 1778.0 2310.0 2429.0 2548.0 2667.0 2786.0 2905.0 3192.0 3360.0
+    3528.0 3696.0 3864.0 4032.0 4074.0 4291.0 4508.0 4725.0 4942.0 5159.0
+    4956.0 5222.0 5488.0 5754.0 6020.0 6286.0 5838.0 6153.0 6468.0 6783.0
+    7098.0 7413.0 22008.0 22372.0 22736.0 23100.0 23464.0 23828.0 24948.0
+    25361.0 25774.0 26187.0 26600.0 27013.0 27888.0 28350.0 28812.0 29274.0
+    29736.0 30198.0 30828.0 31339.0 31850.0 32361.0 32872.0 33383.0 33768.0
+    34328.0 34888.0 35448.0 36008.0 36568.0 36708.0 37317.0 37926.0 38535.0
+    39144.0 39753.0 39648.0 40306.0 40964.0 41622.0 42280.0 42938.0 } } [
+    { 2 7 7 } naturals { 2 7 6 } naturals matmul vec>>
+] unit-test
+
 ! test transpose
 { float-array{ 0.0 2.0 1.0 3.0 } } [
     { 2 2 } naturals transpose vec>>
diff --git a/extra/tensors/tensors.factor b/extra/tensors/tensors.factor
index e44ac4ebff..f97d0c4fac 100644
--- a/extra/tensors/tensors.factor
+++ b/extra/tensors/tensors.factor
@@ -1,12 +1,14 @@
 ! Copyright (C) 2019 HMC Clinic.
 ! See http://factorcode.org/license.txt for BSD license.
 
-USING: accessors alien.data arrays grouping kernel locals math
-math.functions math.ranges multi-methods sequences
-sequences.extras sequences.private specialized-arrays typed ;
+USING: accessors alien alien.c-types alien.data arrays byte-arrays combinators
+grouping kernel locals kernel.private math math.functions math.ranges math.vectors
+math.vectors.simd multi-methods parser prettyprint.custom sequences sequences.extras
+sequences.private specialized-arrays typed ;
 
 QUALIFIED-WITH: alien.c-types c
 SPECIALIZED-ARRAY: c:float
+SPECIALIZED-ARRAY: float-4
 IN: tensors
 
 ! Tensor class definition
@@ -17,6 +19,8 @@ TUPLE: tensor
 ! Errors
 ERROR: non-positive-shape-error shape ;
 ERROR: shape-mismatch-error shape1 shape2 ;
+ERROR: non-uniform-seq-error seq ;
+ERROR: dimension-mismatch-error tensor-dim index-dim ;
 
 <PRIVATE
 
@@ -28,6 +32,7 @@ ERROR: shape-mismatch-error shape1 shape2 ;
 : <tensor> ( shape seq -- tensor )
     tensor boa ;
 
+! Creates a freshly-allocated float-array with the desired c-type values
 : >float-array ( seq -- float-array )
     c:float >c-array ;
 
@@ -50,10 +55,14 @@ PRIVATE>
 : arange ( a b step -- tensor )
     <range> [ length >fixnum 1array ] keep >float-array <tensor> ;
 
-! Construct a tensors with vec { 0 1 2 ... } and reshape to the desired shape
+! Construct a tensor with vec { 0 1 2 ... } and reshape to the desired shape
 : naturals ( shape -- tensor )
     check-shape [ ] [ product [0,b) >float-array ] bi <tensor> ;
 
+! Construct a tensor without initializing its values
+: (tensor) ( shape -- tensor )
+    dup product (float-array) <tensor> ;
+
 <PRIVATE
 
 : check-reshape ( shape1 shape2 -- shape1 shape2 )
@@ -81,6 +90,117 @@ TYPED: tensor>array ( tensor: tensor -- seq: array )
     [ rest-slice reverse [ group ] each ] unless-empty ;
 
