diff --git a/examples/common/utils.py b/examples/common/utils.py
index e4fb1a2..ec0d364 100644
--- a/examples/common/utils.py
+++ b/examples/common/utils.py
@@ -8,6 +8,9 @@
 import nada_numpy.client as na_client
 import numpy as np
 import py_nillion_client as nillion
+from cosmpy.aerial.client import LedgerClient
+from cosmpy.aerial.wallet import LocalWallet
+from nillion_python_helpers import get_quote, get_quote_and_pay, pay_with_quote
 
 
 def async_timer(file_path: os.PathLike) -> Callable:
@@ -54,6 +57,8 @@ async def wrapper(*args, **kwargs) -> Any:
 
 async def store_program(
     client: nillion.NillionClient,
+    payments_wallet: LocalWallet,
+    payments_client: LedgerClient,
     user_id: str,
     cluster_id: str,
     program_name: str,
@@ -74,23 +79,35 @@ async def store_program(
     Returns:
         str: Program ID.
     """
-    action_id = await client.store_program(cluster_id, program_name, program_mir_path)
+    quote_store_program = await get_quote(
+        client, nillion.Operation.store_program(program_mir_path), cluster_id
+    )
+
+    receipt_store_program = await pay_with_quote(
+        quote_store_program, payments_wallet, payments_client
+    )
+
+    action_id = await client.store_program(
+        cluster_id, program_name, program_mir_path, receipt_store_program
+    )
+
     program_id = f"{user_id}/{program_name}"
     if verbose:
         print("Stored program. action_id:", action_id)
         print("Stored program_id:", program_id)
+
     return program_id
 
 
 async def store_secret_array(
     client: nillion.NillionClient,
+    payments_wallet: LocalWallet,
+    payments_client: LedgerClient,
     cluster_id: str,
-    program_id: str,
-    party_id: str,
-    party_name: str,
     secret_array: np.ndarray,
-    name: str,
+    secret_name: str,
     nada_type: Any,
+    ttl_days: int = 1,
     permissions: nillion.Permissions = None,
 ):
     """
@@ -99,43 +116,44 @@ async def store_secret_array(
     Args:
         client (nillion.NillionClient): Nillion client.
         cluster_id (str): Cluster ID.
-        program_id (str): Program ID.
         party_id (str): Party ID.
         party_name (str): Party name.
         secret_array (np.ndarray): Secret array.
         name (str): Secrets name.
         nada_type (Any): Nada type.
         permissions (nillion.Permissions): Optional Permissions.
-        
+
 
     Returns:
         str: Store ID.
     """
-    secret = na_client.array(secret_array, name, nada_type)
-    secrets = nillion.Secrets(secret)
+    stored_secret = nillion.NadaValues(
+        na_client.array(secret_array, secret_name, nada_type)
+    )
+
     store_id = await store_secrets(
         client,
+        payments_wallet,
+        payments_client,
         cluster_id,
-        program_id,
-        party_id,
-        party_name,
-        secrets,
+        stored_secret,
+        ttl_days,
         permissions,
     )
+
     return store_id
 
 
 async def store_secret_value(
     client: nillion.NillionClient,
+    payments_wallet: LocalWallet,
+    payments_client: LedgerClient,
     cluster_id: str,
-    program_id: str,
-    party_id: str,
-    party_name: str,
     secret_value: Any,
-    name: str,
+    secret_name: str,
     nada_type: Any,
+    ttl_days: int = 1,
     permissions: nillion.Permissions = None,
-    
 ):
     """
     Asynchronous function to store secret values on the nillion client.
@@ -143,7 +161,6 @@ async def store_secret_value(
     Args:
         client (nillion.NillionClient): Nillion client.
         cluster_id (str): Cluster ID.
-        program_id (str): Program ID.
         party_id (str): Party ID.
         party_name (str): Party name.
         secret_value (Any): Secret single value.
@@ -156,60 +173,76 @@ async def store_secret_value(
     """
     if nada_type == na.Rational:
         secret_value = round(secret_value * 2 ** na.get_log_scale())
-        nada_type = nillion.PublicVariableInteger
+        nada_type = nillion.Integer
     elif nada_type == na.SecretRational:
         secret_value = round(secret_value * 2 ** na.get_log_scale())
         nada_type = nillion.SecretInteger
 
-    secrets = nillion.Secrets({name: nada_type(secret_value)})
+    stored_secret = nillion.NadaValues(
+        {
+            secret_name: nada_type(secret_value),
+        }
+    )
+
     store_id = await store_secrets(
         client,
+        payments_wallet,
+        payments_client,
         cluster_id,
-        program_id,
-        party_id,
-        party_name,
-        secrets,
+        stored_secret,
+        ttl_days,
         permissions,
     )
+
     return store_id
 
 
 async def store_secrets(
     client: nillion.NillionClient,
+    payments_wallet: LocalWallet,
+    payments_client: LedgerClient,
     cluster_id: str,
-    program_id: str,
-    party_id: str,
-    party_name: str,
-    secrets: nillion.Secrets,
-    permissions: nillion.Permissions = None
+    secret: nillion.NadaValues,
+    ttl_days: int = 1,
+    permissions: nillion.Permissions = None,
 ):
     """
-    Asynchronous function to store secret values on the nillion client.
+    Asynchronous function to store secrets on the nillion client.
 
     Args:
         client (nillion.NillionClient): Nillion client.
         cluster_id (str): Cluster ID.
-        program_id (str): Program ID.
         party_id (str): Party ID.
         party_name (str): Party name.
-        secrets (nillion.Secrets): Secrets.
+        secret (nillion.NadaValues): Stored secret.
         permissions (nillion.Permissions): Optional Permissions.
 
+
     Returns:
         str: Store ID.
     """
-    secret_bindings = nillion.ProgramBindings(program_id)
-    secret_bindings.add_input_party(party_name, party_id)
-    store_id = await client.store_secrets(cluster_id, secret_bindings, secrets, permissions)
+    receipt_store = await get_quote_and_pay(
+        client,
+        nillion.Operation.store_values(secret, ttl_days=ttl_days),
+        payments_wallet,
+        payments_client,
+        cluster_id,
+    )
+
+    store_id = await client.store_values(cluster_id, secret, permissions, receipt_store)
+
     return store_id
 
 
 async def compute(
     client: nillion.NillionClient,
+    payments_wallet: LocalWallet,
+    payments_client: LedgerClient,
+    program_id: str,
     cluster_id: str,
     compute_bindings: nillion.ProgramBindings,
     store_ids: List[str],
-    computation_time_secrets: nillion.Secrets,
+    computation_time_secrets: nillion.NadaValues,
     verbose: bool = True,
 ) -> Dict[str, Any]:
     """
@@ -226,20 +259,26 @@ async def compute(
     Returns:
         Dict[str, Any]: Result of computation.
     """
-    compute_id = await client.compute(
+    receipt_compute = await get_quote_and_pay(
+        client,
+        nillion.Operation.compute(program_id, computation_time_secrets),
+        payments_wallet,
+        payments_client,
+        cluster_id,
+    )
+
+    _ = await client.compute(
         cluster_id,
         compute_bindings,
         store_ids,
         computation_time_secrets,
-        nillion.PublicVariables({}),
+        receipt_compute,
     )
 
-    if verbose:
-        print(f"The computation was sent to the network. compute_id: {compute_id}")
     while True:
         compute_event = await client.next_compute_event()
         if isinstance(compute_event, nillion.ComputeFinishedEvent):
             if verbose:
-                print(f"✅  Compute complete for compute_id {compute_event.uuid}")
+                print(f"✅ Compute complete for compute_id {compute_event.uuid}")
                 print(f"🖥️  The result is {compute_event.result.value}")
-            return compute_event.result.value
\ No newline at end of file
+            return compute_event.result.value
diff --git a/examples/complex_model/main.py b/examples/complex_model/main.py
index 47c1471..1dcddbb 100644
--- a/examples/complex_model/main.py
+++ b/examples/complex_model/main.py
@@ -3,7 +3,7 @@
 import os
 import sys
 
-sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "../..")))
+sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
 
 import asyncio
 
@@ -12,36 +12,55 @@
 import numpy as np
 import py_nillion_client as nillion
 import torch
+from common.utils import compute, store_program, store_secrets
+from cosmpy.aerial.client import LedgerClient
+from cosmpy.aerial.wallet import LocalWallet
+from cosmpy.crypto.keypairs import PrivateKey
 from dotenv import load_dotenv
-# Import helper functions for creating nillion client and getting keys
-from nillion_python_helpers import (create_nillion_client, getNodeKeyFromFile,
-                                    getUserKeyFromFile)
+from nillion_python_helpers import (create_nillion_client,
+                                    create_payments_config)
+from py_nillion_client import NodeKey, UserKey
 
-from examples.common.utils import compute, store_program, store_secrets
 from nada_ai.client import TorchClient
 
-# Load environment variables from a .env file
-load_dotenv()
+home = os.getenv("HOME")
+load_dotenv(f"{home}/.config/nillion/nillion-devnet.env")
 
 
-# Main asynchronous function to coordinate the process
-async def main():
+async def main() -> None:
+    """Main nada program"""
+
     cluster_id = os.getenv("NILLION_CLUSTER_ID")
-    userkey = getUserKeyFromFile(os.getenv("NILLION_USERKEY_PATH_PARTY_1"))
-    nodekey = getNodeKeyFromFile(os.getenv("NILLION_NODEKEY_PATH_PARTY_1"))
+    grpc_endpoint = os.getenv("NILLION_NILCHAIN_GRPC")
+    chain_id = os.getenv("NILLION_NILCHAIN_CHAIN_ID")
+    seed = "my_seed"
+    userkey = UserKey.from_seed((seed))
+    nodekey = NodeKey.from_seed((seed))
     client = create_nillion_client(userkey, nodekey)
     party_id = client.party_id
     user_id = client.user_id
+
     party_names = na_client.parties(2)
     program_name = "complex_model"
-    program_mir_path = f"./target/{program_name}.nada.bin"
-
-    if not os.path.exists("bench"):
-        os.mkdir("bench")
+    program_mir_path = f"target/{program_name}.nada.bin"
+
+    # Configure payments
+    payments_config = create_payments_config(chain_id, grpc_endpoint)
+    payments_client = LedgerClient(payments_config)
+    payments_wallet = LocalWallet(
+        PrivateKey(bytes.fromhex(os.getenv("NILLION_NILCHAIN_PRIVATE_KEY_0"))),
+        prefix="nillion",
+    )
 
-    # Store the program
+    # Store program
     program_id = await store_program(
-        client, user_id, cluster_id, program_name, program_mir_path
+        client,
+        payments_wallet,
+        payments_client,
+        user_id,
+        cluster_id,
+        program_name,
+        program_mir_path,
     )
 
     # Create custom torch Module
@@ -50,7 +69,7 @@ class MyConvModule(torch.nn.Module):
 
         def __init__(self) -> None:
             """Contains some ConvNet components"""
-            super(MyConvModule, self).__init__()
+            super().__init__()
             self.conv = torch.nn.Conv2d(kernel_size=2, in_channels=3, out_channels=2)
             self.pool = torch.nn.AvgPool2d(kernel_size=2, stride=1)
 
@@ -70,7 +89,7 @@ class MyModel(torch.nn.Module):
 
         def __init__(self) -> None:
             """Model is a collection of arbitrary custom components"""
-            super(MyModel, self).__init__()
+            super().__init__()
             self.conv_module = MyConvModule()
             self.my_operations = MyOperations()
             self.linear = torch.nn.Linear(4, 2)
@@ -90,44 +109,64 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
 
     # Create and store model secrets via ModelClient
     model_client = TorchClient(my_model)
-    model_secrets = nillion.Secrets(
+    model_secrets = nillion.NadaValues(
         model_client.export_state_as_secrets("my_model", na.SecretRational)
     )
+    permissions = nillion.Permissions.default_for_user(client.user_id)
+    permissions.add_compute_permissions({client.user_id: {program_id}})
 
     model_store_id = await store_secrets(
-        client, cluster_id, program_id, party_id, party_names[0], model_secrets
+        client,
+        payments_wallet,
+        payments_client,
+        cluster_id,
+        model_secrets,
+        1,
+        permissions,
     )
 
