diff --git a/README.md b/README.md
index 44ceed8..62e6e25 100644
--- a/README.md
+++ b/README.md
@@ -84,6 +84,26 @@ This gives 10 outputs to the neural network. The test program will output the
 accuracy for each digit. Expect above 99% accuracy for the correct digit, and
 less that 1% accuracy for the other digits.
 
+# Tips
+
+* Tinn will never use more than the C standard library.
+
+* Tinn is great for embedded systems. Train a model on your powerful desktop and load
+it onto a microcontroller and use the analog to digital converter to predict real time events.
+
+* The Tinn source code will always be less than 200 lines. Functions externed in the Tinn header
+are protected with the _xt_ namespace standing for _externed tinn_.
+
+* Tinn can easily be multi-threaded with a bit of ingenuity but the master branch will remain
+single threaded to aid development for embedded systems.
+
+* Tinn does not seed the random number generator. Do not forget to do so yourself.
+
+* Always shuffle your input data. Shuffle again after every training iteration.
+
+* Get greater training accuracy by annealing your learning rate. For instance, multiply
+your learning rate by 0.99 every training iteration. This will zero in on a good learning minima.
+
 # Disclaimer
 
 Tinn is not a fully featured neural network C library like Kann, or Genann:
diff --git a/Tinn.c b/Tinn.c
index 3e71b41..6264c2b 100644
--- a/Tinn.c
+++ b/Tinn.c
@@ -121,12 +121,15 @@ static void* ecalloc(const size_t nmemb, const size_t size)
     return mem;
 }
 
+// Returns an output prediction given an input.
 float* xtpredict(const Tinn t, const float* const in)
 {
     fprop(t, in);
     return t.o;
 }
 
+// Trains a tinn with an input and target output with a learning rate.
+// Returns error rate of the neural network.
 float xttrain(const Tinn t, const float* const in, const float* const tg, float rate)
 {
     fprop(t, in);
@@ -134,6 +137,9 @@ float xttrain(const Tinn t, const float* const in, const float* const tg, float
     return toterr(tg, t.o, t.nops);
 }
 
+// Builds a new tinn object given number of inputs (nips),
+// number of hidden neurons for the hidden layer (nhid),
+// and number of outputs (nops).
 Tinn xtbuild(const int nips, const int nhid, const int nops)
 {
     Tinn t;
@@ -152,6 +158,7 @@ Tinn xtbuild(const int nips, const int nhid, const int nops)
     return t;
 }
 
+// Saves the tinn to disk.
 void xtsave(const Tinn t, const char* const path)
 {
     FILE* const file = efopen(path, "w");
@@ -163,6 +170,7 @@ void xtsave(const Tinn t, const char* const path)
     fclose(file);
 }
 
+// Loads a new tinn from disk.
 Tinn xtload(const char* const path)
 {
     FILE* const file = efopen(path, "r");
@@ -180,6 +188,7 @@ Tinn xtload(const char* const path)
     return t;
 }
 
+// Frees a tinn from the heap.
 void xtfree(const Tinn t)
 {
     free(t.w);
diff --git a/Tinn.h b/Tinn.h
index ebea924..af0f927 100644
--- a/Tinn.h
+++ b/Tinn.h
@@ -2,38 +2,39 @@
 
 typedef struct
 {
-    float* w; // All the weights.
-    float* x; // Hidden to output layer weights.
-    float* b; // Biases.
-    float* h; // Hidden layer.
-    float* o; // Output layer.
+    // All the weights.
+    float* w;
+    // Hidden to output layer weights.
+    float* x;
+    // Biases.
+    float* b;
+    // Hidden layer.
+    float* h;
+    // Output layer.
+    float* o;
 
-    int nb; // Number of biases - always two - Tinn only supports a single hidden layer.
-    int nw; // Number of weights.
+    // Number of biases - always two - Tinn only supports a single hidden layer.
+    int nb;
+    // Number of weights.
+    int nw;
 
-    int nips; // Number of inputs.
-    int nhid; // Number of hidden neurons.
-    int nops; // Number of outputs.
+    // Number of inputs.
+    int nips;
+    // Number of hidden neurons.
+    int nhid;
+    // Number of outputs.
+    int nops;
 }
 Tinn;
 
-// Trains a tinn with an input and target output with a learning rate.
-// Returns error rate of the neural network.
-float xttrain(Tinn, const float* in, const float* tg, float rate);
-
-// Builds a new tinn object given number of inputs (nips),
-// number of hidden neurons for the hidden layer (nhid),
-// and number of outputs (nops).
-Tinn xtbuild(int nips, int nhid, int nops);
-
-// Returns an output prediction given an input.
 float* xtpredict(Tinn, const float* in);
 
-// Saves the tinn to disk.
+float xttrain(Tinn, const float* in, const float* tg, float rate);
+
+Tinn xtbuild(int nips, int nhid, int nops);
+
 void xtsave(Tinn, const char* path);
 
-// Loads a new tinn from disk.
 Tinn xtload(const char* path);
 
-// Frees a tinn from the heap.
 void xtfree(Tinn);