 <PRIVATE
+! recursively finds shape of nested array
+! assumes properly shaped array (all sub-arrays are same size)
+:: find-shape ( seq shape -- shape' )
+    seq empty? [ { 0 } ] [
+        ! add length of seq element to shape
+        shape seq length 1array append :> shape'
+        ! base case: check if the first element is a seq
+        seq first :> 1st
+        1st sequence?
+        ! is a sequence: recurse on 1st element
+        [ 1st shape' find-shape ]
+        ! not a sequence: return shape'
+        [ shape' ] if
+    ] if ;
+PRIVATE>
+
+! turns a nested array into a tensor
+:: >tensor ( seq -- tensor )
+    ! get the shape
+    seq { } find-shape :> shape
+    ! flatten the array
+    seq
+    shape length 1 - [
+        drop concat
+    ] each-integer :> flatseq
+    ! check that the size is good
+    shape product flatseq length =
+    [ seq non-uniform-seq-error ] unless
+    ! turn into a tensor
+    shape flatseq >float-array <tensor> ;
+
+SYNTAX: t{ \ } [ >tensor ] parse-literal ;
+
+! Pretty printing
+syntax:M: tensor pprint-delims drop \ t{ \ } ;
+syntax:M: tensor >pprint-sequence tensor>array ;
+syntax:M: tensor pprint* pprint-object ;
+
+
+<PRIVATE
+! turns a shape into a list of things by which to multiply 
+! indices to get a full index (e.g. { 2 3 4 } -> { 12 4 1 })
+: ind-mults ( shape -- seq )
+    <reversed> 1 swap [ swap [ * ] keep ] map nip reverse ;
+
+! turns a num/seq index & tensor into num index & tensor
+! also throws a dimension mismatch if seq & tens shape>> arent the same len
+: num-index ( n/seq tensor -- n tensor )
+    ! check form of index (num or seq)
+    swap dup array? not
+    [ ! if array, first check if it's a valid index
+        2dup [ shape>> length ] dip length 2dup = 
+        [ dimension-mismatch-error ] unless 2drop
+        ! turn into num
+        [ dup shape>> ind-mults ] dip [ * ] 2map-sum
+    ] unless swap ;
+
+PRIVATE>
+
+
+! Sequence protocol implementation
+syntax:M: tensor clone [ shape>> clone ] [ vec>> clone ] bi <tensor> ;
+
+syntax:M: tensor length vec>> length ;
+
+syntax:M: tensor nth num-index vec>> nth ;
+
+syntax:M: tensor nth-unsafe num-index vec>> nth-unsafe ;
+
+syntax:M: tensor set-nth num-index vec>> set-nth ;
+
+syntax:M: tensor set-nth-unsafe num-index vec>> set-nth-unsafe ;
+
+syntax:M: tensor new-sequence
+    ! Check if the old and new tensors are the same size
+    shape>> 2dup product =
+    ! If so preserve the shape, otherwise create a 1D tensor
+    [ nip (tensor) ] [ drop 1array (tensor) ] if ;
+
+syntax:M: tensor like
+    ! If the original sequence is already a tensor, we are done
+    over tensor?
+    [ drop ] [
+        over float-array? [
+            [ dup [ length 1array ] dip <tensor> ] dip
+        ] [
+            [ >tensor ] dip
+        ] if
+        2dup [ length ] bi@ = [ shape>> reshape ] [ drop ] if
+    ] if ;
+
+syntax:M: tensor clone-like
+    ! If the original sequence is already a tensor, we just need to clone it
+    over tensor?
+    [ drop clone ] [
+        [ >tensor ] dip
+        2dup [ length ] bi@ = [ shape>> reshape ] [ drop ] if
+    ] if ;
+
+INSTANCE: tensor sequence
+
+
+<PRIVATE
+
+:: make-subseq ( arr start len -- arr )
+    ! Find the index
+    c:float heap-size start *
+    ! Compute the starting pointer
+    arr underlying>> <displaced-alien>
+    ! Push length and type to create the new array
+    len c:float <c-direct-array> ; inline
 