     # Store inputs to perform inference for
     my_input = na_client.array(np.ones((3, 4, 3)), "my_input", na.SecretRational)
-    input_secrets = nillion.Secrets(my_input)
+    input_secrets = nillion.NadaValues(my_input)
 
     data_store_id = await store_secrets(
-        client, cluster_id, program_id, party_id, party_names[1], input_secrets
+        client,
+        payments_wallet,
+        payments_client,
+        cluster_id,
+        input_secrets,
+        1,
+        permissions,
     )
 
     # Set up the compute bindings for the parties
     compute_bindings = nillion.ProgramBindings(program_id)
-    [
+
+    for party_name in party_names:
         compute_bindings.add_input_party(party_name, party_id)
-        for party_name in party_names
-    ]
-    compute_bindings.add_output_party(party_names[1], party_id)
+    compute_bindings.add_output_party(party_names[-1], party_id)
 
     print(f"Computing using program {program_id}")
-    print(f"Use secret store_id: {model_store_id} {data_store_id}")
+    print(f"Use secret store_id: {model_store_id}, {data_store_id}")
+
+    # Create a computation time secret to use
+    computation_time_secrets = nillion.NadaValues({})
 
-    # Perform the computation and return the result
+    # Compute, passing all params including the receipt that shows proof of payment
     result = await compute(
         client,
+        payments_wallet,
+        payments_client,
+        program_id,
         cluster_id,
         compute_bindings,
         [model_store_id, data_store_id],
-        nillion.Secrets({}),
+        computation_time_secrets,
+        verbose=True,
     )
 
     # Sort & rescale the obtained results by the quantization scale
-    outputs = outputs = [
+    outputs = [
         na_client.float_from_rational(result[1])
         for result in sorted(
             result.items(),
@@ -135,13 +174,12 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
         )
     ]
 
-    print(f"🖥️  The result is {outputs}")
+    print(f"🖥️  The processed result is {outputs} @ {na.get_log_scale()}-bit precision")
 
     expected = my_model.forward(torch.ones((3, 4, 3))).detach().numpy().tolist()
-    print(f"🖥️  VS expected plain-text result {expected}")
-    return result
+
+    print(f"🖥️  VS expected result {expected}")
 
 
-# Run the main function if the script is executed directly
 if __name__ == "__main__":
     asyncio.run(main())
diff --git a/examples/complex_model/tests/complex_model.yaml b/examples/complex_model/tests/complex_model.yaml
index 9a30a2c..95492b1 100644
--- a/examples/complex_model/tests/complex_model.yaml
+++ b/examples/complex_model/tests/complex_model.yaml
@@ -1,161 +1,151 @@
----
 program: complex_model
 inputs:
-  secrets:
-    # We assume all values were originally floats, scaled & rounded by a factor of 2**16
-
-    # For simplicity's sake, we assume all coefficients and inputs are 2.5
-    # 2.5 * 2**16 = 163840
-
-    my_input_2_1_0:
-      SecretInteger: "163840"
-    my_input_2_2_0:
-      SecretInteger: "163840"
-    my_input_0_0_2:
-      SecretInteger: "163840"
-    my_model_linear.bias_0:
-      SecretInteger: "163840"
-    my_model_conv_module.conv.weight_1_0_0_0:
-      SecretInteger: "163840"
-    my_model_linear.weight_1_1:
-      SecretInteger: "163840"
-    my_input_0_1_2:
-      SecretInteger: "163840"
-    my_model_conv_module.conv.weight_0_1_1_0:
-      SecretInteger: "163840"
-    my_model_conv_module.conv.weight_0_2_0_1:
-      SecretInteger: "163840"
-    my_model_conv_module.conv.weight_1_0_1_0:
-      SecretInteger: "163840"
-    my_input_2_1_1:
-      SecretInteger: "163840"
-    my_input_1_1_2:
-      SecretInteger: "163840"
-    my_input_2_1_2:
-      SecretInteger: "163840"
-    my_input_1_2_2:
-      SecretInteger: "163840"
-    my_input_2_3_0:
-      SecretInteger: "163840"
-    my_input_0_2_0:
-      SecretInteger: "163840"
-    my_model_conv_module.conv.weight_1_2_0_1:
-      SecretInteger: "163840"
-    my_input_1_1_1:
-      SecretInteger: "163840"
-    my_input_2_2_1:
-      SecretInteger: "163840"
-    my_model_conv_module.conv.weight_0_1_0_0:
-      SecretInteger: "163840"
-    my_input_0_2_1:
-      SecretInteger: "163840"
-    my_input_0_0_1:
-      SecretInteger: "163840"
-    my_model_linear.weight_1_2:
-      SecretInteger: "163840"
-    my_model_conv_module.conv.weight_1_1_0_1:
-      SecretInteger: "163840"
-    my_model_linear.weight_1_3:
-      SecretInteger: "163840"
-    my_input_0_0_0:
-      SecretInteger: "163840"
-    my_model_conv_module.conv.weight_0_0_1_1:
-      SecretInteger: "163840"
-    my_model_conv_module.conv.bias_1:
-      SecretInteger: "163840"
-    my_input_1_1_0:
-      SecretInteger: "163840"
-    my_model_conv_module.conv.weight_1_2_1_0:
-      SecretInteger: "163840"
-    my_input_1_3_0:
-      SecretInteger: "163840"
-    my_model_conv_module.conv.bias_0:
-      SecretInteger: "163840"
-    my_model_conv_module.conv.weight_1_1_1_1:
-      SecretInteger: "163840"
-    my_input_0_3_1:
-      SecretInteger: "163840"
-    my_input_2_0_0:
-      SecretInteger: "163840"
-    my_input_2_2_2:
-      SecretInteger: "163840"
-    my_model_linear.weight_0_0:
-      SecretInteger: "163840"
-    my_model_linear.weight_0_3:
-      SecretInteger: "163840"
-    my_model_conv_module.conv.weight_0_2_1_1:
-      SecretInteger: "163840"
-    my_model_conv_module.conv.weight_1_0_0_1:
-      SecretInteger: "163840"
-    my_model_linear.weight_0_1:
-      SecretInteger: "163840"
-    my_input_2_3_1:
-      SecretInteger: "163840"
-    my_model_conv_module.conv.weight_0_1_1_1:
-      SecretInteger: "163840"
-    my_model_conv_module.conv.weight_0_0_1_0:
-      SecretInteger: "163840"
-    my_input_2_0_1:
-      SecretInteger: "163840"
-    my_input_1_3_2:
-      SecretInteger: "163840"
-    my_input_1_0_1:
-      SecretInteger: "163840"
-    my_model_conv_module.conv.weight_0_0_0_1:
-      SecretInteger: "163840"
-    my_model_conv_module.conv.weight_1_1_0_0:
-      SecretInteger: "163840"
-    my_model_linear.weight_1_0:
-      SecretInteger: "163840"
-    my_input_2_0_2:
-      SecretInteger: "163840"
-    my_model_conv_module.conv.weight_0_2_0_0:
-      SecretInteger: "163840"
-    my_input_1_0_2:
-      SecretInteger: "163840"
-    my_model_conv_module.conv.weight_0_0_0_0:
-      SecretInteger: "163840"
-    my_model_conv_module.conv.weight_1_2_1_1:
-      SecretInteger: "163840"
-    my_input_0_2_2:
-      SecretInteger: "163840"
-    my_model_conv_module.conv.weight_1_2_0_0:
-      SecretInteger: "163840"
-    my_model_linear.bias_1:
-      SecretInteger: "163840"
-    my_input_1_0_0:
-      SecretInteger: "163840"
-    my_input_0_3_2:
-      SecretInteger: "163840"
-    my_model_conv_module.conv.weight_1_0_1_1:
-      SecretInteger: "163840"
-    my_model_conv_module.conv.weight_1_1_1_0:
-      SecretInteger: "163840"
-    my_input_1_2_0:
-      SecretInteger: "163840"
-    my_input_0_1_1:
-      SecretInteger: "163840"
-    my_input_0_3_0:
-      SecretInteger: "163840"
-    my_model_linear.weight_0_2:
-      SecretInteger: "163840"
-    my_model_conv_module.conv.weight_0_2_1_0:
-      SecretInteger: "163840"
-    my_model_conv_module.conv.weight_0_1_0_1:
-      SecretInteger: "163840"
-    my_input_1_2_1:
-      SecretInteger: "163840"
-    my_input_1_3_1:
-      SecretInteger: "163840"
-    my_input_0_1_0:
-      SecretInteger: "163840"
-    my_input_2_3_2:
-      SecretInteger: "163840"
-  public_variables: {}
+  my_input_2_1_0:
+    SecretInteger: '163840'
+  my_input_2_2_0:
+    SecretInteger: '163840'
+  my_input_0_0_2:
+    SecretInteger: '163840'
+  my_model_linear.bias_0:
+    SecretInteger: '163840'
+  my_model_conv_module.conv.weight_1_0_0_0:
+    SecretInteger: '163840'
+  my_model_linear.weight_1_1:
+    SecretInteger: '163840'
+  my_input_0_1_2:
+    SecretInteger: '163840'
+  my_model_conv_module.conv.weight_0_1_1_0:
+    SecretInteger: '163840'
+  my_model_conv_module.conv.weight_0_2_0_1:
+    SecretInteger: '163840'
+  my_model_conv_module.conv.weight_1_0_1_0:
+    SecretInteger: '163840'
+  my_input_2_1_1:
+    SecretInteger: '163840'
+  my_input_1_1_2:
+    SecretInteger: '163840'
+  my_input_2_1_2:
+    SecretInteger: '163840'
+  my_input_1_2_2:
+    SecretInteger: '163840'
+  my_input_2_3_0:
+    SecretInteger: '163840'
+  my_input_0_2_0:
+    SecretInteger: '163840'
+  my_model_conv_module.conv.weight_1_2_0_1:
+    SecretInteger: '163840'
+  my_input_1_1_1:
+    SecretInteger: '163840'
+  my_input_2_2_1:
+    SecretInteger: '163840'
+  my_model_conv_module.conv.weight_0_1_0_0:
+    SecretInteger: '163840'
+  my_input_0_2_1:
+    SecretInteger: '163840'
+  my_input_0_0_1:
+    SecretInteger: '163840'
+  my_model_linear.weight_1_2:
+    SecretInteger: '163840'
+  my_model_conv_module.conv.weight_1_1_0_1:
+    SecretInteger: '163840'
+  my_model_linear.weight_1_3:
+    SecretInteger: '163840'
+  my_input_0_0_0:
+    SecretInteger: '163840'
+  my_model_conv_module.conv.weight_0_0_1_1:
+    SecretInteger: '163840'
+  my_model_conv_module.conv.bias_1:
+    SecretInteger: '163840'
+  my_input_1_1_0:
+    SecretInteger: '163840'
+  my_model_conv_module.conv.weight_1_2_1_0:
+    SecretInteger: '163840'
+  my_input_1_3_0:
+    SecretInteger: '163840'
+  my_model_conv_module.conv.bias_0:
+    SecretInteger: '163840'
+  my_model_conv_module.conv.weight_1_1_1_1:
+    SecretInteger: '163840'
+  my_input_0_3_1:
+    SecretInteger: '163840'
+  my_input_2_0_0:
+    SecretInteger: '163840'
+  my_input_2_2_2:
+    SecretInteger: '163840'
+  my_model_linear.weight_0_0:
+    SecretInteger: '163840'
+  my_model_linear.weight_0_3:
+    SecretInteger: '163840'
+  my_model_conv_module.conv.weight_0_2_1_1:
+    SecretInteger: '163840'
+  my_model_conv_module.conv.weight_1_0_0_1:
+    SecretInteger: '163840'
+  my_model_linear.weight_0_1:
+    SecretInteger: '163840'
+  my_input_2_3_1:
+    SecretInteger: '163840'
+  my_model_conv_module.conv.weight_0_1_1_1:
+    SecretInteger: '163840'
+  my_model_conv_module.conv.weight_0_0_1_0:
+    SecretInteger: '163840'
+  my_input_2_0_1:
+    SecretInteger: '163840'
+  my_input_1_3_2:
+    SecretInteger: '163840'
+  my_input_1_0_1:
+    SecretInteger: '163840'
+  my_model_conv_module.conv.weight_0_0_0_1:
+    SecretInteger: '163840'
+  my_model_conv_module.conv.weight_1_1_0_0:
+    SecretInteger: '163840'
+  my_model_linear.weight_1_0:
+    SecretInteger: '163840'
+  my_input_2_0_2:
+    SecretInteger: '163840'
+  my_model_conv_module.conv.weight_0_2_0_0:
+    SecretInteger: '163840'
+  my_input_1_0_2:
+    SecretInteger: '163840'
+  my_model_conv_module.conv.weight_0_0_0_0:
+    SecretInteger: '163840'
+  my_model_conv_module.conv.weight_1_2_1_1:
+    SecretInteger: '163840'
+  my_input_0_2_2:
+    SecretInteger: '163840'
+  my_model_conv_module.conv.weight_1_2_0_0:
+    SecretInteger: '163840'
+  my_model_linear.bias_1:
+    SecretInteger: '163840'
+  my_input_1_0_0:
+    SecretInteger: '163840'
+  my_input_0_3_2:
+    SecretInteger: '163840'
+  my_model_conv_module.conv.weight_1_0_1_1:
+    SecretInteger: '163840'
+  my_model_conv_module.conv.weight_1_1_1_0:
+    SecretInteger: '163840'
+  my_input_1_2_0:
+    SecretInteger: '163840'
+  my_input_0_1_1:
+    SecretInteger: '163840'
+  my_input_0_3_0:
+    SecretInteger: '163840'
+  my_model_linear.weight_0_2:
+    SecretInteger: '163840'
+  my_model_conv_module.conv.weight_0_2_1_0:
+    SecretInteger: '163840'
+  my_model_conv_module.conv.weight_0_1_0_1:
+    SecretInteger: '163840'
+  my_input_1_2_1:
+    SecretInteger: '163840'
+  my_input_1_3_1:
+    SecretInteger: '163840'
+  my_input_0_1_0:
+    SecretInteger: '163840'
+  my_input_2_3_2:
+    SecretInteger: '163840'
 expected_outputs:
-  # If you go in and crunch the numbers of this one, the result should be 1542.5
-  # 1542.5 * 2**16 = 101_089_280
   my_output_1:
-    SecretInteger: "101089280"
+    SecretInteger: '101089280'
   my_output_0:
-    SecretInteger: "101089280"
+    SecretInteger: '101089280'
diff --git a/examples/linear_regression/main.py b/examples/linear_regression/main.py
index 81a987a..efdb3cd 100644
--- a/examples/linear_regression/main.py
+++ b/examples/linear_regression/main.py
@@ -3,7 +3,7 @@
 import os
 import sys
 
-sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "../..")))
+sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
 
 import asyncio
 
@@ -11,39 +11,59 @@
 import nada_numpy.client as na_client
 import numpy as np
 import py_nillion_client as nillion
+from common.utils import compute, store_program, store_secrets
+from cosmpy.aerial.client import LedgerClient
+from cosmpy.aerial.wallet import LocalWallet
+from cosmpy.crypto.keypairs import PrivateKey
 from dotenv import load_dotenv
-# Import helper functions for creating nillion client and getting keys
-from nillion_python_helpers import (create_nillion_client, getNodeKeyFromFile,
-                                    getUserKeyFromFile)
+from nillion_python_helpers import (create_nillion_client,
+                                    create_payments_config)
+from py_nillion_client import NodeKey, UserKey
 from sklearn.linear_model import LinearRegression
 
-from examples.common.utils import compute, store_program, store_secrets
 from nada_ai.client import SklearnClient
 
-# Load environment variables from a .env file
-load_dotenv()
+home = os.getenv("HOME")
+load_dotenv(f"{home}/.config/nillion/nillion-devnet.env")
 
 NUM_FEATS = 10
 
 
 # Main asynchronous function to coordinate the process
-async def main():
+async def main() -> None:
+    """Main nada program"""
+
     cluster_id = os.getenv("NILLION_CLUSTER_ID")
-    userkey = getUserKeyFromFile(os.getenv("NILLION_USERKEY_PATH_PARTY_1"))
-    nodekey = getNodeKeyFromFile(os.getenv("NILLION_NODEKEY_PATH_PARTY_1"))
+    grpc_endpoint = os.getenv("NILLION_NILCHAIN_GRPC")
+    chain_id = os.getenv("NILLION_NILCHAIN_CHAIN_ID")
+    seed = "my_seed"
+    userkey = UserKey.from_seed((seed))
+    nodekey = NodeKey.from_seed((seed))
     client = create_nillion_client(userkey, nodekey)
     party_id = client.party_id
     user_id = client.user_id
+
     party_names = na_client.parties(2)
     program_name = "linear_regression"
-    program_mir_path = f"./target/{program_name}.nada.bin"
-
-    if not os.path.exists("bench"):
-        os.mkdir("bench")
+    program_mir_path = f"target/{program_name}.nada.bin"
+
+    # Configure payments
+    payments_config = create_payments_config(chain_id, grpc_endpoint)
+    payments_client = LedgerClient(payments_config)
+    payments_wallet = LocalWallet(
+        PrivateKey(bytes.fromhex(os.getenv("NILLION_NILCHAIN_PRIVATE_KEY_0"))),
+        prefix="nillion",
+    )
 
-    # Store the program
+    # Store program
     program_id = await store_program(
-        client, user_id, cluster_id, program_name, program_mir_path
+        client,
+        payments_wallet,
+        payments_client,
+        user_id,
+        cluster_id,
+        program_name,
+        program_mir_path,
     )
 
     # Train a linear regression
@@ -65,50 +85,69 @@ async def main():
 
     # Create and store model secrets via ModelClient
     model_client = SklearnClient(fit_model)
-    model_secrets = nillion.Secrets(
+    model_secrets = nillion.NadaValues(
         model_client.export_state_as_secrets("my_model", na.SecretRational)
     )
+    permissions = nillion.Permissions.default_for_user(client.user_id)
+    permissions.add_compute_permissions({client.user_id: {program_id}})
 
     model_store_id = await store_secrets(
-        client, cluster_id, program_id, party_id, party_names[0], model_secrets
+        client,
+        payments_wallet,
+        payments_client,
+        cluster_id,
+        model_secrets,
+        1,
+        permissions,
     )
 
     # Store inputs to perform inference for
     my_input = na_client.array(np.ones((NUM_FEATS,)), "my_input", na.SecretRational)
-    input_secrets = nillion.Secrets(my_input)
+    input_secrets = nillion.NadaValues(my_input)
 
     data_store_id = await store_secrets(
-        client, cluster_id, program_id, party_id, party_names[1], input_secrets
+        client,
+        payments_wallet,
+        payments_client,
+        cluster_id,
+        input_secrets,
+        1,
+        permissions,
     )
 
     # Set up the compute bindings for the parties
     compute_bindings = nillion.ProgramBindings(program_id)
-    [
+
+    for party_name in party_names:
         compute_bindings.add_input_party(party_name, party_id)
-        for party_name in party_names
-    ]
-    compute_bindings.add_output_party(party_names[1], party_id)
+    compute_bindings.add_output_party(party_names[-1], party_id)
 
     print(f"Computing using program {program_id}")
     print(f"Use secret store_id: {model_store_id} {data_store_id}")
 
-    # Perform the computation and return the result
+    # Create a computation time secret to use
+    computation_time_secrets = nillion.NadaValues({})
+
+    # Compute, passing all params including the receipt that shows proof of payment
     result = await compute(
         client,
+        payments_wallet,
+        payments_client,
+        program_id,
         cluster_id,
         compute_bindings,
         [model_store_id, data_store_id],
-        nillion.Secrets({}),
+        computation_time_secrets,
+        verbose=True,
     )
+
     # Rescale the obtained result by the quantization scale
     outputs = [na_client.float_from_rational(result["my_output"])]
-    print(f"🖥️  The result is {outputs}")
+    print(f"🖥️  The result is {outputs} @ {na.get_log_scale()}-bit precision")
 
     expected = fit_model.predict(np.ones((NUM_FEATS,)).reshape(1, -1))
-    print(f"🖥️  VS expected plain-text result {expected}")
-    return result
+    print(f"🖥️  VS expected result {expected}")
 
