diff --git a/.github/workflows/publish.yml b/.github/workflows/publish.yml
index f48ffa6..7649a89 100644
--- a/.github/workflows/publish.yml
+++ b/.github/workflows/publish.yml
@@ -72,6 +72,25 @@ jobs:
           tags: ${{ steps.s3fs-meta.outputs.tags }}
           labels: ${{ steps.s3fs-meta.outputs.labels }}
 
+      - name: Extract tensorrt metadata
+        id: tensorrt-meta
+        uses: docker/metadata-action@v5
+        with:
+          images: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}/tensorrt
+          tags: |
+            type=ref,event=tag
+            type=raw,value=latest,enable=${{ github.ref_type == 'tag' || github.ref == 'refs/heads/main' }}
+            type=sha,prefix=
+
+      - name: Build and push tensorrt
+        uses: docker/build-push-action@v5
+        with:
+          context: examples/tensorrt
+          platforms: linux/amd64
+          push: true
+          tags: ${{ steps.tensorrt-meta.outputs.tags }}
+          labels: ${{ steps.tensorrt-meta.outputs.labels }}
+
       - name: Notify release result
         if: always() && startsWith(github.ref, 'refs/tags/v')
         uses: marimo-team/internal-gh-actions/release-notification@main
diff --git a/examples/jax/jax_example.py b/examples/jax/jax_example.py
new file mode 100644
index 0000000..fbbfb66
--- /dev/null
+++ b/examples/jax/jax_example.py
@@ -0,0 +1,131 @@
+# /// script
+# requires-python = ">=3.11"
+# dependencies = [
+#     "flax>=0.10.0",
+#     "jax[cuda12]>=0.4.0",
+#     "marimo>=0.21.1",
+#     "matplotlib==3.10.8",
+#     "mofresh",
+#     "optax>=0.2.0",
+#     "polars>=1.0",
+# ]
+#
+# [tool.marimo.runtime]
+# auto_instantiate = false
+#
+# [tool.marimo.k8s]
+# storage = "5Gi"
+#
+# [tool.marimo.k8s.resources]
+# limits."nvidia.com/gpu" = 1
+#
+# [tool.marimo.k8s.nodeSelector]
+# "gpu.nvidia.com/class" = "L40"
+# ///
+
+import marimo
+
+__generated_with = "0.21.1"
+app = marimo.App(width="columns", auto_download=["html"])
+
+with app.setup(hide_code=True):
+    import marimo as mo
+    import jax
+    import jax.numpy as jnp
+    import flax.linen as nn
+    import optax
+    import polars as pl
+    import numpy as np
+
+
+@app.cell(hide_code=True)
+def _():
+    mo.md(r"""
+    marimo's multi-column layout offers a unique live preview as you work.
+    """)
+    return
+
+
+@app.cell
+def _():
+    import matplotlib.pylab as plt
+    from mofresh import refresh_matplotlib, ImageRefreshWidget
+
+    widget = ImageRefreshWidget(src="")
+
+    @refresh_matplotlib
+    def losschart(data):
+        df = pl.DataFrame(data)
+        plt.plot(df["epoch"], df["loss_train"])
+
+    widget
+    return losschart, widget
+
+
+@app.cell
+def _():
+    return
+
+
+@app.cell(column=1, hide_code=True)
+def _():
+    mo.md(r"""
+    Example network and training with `jax`
+    """)
+    return
+
+
+@app.cell
+def _(losschart, widget):
+    datalogs = []
+
+    def train_identity_network(data, epochs=5000, learning_rate=0.025, iteration=0):
+        rng = jax.random.PRNGKey(42)
+        x = jnp.array(data, dtype=jnp.float32)
+
+        model = IdentityNetwork()
+        params = model.init(rng, x)
+
+        optimizer = optax.sgd(learning_rate)
+        opt_state = optimizer.init(params)
+
+        def loss_fn(p, batch):
+            return jnp.mean((model.apply(p, batch) - batch) ** 2)
+
+        for epoch in range(epochs):
+            loss, grads = jax.value_and_grad(loss_fn)(params, x)
+            updates, opt_state = optimizer.update(grads, opt_state)
+            params = optax.apply_updates(params, updates)
+
+            if (epoch + 1) % 25 == 0:
+                test_data = jnp.array(np.random.rand(n, k), dtype=jnp.float32)
+                test_loss = loss_fn(params, test_data)
+                datalogs.append({
+                    "epoch": epoch + iteration * epochs,