 : check-bop-shape ( shape1 shape2 -- shape )
     2dup = [ shape-mismatch-error ] unless drop ;
@@ -90,42 +210,245 @@ TYPED:: t-bop ( tensor1: tensor tensor2: tensor quot: ( x y -- z ) -- tensor: te
     tensor1 shape>> tensor2 shape>> check-bop-shape
     tensor1 vec>> tensor2 vec>> quot 2map <tensor> ; inline
 
+! Create an array of 4-element SIMD arrays for processing floats
+: simd-for-bop ( array -- simd-array rest-slice/f )
+    dup length dup 4 mod [ drop f ] [ - cut-slice ] if-zero
+    [ float-4 cast-array ] dip ; inline
+
+! Create an array of 4-element SIMD arrays for processing floats
+! Tensor class definition
+TUPLE: simd-slice
+    { first-slice float-array }
+    { simd-slice float-4-array }
+    { end-slice float-array } ;
+
+:: (simd-slice) ( arr start len -- arr/f )
+    len [ float-array{ } ] [ drop arr start len make-subseq ] if-zero ; inline
+
+:: <simd-slice> ( arr start -- simd-slice )
+    ! Compute the beginning
+    arr 0 start (simd-slice)
+    ! Compute the SIMD part
+    arr length start - :> len
+    len 4 mod :> end
+    arr start len end - (simd-slice) float-4 cast-array
+    ! Compute the end
+    arr dup length end - end (simd-slice)
+    simd-slice boa ; inline
+
+! Apply the binary operators simd-quot and quot to quickly combine the tensors
+:: t-bop-simd ( tensor1 tensor2 simd-quot: ( x y -- z ) quot: ( x y -- z ) -- tensor )
+    tensor1 shape>> tensor2 shape>> check-bop-shape
+    tensor1 vec>> tensor2 vec>>
+    dup length (float-array) dup :> vec3
+    [ simd-for-bop ] tri@ :> ( simd1 rest1 simd2 rest2 simd3 rest3 )
+    simd1 simd2 simd-quot simd3 2map-into
+    rest1 rest2 quot rest3 2map-into
+    vec3 <tensor> ; inline
+
 ! Apply the operation to the tensor
 TYPED:: t-uop ( tensor: tensor quot: ( x -- y ) -- tensor: tensor )
     tensor vec>> quot map [ tensor shape>> ] dip <tensor> ; inline
 
+! Apply the binary operators simd-quot and quot to quickly combine a tensor and
+! a number
+:: t-uop-simd ( tensor n simd-quot: ( x y -- z ) quot: ( x y -- z ) -- tensor )
+    tensor dup [ shape>> ] [ vec>> ] bi*
+    dup length (float-array) dup :> vec2
+    [ simd-for-bop ] bi@ :> ( simd1 rest1 simd2 rest2 )
+    simd1 n n n n float-4-boa simd-quot curry simd2 map-into
+    rest1 n quot curry rest2 map-into
+    vec2 <tensor> ; inline
+
 PRIVATE>
 
 ! Add a tensor to either another tensor or a scalar
 multi-methods:GENERIC: t+ ( x y -- tensor )
-METHOD: t+ { tensor tensor } [ + ] t-bop ;
-METHOD: t+ { tensor number } >float [ + ] curry t-uop ;
-METHOD: t+ { number tensor } [ >float ] dip [ + ] with t-uop ;
+METHOD: t+ { tensor tensor } [ v+ ] [ + ] t-bop-simd ;
+METHOD: t+ { tensor number } >float [ v+ ] [ + ] t-uop-simd ;
+METHOD: t+ { number tensor } swap >float [ swap v+ ] [ swap + ] t-uop-simd ;
 