 
-# Run the main function if the script is executed directly
 if __name__ == "__main__":
     asyncio.run(main())
diff --git a/examples/linear_regression/tests/linear_regression.yaml b/examples/linear_regression/tests/linear_regression.yaml
index 9b4892c..fbebae9 100644
--- a/examples/linear_regression/tests/linear_regression.yaml
+++ b/examples/linear_regression/tests/linear_regression.yaml
@@ -1,58 +1,47 @@
----
 program: linear_regression
 inputs:
-  secrets:
-    # We assume all values were originally floats, scaled & rounded by a factor of 2**16
-
-    # All coefficients are scaled random floats between 0 and 1
-    my_model_coef_0:
-      SecretInteger: "30669"  # e.g. 30669 = 0.4679718017578125 * 2**16
-    my_model_coef_1:
-      SecretInteger: "22411"
-    my_model_coef_2:
-      SecretInteger: "61182"
-    my_model_coef_3:
-      SecretInteger: "23548"
-    my_model_coef_4:
-      SecretInteger: "52068"
-    my_model_coef_5:
-      SecretInteger: "49177"
-    my_model_coef_6:
-      SecretInteger: "60541"
-    my_model_coef_7:
-      SecretInteger: "55932"
-    my_model_coef_8:
-      SecretInteger: "7771"
-    my_model_coef_9:
-      SecretInteger: "61119"
-    my_model_intercept_0:
-      SecretInteger: "45040"
-
-    # Inputs are an array of 2.5 (2.5 * 2**16 = 163840)
-
-    my_input_0:
-      SecretInteger: "163840"
-    my_input_1:
-      SecretInteger: "163840"
-    my_input_2:
-      SecretInteger: "163840"
-    my_input_3:
-      SecretInteger: "163840"
-    my_input_4:
-      SecretInteger: "163840"
-    my_input_5:
-      SecretInteger: "163840"
-    my_input_6:
-      SecretInteger: "163840"
-    my_input_7:
-      SecretInteger: "163840"
-    my_input_8:
-      SecretInteger: "163840"
-    my_input_9:
-      SecretInteger: "163840"
-  public_variables: {}
+  my_model_coef_0:
+    SecretInteger: '30669'
+  my_model_coef_1:
+    SecretInteger: '22411'
+  my_model_coef_2:
+    SecretInteger: '61182'
+  my_model_coef_3:
+    SecretInteger: '23548'
+  my_model_coef_4:
+    SecretInteger: '52068'
+  my_model_coef_5:
+    SecretInteger: '49177'
+  my_model_coef_6:
+    SecretInteger: '60541'
+  my_model_coef_7:
+    SecretInteger: '55932'
+  my_model_coef_8:
+    SecretInteger: '7771'
+  my_model_coef_9:
+    SecretInteger: '61119'
+  my_model_intercept_0:
+    SecretInteger: '45040'
+  my_input_0:
+    SecretInteger: '163840'
+  my_input_1:
+    SecretInteger: '163840'
+  my_input_2:
+    SecretInteger: '163840'
+  my_input_3:
+    SecretInteger: '163840'
+  my_input_4:
+    SecretInteger: '163840'
+  my_input_5:
+    SecretInteger: '163840'
+  my_input_6:
+    SecretInteger: '163840'
+  my_input_7:
+    SecretInteger: '163840'
+  my_input_8:
+    SecretInteger: '163840'
+  my_input_9:
+    SecretInteger: '163840'
 expected_outputs:
-  # If you go in and crunch the numbers of this one, the result should be 16.877471923828125
-  # 16.877471923828125 * 2**16 = 1106085
   my_output:
-    SecretInteger: "1106085"
+    SecretInteger: '1106085'
diff --git a/examples/multi_layer_perceptron/01_model_provider.ipynb b/examples/multi_layer_perceptron/01_model_provider.ipynb
index cfd6e71..c6f2639 100644
--- a/examples/multi_layer_perceptron/01_model_provider.ipynb
+++ b/examples/multi_layer_perceptron/01_model_provider.ipynb
@@ -7,13 +7,6 @@
     "# Multi-layer perceptron"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This demo shows MLP"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 1,
@@ -44,22 +37,44 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/home/vscode/.local/lib/python3.12/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
-      "  from .autonotebook import tqdm as notebook_tqdm\n",
-      "Importing plotly failed. Interactive plots will not work.\n"
+      "/Users/mathiasleys/projects/venv/lib/python3.12/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n"
      ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "import json\n",
     "import os\n",
-    "from typing import Dict\n",
+    "import sys\n",
     "\n",
-    "import os\n",
+    "sys.path.append(os.path.abspath(os.path.join(os.getcwd(), os.pardir)))\n",
+    "\n",
+    "import json\n",
+    "import nada_numpy as na\n",
+    "import numpy as np\n",
+    "import py_nillion_client as nillion\n",
     "import torch\n",
+    "from common.utils import store_program, store_secrets\n",
+    "from cosmpy.aerial.client import LedgerClient\n",
+    "from cosmpy.aerial.wallet import LocalWallet\n",
+    "from cosmpy.crypto.keypairs import PrivateKey\n",
+    "from dotenv import load_dotenv\n",
+    "from nada_ai.client import TorchClient\n",
+    "from nillion_python_helpers import (create_nillion_client,\n",
+    "                                    create_payments_config)\n",
+    "from py_nillion_client import NodeKey, UserKey\n",
+    "\n",
     "from torch import nn\n",
     "from torchvision import transforms\n",
-    "import py_nillion_client as nillion\n",
     "from sklearn.metrics import (\n",
     "    confusion_matrix,\n",
     "    ConfusionMatrixDisplay,\n",
@@ -69,24 +84,15 @@
     "from PIL import Image\n",
     "import numpy as np\n",
     "\n",
-    "from dotenv import load_dotenv\n",
-    "\n",
-    "# Using Nada AI model client\n",
-    "from nada_ai.client import TorchClient\n",
-    "import nada_numpy as na\n",
-    "import py_nillion_client as nillion\n",
-    "from nillion_python_helpers import (\n",
-    "    create_nillion_client,\n",
-    "    getUserKeyFromFile,\n",
-    "    getNodeKeyFromFile,\n",
-    ")"
+    "home = os.getenv(\"HOME\")\n",
+    "load_dotenv(f\"{home}/.config/nillion/nillion-devnet.env\")"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Train an Covid classification model\n",
+    "### Train a Covid image classification model on CT scans\n",
     "\n",
     "Before this step you must install kaggle"
    ]
@@ -100,12 +106,13 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "Warning: Looks like you're using an outdated API Version, please consider updating (server 1.6.14 / client 1.6.12)\n",
       "Dataset URL: https://www.kaggle.com/datasets/mehradaria/covid19-lung-ct-scans\n",
       "License(s): Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)\n",
       "Downloading covid19-lung-ct-scans.zip to data\n",
-      "... resuming from 331350016 bytes (767065127 bytes left) ...\n",
-      "100%|█████████████████████████████████████▉| 1.02G/1.02G [00:30<00:00, 21.3MB/s]\n",
-      "100%|██████████████████████████████████████| 1.02G/1.02G [00:30<00:00, 24.9MB/s]\n"
+      "... resuming from 846200832 bytes (252214311 bytes left) ...\n",
+      "100%|█████████████████████████████████████▉| 1.02G/1.02G [00:31<00:00, 9.06MB/s]\n",
+      "100%|██████████████████████████████████████| 1.02G/1.02G [00:31<00:00, 8.05MB/s]\n"
      ]
     }
    ],
@@ -157,23 +164,22 @@
    "outputs": [],
    "source": [
     "# Create custom torch Module\n",
-    "class MyNN(torch.nn.Module):\n",
-    "    \"\"\"My simple neural net\"\"\"\n",
+    "class MyNN(nn.Module):\n",
+    "    \"\"\"My brand new model\"\"\"\n",
     "\n",
     "    def __init__(self) -> None:\n",
     "        \"\"\"Model is a two layers and an activations\"\"\"\n",
-    "        super(MyNN, self).__init__()\n",
-    "        self.conv1 = torch.nn.Conv2d(\n",
-    "            in_channels=1, out_channels=2, kernel_size=3, stride=4, padding=1\n",
+    "        super().__init__()\n",
+    "        self.conv1 = nn.Conv2d(\n",
+    "            in_channels=1, out_channels=2, kernel_size=3, padding=1, stride=3\n",
     "        )\n",
-    "        self.pool = torch.nn.AvgPool2d(kernel_size=2, stride=2)\n",
-    "\n",
-    "        self.fc1 = torch.nn.Linear(in_features=8, out_features=2)\n",
+    "        self.pool = nn.AvgPool2d(kernel_size=2, stride=2)\n",
+    "        self.fc1 = nn.Linear(in_features=18, out_features=2)\n",
     "\n",
-    "        self.relu = torch.nn.ReLU()\n",
-    "        self.flatten = torch.nn.Flatten()\n",
+    "        self.relu = nn.ReLU()\n",
+    "        self.flatten = nn.Flatten()\n",
     "\n",
-    "    def forward(self, x: np.ndarray) -> np.ndarray:\n",
+    "    def forward(self, x: na.NadaArray) -> na.NadaArray:\n",
     "        \"\"\"My forward pass logic\"\"\"\n",
     "        x = self.relu(self.conv1(x))\n",
     "        x = self.pool(x)\n",
@@ -181,7 +187,6 @@
     "        x = self.fc1(x)\n",
     "        return x\n",
     "\n",
-    "\n",
     "my_model = MyNN()"
    ]
   },
@@ -219,95 +224,95 @@
      "output_type": "stream",
      "text": [
       "Starting epoch 1...\n",
-      "Loss after mini-batch   100: 0.541\n",
-      "Accuracy after mini-batch   100: 88.938\n",
-      "Loss after mini-batch   200: 0.516\n",
-      "Accuracy after mini-batch   200: 87.812\n",
-      "Loss after mini-batch   300: 0.487\n",
-      "Accuracy after mini-batch   300: 89.500\n",
-      "Loss after mini-batch   400: 0.471\n",
-      "Accuracy after mini-batch   400: 88.375\n",
+      "Loss after mini-batch   100: 0.692\n",
+      "Accuracy after mini-batch   100: 56.812\n",
+      "Loss after mini-batch   200: 0.671\n",
+      "Accuracy after mini-batch   200: 89.125\n",
+      "Loss after mini-batch   300: 0.649\n",
+      "Accuracy after mini-batch   300: 87.812\n",
+      "Loss after mini-batch   400: 0.617\n",
+      "Accuracy after mini-batch   400: 89.062\n",
       "Starting epoch 2...\n",
-      "Loss after mini-batch   100: 0.442\n",
-      "Accuracy after mini-batch   100: 88.688\n",
-      "Loss after mini-batch   200: 0.441\n",
-      "Accuracy after mini-batch   200: 87.562\n",
-      "Loss after mini-batch   300: 0.404\n",
-      "Accuracy after mini-batch   300: 89.375\n",
-      "Loss after mini-batch   400: 0.403\n",
-      "Accuracy after mini-batch   400: 88.938\n",
+      "Loss after mini-batch   100: 0.579\n",
+      "Accuracy after mini-batch   100: 87.375\n",
+      "Loss after mini-batch   200: 0.531\n",
+      "Accuracy after mini-batch   200: 89.562\n",
+      "Loss after mini-batch   300: 0.500\n",
+      "Accuracy after mini-batch   300: 87.750\n",
+      "Loss after mini-batch   400: 0.462\n",
+      "Accuracy after mini-batch   400: 88.812\n",
       "Starting epoch 3...\n",
-      "Loss after mini-batch   100: 0.381\n",
-      "Accuracy after mini-batch   100: 89.500\n",
-      "Loss after mini-batch   200: 0.383\n",
-      "Accuracy after mini-batch   200: 88.938\n",
-      "Loss after mini-batch   300: 0.380\n",
-      "Accuracy after mini-batch   300: 87.812\n",
-      "Loss after mini-batch   400: 0.376\n",
-      "Accuracy after mini-batch   400: 88.312\n",
+      "Loss after mini-batch   100: 0.437\n",
+      "Accuracy after mini-batch   100: 87.250\n",
+      "Loss after mini-batch   200: 0.404\n",
+      "Accuracy after mini-batch   200: 88.375\n",
+      "Loss after mini-batch   300: 0.389\n",
+      "Accuracy after mini-batch   300: 88.875\n",
+      "Loss after mini-batch   400: 0.350\n",
+      "Accuracy after mini-batch   400: 89.875\n",
       "Starting epoch 4...\n",
-      "Loss after mini-batch   100: 0.369\n",
-      "Accuracy after mini-batch   100: 88.562\n",
-      "Loss after mini-batch   200: 0.352\n",
-      "Accuracy after mini-batch   200: 89.312\n",
-      "Loss after mini-batch   300: 0.363\n",
-      "Accuracy after mini-batch   300: 88.375\n",
-      "Loss after mini-batch   400: 0.372\n",
-      "Accuracy after mini-batch   400: 88.062\n",
+      "Loss after mini-batch   100: 0.347\n",
+      "Accuracy after mini-batch   100: 89.500\n",
+      "Loss after mini-batch   200: 0.371\n",
+      "Accuracy after mini-batch   200: 88.000\n",
+      "Loss after mini-batch   300: 0.357\n",
+      "Accuracy after mini-batch   300: 88.688\n",
+      "Loss after mini-batch   400: 0.381\n",
+      "Accuracy after mini-batch   400: 87.875\n",
       "Starting epoch 5...\n",
-      "Loss after mini-batch   100: 0.340\n",
-      "Accuracy after mini-batch   100: 89.625\n",
-      "Loss after mini-batch   200: 0.362\n",
-      "Accuracy after mini-batch   200: 88.438\n",
-      "Loss after mini-batch   300: 0.365\n",
-      "Accuracy after mini-batch   300: 88.312\n",
-      "Loss after mini-batch   400: 0.369\n",
-      "Accuracy after mini-batch   400: 87.938\n",
+      "Loss after mini-batch   100: 0.360\n",
+      "Accuracy after mini-batch   100: 88.625\n",
+      "Loss after mini-batch   200: 0.365\n",
+      "Accuracy after mini-batch   200: 87.938\n",
+      "Loss after mini-batch   300: 0.363\n",
+      "Accuracy after mini-batch   300: 88.562\n",
+      "Loss after mini-batch   400: 0.332\n",
+      "Accuracy after mini-batch   400: 89.812\n",
       "Starting epoch 6...\n",
-      "Loss after mini-batch   100: 0.354\n",
-      "Accuracy after mini-batch   100: 88.688\n",
-      "Loss after mini-batch   200: 0.366\n",
-      "Accuracy after mini-batch   200: 88.312\n",
-      "Loss after mini-batch   300: 0.342\n",
-      "Accuracy after mini-batch   300: 89.562\n",
-      "Loss after mini-batch   400: 0.365\n",
-      "Accuracy after mini-batch   400: 87.812\n",
+      "Loss after mini-batch   100: 0.343\n",
+      "Accuracy after mini-batch   100: 89.312\n",
+      "Loss after mini-batch   200: 0.362\n",
+      "Accuracy after mini-batch   200: 88.250\n",
+      "Loss after mini-batch   300: 0.358\n",
+      "Accuracy after mini-batch   300: 88.438\n",
+      "Loss after mini-batch   400: 0.362\n",
+      "Accuracy after mini-batch   400: 88.438\n",
       "Starting epoch 7...\n",
-      "Loss after mini-batch   100: 0.373\n",