+                    "loss_train": float(loss),
+                    "loss_test": float(test_loss),
+                })
+                widget.src = losschart(datalogs)
+
+        return params
+
+    for i in range(1):
+        n = 10000
+        k = 10
+        random_data = np.random.rand(n, k)
+        trained_params = train_identity_network(random_data, epochs=5000, iteration=i)
+    return
+
+
+@app.class_definition
+class IdentityNetwork(nn.Module):
+    @nn.compact
+    def __call__(self, x):
+        features = x.shape[-1]
+        x = nn.relu(nn.Dense(features)(x))
+        x = nn.relu(nn.Dense(features)(x))
+        return x
+
+
+if __name__ == "__main__":
+    app.run()
diff --git a/examples/tensorrt/Dockerfile b/examples/tensorrt/Dockerfile
new file mode 100644
index 0000000..0dec7f0
--- /dev/null
+++ b/examples/tensorrt/Dockerfile
@@ -0,0 +1,21 @@
+FROM nvcr.io/nvidia/tritonserver:25.01-trtllm-python-py3
+
+# The marimo operator requires:
+# 1. uv  — setup-venv init container runs: uv venv /opt/venv
+# 2. marimo binary in PATH — main container command is: marimo run/edit ...
+RUN pip install uv "marimo>=0.21.1"
+
+# The operator launches the container without running the NVIDIA entrypoint, so
+# env vars it would normally set must be baked in here instead.
+
+# uv is pip-installed to /usr/local/bin; operator hardcodes /usr/bin
+ENV UV="/usr/local/bin/uv"
+
+# TensorRT-LLM is in dist-packages (Ubuntu pip convention); operator venv lands at /opt/venv
+ENV PYTHONPATH="/usr/local/lib/python3.12/dist-packages:/opt/venv/lib/python3.12/site-packages"
+
+# TensorRT/cuBLAS .so files (entrypoint normally adds these to LD_LIBRARY_PATH)
+ENV LD_LIBRARY_PATH="/usr/local/tensorrt/targets/x86_64-linux-gnu/lib:/usr/local/cuda/lib64:/usr/local/cuda/compat/lib:/usr/local/lib/python3.12/dist-packages/tensorrt_llm/libs:/usr/local/lib/python3.12/dist-packages/torch/lib:/usr/local/lib/python3.12/dist-packages/nvidia/cublas/lib:/usr/local/lib/python3.12/dist-packages/nvidia/cuda_runtime/lib"
+
+# Reduce TRT-LLM log noise by default; notebooks can override if needed.
+ENV TRTLLM_LOG_LEVEL="WARNING"
diff --git a/examples/tensorrt/README.md b/examples/tensorrt/README.md
new file mode 100644
index 0000000..a037333
--- /dev/null
+++ b/examples/tensorrt/README.md
@@ -0,0 +1,62 @@
+# TensorRT-LLM Example
+
+Interactive [TensorRT-LLM](https://github.com/NVIDIA/TensorRT-LLM) inference notebook running on the marimo operator. Includes a model picker with several open-weight models and a reactive prompt selector.
+
+Requires a GPU node with ≥ 24 GB VRAM. The included models range from ~3 GB (TinyLlama 1.1B) up to ~16 GB (Mistral 7B / Minitron 8B).
+
+## Files
+
+| File | Purpose |
+|---|---|
+| `tensorrt.py` | marimo notebook — deploy with the `kubectl-marimo` plugin |
+| `tensorrt.yaml` | Plain `MarimoNotebook` manifest — deploy with `kubectl apply` |
+| `Dockerfile` | Thin layer over the NVIDIA NGC TRT-LLM container |
+
+## Deploy with the plugin
+
+```bash
+uv tool install kubectl-marimo
+
+kubectl marimo edit tensorrt.py -n <namespace>
+```
+
+## Deploy with kubectl
+
+```bash
+# Edit nodeSelector if needed, then apply
+kubectl apply -f tensorrt.yaml -n <namespace>
+
+# Get the access token
+kubectl logs -n <namespace> tensorrt -c marimo | grep access_token
+
+# Port-forward to access locally
+kubectl port-forward -n <namespace> svc/tensorrt 2718:2718
+```
+
+## Build the image
+
+The pre-built image is at `ghcr.io/marimo-team/marimo-operator/tensorrt:latest` and is rebuilt on every push to main. To build your own:
+
+```bash
+docker build --platform linux/amd64 -t <your-registry>/tensorrt:latest .
+docker push <your-registry>/tensorrt:latest
+```
+
+If your registry is private, create an image pull secret:
+
+```bash
+kubectl create secret docker-registry registry-secret \