 ! Subtraction between two tensors or a tensor and a scalar
 multi-methods:GENERIC: t- ( x y -- tensor )
-METHOD: t- { tensor tensor } [ - ] t-bop ;
-METHOD: t- { tensor number } >float [ - ] curry t-uop ;
-METHOD: t- { number tensor } [ >float ] dip [ - ] with t-uop ;
+METHOD: t- { tensor tensor } [ v- ] [ - ] t-bop-simd ;
+METHOD: t- { tensor number } >float [ v- ] [ - ] t-uop-simd ;
+METHOD: t- { number tensor } swap >float [ swap v- ] [ swap - ] t-uop-simd ;
 
 ! Multiply a tensor with either another tensor or a scalar
 multi-methods:GENERIC: t* ( x y -- tensor )
-METHOD: t* { tensor tensor } [ * ] t-bop ;
-METHOD: t* { tensor number } >float [ * ] curry t-uop ;
-METHOD: t* { number tensor } [ >float ] dip [ * ] with t-uop ;
+METHOD: t* { tensor tensor } [ v* ] [ * ] t-bop-simd ;
+METHOD: t* { tensor number } >float [ v* ] [ * ] t-uop-simd ;
+METHOD: t* { number tensor } swap >float [ swap v* ] [ swap * ] t-uop-simd ;
 
 ! Divide two tensors or a tensor and a scalar
 multi-methods:GENERIC: t/ ( x y -- tensor )
-METHOD: t/ { tensor tensor } [ / ] t-bop ;
-METHOD: t/ { tensor number } >float [ / ] curry t-uop ;
-METHOD: t/ { number tensor } [ >float ] dip [ / ] with t-uop ;
+METHOD: t/ { tensor tensor } [ v/ ] [ / ] t-bop-simd ;
+METHOD: t/ { tensor number } >float [ v/ ] [ / ] t-uop-simd ;
+METHOD: t/ { number tensor } swap >float [ swap v/ ] [ swap / ] t-uop-simd ;
 
-! Divide two tensors or a tensor and a scalar
+! Mod two tensors or a tensor and a scalar
 multi-methods:GENERIC: t% ( x y -- tensor )
 METHOD: t% { tensor tensor } [ mod ] t-bop ;
 METHOD: t% { tensor number } >float [ mod ] curry t-uop ;
 METHOD: t% { number tensor } [ >float ] dip [ mod ] with t-uop ;
 