-      "Accuracy after mini-batch   100: 87.812\n",
+      "Loss after mini-batch   100: 0.352\n",
+      "Accuracy after mini-batch   100: 88.875\n",
       "Loss after mini-batch   200: 0.348\n",
-      "Accuracy after mini-batch   200: 88.938\n",
-      "Loss after mini-batch   300: 0.338\n",
-      "Accuracy after mini-batch   300: 89.438\n",
-      "Loss after mini-batch   400: 0.350\n",
-      "Accuracy after mini-batch   400: 89.062\n",
+      "Accuracy after mini-batch   200: 89.000\n",
+      "Loss after mini-batch   300: 0.357\n",
+      "Accuracy after mini-batch   300: 88.312\n",
+      "Loss after mini-batch   400: 0.345\n",
+      "Accuracy after mini-batch   400: 89.000\n",
       "Starting epoch 8...\n",
-      "Loss after mini-batch   100: 0.352\n",
-      "Accuracy after mini-batch   100: 88.938\n",
-      "Loss after mini-batch   200: 0.354\n",
-      "Accuracy after mini-batch   200: 88.438\n",
-      "Loss after mini-batch   300: 0.341\n",
-      "Accuracy after mini-batch   300: 89.500\n",
-      "Loss after mini-batch   400: 0.373\n",
-      "Accuracy after mini-batch   400: 87.812\n",
+      "Loss after mini-batch   100: 0.342\n",
+      "Accuracy after mini-batch   100: 89.188\n",
+      "Loss after mini-batch   200: 0.366\n",
+      "Accuracy after mini-batch   200: 87.938\n",
+      "Loss after mini-batch   300: 0.357\n",
+      "Accuracy after mini-batch   300: 88.562\n",
+      "Loss after mini-batch   400: 0.343\n",
+      "Accuracy after mini-batch   400: 88.812\n",
       "Starting epoch 9...\n",
-      "Loss after mini-batch   100: 0.358\n",
-      "Accuracy after mini-batch   100: 88.438\n",
-      "Loss after mini-batch   200: 0.367\n",
-      "Accuracy after mini-batch   200: 88.062\n",
-      "Loss after mini-batch   300: 0.360\n",
-      "Accuracy after mini-batch   300: 88.625\n",
-      "Loss after mini-batch   400: 0.340\n",
-      "Accuracy after mini-batch   400: 89.062\n",
+      "Loss after mini-batch   100: 0.334\n",
+      "Accuracy after mini-batch   100: 89.875\n",
+      "Loss after mini-batch   200: 0.364\n",
+      "Accuracy after mini-batch   200: 87.938\n",
+      "Loss after mini-batch   300: 0.363\n",
+      "Accuracy after mini-batch   300: 87.812\n",
+      "Loss after mini-batch   400: 0.343\n",
+      "Accuracy after mini-batch   400: 88.938\n",
       "Starting epoch 10...\n",
-      "Loss after mini-batch   100: 0.351\n",
-      "Accuracy after mini-batch   100: 88.812\n",
-      "Loss after mini-batch   200: 0.347\n",
-      "Accuracy after mini-batch   200: 88.875\n",
-      "Loss after mini-batch   300: 0.370\n",
-      "Accuracy after mini-batch   300: 87.938\n",
-      "Loss after mini-batch   400: 0.342\n",
-      "Accuracy after mini-batch   400: 89.250\n"
+      "Loss after mini-batch   100: 0.336\n",
+      "Accuracy after mini-batch   100: 89.812\n",
+      "Loss after mini-batch   200: 0.351\n",
+      "Accuracy after mini-batch   200: 88.312\n",
+      "Loss after mini-batch   300: 0.361\n",
+      "Accuracy after mini-batch   300: 88.000\n",
+      "Loss after mini-batch   400: 0.355\n",
+      "Accuracy after mini-batch   400: 88.375\n"
      ]
     }
    ],
@@ -355,7 +360,7 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0xffff33992cf0>]"
+       "[<matplotlib.lines.Line2D at 0x336e152e0>]"
       ]
      },
      "execution_count": 8,
@@ -364,7 +369,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -385,7 +390,7 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0xffff6948f8c0>]"
+       "[<matplotlib.lines.Line2D at 0x336e8dc10>]"
       ]
      },
      "execution_count": 9,
@@ -394,7 +399,7 @@
     },
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -434,17 +439,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "precision: 89.389%\n",
+      "precision: 89.686%\n",
       "recall: 100.000%\n",
-      "f1: 94.397%\n",
-      "support: 1508\n"
+      "f1: 94.562%\n",
+      "support: 1513\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/home/vscode/.local/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1517: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
+      "/Users/mathiasleys/projects/venv/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1517: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
       "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n"
      ]
     }
@@ -465,7 +470,7 @@
     {
      "data": {
       "text/plain": [
-       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0xffffb08d1e50>"
+       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x11a4acad0>"
       ]
      },
      "execution_count": 12,
@@ -474,7 +479,7 @@
     },
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 640x480 with 2 Axes>"
       ]
@@ -541,23 +546,15 @@
    "cell_type": "code",
    "execution_count": 14,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "/tmp/tmpqbvm6we8\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "cluster_id = os.getenv(\"NILLION_CLUSTER_ID\")\n",
-    "print(os.getenv(\"NILLION_USERKEY_PATH_PARTY_1\"))\n",
-    "model_provider_userkey = getUserKeyFromFile(os.getenv(\"NILLION_USERKEY_PATH_PARTY_1\"))\n",
-    "model_provider_nodekey = getNodeKeyFromFile(os.getenv(\"NILLION_NODEKEY_PATH_PARTY_1\"))\n",
-    "model_provider_client = create_nillion_client(\n",
-    "    model_provider_userkey, model_provider_nodekey\n",
-    ")\n",
+    "grpc_endpoint = os.getenv(\"NILLION_NILCHAIN_GRPC\")\n",
+    "chain_id = os.getenv(\"NILLION_NILCHAIN_CHAIN_ID\")\n",
+    "seed = \"my_seed\"\n",
+    "model_provider_userkey = UserKey.from_seed((seed))\n",
+    "model_provider_nodekey = NodeKey.from_seed((seed))\n",
+    "model_provider_client = create_nillion_client(model_provider_userkey, model_provider_nodekey)\n",
     "model_provider_party_id = model_provider_client.party_id\n",
     "model_provider_user_id = model_provider_client.user_id"
    ]
@@ -568,60 +565,37 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "model_user_userkey = getUserKeyFromFile(os.getenv(\"NILLION_USERKEY_PATH_PARTY_2\"))\n",
-    "model_user_nodekey = getNodeKeyFromFile(os.getenv(\"NILLION_NODEKEY_PATH_PARTY_2\"))\n",
-    "model_user_client = create_nillion_client(model_user_userkey, model_user_nodekey)\n",
-    "model_user_party_id = model_user_client.party_id\n",
-    "model_user_user_id = create_nillion_client(\n",
-    "    model_user_userkey, model_user_nodekey\n",
-    ").user_id"
+    "party_names = [\"Provider\", \"User\"]\n",
+    "program_name = \"multi_layer_perceptron\"\n",
+    "program_mir_path = f\"target/{program_name}.nada.bin\""
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 16,
    "metadata": {},
+   "outputs": [],
    "source": [
-    "### Upload Nada program to Nillion"
+    "payments_config = create_payments_config(chain_id, grpc_endpoint)\n",
+    "payments_client = LedgerClient(payments_config)\n",
+    "payments_wallet = LocalWallet(\n",
+    "    PrivateKey(bytes.fromhex(os.getenv(\"NILLION_NILCHAIN_PRIVATE_KEY_0\"))),\n",
+    "    prefix=\"nillion\",\n",
+    ")"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "TODO: explain what the Nada program does"
+    "### Upload Nada program to Nillion"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 16,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "async def store_program(\n",
-    "    *,\n",
-    "    client: nillion.NillionClient,\n",
-    "    cluster_id: str,\n",
-    "    user_id: str,\n",
-    "    nada_program_path: str,\n",
-    ") -> Dict[str, str]:\n",
-    "    \"\"\"Stores Nada program binary in Nillion network.\n",
-    "\n",
-    "    Args:\n",
-    "        client (nillion.NillionClient): Client that will upload Nada program.\n",
-    "        cluster_id (str): Nillion cluster ID.\n",
-    "        user_id (str): User ID of user that will upload Nada program.\n",
-    "        nada_program_path (str): Path to Nada program binary.\n",
-    "\n",
-    "    Returns:\n",
-    "        Dict[str, str]: Resulting `action_id` and `program_id`.\n",
-    "    \"\"\"\n",
-    "    action_id = await client.store_program(cluster_id, \"multi_layer_perceptron\", nada_program_path)\n",
-    "    program_id = f\"{user_id}/multi_layer_perceptron\"\n",
-    "\n",
-    "    return {\n",
-    "        \"action_id\": action_id,\n",
-    "        \"program_id\": program_id,\n",
-    "    }"
+    "TODO: explain what the Nada program does"
    ]
   },
   {
@@ -633,26 +607,23 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "✅ Program saved successfully!\n",
-      "action_id: bbdc0628-2272-4405-a63a-7ddf1c238e48\n",
-      "program_id: 3tkrMbd2fQYTX2MSK31drWKepcmYYbE1MKEQUGRrkdAP9kPCaiVMYaMWUd5xkeeZjaxYFq3bKd1Rhki77oqGVQTR/main\n"
+      "Getting quote for operation...\n",
+      "Submitting payment receipt 2 unil, tx hash 32054F2BE5EED437390CDF7A782C6659B8F0C178A398CCF346178E2827C554DB\n",
+      "Stored program. action_id: 3rgqxWd47e171EUwe4RXP9hm45tmoXfuF8fC52S7jcFoQTnU8wPiL7hqWzyV1muak6bEg7iWhudwg4t2pM9XnXcp/multi_layer_perceptron\n",
+      "Stored program_id: 3rgqxWd47e171EUwe4RXP9hm45tmoXfuF8fC52S7jcFoQTnU8wPiL7hqWzyV1muak6bEg7iWhudwg4t2pM9XnXcp/multi_layer_perceptron\n"
      ]
     }
    ],
    "source": [
-    "result_store_program = await store_program(\n",
-    "    client=model_provider_client,\n",
-    "    cluster_id=cluster_id,\n",
-    "    user_id=model_provider_user_id,\n",
-    "    nada_program_path=\"target/multi_layer_perceptron.nada.bin\",\n",
-    ")\n",
-    "\n",
-    "action_id = result_store_program[\"action_id\"]\n",
-    "program_id = result_store_program[\"program_id\"]\n",
-    "\n",
-    "print(\"✅ Program saved successfully!\")\n",
-    "print(\"action_id:\", action_id)\n",
-    "print(\"program_id:\", program_id)"
+    "program_id = await store_program(\n",
+    "    model_provider_client,\n",
+    "    payments_wallet,\n",
+    "    payments_client,\n",
+    "    model_provider_user_id,\n",
+    "    cluster_id,\n",
+    "    program_name,\n",
+    "    program_mir_path,\n",
+    ")"
    ]
   },
   {
@@ -676,95 +647,38 @@
    "cell_type": "code",
    "execution_count": 19,
    "metadata": {},
-   "outputs": [],
-   "source": [
-    "async def store_model(\n",
-    "    *,\n",
-    "    model_client: TorchClient,\n",
-    "    client: nillion.NillionClient,\n",
-    "    cluster_id: str,\n",
-    "    program_id: str,\n",
-    "    party_id: str,\n",
-    "    model_user_user_id: str,\n",
-    "    model_provider_user_id: str,\n",
-    ") -> Dict[str, str]:\n",
-    "    \"\"\"Stores model params in Nillion network.\n",
-    "\n",
-    "    Args:\n",
-    "        model (MyModel): Model object to store in network.\n",
-    "        client (nillion.NillionClient): Nillion client that stores model params.\n",
-    "        cluster_id (str): Nillion cluster ID.\n",
-    "        program_id (str): Program ID of Nada program.\n",
-    "        party_id (str): Party ID of party that will store model params.\n",
-    "        model_user_user_id (str): User ID of user that will get compute permissions.\n",
-    "        model_provider_user_id (str): User ID of user that will provide model params.\n",
-    "        precision (int): Desired precision.\n",
-    "\n",
-    "    Returns:\n",
-    "        Dict[str, str]: Resulting `provider_party_id` and `model_store_id`.\n",
-    "    \"\"\"\n",
-    "\n",
-    "    model_secrets = nillion.Secrets(\n",
-    "        model_client.export_state_as_secrets(\"my_nn\", na.SecretRational)\n",
-    "    )\n",
-    "\n",
-    "    secret_bindings = nillion.ProgramBindings(program_id)\n",
-    "    secret_bindings.add_input_party(\"Party0\", party_id)\n",
-    "\n",
-    "    permissions = nillion.Permissions.default_for_user(model_provider_user_id)\n",
-    "    compute_permissions = {\n",
-    "        model_user_user_id: {program_id},\n",
-    "    }\n",
-    "    # Give permission to model user to run inference\n",
-    "    permissions.add_compute_permissions(compute_permissions)\n",
-    "\n",
-    "    store_id = await client.store_secrets(\n",
-    "        cluster_id, secret_bindings, model_secrets, permissions\n",
-    "    )\n",
-    "\n",
-    "    return {\n",
-    "        \"provider_party_id\": party_id,\n",
-    "        \"model_store_id\": store_id,\n",
-    "    }"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "✅ Model params uploaded successfully!\n",
-      "provider_party_id: 12D3KooWAcTbAaa6LGoCvgB1BPSedAME2ynaEwPLnLYusBDjmSzM\n",
-      "model_store_id: 4b2b1ebb-b3d5-4811-a980-ac858fd6c0cd\n"
+      "Getting quote for operation...\n",
+      "Quote cost is 5570 unil\n",
+      "Submitting payment receipt 5570 unil, tx hash 6A9CB2D7060BE85272B383A6F23E71D34B10B87D27F21173004990831868683A\n"
      ]
     }
    ],
    "source": [
-    "result_store_model = await store_model(\n",
-    "    model_client=model_client,\n",
-    "    client=model_provider_client,\n",
-    "    cluster_id=cluster_id,\n",
-    "    program_id=program_id,\n",
-    "    party_id=model_provider_party_id,\n",
-    "    model_user_user_id=model_user_user_id,\n",
-    "    model_provider_user_id=model_provider_user_id,\n",
+    "model_secrets = nillion.NadaValues(\n",
+    "    model_client.export_state_as_secrets(\"my_nn\", na.SecretRational)\n",
     ")\n",
-    "\n",
-    "provider_party_id = result_store_model[\"provider_party_id\"]\n",
-    "model_store_id = result_store_model[\"model_store_id\"]\n",
-    "\n",
-    "print(\"✅ Model params uploaded successfully!\")\n",
-    "print(\"provider_party_id:\", provider_party_id)\n",