+  --docker-server=<registry> \
+  --docker-username=<user> \
+  --docker-password=<token> \
+  -n <namespace>
+
+kubectl patch serviceaccount default -n <namespace> \
+  -p '{"imagePullSecrets":[{"name":"registry-secret"}]}'
+```
+
+## Further reading
+
+- [TensorRT-LLM documentation](https://nvidia.github.io/TensorRT-LLM/)
+- [FP8 quantization guide](https://nvidia.github.io/TensorRT-LLM/performance/performance-tuning-guide/fp8-quantization.html)
+- [marimo operator](https://github.com/marimo-team/marimo-operator)
diff --git a/examples/tensorrt/tensorrt.py b/examples/tensorrt/tensorrt.py
new file mode 100644
index 0000000..b3a5e8d
--- /dev/null
+++ b/examples/tensorrt/tensorrt.py
@@ -0,0 +1,107 @@
+# /// script
+# dependencies = ["marimo>=0.21.1", "setuptools"]
+#
+# [tool.marimo.k8s]
+# image = "ghcr.io/marimo-team/marimo-operator/tensorrt:latest"
+# storage = "20Gi"
+#
+# [tool.marimo.k8s.resources]
+# limits."nvidia.com/gpu" = 1
+#
+# [tool.marimo.k8s.nodeSelector]
+# "gpu.nvidia.com/class" = "L40"
+#
+# ///
+
+import marimo
+
+__generated_with = "0.21.1"
+app = marimo.App(width="medium")
+
+with app.setup:
+    import gc
+    import io
+    import logging
+
+    import marimo as mo
+    import torch
+    from tensorrt_llm import LLM, SamplingParams
+
+
+@app.cell
+def _():
+    # Collect TRT-LLM / torch log lines emitted during model load so we can
+    # surface them in the notebook rather than losing them to pod stdout.
+    log_stream = io.StringIO()
+    logging.basicConfig(stream=log_stream, level=logging.WARNING)
+
+    models = {
+        "TinyLlama 1.1B (fast)": "TinyLlama/TinyLlama-1.1B-Chat-v1.0",
+        "Phi-3.5-mini 3.8B": "microsoft/Phi-3.5-mini-instruct",
+        "Mistral 7B": "mistralai/Mistral-7B-Instruct-v0.3",
+        "Llama-3.1 8B FP8 (NVIDIA)": "nvidia/Llama-3.1-8B-Instruct-FP8",
+        "Minitron 8B (NVIDIA)": "nvidia/Mistral-NeMo-Minitron-8B-Instruct",
+    }
+
+    prompts = [
+        "Hello, my name is",
+        "The capital of France is",
+        "The future of AI is",
+    ]
+
+    prompt = mo.ui.dropdown(
+        label="Select a Prompt",
+        options=prompts,
+        value=prompts[0],
+    )
+
+    model_picker = mo.ui.dropdown(
+        label="Model",
+        options=models,
+        value="TinyLlama 1.1B (fast)",
+    )
+
+    mo.vstack([
+        mo.md("## TensorRT-LLM Inference Example, select a model to download and run"),
+        model_picker,
+    ])
+    return log_stream, model_picker, models, prompt, prompts
+
+
+@app.cell
+def _(log_stream, model_picker):
+    mo.stop(model_picker.value is None)
+
+    # Free the previous model's GPU memory before loading the next one.
+    # Without this, two models can coexist briefly and OOM a 48 GB L40.
+    gc.collect()
+    torch.cuda.empty_cache()
+
+    # Model can be a HuggingFace model name, a local path, or a quantized
+    # checkpoint such as nvidia/Llama-3.1-8B-Instruct-FP8 on HF.
+    llm = LLM(model=model_picker.value)
+
+    # Show any WARNING+ log lines collected during model load.
+    logs = log_stream.getvalue()
+    mo.accordion({"Model load logs": mo.plain_text(logs) if logs else mo.md("_No warnings._")})
+    return (llm,)
+
+
+@app.cell(hide_code=True)
+def _(llm, prompt):
+    sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
+
+    results = []
+    for output in llm.generate([prompt.value], sampling_params):
+        results.append(
+            mo.md(
+                f"**Prompt:** {output.prompt}  \n"
+                f"**Generated:** {output.outputs[0].text}"
+            )
+        )
+
+    mo.vstack([prompt, mo.vstack(results)])
+
+
+if __name__ == "__main__":
+    app.run()
diff --git a/examples/tensorrt/tensorrt.yaml b/examples/tensorrt/tensorrt.yaml
new file mode 100644
index 0000000..3e0df27
--- /dev/null
