diff --git a/config.toml b/config.toml
index 422fdbd87..149fb8e79 100644
--- a/config.toml
+++ b/config.toml
@@ -1,47 +1,33 @@
 batch_size = 64
-steps = 500000
-print_every = 10000
+steps = 1000000
+print_every = 50000
 seed = 0
 cnn_storage = "./res/cnn.eqx"
 
 [[sae]]
 layer = 6
-hidden_size = 1000
-input_size = 64
-learning_rate = 0.1
-
-[[sae]]
-layer = 6
-hidden_size = 1000
-input_size = 64
-learning_rate = 3e-2
-
-[[sae]]
-layer = 6
-hidden_size = 1000
-input_size = 64
-learning_rate = 1e-2
-
-[[sae]]
-layer = 6
-hidden_size = 1000
-input_size = 64
-learning_rate = 3e-3
-
-[[sae]]
-layer = 6
-hidden_size = 1000
+hidden_size = 300
 input_size = 64
 learning_rate = 1e-3
+l1 = 3e-4 # from Neel Nanda's sae git
 
 [[sae]]
 layer = 6
-hidden_size = 1000
+hidden_size = 300
 input_size = 64
 learning_rate = 3e-4
+l1 = 3e-4
 
 [[sae]]
 layer = 6
-hidden_size = 1000
+hidden_size = 300
 input_size = 64
 learning_rate = 1e-4
+l1 = 3e-4
+
+[[sae]]
+layer = 6
+hidden_size = 300
+input_size = 64
+learning_rate = 3e-5
+l1 = 3e-4
diff --git a/src/__main__.py b/src/__main__.py
index e9772f5fc..97ec07146 100644
--- a/src/__main__.py
+++ b/src/__main__.py
@@ -10,6 +10,7 @@ import jax.numpy as jnp
 import jo3mnist
 from jo3util import eqx as jo3eqx
 from jo3util.root import run_dir
+from torch.utils.tensorboard import SummaryWriter
 from tqdm import tqdm
 
 from .cnn import CNN, train_cnn
@@ -49,6 +50,7 @@ for sae_hyperparams in O.sae:
         sae_hyperparams.learning_rate,
         O.steps,
         O.print_every,
+        SummaryWriter(sae_dir / "log")
     )
 
     sae_dir.mkdir()
diff --git a/src/sae.py b/src/sae.py
index 7afc9190e..2b8585fa5 100644
--- a/src/sae.py
+++ b/src/sae.py
@@ -5,6 +5,7 @@
 
 """
 
+from datetime import datetime
 from typing import Callable, List
 
 import equinox as eqx
@@ -15,6 +16,7 @@ import optax
 from jaxtyping import Array, Float, Int, PyTree
 from jo3util.eqx import insert_after, sow
 from torch.utils.data import DataLoader
+from torch.utils.tensorboard import SummaryWriter
 
 
 class SAE(eqx.Module):
@@ -87,10 +89,10 @@ def make_step(
     optim,
     opt_state: PyTree,
     x: Float[Array, "batch 1 28 28"],
-    y: Int[Array, " batch"],
+    λ: float,
 ):
     activ = jax.vmap(model)(x)[0]
-    loss_value, grads = SAE.loss(sae, activ[0], 1)
+    loss_value, grads = SAE.loss(sae, activ[0], λ)
     updates, opt_state = optim.update(grads, opt_state, sae)
     sae = eqx.apply_updates(sae, updates)
     return sae, opt_state, loss_value
@@ -98,39 +100,43 @@ def make_step(
 
 def train_loop(
     sae: SAE,
-    model: eqx.Module,
+    cnn: eqx.Module,
     sae_pos: Callable,
     trainloader: DataLoader,
     testloader: DataLoader,
     optim: optax.GradientTransformation,
     steps: int,
     print_every: int,
+    tensorboard,
+    λ,
 ) -> eqx.Module:
     opt_state = optim.init(eqx.filter(sae, eqx.is_array))
 
-    model = sow(sae_pos, model)
+    cnn = sow(sae_pos, cnn)
 
-    print(f"test_accuracy={evaluate(model, testloader).item()}")
+    print(f"test_accuracy={evaluate(cnn, testloader).item()}")
 
-    
     # Loop over our training dataset as many times as we need.
     def infinite_trainloader():
         while True:
             yield from trainloader
 
-    for step, (x, y) in zip(range(steps), infinite_trainloader()):
+    for step, (x, _) in zip(range(steps), infinite_trainloader()):
         # PyTorch dataloaders give PyTorch tensors by default,
         # so convert them to NumPy arrays.
-        x = x.numpy()
-        y = y.numpy()
-        sae, opt_state, train_loss = make_step(sae, model, optim, opt_state, x, y)
+        sae, opt_state, train_loss = make_step(
+            sae, cnn, optim, opt_state, x.numpy(), λ
+        )
+        tensorboard.add_scalar("loss", train_loss.item(), step)
         if (step % print_every) == 0 or (step == steps - 1):
-            model_with_sae = insert_after(sae_pos, model, sae)
-            test_accuracy = evaluate(model_with_sae, testloader)
+            cnn_with_sae = insert_after(sae_pos, cnn, sae)
+            test_accuracy = evaluate(cnn_with_sae, testloader)
             print(
+                datetime.now().strftime("%H:%M"),
                 f"{step=}, train_loss={train_loss.item()}, "
-                f"test_accuracy={test_accuracy.item()}"
+                f"test_accuracy={test_accuracy.item()}",
             )
+            tensorboard.add_scalar("accu", test_accuracy.item(), step)
     return sae
 
 
@@ -144,6 +150,8 @@ def train_sae(
     learning_rate,
     steps,
     print_every,
+    tensorboard,
+    λ,
 ):
     trainloader, testloader = jo3mnist.load(batch_size=batch_size)
     sae = SAE(activ_size, hidden_size, key)
@@ -157,4 +165,6 @@ def train_sae(
         optim,
         steps,
         print_every,
+        tensorboard,
+        λ
     )