-    "print(\"model_store_id:\", model_store_id)"
+    "permissions = nillion.Permissions.default_for_user(model_provider_client.user_id)\n",
+    "permissions.add_compute_permissions({model_provider_client.user_id: {program_id}})\n",
+    "\n",
+    "model_store_id = await store_secrets(\n",
+    "    model_provider_client,\n",
+    "    payments_wallet,\n",
+    "    payments_client,\n",
+    "    cluster_id,\n",
+    "    model_secrets,\n",
+    "    1,\n",
+    "    permissions,\n",
+    ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -780,7 +694,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
diff --git a/examples/multi_layer_perceptron/02_model_inference.ipynb b/examples/multi_layer_perceptron/02_model_inference.ipynb
index 215799b..db4fcf1 100644
--- a/examples/multi_layer_perceptron/02_model_inference.ipynb
+++ b/examples/multi_layer_perceptron/02_model_inference.ipynb
@@ -9,7 +9,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -30,28 +30,35 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "from typing import Dict\n",
-    "import torch\n",
+    "import os\n",
+    "import sys\n",
+    "\n",
+    "sys.path.append(os.path.abspath(os.path.join(os.getcwd(), os.pardir)))\n",
+    "\n",
     "import json\n",
     "import os\n",
+    "from common.utils import compute, store_secret_array\n",
+    "from nillion_python_helpers import (create_nillion_client,\n",
+    "                                    create_payments_config)\n",
+    "from py_nillion_client import NodeKey, UserKey\n",
     "import py_nillion_client as nillion\n",
     "from torchvision import transforms\n",
+    "import nada_numpy as na\n",
     "from PIL import Image\n",
     "from dotenv import load_dotenv\n",
     "import numpy as np\n",
+    "import torch\n",
     "\n",
-    "import nada_numpy as na\n",
-    "import nada_numpy.client as na_client\n",
-    "import py_nillion_client as nillion\n",
-    "from nillion_python_helpers import (\n",
-    "    create_nillion_client,\n",
-    "    getUserKeyFromFile,\n",
-    "    getNodeKeyFromFile,\n",
-    ")"
+    "from cosmpy.aerial.client import LedgerClient\n",
+    "from cosmpy.aerial.wallet import LocalWallet\n",
+    "from cosmpy.crypto.keypairs import PrivateKey\n",
+    "\n",
+    "home = os.getenv(\"HOME\")\n",
+    "load_dotenv(f\"{home}/.config/nillion/nillion-devnet.env\")"
    ]
   },
   {
@@ -74,20 +81,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Load all Nillion network environment variables\n",
     "assert os.getcwd().endswith(\n",
@@ -98,13 +94,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "cluster_id = os.getenv(\"NILLION_CLUSTER_ID\")\n",
-    "model_user_userkey = getUserKeyFromFile(os.getenv(\"NILLION_USERKEY_PATH_PARTY_2\"))\n",
-    "model_user_nodekey = getNodeKeyFromFile(os.getenv(\"NILLION_NODEKEY_PATH_PARTY_2\"))\n",
+    "grpc_endpoint = os.getenv(\"NILLION_NILCHAIN_GRPC\")\n",
+    "chain_id = os.getenv(\"NILLION_NILCHAIN_CHAIN_ID\")\n",
+    "seed = \"my_seed\"\n",
+    "model_user_userkey = UserKey.from_seed((seed))\n",
+    "model_user_nodekey = NodeKey.from_seed((seed))\n",
     "model_user_client = create_nillion_client(model_user_userkey, model_user_nodekey)\n",
     "model_user_party_id = model_user_client.party_id\n",
     "model_user_user_id = model_user_client.user_id"
@@ -112,19 +111,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "payments_config = create_payments_config(chain_id, grpc_endpoint)\n",
+    "payments_client = LedgerClient(payments_config)\n",
+    "payments_wallet = LocalWallet(\n",
+    "    PrivateKey(bytes.fromhex(os.getenv(\"NILLION_NILCHAIN_PRIVATE_KEY_0\"))),\n",
+    "    prefix=\"nillion\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Program ID:  3tkrMbd2fQYTX2MSK31drWKepcmYYbE1MKEQUGRrkdAP9kPCaiVMYaMWUd5xkeeZjaxYFq3bKd1Rhki77oqGVQTR/main\n",
-      "Model Store ID:  4b2b1ebb-b3d5-4811-a980-ac858fd6c0cd\n",
-      "Model Provider Party ID:  12D3KooWAcTbAaa6LGoCvgB1BPSedAME2ynaEwPLnLYusBDjmSzM\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# This information was provided by the model provider\n",
     "with open(\"data/tmp.json\", \"r\") as provider_variables_file:\n",
@@ -155,7 +158,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -170,20 +173,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(1, 1, 16, 16)"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "test_image_batch = np.array(test_image.unsqueeze(0))\n",
     "test_image_batch.shape  # (B, channels, H, W)"
@@ -198,175 +190,73 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "async def store_images(\n",
-    "    *,\n",
-    "    client: nillion.NillionClient,\n",
-    "    cluster_id: str,\n",
-    "    program_id: str,\n",
-    "    party_id: str,\n",
-    "    user_id: str,\n",
-    "    images: torch.Tensor,\n",
-    ") -> Dict[str, str]:\n",
-    "    \"\"\"Stores text features in Nillion network.\n",
-    "\n",
-    "    Args:\n",
-    "        client (nillion.NillionClient): Nillion client that stores features.\n",
-    "        cluster_id (str): Nillion cluster ID.\n",
-    "        program_id (str): Program ID of Nada program.\n",
-    "        party_id (str): Party ID of party that will store text features.\n",
-    "        user_id (str): User ID of user that will get compute permissions.\n",
-    "        images (torch.Tensor): Image batch.\n",
-    "        precision (int): Scaling factor to convert float to ints.\n",
-    "\n",
-    "    Returns:\n",
-    "        Dict[str, str]: Resulting `model_user_party_id` and `images_store_id`.\n",
-    "    \"\"\"\n",
-    "    secrets = nillion.Secrets(\n",
-    "        na_client.array(images, \"my_input\", nada_type=na.SecretRational)\n",
-    "    )\n",
-    "\n",
-    "    secret_bindings = nillion.ProgramBindings(program_id)\n",
-    "    secret_bindings.add_input_party(\"Party1\", party_id)\n",
+    "permissions = nillion.Permissions.default_for_user(model_user_client.user_id)\n",
+    "permissions.add_compute_permissions({model_user_client.user_id: {program_id}})\n",
     "\n",
-    "    images_store_id = await client.store_secrets(\n",
-    "        cluster_id, secret_bindings, secrets, None\n",
-    "    )\n",
-    "\n",
-    "    return {\n",
-    "        \"model_user_user_id\": user_id,\n",
-    "        \"images_store_id\": images_store_id,\n",
-    "    }"
+    "images_store_id = await store_secret_array(\n",
+    "    model_user_client,\n",
+    "    payments_wallet,\n",
+    "    payments_client,\n",
+    "    cluster_id,\n",
+    "    test_image_batch,\n",
+    "    \"my_input\",\n",
+    "    na.SecretRational,\n",
+    "    1,\n",
+    "    permissions,\n",
+    ")"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 9,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "✅ Images uploaded successfully!\n",
-      "model_user_user_id: 22K41jnxYNoDDck5ZPgZyAG6Lv52G7xjVHVnfvLovYGCMFUsBB7yYRCaeebnDFh9qEW8ki7a7hbjSnNwBUWNkdmv\n",
-      "images_store_id: e057d84b-cea0-4a63-8946-bef676759739\n"
-     ]
-    }
-   ],
    "source": [
-    "result_store_features = await store_images(\n",
-    "    client=model_user_client,\n",
-    "    cluster_id=cluster_id,\n",
-    "    program_id=program_id,\n",
-    "    party_id=model_user_party_id,\n",
-    "    user_id=model_user_user_id,\n",
-    "    images=test_image_batch,\n",
-    ")\n",
-    "\n",
-    "model_user_user_id = result_store_features[\"model_user_user_id\"]\n",
-    "images_store_id = result_store_features[\"images_store_id\"]\n",
-    "\n",
-    "print(\"✅ Images uploaded successfully!\")\n",
-    "print(\"model_user_user_id:\", model_user_user_id)\n",
-    "print(\"images_store_id:\", images_store_id)"
+    "### Run inference & check result"
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": null,
    "metadata": {},
+   "outputs": [],
    "source": [
-    "### Run inference & check result"
+    "compute_bindings = nillion.ProgramBindings(program_id)\n",
+    "\n",
+    "compute_bindings.add_input_party(\"Provider\", model_provider_party_id)\n",
+    "compute_bindings.add_input_party(\"User\", model_user_party_id)\n",
+    "compute_bindings.add_output_party(\"User\", model_user_party_id)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "async def run_inference(\n",
-    "    *,\n",
-    "    client: nillion.NillionClient,\n",
-    "    cluster_id: str,\n",
-    "    program_id: str,\n",
-    "    model_user_party_id: str,\n",
-    "    model_provider_party_id: str,\n",
-    "    model_store_id: str,\n",
-    "    images_store_id: str,\n",
-    ") -> Dict[str, str | float]:\n",
-    "    \"\"\"Runs blind inference on the Nillion network by executing the Nada program on the uploaded data.\n",
-    "\n",
-    "    Args:\n",
-    "        client (nillion.NillionClient): Nillion client that runs inference.\n",
-    "        cluster_id (str): Nillion cluster ID.\n",
-    "        program_id (str): Program ID of Nada program.\n",
-    "        model_user_party_id (str): Party ID of party that will run inference.\n",
-    "        model_user_party_id (str): Party ID of party that will provide model params.\n",
-    "        model_store_id (str): Store ID that points to the model params in the Nillion network.\n",
-    "        images_store_id (str): Store ID that points to the images in the Nillion network.\n",
-    "        precision (int): Scaling factor to convert float to ints.s\n",
-    "\n",
-    "    Returns:\n",
-    "        Dict[str, str | float]: Resulting `compute_id`, `output_0` and `output_1`.\n",
-    "    \"\"\"\n",
-    "    compute_bindings = nillion.ProgramBindings(program_id)\n",
-    "    compute_bindings.add_input_party(\"Party0\", model_user_party_id)\n",
-    "    compute_bindings.add_input_party(\"Party1\", model_provider_party_id)\n",
-    "    compute_bindings.add_output_party(\"Party1\", model_user_party_id)\n",
-    "\n",
-    "    _ = await client.compute(\n",
-    "        cluster_id,\n",
-    "        compute_bindings,\n",
-    "        [images_store_id, model_store_id],\n",
-    "        nillion.Secrets({}),\n",
-    "        nillion.PublicVariables({}),\n",
-    "    )\n",
-    "\n",
-    "    while True:\n",
-    "        compute_event = await client.next_compute_event()\n",
-    "        if isinstance(compute_event, nillion.ComputeFinishedEvent):\n",
-    "            inference_result = compute_event.result.value\n",
-    "            break\n",
-    "\n",
-    "    return {\n",
-    "        \"compute_id\": compute_event.uuid,\n",
-    "        \"output_0\": na_client.float_from_rational(inference_result[\"my_output_0_0\"]),\n",
-    "        \"output_1\": na_client.float_from_rational(inference_result[\"my_output_0_1\"]),\n",
-    "    }"
+    "result = await compute(\n",
+    "    model_user_client,\n",
+    "    payments_wallet,\n",
+    "    payments_client,\n",
+    "    program_id,\n",
+    "    cluster_id,\n",
+    "    compute_bindings,\n",
+    "    [model_store_id, images_store_id],\n",
+    "    nillion.NadaValues({}),\n",
+    "    verbose=True,\n",
+    ")\n",
+    "result"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'compute_id': '67557c31-d7db-4cd3-bb77-e1029011cea7',\n",
-       " 'output_0': -1.665313720703125,\n",
-       " 'output_1': 0.876068115234375}"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "result_inference = await run_inference(\n",
-    "    client=model_user_client,\n",
-    "    cluster_id=cluster_id,\n",
-    "    program_id=program_id,\n",
-    "    model_user_party_id=model_user_party_id,\n",
-    "    model_provider_party_id=model_provider_party_id,\n",
-    "    model_store_id=model_store_id,\n",
-    "    images_store_id=images_store_id,\n",
-    ")\n",
+    "result_inference = {key: na.float_from_rational(value) for key, value in result.items()}\n",
     "result_inference"
    ]
   },
@@ -379,7 +269,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -389,7 +279,7 @@
     "\n",
     "    def __init__(self) -> None:\n",
     "        \"\"\"Model is a two layers and an activations\"\"\"\n",
-    "        super(MyNN, self).__init__()\n",
+    "        super().__init__()\n",
     "        self.conv1 = torch.nn.Conv2d(\n",
     "            in_channels=1, out_channels=2, kernel_size=3, stride=4, padding=1\n",
     "        )\n",
@@ -411,20 +301,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<All keys matched successfully>"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "my_model = MyNN()\n",
     "my_model.load_state_dict(torch.load(\"./data/my_model.pt\"))"
@@ -432,40 +311,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tensor([0.0730, 0.9270], grad_fn=<SoftmaxBackward0>)"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "torch.softmax(my_model(test_image.unsqueeze(0))[0], dim=0)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tensor([0.0730, 0.9270])"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "torch.softmax(\n",
     "    torch.Tensor([result_inference[\"output_0\"], result_inference[\"output_1\"]]), dim=0\n",
@@ -489,7 +346,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.3"
+   "version": "3.12.2"
   }
  },
  "nbformat": 4,
diff --git a/examples/multi_layer_perceptron/src/multi_layer_perceptron.py b/examples/multi_layer_perceptron/src/multi_layer_perceptron.py
index 3ace617..7f21328 100644
--- a/examples/multi_layer_perceptron/src/multi_layer_perceptron.py
+++ b/examples/multi_layer_perceptron/src/multi_layer_perceptron.py
@@ -1,24 +1,26 @@
 import nada_numpy as na
 from my_nn import MyNN
+from nada_dsl import Party
 