+++ b/examples/tensorrt/tensorrt.yaml
@@ -0,0 +1,106 @@
+apiVersion: marimo.io/v1alpha1
+kind: MarimoNotebook
+metadata:
+  name: tensorrt
+spec:
+  image: ghcr.io/marimo-team/marimo-operator/tensorrt:latest
+  mode: edit
+  storage:
+    size: 20Gi
+  resources:
+    limits:
+      nvidia.com/gpu: "1"
+  # Adjust nodeSelector to match the GPU nodes in your cluster.
+  podOverrides:
+    nodeSelector:
+      gpu.nvidia.com/class: L40
+  content: |
+    # /// script
+    # dependencies = ["marimo>=0.21.1", "setuptools"]
+    # ///
+
+    import marimo
+
+    __generated_with = "0.21.1"
+    app = marimo.App(width="medium")
+
+    with app.setup:
+        import gc
+        import io
+        import logging
+
+        import marimo as mo
+        import torch
+        from tensorrt_llm import LLM, SamplingParams
+
+
+    @app.cell
+    def _():
+        log_stream = io.StringIO()
+        logging.basicConfig(stream=log_stream, level=logging.WARNING)
+
+        models = {
+            "TinyLlama 1.1B (fast)": "TinyLlama/TinyLlama-1.1B-Chat-v1.0",
+            "Phi-3.5-mini 3.8B": "microsoft/Phi-3.5-mini-instruct",
+            "Mistral 7B": "mistralai/Mistral-7B-Instruct-v0.3",
+            "Llama-3.1 8B FP8 (NVIDIA)": "nvidia/Llama-3.1-8B-Instruct-FP8",
+            "Minitron 8B (NVIDIA)": "nvidia/Mistral-NeMo-Minitron-8B-Instruct",
+        }
+
+        prompts = [
+            "Hello, my name is",
+            "The capital of France is",
+            "The future of AI is",
+        ]
+
+        prompt = mo.ui.dropdown(
+            label="Select a Prompt",
+            options=prompts,
+            value=prompts[0],
+        )
+
+        model_picker = mo.ui.dropdown(
+            label="Model",
+            options=models,
+            value="TinyLlama 1.1B (fast)",
+        )
+
+        mo.vstack([
+            mo.md("## TensorRT-LLM Inference Example, select a model to download and run"),
+            model_picker,
+        ])
+        return log_stream, model_picker, models, prompt, prompts
+
+
+    @app.cell
+    def _(log_stream, model_picker):
+        mo.stop(model_picker.value is None)
+
+        gc.collect()
+        torch.cuda.empty_cache()
+
+        llm = LLM(model=model_picker.value)
+
+        logs = log_stream.getvalue()
+        mo.accordion({"Model load logs": mo.plain_text(logs) if logs else mo.md("_No warnings._")})
+        return (llm,)
+
+
+    @app.cell(hide_code=True)
+    def _(llm, prompt):
+        sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
+
+        results = []
+        for output in llm.generate([prompt.value], sampling_params):
+            results.append(
+                mo.md(
+                    f"**Prompt:** {output.prompt}  \n"
+                    f"**Generated:** {output.outputs[0].text}"
+                )
+            )
+
+        mo.vstack([prompt, mo.vstack(results)])
+
+
+    if __name__ == "__main__":
+        app.run()
diff --git a/pkg/resources/pod.go b/pkg/resources/pod.go
index 7794fa1..e79eba5 100644
--- a/pkg/resources/pod.go
+++ b/pkg/resources/pod.go
@@ -87,10 +87,12 @@ func BuildPod(notebook *marimov1alpha1.MarimoNotebook) *corev1.Pod {
 				Name:  "copy-content",
 				Image: config.DefaultInitImage,
 				Command: []string{"sh", "-c", fmt.Sprintf(
-					"cp /content/%s %s/%s",
+					"cp /content/%s %s/%s && chmod 666 %s/%s",
 					ContentKey,
 					NotebookDir,
 					contentKey,
+					NotebookDir,
+					contentKey,
 				)},
 				VolumeMounts: []corev1.VolumeMount{
 					{Name: PVCVolumeName, MountPath: NotebookDir},
diff --git a/plugin/kubectl_marimo/formats/python.py b/plugin/kubectl_marimo/formats/python.py
index b7a4ff3..d1aea8b 100644
--- a/plugin/kubectl_marimo/formats/python.py
+++ b/plugin/kubectl_marimo/formats/python.py
@@ -1,6 +1,7 @@
 """Parser for marimo Python notebooks."""
 