+! Sum together all elements in the tensor
+syntax:M: tensor sum vec>> 0 <simd-slice>
+    [ simd-slice>> 0 [ sum + ] reduce ]
+    [ end-slice>> sum ] bi + ;
+
+<PRIVATE
+
+! Also converts all elements of the sequence to tensors
+:: check-concat-shape ( seq -- seq )
+    ! Compute the bottom shape of the first element in the sequence
+    seq first { } >tensor dup :> empty-tensor
+    like shape>> dup :> first-shape rest :> rest-shape
+    seq [
+        ! Compute the bottom shape of this element
+        empty-tensor like dup shape>> rest
+        ! Compare; if they are different, throw an error
+        rest-shape = [ shape>> first-shape swap shape-mismatch-error ] unless
+    ] map ;
+
+! Also converts all elements of the sequence to tensors
+:: check-stack-shape ( seq -- seq )
+    ! Compute the bottom shape of the first element in the sequence
+    seq first { } >tensor dup :> empty-tensor
+    like shape>> :> first-shape
+    seq [
+        ! Compute the bottom shape of this element
+        empty-tensor like dup shape>>
+        ! Compare; if they are different, throw an error
+        first-shape = [ shape>> first-shape swap shape-mismatch-error ] unless
+    ] map ;
+
+! Also converts all elements of the sequence to tensors
+:: check-hstack-shape ( seq -- seq )
+    ! Compute the top shape of the first element in the sequence
+    seq first { } >tensor dup :> empty-tensor
+    like shape>> dup :> first-shape but-last :> but-last-shape
+    seq [
+        ! Compute the top shape of this element
+        empty-tensor like dup shape>> but-last
+        ! Compare; if they are different, throw an error
+        but-last-shape = [ shape>> first-shape swap shape-mismatch-error ] unless
+    ] map ;
+
+: final-hstack-shape ( seq -- shape )
+    ! Get the top part
+    dup first shape>> but-last swap
+    ! Compute the last part of the shape
+    [ shape>> last ] map sum 1array append ;
+
+! Returns an guide for hstacking where the index corresponds to the postion
+! in the last dimension of the resulting tensor, and the elements are
+! { which tensor, len of tensor, index }
+:: hstack-guide ( seq -- guide )
+    ! Compute the list of last shape parts
+    seq [ shape>> last ] map :> last-dims
+    ! Curr tensor and index in tensor
+    0 0
+    last-dims sum [0,b) [
+        drop :> old-t-ind :> last-dims-i
+        last-dims-i last-dims nth
+        old-t-ind -
+        ! If we need to move onto the next tensor
+        [ last-dims-i 1 + 0 ]
+        ! Otherwise, stay with the current tensor
+        [ drop last-dims-i old-t-ind ] if-zero
+        2dup [ dup last-dims nth ] dip 3array
+        [ 1 + ] dip
+    ] map nip nip ;
+
+! Given a sequence of tensors, stack them across the last dimension
+:: hstack-unsafe ( tseq -- tensor )
+    ! Create the final tensor
+    tseq final-hstack-shape (tensor)
+    ! Compute the guide information
+    tseq hstack-guide dup length :> repeat :> guide
+    dup vec>> [
+        :> i drop
+        ! First get the correct tensor
+        i repeat /mod guide nth
+        dup first tseq nth
+        ! Now find the correct value within that tensor
+        [ [ second ] [ third ] bi -rot * + ] dip nth
+    ] map-index! drop ;
+
+! Also converts all elements of the sequence to tensors
+:: check-vstack-shape ( seq -- seq )
+    ! Compute the shape of the first sequence
+    seq first { } >tensor dup :> empty-tensor
+    like shape>> dup :> first-shape
+    ! Compute the index of the dimension to be stacked across
+    length 2 - :> vdim
+    seq [
+        ! Convert this element to a tensor
+        empty-tensor like dup
+        ! Compare the shapes
+        shape>> first-shape [ = ] 2map
+        vdim swap remove-nth
+        ! If the shapes differ in anything except the second-to-last dimension
+        ! this sequence cannot be vstacked
+        t [ = ] reduce [ shape>> first-shape swap shape-mismatch-error ] unless
+    ] map ;
+
+! Compute the shape after the vstack has been completed
+:: final-vstack-shape ( seq -- shape )
+    ! Compute the new second-to-last dimension
+    seq first dims 2 - :> vdim
+    seq 0 [ shape>> vdim swap nth + ] reduce
+    ! Combine it to create the new shape
+    seq first shape>> clone :> new-shape
+    vdim new-shape set-nth
+    new-shape ;
+
+! Combine the second-to-last and last dimensions of each tensor for stacking
+:: reshape-for-vstack ( seq -- seq )
+    seq first dims 2 - :> vdim
+    seq [
+        dup shape>> vdim cut product 1array append >>shape
+    ] map! ;
+
+
+PRIVATE>
+
+! Concatenation operations
+! Concatenate across the last dimension
+: t-concat ( seq -- tensor )
+    check-concat-shape
+    ! Compute the final shape
+    [
+        ! Compute the first dimension
+        [ 0 [ shape>> first + ] reduce 1array ]
+        ! Compute the other dimensions
+        [ first shape>> rest ] bi  append
+    ]
+    ! Concatenate all of the float-arrays
+    [ [ vec>> ] map concat ] bi <tensor> ;
+
+: stack ( seq -- tensor )
+    check-stack-shape
+    ! Compute the new shape
+    [ [ length 1array ] [ first shape>> ] bi append ]
+    ! Concatenate all of the tensors
+    [ [ vec>> ] map concat ] bi <tensor> ;
+
+: hstack ( seq -- tensor )
+    ! Check shape and convert everything to tensors
+    check-hstack-shape hstack-unsafe ;
+
+: vstack ( seq -- tensor )
+    ! Check shape and convert everything to tensors
+    check-vstack-shape
+    ! Find the final shape
+    [ final-vstack-shape ]
+    ! Reshape each of the tensors and stack
+    [ reshape-for-vstack hstack-unsafe ] bi
+    ! Finally reshape and return
+    swap >>shape ;
+
 <PRIVATE
 