 
 def nada_main():
-    # Step 1: We use Nada NumPy wrapper to create "Party0" and "Party1"
-    parties = na.parties(2)
+    # Step 1: We use Nada NumPy wrapper to create "User" and "Provider"
+    user = Party("User")
+    provider = Party("Provider")
 
     # Step 2: Instantiate model object
     my_model = MyNN()
 
     # Step 3: Load model weights from Nillion network by passing model name (acts as ID)
     # In this examples Party0 provides the model and Party1 runs inference
-    my_model.load_state_from_network("my_nn", parties[0], na.SecretRational)
+    my_model.load_state_from_network("my_nn", provider, na.SecretRational)
 
     # Step 4: Load input data to be used for inference (provided by Party1)
-    my_input = na.array((1, 1, 16, 16), parties[1], "my_input", na.SecretRational)
+    my_input = na.array((1, 1, 16, 16), user, "my_input", na.SecretRational)
 
     # Step 5: Compute inference
     # Note: completely equivalent to `my_model.forward(...)`
     result = my_model(my_input)
 
     # Step 6: We can use result.output() to produce the output for Party1 and variable name "my_output"
-    return result.output(parties[1], "my_output")
+    return result.output(user, "my_output")
diff --git a/examples/multi_layer_perceptron/src/my_nn.py b/examples/multi_layer_perceptron/src/my_nn.py
index 9ad6746..20e9caf 100644
--- a/examples/multi_layer_perceptron/src/my_nn.py
+++ b/examples/multi_layer_perceptron/src/my_nn.py
@@ -8,14 +8,12 @@ class MyNN(nn.Module):
 
     def __init__(self) -> None:
         """Model is a two layers and an activations"""
-        super(MyNN, self).__init__()
-        # Input size (1, 1, 16, 16) --> Output size (1, 2)
+        super().__init__()
         self.conv1 = nn.Conv2d(
-            in_channels=1, out_channels=2, kernel_size=3, padding=1, stride=4
+            in_channels=1, out_channels=2, kernel_size=3, padding=1, stride=3
         )
-        # Input size (1, 2) --> Output size (1, 2)
         self.pool = nn.AvgPool2d(kernel_size=2, stride=2)
-        self.fc1 = nn.Linear(in_features=8, out_features=2)
+        self.fc1 = nn.Linear(in_features=18, out_features=2)
 