 import re
+import tomllib
 from typing import Any
 
 
@@ -17,145 +18,59 @@ def parse_python(content: str) -> tuple[str, dict[str, Any] | None]:
 
 
 def extract_pep723_metadata(content: str) -> dict[str, Any] | None:
-    """Extract PEP 723 inline script metadata.
+    """Extract PEP 723 inline script metadata and marimo k8s config.
 
-    PEP 723 metadata is embedded in a comment block:
+    PEP 723 metadata is embedded in a comment block at the top of the file:
 
-    # /// script
-    # dependencies = ["marimo", "pandas"]
-    # ///
+        # /// script
+        # dependencies = ["marimo", "pandas"]
+        #
+        # [tool.marimo.k8s]
+        # image = "custom-image:latest"
+        # storage = "5Gi"
+        # ///
 
-    We also look for custom marimo fields:
-    # [tool.marimo.k8s]
-    # image = "custom-image:latest"
-    # storage = "5Gi"
+    The entire block is valid TOML once the leading `# ` is stripped from each line.
+    Marimo k8s config lives under [tool.marimo.k8s] and its subsections.
     """
-    # Look for PEP 723 script block
-    # Pattern allows empty comment lines (# or #\n) as well as # followed by content
     pattern = r"# /// script\n((?:#(?: .*)?\n)*?)# ///"
     match = re.search(pattern, content)
-
     if not match:
         return None
 
-    metadata = {}
-    block = match.group(1)
-
-    # Parse TOML-like lines
-    for line in block.split("\n"):
-        line = line.lstrip("# ").strip()
-        if "=" in line:
-            key, _, value = line.partition("=")
-            key = key.strip()
-            value = value.strip()
-            # Parse simple values
-            if value.startswith('"') and value.endswith('"'):
-                value = value[1:-1]
-            elif value.startswith("'") and value.endswith("'"):
-                value = value[1:-1]
-            elif value.startswith("["):
-                # List - for now just store as string
-                pass
-            metadata[key] = value
-
-    # Look for marimo k8s config
-    k8s_pattern = r"# \[tool\.marimo\.k8s\]\n((?:# .*\n)*)"
-    k8s_match = re.search(k8s_pattern, content)
-    if k8s_match:
-        for line in k8s_match.group(1).split("\n"):
-            line = line.lstrip("# ").strip()
-            if line.startswith("["):
-                # Stop at next section
-                break
-            if "=" in line:
-                key, _, value = line.partition("=")
-                key = key.strip()
-                value = value.strip()
-                value = _parse_toml_value(value)
-                metadata[key] = value
-
-    # Look for marimo k8s env config
-    env_pattern = r"# \[tool\.marimo\.k8s\.env\]\n((?:# .*\n)*)"
-    env_match = re.search(env_pattern, content)
-    if env_match:
-        env = {}
-        for line in env_match.group(1).split("\n"):
-            line = line.lstrip("# ").strip()
-            if line.startswith("["):
-                # Stop at next section
-                break
-            if "=" in line:
-                key, _, value = line.partition("=")
-                key = key.strip()
-                value = value.strip()
-                value = _parse_toml_value(value)
-                env[key] = value
-        if env:
-            metadata["env"] = env
-
-    # Look for marimo k8s nodeSelector config
-    node_selector_pattern = r"# \[tool\.marimo\.k8s\.nodeSelector\]\n((?:# .*\n)*)"
-    node_selector_match = re.search(node_selector_pattern, content)
-    if node_selector_match:
-        node_selector = {}
-        for line in node_selector_match.group(1).split("\n"):
-            line = line.lstrip("# ").strip()
-            if line.startswith("["):
-                # Stop at next section
-                break
-            if "=" in line:
-                key, _, value = line.partition("=")
-                key = key.strip()
-                value = value.strip()
-                value = _parse_toml_value(value)
-                node_selector[key] = value
-        if node_selector:
-            metadata["nodeSelector"] = node_selector
+    # Strip exactly the `# ` prefix (2 chars) from each comment line.
+    # Bare `#` lines (blank comments) become empty lines, preserving TOML structure.
+    lines = []
+    for line in match.group(1).splitlines():
+        if line.startswith("# "):
+            lines.append(line[2:])
+        elif line.rstrip() == "#":
+            lines.append("")
+    toml_str = "\n".join(lines)
+
+    try:
+        data = tomllib.loads(toml_str)
+    except tomllib.TOMLDecodeError:
+        return None
 