 ! Check that the tensor has an acceptable shape for matrix multiplication
@@ -147,25 +470,101 @@ METHOD: t% { number tensor } [ >float ] dip [ mod ] with t-uop ;
     ! Take a slice
     rot <slice> ;
 
+! much quicker transpose for 2d tensors
+TYPED:: 2d-transpose ( tensor: tensor -- tensor': tensor )
+    tensor shape>> :> old-shape
+    tensor vec>> :> vec
+    old-shape first2 :> ( s1 s2 )
+    ! loop through new tensor
+    old-shape reverse dup product <iota> [
+        ! find y*b val in original tensor
+        s1 /mod s2 *
+        ! find x val in original tensor
+        [ s2 /mod ] dip + nip
+        ! get that index in original tensor
+        vec nth-unsafe
+    ] float-array{ } map-as <tensor> ;
+
 ! Perform matrix multiplication muliplying an
 ! mxn matrix with a nxp matrix
-TYPED:: 2d-matmul ( vec1: float-array start1: fixnum
-                    vec2: float-array start2: fixnum
-                    res: float-array start3: fixnum
+TYPED:: 2d-matmul ( vec1: float-array vec2: float-array res: float-array
                     m: fixnum n: fixnum p: fixnum -- )
     ! For each element in the range, we want to compute the dot product of the
     ! corresponding row and column
+    ! Transpose vec2 so that we are doing row * row (as opposed to row * col)
+    { n p } vec2 <tensor> 2d-transpose vec>> :> vec2
+
     m [ :> i
+        i n * :> in
+        i p * :> ip
+        vec1 in n make-subseq
         p [ :> j
-            0.0 ! This is the sum
-            n [ :> k
-                ! Add to the sum
-                i n * k + start1 + vec1 nth-unsafe
-                k p * j + start2 + vec2 nth-unsafe
-                * +
-            ] each-integer
-            i p * j + start3 + res set-nth-unsafe
+            dup
+            vec2 j n * n make-subseq
+            0.0 [ * + ] 2reduce
+            ip j + res set-nth-unsafe
         ] each-integer
+        drop
+    ] each-integer ;
+
+! Perform matrix multiplication muliplying an
+! mxn matrix with a nxp matrix
+TYPED:: 2d-matmul-mixed ( vec1: float-array vec2: float-array res: float-array
+                    m: fixnum n: fixnum p: fixnum start: fixnum -- )
+    ! For each element in the range, we want to compute the dot product of the
+    ! corresponding row and column
+    ! Transpose vec2 so that we are doing row * row (as opposed to row * col)
+    { n p } vec2 <tensor> 2d-transpose vec>> :> vec2
+
+    ! Compute the location in the float-array each 2D matrix will start at
+    start m n * * :> start1
+    start n p * * :> start2
+
+    m [ :> i
+        i n * :> in
+        4 4 in start1 + 4 mod - swap mod :> in4m
+        i p * :> ip
+        vec1 in n make-subseq :> sub1
+        sub1 in4m <simd-slice> :> slice1
+        p [ :> j
+            j n * :> jn
+            4 4 jn 4 mod - swap mod :> jn4m
+            vec2 jn n make-subseq
+            in4m jn4m = [
+                jn4m <simd-slice> slice1 swap
+                2dup [ first-slice>> ] bi@ 0.0 [ * + ] 2reduce
+                [ 2dup [ simd-slice>> ] bi@ ] dip [ vdot + ] 2reduce
+                [ [ end-slice>> ] bi@ ] dip [ * + ] 2reduce
+            ] [
+                sub1 swap
+                0.0 [ * + ] 2reduce
+            ] if
+            ip j + res set-nth-unsafe
+        ] each-integer
+    ] each-integer ;
+
+! ! Perform matrix multiplication muliplying an
+! mxn matrix with a nxp matrix
+! Should only be called when n is a multiple of 4
+TYPED:: 2d-matmul-simd ( vec1: float-array vec2: float-array
+                             res: float-array
+                             m: fixnum n: fixnum p: fixnum -- )
+    ! For each element in the range, we want to compute the dot product of the
+    ! corresponding row and column
+    ! Transpose vec2 so that we are doing row * row (as opposed to row * col)
+    { n p } vec2 <tensor> 2d-transpose vec>> :> vec2
+
+    m [ :> i
+        i n * :> in
+        i p * :> ip
+        vec1 in n make-subseq float-4 cast-array
+        p [ :> j
+            dup
+            vec2 j n * n make-subseq float-4 cast-array
+            0.0 [ vdot + ] 2reduce
+            ip j + res set-nth-unsafe
+        ] each-integer
+        drop
     ] each-integer ;
 
