diff --git a/06_gpu_and_ml/protein_fold/protein_fold.py b/06_gpu_and_ml/protein_fold/protein_fold.py
index ea478ba3c..02b4a366b 100644
--- a/06_gpu_and_ml/protein_fold/protein_fold.py
+++ b/06_gpu_and_ml/protein_fold/protein_fold.py
@@ -54,18 +54,16 @@
 
 gpu = "A10G"
 
-# ### Create a Volume to store weights and pdb data
+# ### Create a Volume to store cached pdb data
 
-# To minimize cold start times we'll store the model weights on a modal
-# [Volume](https://modal.com/docs/guide/volumes) so they can be quickly loaded
-# onto the GPU. We'll also store pdb data on the volume to minimize hitting
-# the RCSB server for the same data multiple times.
+# To minimize htting the RCSB server for pdb data we'll cache pdb data on a modal
+# [Volume](https://modal.com/docs/guide/volumes) so we never have to read the
+# same data twice.
 
 
 volume = modal.Volume.from_name("example-protein-fold", create_if_missing=True)
 VOLUME_PATH = Path("/vol/data")
 PDBS_PATH = VOLUME_PATH / "pdbs"
-HTMLS_PATH = VOLUME_PATH / "htmls"
 
 
 # ### Define dependencies in container images
@@ -144,7 +142,6 @@ class Model:
     def setup_model(self):
         self.model: ESM3InferenceClient = ESM3.from_pretrained(
             "esm3_sm_open_v1"
-            # How to store on volume FIXME
         )
 
     @modal.build()
@@ -165,8 +162,6 @@ def enter(self):
 
     @modal.method()
     def inference(self, sequence: str) -> bool:
-        start_time = time.monotonic()  # TODO Remove
-
         num_steps = min(len(sequence), self.max_steps)
         structure_generation_config = GenerationConfig(
             track="structure",
@@ -177,9 +172,6 @@ def inference(self, sequence: str) -> bool:
             ESMProtein(sequence=sequence), structure_generation_config
         )
 
-        latency_s = time.monotonic() - start_time
-        L.info(f"Inference latency: {latency_s:.2f} seconds.")
-
         L.info("Checking for errors...")
         if hasattr(esm_protein, "error_msg"):
             raise ValueError(esm_protein.error_msg)
@@ -189,7 +181,7 @@ def inference(self, sequence: str) -> bool:
         return esm_protein
 
 
-# ### Serving a Gradio UI `web_endpoint` with an `asgi_app`
+# ### Serving a Gradio UI with an `asgi_app`
 
 # The `ModelInference` class above is available for use
 # from any other Python environment with the right Modal credentials
@@ -198,7 +190,10 @@ def inference(self, sequence: str) -> bool:
 # But we can also expose it via a web app using the `@asgi_app` decorator. Here
 # we will specifically create a Gradio web app that allows us to visualize
 # the 3D structure output of the ESM3 model as well as any known structures from
-# the RCSB Protein Data Bank.
+# the RCSB Protein Data Bank. In addition, to understand the confidence level
+# of the ESM3 output, we'll include a visualization of the [pLDDT](https://www.ebi.ac.uk/training/online/courses/alphafold/inputs-and-outputs/evaluating-alphafolds-predicted-structures-using-confidence-scores/plddt-understanding-local-confidence/) (predicted Local Distance Difference Test) scores
+# for each residue (amino acid) in the folded protein structure.
+#
 
 # You should see the URL for this UI in the output of `modal deploy`
 # or on your [Modal app dashboard](https://modal.com/apps) for this app.
@@ -231,7 +226,6 @@ def extract_data(esm_protein):
     def run_esm(sequence, maybe_stale_pdb_id):
         L.info("Removing whitespace from text input")
         sequence = sequence.strip()
-        maybe_stale_pdb_id = maybe_stale_pdb_id.strip()
 
         L.info("Running ESM")
         esm_protein: ESMProtein = Model().inference.remote(sequence)
@@ -247,11 +241,10 @@ def run_esm(sequence, maybe_stale_pdb_id):
             width, height, esm_protein.to_pdb_string(), residue_pLDDTs
         )
 
+        maybe_stale_pdb_id = maybe_stale_pdb_id.strip()
         pdb_sequence = get_sequence(maybe_stale_pdb_id)
         if pdb_sequence == sequence:
             pdb_id = maybe_stale_pdb_id
-            # if pdb_id.find("_") != -1: # "1CRN_A" -> "1CRN"
-            # pdb_id = pdb_id[:pdb_id.index("_")] # WHy was this here.
             L.info(f"Constructing HTML for RCSB entry for {pdb_id}")
             pdb_string, residue_id_to_sse = extract_pdb_and_residues(pdb_id)
             rcsb_sse_html = (
@@ -386,7 +379,7 @@ def extract_pdb_id(example_idx):
                 )
                 htmls.append(gr.HTML())
 
-            # Column 3: Database showing Crystalized form of Protein if avail.
+            # Column 3: Crystalized form of Protein if available.
             with gr.Column():
                 gr.Markdown("## Crystalized Structure from RCSB's PDB")
                 gr.Image(  # output image component
@@ -413,7 +406,6 @@ def extract_pdb_id(example_idx):
         path="/",
     )
 
-
 @app.local_entrypoint()
 def main():
     sequence = (
@@ -429,11 +421,15 @@ def main():
 
 # The remainder of this code is boilerplate.
 
+
 # ### Extracting Sequences and Residues from PDBs
 
 # A fair amount of code is required to extract sequences and residue
 # information from pdb strings, we build several helper functions for it below.
 
+# There is more complex code for running py3Dmol which you can find in
+# the `py3DmolWrapper.py` file.
+
 
 def fetch_pdb_if_necessary(pdb_id, pdb_type):
     """Fetch PDB from RCSB if not already cached."""
@@ -514,5 +510,4 @@ def postprocess_html(html):
         f"""allow="midi; display-capture;" frameborder="0" """
         f"""srcdoc='{html_wrapped}'></iframe>"""
     )
-    return iframe_html  # FIXME?
-    # return html_wrapped
+    return iframe_html