-    return metadata if metadata else None
+    metadata: dict[str, Any] = {}
 
+    # PEP 723 standard top-level fields (resources.py ignores these, but callers may use them)
+    for key in ("dependencies", "requires-python"):
+        if key in data:
+            metadata[key] = data[key]
 
-def _parse_toml_value(value: str) -> Any:
-    """Parse a TOML-like value (string, list, etc.)."""
-    value = value.strip()
-
-    # String with double quotes
-    if value.startswith('"') and value.endswith('"'):
-        return value[1:-1]
-
-    # String with single quotes
-    if value.startswith("'") and value.endswith("'"):
-        return value[1:-1]
-
-    # List
-    if value.startswith("[") and value.endswith("]"):
-        # Simple list parsing - handles ["a", "b", "c"]
-        inner = value[1:-1].strip()
-        if not inner:
-            return []
-        items = []
-        for item in inner.split(","):
-            item = item.strip()
-            if item.startswith('"') and item.endswith('"'):
-                items.append(item[1:-1])
-            elif item.startswith("'") and item.endswith("'"):
-                items.append(item[1:-1])
-            else:
-                items.append(item)
-        return items
-
-    # Number
-    if value.isdigit():
-        return int(value)
-
-    # Boolean
-    if value.lower() == "true":
-        return True
-    if value.lower() == "false":
-        return False
-
-    # Default: return as string
-    return value
+    # k8s config lives under [tool.marimo.k8s]
+    k8s = data.get("tool", {}).get("marimo", {}).get("k8s", {})
+
+    # Kubernetes resource quantities must be strings; tomllib parses bare integers as int.
+    if "resources" in k8s:
+        for section, limits in k8s["resources"].items():
+            if isinstance(limits, dict):
+                k8s["resources"][section] = {k: str(v) for k, v in limits.items()}
+
+    metadata.update(k8s)
+    return metadata if metadata else None
 
 
 def is_marimo_python(content: str) -> bool:
diff --git a/plugin/pyproject.toml b/plugin/pyproject.toml
index e0f7fb4..f1660a0 100644
--- a/plugin/pyproject.toml
+++ b/plugin/pyproject.toml
@@ -4,7 +4,7 @@ version = "0.2.5"
 description = "kubectl plugin for deploying marimo notebooks to Kubernetes"
 readme = "README.md"
 license = {text = "Apache-2.0"}
-requires-python = ">=3.10"
+requires-python = ">=3.11"
 authors = [
     {name = "marimo-team"}
 ]
@@ -16,7 +16,6 @@ classifiers = [
     "License :: OSI Approved :: Apache Software License",
     "Operating System :: OS Independent",
     "Programming Language :: Python :: 3",
-    "Programming Language :: Python :: 3.10",
     "Programming Language :: Python :: 3.11",
     "Programming Language :: Python :: 3.12",
     "Topic :: Software Development",
diff --git a/plugin/tests/test_formats.py b/plugin/tests/test_formats.py
index 650a445..85e483c 100644
--- a/plugin/tests/test_formats.py
+++ b/plugin/tests/test_formats.py
@@ -128,7 +128,7 @@ def test_parse_with_pep723(self):
 app = marimo.App()"""
         result_content, metadata = parse_python(content)
         assert result_content == content
-        assert metadata["dependencies"] == '["marimo", "pandas"]'
+        assert metadata["dependencies"] == ["marimo", "pandas"]
 