 PRIVATE>
@@ -187,47 +586,43 @@ TYPED:: matmul ( tensor1: tensor tensor2: tensor -- tensor3: tensor )
     top-shape { m p } append
 
     ! Now create the new float array to store the underlying result
-    dup product c:float (c-array) :> vec3
+    dup product (float-array) :> vec3
 
     ! Now update the tensor3 to contain the multiplied matricies
     top-prod [
         :> i
-        ! Compute vec1 and start1
-        tensor1 vec>> m n * i *
+
+        ! Compute vec1 using direct C arrays
+        tensor1 vec>> m n * i * m n * make-subseq
+
         ! Compute vec2 and start2
-        tensor2 vec>> n p * i *
+        tensor2 vec>> n p * i * n p * make-subseq
+
         ! Compute the result
-        vec3 m p * i *
+        vec3 m p * i * m p * make-subseq
         ! Push m, n, and p and multiply the arrays
-        m n p 2d-matmul
+        m n p
+        { { [ n 4 mod 0 = ] [ 2d-matmul-simd ] }
+          { [ n 4 < ] [ 2d-matmul ] }
+          [ i 2d-matmul-mixed ]
+        } cond
+
     ] each-integer
     vec3 <tensor> ;
 
-
-<PRIVATE
-! helper for transpose: turns a shape into a list of things
-! by which to multiply indices to get a full index
-: ind-mults ( shape -- seq )
-    <reversed> 1 swap [ swap [ * ] keep ] map nip ;
-
-! helper for transpose: given shape, flat index, & mults for
-! the shape, gives nd index
-: transpose-index ( i shape -- seq )
-    <reversed> [ /mod ] map reverse nip ;
-PRIVATE>
-
 ! Transpose an n-dimensional tensor by flipping the axes
 TYPED:: transpose ( tensor: tensor -- tensor': tensor )
-    tensor shape>> :> old-shape
-    tensor vec>> :> vec
-    old-shape reverse :> new-shape
-    ! check that the size is fine
-    new-shape product vec length assert=
-    old-shape ind-mults reverse :> mults
-    ! loop through new tensor
-    new-shape dup product <iota> [
-        ! find index in original tensor
-        old-shape mults [ [ /mod ] dip * ] 2map-sum nip
-        ! get that index in original tensor
-        vec nth-unsafe
-    ] float-array{ } map-as <tensor> ;
+    tensor shape>> length 2 =
+    [ tensor 2d-transpose ]
+    [ tensor shape>> :> old-shape
+        tensor vec>> :> vec
+        old-shape reverse :> new-shape
+        old-shape ind-mults :> mults
+        ! loop through new tensor
+        new-shape dup product <iota> [
+            ! find index in original tensor
+            old-shape mults [ [ /mod ] dip * ] 2map-sum nip
+            ! get that index in original tensor
+            vec nth-unsafe
+        ] float-array{ } map-as <tensor>
+    ] if ;