diff --git a/bridge.pyx b/bridge.pyx
index 84087c0..d2025ae 100644
--- a/bridge.pyx
+++ b/bridge.pyx
@@ -75,9 +75,10 @@ cdef public void f_idx_list_to_print(float* f_idxs, size_t num):
 
 
 cdef public void c_onehot(float* y, float* idxs, size_t n_idx):
-    oh = nn.onehot(np.asarray(<float[:n_idx]>idxs))
+    oh = nn.onehot(np.asarray(<float[:n_idx]>idxs), nc=len(nn.vocab))
     ensure_contiguous(oh)
     memcpy(y, PyArray_DATA(oh), oh.size * sizeof(float))
+    # eprint(np.argmax(oh, axis=1))
 
 
 cdef public void c_slices(float* X, float* idxs, size_t bs, size_t win):
@@ -85,7 +86,8 @@ cdef public void c_slices(float* X, float* idxs, size_t bs, size_t win):
     idxs_np = np.asarray(<float[:bs + 2*win]>idxs)
     for r in range(bs):
         X_np[r, :win] = idxs_np[r:r+win]
-        X_np[r, win+1:] = idxs_np[r+win+1:r+2*win+1]
+        X_np[r, win:] = idxs_np[r+win+1:r+win+1+win]
+    # eprint(X_np)
 
 
 cdef public void debug_print(object o):
@@ -93,7 +95,7 @@ cdef public void debug_print(object o):
 
 
 cdef public object create_network(int win, int embed):
-    return nn.create_cbow_network(win, len(nn.vocab), embed)
+    return nn.create_cbow_network(win, embed)
 
 
 cdef public void set_net_weights(object net, WeightList* wl):
@@ -214,7 +216,7 @@ def inspect_array(a):
 
 
 def ensure_contiguous(a):
-    assert a.flats['C_CONTIGUOUS']
+    assert a.flags['C_CONTIGUOUS']
 
 
 def eprint(*args, **kwargs):
diff --git a/library.py b/library.py
index a573384..25561a8 100644
--- a/library.py
+++ b/library.py
@@ -34,15 +34,17 @@ def create_mnist_network():
     return model
 
 
-def create_cbow_network(win, vocsize, embed):
+def create_cbow_network(win, embed):
     ctxt = tf.keras.layers.Input(shape=[win])
-    ed = tf.keras.layers.Embedding(vocsize, embed, input_length=win)(ctxt)
-    avgd = tf.keras.layers.Lambda(lambda x: tf.reduce_mean(x, axis=1))(ed)
-    mod = tf.keras.Model(inputs=ctxt, outputs=avgd)
+    ed = tf.keras.layers.Embedding(len(vocab), embed, input_length=win)(ctxt)
+    cbow = tf.keras.layers.Lambda(lambda x: tf.reduce_mean(x, axis=1))(ed)
+    blowup = tf.keras.layers.Dense(len(vocab), activation='softmax')(cbow)
+    mod = tf.keras.Model(inputs=ctxt, outputs=blowup)
     mod.compile(
         optimizer='sgd',
         loss='categorical_crossentropy',
     )
+    print(mod, flush=True)
     return mod
 
 
diff --git a/main.c b/main.c
index 34523ef..98e30bb 100644
--- a/main.c
+++ b/main.c
@@ -15,8 +15,8 @@
 #define TAG_SWORD 7
 #define TAG_IWORD 8
 
-#define COMM 100
-#define ITER 20
+#define COMM 1
+#define ITER 1
 #define BS 10
 #define EMB 20
 #define WIN 2
@@ -67,7 +67,8 @@ size_t number_of(Role what) {
                 - number_of(BATCHER)
                 - number_of(MASTER);
         case MASTER:
-            return 1;
+            return 0;
+#warning "set to real number of masters!"
     }
 }
 
@@ -167,7 +168,7 @@ void filterer() {
 void batcher() {
     // Reads some data and converts it to a float array
     // INFO_PRINTF("Starting batcher %d\n", getpid());
-    // int s = 0;
+    int s = 0;
     const size_t n_words = BS + WIN + WIN;
     float* f_widx = malloc(n_words * sizeof(float));
     long l_wid = 0;
@@ -187,11 +188,11 @@ void batcher() {
             // INFO_PRINTF("%5.0f ", f_widx[i]);
         // }
         // INFO_PRINTLN("");
-        // MPI_Recv(&s, 1, MPI_INT, MPI_ANY_SOURCE, TAG_READY, MPI_COMM_WORLD,
-                // MPI_STATUS_IGNORE);
-        // if (s != -1) {
-            // MPI_Send(f_widx, n_words, MPI_FLOAT, s, TAG_BATCH, MPI_COMM_WORLD);
-        // }
+        MPI_Recv(&s, 1, MPI_INT, MPI_ANY_SOURCE, TAG_READY, MPI_COMM_WORLD,
+                MPI_STATUS_IGNORE);
+        if (s != -1) {
+            MPI_Send(f_widx, n_words, MPI_FLOAT, s, TAG_BATCH, MPI_COMM_WORLD);
+        }
     }
     free(f_widx);
 }
@@ -253,10 +254,10 @@ void slave_node() {
     float* f_widx = malloc(n_words * sizeof(float));
 
     for in_range(i, COMM) {
-        MPI_Send(&me, 1, MPI_INT, mpi_id_from_role_id(MASTER, 0),
-                TAG_READY, MPI_COMM_WORLD);
-        recv_weights(&wl, mpi_id_from_role_id(MASTER, 0), TAG_WEIGH);
-        set_net_weights(net, &wl);
+        // MPI_Send(&me, 1, MPI_INT, mpi_id_from_role_id(MASTER, 0),
+                // TAG_READY, MPI_COMM_WORLD);
+        // recv_weights(&wl, mpi_id_from_role_id(MASTER, 0), TAG_WEIGH);
+        // set_net_weights(net, &wl);
         for in_range(k, ITER) {
             MPI_Send(&me, 1, MPI_INT, mpi_id_from_role_id(BATCHER, 0),
                     TAG_READY, MPI_COMM_WORLD);
@@ -267,9 +268,9 @@ void slave_node() {
             c_onehot(y, f_widx + WIN, BS);
             step_net(net, X, y, BS);
         }
-        printf("%d net: %f\n", my_mpi_id(), eval_net(net));
+        // printf("%d net: %f\n", my_mpi_id(), eval_net(net));
         update_weightlist(&wl, net);
-        send_weights(&wl, mpi_id_from_role_id(MASTER, 0), TAG_WEIGH);
+        // send_weights(&wl, mpi_id_from_role_id(MASTER, 0), TAG_WEIGH);
     }
     Py_DECREF(net);
     free_weightlist(&wl);
@@ -345,6 +346,9 @@ int main (int argc, const char **argv) {
         case BATCHER:
             batcher();
             break;
+        case SLAVE:
+            slave_node();
+            break;
         default:
             INFO_PRINTLN("DYING HORRIBLY!");
         // case SLAVE: slave_node(); break;