     def test_parse_no_metadata(self):
         content = "import marimo\napp = marimo.App()"
@@ -141,20 +141,21 @@ class TestExtractPep723Metadata:
     def test_basic(self):
         content = """# /// script
 # dependencies = ["marimo"]
-# requires-python = ">=3.10"
+# requires-python = ">=3.11"
 # ///
 code"""
         meta = extract_pep723_metadata(content)
-        assert meta["dependencies"] == '["marimo"]'
-        assert meta["requires-python"] == ">=3.10"
+        assert meta["dependencies"] == ["marimo"]
+        assert meta["requires-python"] == ">=3.11"
 
     def test_k8s_config(self):
         content = """# /// script
 # dependencies = ["marimo"]
-# ///
+#
 # [tool.marimo.k8s]
 # image = "custom:latest"
 # storage = "5Gi"
+# ///
 import marimo"""
         meta = extract_pep723_metadata(content)
         assert meta["image"] == "custom:latest"
@@ -168,10 +169,11 @@ def test_no_metadata(self):
     def test_k8s_mounts_list(self):
         content = """# /// script
 # dependencies = ["marimo"]
-# ///
+#
 # [tool.marimo.k8s]
 # storage = "1Gi"
 # mounts = ["sshfs://user@host:/data", "cw://bucket/prefix"]
+# ///
 import marimo"""
         meta = extract_pep723_metadata(content)
         assert meta["storage"] == "1Gi"
@@ -180,13 +182,14 @@ def test_k8s_mounts_list(self):
     def test_k8s_node_selector(self):
         content = """# /// script
 # dependencies = ["marimo"]
-# ///
+#
 # [tool.marimo.k8s]
 # storage = "1Gi"
 #
 # [tool.marimo.k8s.nodeSelector]
-# compute.coreweave.com/node-pool = "gpu-node-pool"
+# "compute.coreweave.com/node-pool" = "gpu-node-pool"
 # gpu = "true"
+# ///
 import marimo"""
         meta = extract_pep723_metadata(content)
         assert meta["storage"] == "1Gi"
diff --git a/plugin/uv.lock b/plugin/uv.lock
index 84adedf..3591355 100644
--- a/plugin/uv.lock
+++ b/plugin/uv.lock
@@ -1,6 +1,6 @@
 version = 1
 revision = 3
-requires-python = ">=3.10"
+requires-python = ">=3.11"
 
 [[package]]
 name = "cachetools"
@@ -26,22 +26,6 @@ version = "3.4.4"
 source = { registry = "https://pypi.org/simple" }
 sdist = { url = "https://files.pythonhosted.org/packages/13/69/33ddede1939fdd074bce5434295f38fae7136463422fe4fd3e0e89b98062/charset_normalizer-3.4.4.tar.gz", hash = "sha256:94537985111c35f28720e43603b8e7b43a6ecfb2ce1d3058bbe955b73404e21a", size = 129418, upload-time = "2025-10-14T04:42:32.879Z" }
 wheels = [
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@@ -139,18 +123,6 @@ wheels = [
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 ]
 
-[[package]]
-name = "exceptiongroup"
-version = "1.3.1"
-source = { registry = "https://pypi.org/simple" }
-dependencies = [
-    { name = "typing-extensions", marker = "python_full_version < '3.13'" },
-]
-sdist = { url = "https://files.pythonhosted.org/packages/50/79/66800aadf48771f6b62f7eb014e352e5d06856655206165d775e675a02c9/exceptiongroup-1.3.1.tar.gz", hash = "sha256:8b412432c6055b0b7d14c310000ae93352ed6754f70fa8f7c34141f91c4e3219", size = 30371, upload-time = "2025-11-21T23:01:54.787Z" }
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-
 [[package]]
 name = "google-auth"
 version = "2.43.0"
@@ -303,12 +275,10 @@ version = "9.0.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "colorama", marker = "sys_platform == 'win32'" },
-    { name = "exceptiongroup", marker = "python_full_version < '3.11'" },
     { name = "iniconfig" },
     { name = "packaging" },
     { name = "pluggy" },
     { name = "pygments" },
-    { name = "tomli", marker = "python_full_version < '3.11'" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/07/56/f013048ac4bc4c1d9be45afd4ab209ea62822fb1598f40687e6bf45dcea4/pytest-9.0.1.tar.gz", hash = "sha256:3e9c069ea73583e255c3b21cf46b8d3c56f6e3a1a8f6da94ccb0fcf57b9d73c8", size = 1564125, upload-time = "2025-11-12T13:05:09.333Z" }
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