         self.relu = nn.ReLU()
         self.flatten = nn.Flatten()
diff --git a/examples/multi_layer_perceptron/tests/multi_layer_perceptron.yaml b/examples/multi_layer_perceptron/tests/multi_layer_perceptron.yaml
index f2de756..9a44f01 100644
--- a/examples/multi_layer_perceptron/tests/multi_layer_perceptron.yaml
+++ b/examples/multi_layer_perceptron/tests/multi_layer_perceptron.yaml
@@ -1,326 +1,634 @@
 ---
 program: multi_layer_perceptron
 inputs:
-  secrets:
-    my_input_0_0_12_7:
-      SecretInteger: "3"
-    my_input_0_0_11_12:
-      SecretInteger: "3"
-    my_input_0_0_0_11:
-      SecretInteger: "3"
-    my_input_0_0_8_0:
-      SecretInteger: "3"
-    my_nn_fc1.weight_0_4:
-      SecretInteger: "3"
-    my_input_0_0_8_5:
-      SecretInteger: "3"
-    my_input_0_0_3_0:
-      SecretInteger: "3"
-    my_input_0_0_9_3:
-      SecretInteger: "3"
-    my_nn_fc1.weight_0_7:
-      SecretInteger: "3"
-    my_input_0_0_13_3:
-      SecretInteger: "3"
-    my_input_0_0_11_9:
-      SecretInteger: "3"
-    my_input_0_0_9_8:
-      SecretInteger: "3"
-    my_input_0_0_3_12:
-      SecretInteger: "3"
-    my_input_0_0_4_3:
-      SecretInteger: "3"
-    my_nn_conv1.weight_1_0_2_2:
-      SecretInteger: "3"
-    my_input_0_0_1_1:
-      SecretInteger: "3"
-    my_nn_fc1.weight_0_5:
-      SecretInteger: "3"
-    my_input_0_0_1_0:
-      SecretInteger: "3"
-    my_input_0_0_9_1:
-      SecretInteger: "3"
-    my_input_0_0_4_8:
-      SecretInteger: "3"
-    my_input_0_0_4_9:
-      SecretInteger: "3"
-    my_input_0_0_1_7:
-      SecretInteger: "3"
-    my_nn_fc1.bias_0:
-      SecretInteger: "3"
-    my_nn_conv1.weight_0_0_2_2:
-      SecretInteger: "3"
-    my_nn_fc1.weight_0_1:
-      SecretInteger: "3"
-    my_nn_conv1.weight_1_0_1_0:
-      SecretInteger: "3"
-    my_input_0_0_12_9:
-      SecretInteger: "3"
-    my_input_0_0_8_1:
-      SecretInteger: "3"
-    my_nn_conv1.weight_1_0_0_1:
-      SecretInteger: "3"
-    my_input_0_0_13_5:
-      SecretInteger: "3"
-    my_input_0_0_0_4:
-      SecretInteger: "3"
-    my_input_0_0_11_7:
-      SecretInteger: "3"
-    my_input_0_0_0_9:
-      SecretInteger: "3"
-    my_nn_fc1.weight_1_1:
-      SecretInteger: "3"
-    my_input_0_0_0_0:
-      SecretInteger: "3"
-    my_input_0_0_8_4:
-      SecretInteger: "3"
-    my_input_0_0_7_9:
-      SecretInteger: "3"
-    my_nn_fc1.weight_1_7:
-      SecretInteger: "3"
-    my_input_0_0_4_13:
-      SecretInteger: "3"
-    my_input_0_0_12_0:
-      SecretInteger: "3"
-    my_input_0_0_1_12:
-      SecretInteger: "3"
-    my_nn_conv1.weight_0_0_1_2:
-      SecretInteger: "3"
-    my_nn_fc1.weight_0_6:
-      SecretInteger: "3"
-    my_input_0_0_0_1:
-      SecretInteger: "3"
-    my_input_0_0_7_1:
-      SecretInteger: "3"
-    my_nn_conv1.weight_0_0_2_1:
-      SecretInteger: "3"
-    my_input_0_0_3_7:
-      SecretInteger: "3"
-    my_input_0_0_0_3:
-      SecretInteger: "3"
-    my_input_0_0_5_1:
-      SecretInteger: "3"
-    my_nn_conv1.weight_1_0_0_2:
-      SecretInteger: "3"
-    my_input_0_0_3_13:
-      SecretInteger: "3"
-    my_input_0_0_4_1:
-      SecretInteger: "3"
-    my_input_0_0_7_11:
-      SecretInteger: "3"
-    my_input_0_0_3_9:
-      SecretInteger: "3"
-    my_input_0_0_8_9:
-      SecretInteger: "3"
-    my_input_0_0_11_1:
-      SecretInteger: "3"
-    my_input_0_0_12_12:
-      SecretInteger: "3"
-    my_input_0_0_9_0:
-      SecretInteger: "3"
-    my_input_0_0_4_0:
-      SecretInteger: "3"
-    my_input_0_0_8_7:
-      SecretInteger: "3"
-    my_input_0_0_3_3:
-      SecretInteger: "3"
-    my_input_0_0_7_5:
-      SecretInteger: "3"
-    my_input_0_0_9_5:
-      SecretInteger: "3"
-    my_input_0_0_8_11:
-      SecretInteger: "3"
-    my_input_0_0_13_8:
-      SecretInteger: "3"
-    my_input_0_0_5_5:
-      SecretInteger: "3"
-    my_input_0_0_7_7:
-      SecretInteger: "3"
-    my_input_0_0_4_11:
-      SecretInteger: "3"
-    my_input_0_0_12_5:
-      SecretInteger: "3"
-    my_nn_conv1.weight_1_0_2_0:
-      SecretInteger: "3"
-    my_nn_fc1.weight_1_2:
-      SecretInteger: "3"
-    my_input_0_0_5_12:
-      SecretInteger: "3"
-    my_input_0_0_13_13:
-      SecretInteger: "3"
-    my_input_0_0_5_7:
-      SecretInteger: "3"
-    my_input_0_0_7_3:
-      SecretInteger: "3"
-    my_input_0_0_1_5:
-      SecretInteger: "3"
-    my_input_0_0_1_9:
-      SecretInteger: "3"
-    my_nn_fc1.weight_1_0:
-      SecretInteger: "3"
-    my_input_0_0_7_12:
-      SecretInteger: "3"
-    my_input_0_0_11_11:
-      SecretInteger: "3"
-    my_input_0_0_8_8:
-      SecretInteger: "3"
-    my_input_0_0_5_8:
-      SecretInteger: "3"
-    my_input_0_0_12_3:
-      SecretInteger: "3"
-    my_input_0_0_1_4:
-      SecretInteger: "3"
-    my_input_0_0_11_8:
-      SecretInteger: "3"
-    my_input_0_0_0_13:
-      SecretInteger: "3"
-    my_input_0_0_12_4:
-      SecretInteger: "3"
-    my_input_0_0_3_11:
-      SecretInteger: "3"
-    my_input_0_0_11_0:
-      SecretInteger: "3"
-    my_input_0_0_8_12:
-      SecretInteger: "3"
-    my_input_0_0_0_7:
-      SecretInteger: "3"
-    my_input_0_0_9_12:
-      SecretInteger: "3"
-    my_nn_conv1.weight_1_0_1_2:
-      SecretInteger: "3"
-    my_input_0_0_0_12:
-      SecretInteger: "3"
-    my_input_0_0_13_4:
-      SecretInteger: "3"
-    my_input_0_0_5_13:
-      SecretInteger: "3"
-    my_nn_conv1.bias_0:
-      SecretInteger: "3"
-    my_nn_conv1.weight_1_0_1_1:
-      SecretInteger: "3"
-    my_input_0_0_7_8:
-      SecretInteger: "3"
-    my_input_0_0_1_11:
-      SecretInteger: "3"
-    my_input_0_0_8_3:
-      SecretInteger: "3"
-    my_nn_conv1.weight_1_0_2_1:
-      SecretInteger: "3"
-    my_input_0_0_11_5:
-      SecretInteger: "3"
-    my_nn_fc1.weight_1_3:
-      SecretInteger: "3"
-    my_input_0_0_3_8:
-      SecretInteger: "3"
-    my_input_0_0_9_7:
-      SecretInteger: "3"
-    my_input_0_0_12_11:
-      SecretInteger: "3"
-    my_nn_fc1.weight_1_6:
-      SecretInteger: "3"
-    my_nn_fc1.bias_1:
-      SecretInteger: "3"
-    my_input_0_0_7_13:
-      SecretInteger: "3"
-    my_input_0_0_11_3:
-      SecretInteger: "3"
-    my_input_0_0_9_4:
-      SecretInteger: "3"
-    my_nn_conv1.weight_1_0_0_0:
-      SecretInteger: "3"
-    my_input_0_0_13_12:
-      SecretInteger: "3"
-    my_input_0_0_0_5:
-      SecretInteger: "3"
-    my_input_0_0_5_0:
-      SecretInteger: "3"
-    my_input_0_0_7_0:
-      SecretInteger: "3"
-    my_input_0_0_4_4:
-      SecretInteger: "3"
-    my_input_0_0_4_12:
-      SecretInteger: "3"
-    my_input_0_0_5_9:
-      SecretInteger: "3"
-    my_input_0_0_12_8:
-      SecretInteger: "3"
-    my_input_0_0_13_9:
-      SecretInteger: "3"
-    my_input_0_0_1_13:
-      SecretInteger: "3"
-    my_input_0_0_12_13:
-      SecretInteger: "3"
-    my_input_0_0_3_5:
-      SecretInteger: "3"
-    my_input_0_0_11_4:
-      SecretInteger: "3"
-    my_nn_conv1.weight_0_0_0_0:
-      SecretInteger: "3"
-    my_input_0_0_5_11:
-      SecretInteger: "3"
-    my_input_0_0_13_1:
-      SecretInteger: "3"
-    my_input_0_0_8_13:
-      SecretInteger: "3"
-    my_input_0_0_3_4:
-      SecretInteger: "3"
-    my_nn_conv1.bias_1:
-      SecretInteger: "3"
-    my_input_0_0_5_4:
-      SecretInteger: "3"
-    my_input_0_0_4_5:
-      SecretInteger: "3"
-    my_input_0_0_0_8:
-      SecretInteger: "3"
-    my_input_0_0_4_7:
-      SecretInteger: "3"
-    my_input_0_0_9_11:
-      SecretInteger: "3"
-    my_input_0_0_13_11:
-      SecretInteger: "3"
-    my_nn_conv1.weight_0_0_2_0:
-      SecretInteger: "3"
-    my_nn_conv1.weight_0_0_0_1:
-      SecretInteger: "3"
-    my_input_0_0_13_7:
-      SecretInteger: "3"
-    my_nn_conv1.weight_0_0_0_2:
-      SecretInteger: "3"
-    my_nn_conv1.weight_0_0_1_1:
-      SecretInteger: "3"
-    my_nn_fc1.weight_0_0:
-      SecretInteger: "3"
-    my_input_0_0_1_3:
-      SecretInteger: "3"
-    my_input_0_0_5_3:
-      SecretInteger: "3"
-    my_nn_conv1.weight_0_0_1_0:
-      SecretInteger: "3"
-    my_input_0_0_12_1:
-      SecretInteger: "3"
-    my_input_0_0_9_9:
-      SecretInteger: "3"
-    my_input_0_0_9_13:
-      SecretInteger: "3"
-    my_input_0_0_11_13:
-      SecretInteger: "3"
-    my_nn_fc1.weight_1_4:
-      SecretInteger: "3"
-    my_input_0_0_3_1:
-      SecretInteger: "3"
-    my_nn_fc1.weight_1_5:
-      SecretInteger: "3"
-    my_nn_fc1.weight_0_2:
-      SecretInteger: "3"
-    my_input_0_0_13_0:
-      SecretInteger: "3"
-    my_input_0_0_1_8:
-      SecretInteger: "3"
-    my_input_0_0_7_4:
-      SecretInteger: "3"
-    my_nn_fc1.weight_0_3:
-      SecretInteger: "3"
-  public_variables: {}
+  my_input_0_0_1_6:
+    SecretInteger: "3"
+  my_nn_fc1.weight_0_16:
+    SecretInteger: "3"
+  my_input_0_0_8_15:
+    SecretInteger: "3"
+  my_nn_fc1.weight_0_12:
+    SecretInteger: "3"
+  my_input_0_0_14_9:
+    SecretInteger: "3"
+  my_input_0_0_14_7:
+    SecretInteger: "3"
+  my_input_0_0_2_7:
+    SecretInteger: "3"
+  my_input_0_0_11_3:
+    SecretInteger: "3"
+  my_input_0_0_13_9:
+    SecretInteger: "3"
+  my_input_0_0_8_2:
+    SecretInteger: "3"
+  my_input_0_0_0_11:
+    SecretInteger: "3"
+  my_input_0_0_4_10:
+    SecretInteger: "3"
+  my_input_0_0_0_1:
+    SecretInteger: "3"
+  my_input_0_0_13_7:
+    SecretInteger: "3"
+  my_input_0_0_10_1:
+    SecretInteger: "3"
+  my_nn_fc1.bias_1:
+    SecretInteger: "3"
+  my_input_0_0_12_14:
+    SecretInteger: "3"
+  my_input_0_0_11_12:
+    SecretInteger: "3"
+  my_input_0_0_15_10:
+    SecretInteger: "3"
+  my_input_0_0_14_5:
+    SecretInteger: "3"
+  my_input_0_0_15_3:
+    SecretInteger: "3"
+  my_input_0_0_5_15:
+    SecretInteger: "3"
+  my_input_0_0_7_4:
+    SecretInteger: "3"
+  my_input_0_0_3_14:
+    SecretInteger: "3"
+  my_input_0_0_2_13:
+    SecretInteger: "3"
+  my_input_0_0_2_12:
+    SecretInteger: "3"
+  my_input_0_0_0_12:
+    SecretInteger: "3"
+  my_input_0_0_11_6:
+    SecretInteger: "3"
+  my_input_0_0_4_8:
+    SecretInteger: "3"
+  my_nn_fc1.weight_0_9:
+    SecretInteger: "3"
+  my_input_0_0_13_3:
+    SecretInteger: "3"
+  my_nn_conv1.weight_1_0_0_0:
+    SecretInteger: "3"
+  my_input_0_0_2_14:
+    SecretInteger: "3"
+  my_input_0_0_10_10:
+    SecretInteger: "3"
+  my_nn_fc1.weight_1_12:
+    SecretInteger: "3"
+  my_input_0_0_13_8:
+    SecretInteger: "3"
+  my_nn_conv1.weight_1_0_2_0:
+    SecretInteger: "3"
+  my_input_0_0_1_11:
+    SecretInteger: "3"
+  my_nn_conv1.weight_1_0_2_2:
+    SecretInteger: "3"
+  my_input_0_0_3_15:
+    SecretInteger: "3"
+  my_input_0_0_4_14:
+    SecretInteger: "3"
+  my_input_0_0_9_4:
+    SecretInteger: "3"
+  my_nn_fc1.weight_1_0:
+    SecretInteger: "3"
+  my_input_0_0_11_14:
+    SecretInteger: "3"
+  my_input_0_0_12_6:
+    SecretInteger: "3"
+  my_input_0_0_7_11:
+    SecretInteger: "3"
+  my_input_0_0_5_1:
+    SecretInteger: "3"
+  my_input_0_0_0_8:
+    SecretInteger: "3"
+  my_input_0_0_12_3:
+    SecretInteger: "3"
+  my_input_0_0_15_1:
+    SecretInteger: "3"
+  my_nn_fc1.weight_1_13:
+    SecretInteger: "3"
+  my_input_0_0_12_0:
+    SecretInteger: "3"
+  my_input_0_0_8_9:
+    SecretInteger: "3"
+  my_nn_fc1.weight_1_6:
+    SecretInteger: "3"
+  my_input_0_0_7_7:
+    SecretInteger: "3"
+  my_input_0_0_0_15:
+    SecretInteger: "3"
+  my_nn_conv1.weight_1_0_1_0:
+    SecretInteger: "3"
+  my_input_0_0_13_14:
+    SecretInteger: "3"
+  my_input_0_0_1_1:
+    SecretInteger: "3"
+  my_input_0_0_11_0:
+    SecretInteger: "3"
+  my_input_0_0_5_14:
+    SecretInteger: "3"
+  my_input_0_0_15_2:
+    SecretInteger: "3"
+  my_input_0_0_8_3:
+    SecretInteger: "3"
+  my_input_0_0_9_2:
+    SecretInteger: "3"
+  my_input_0_0_9_1:
+    SecretInteger: "3"
+  my_nn_fc1.weight_1_10:
+    SecretInteger: "3"
+  my_input_0_0_10_4:
+    SecretInteger: "3"
+  my_input_0_0_6_0:
+    SecretInteger: "3"
+  my_input_0_0_6_4:
+    SecretInteger: "3"
+  my_nn_conv1.weight_1_0_1_1:
+    SecretInteger: "3"
+  my_input_0_0_12_7:
+    SecretInteger: "3"
+  my_nn_conv1.bias_1:
+    SecretInteger: "3"
+  my_nn_conv1.weight_0_0_1_1:
+    SecretInteger: "3"
+  my_input_0_0_5_12:
+    SecretInteger: "3"
+  my_input_0_0_5_10:
+    SecretInteger: "3"
+  my_input_0_0_9_0:
+    SecretInteger: "3"
+  my_nn_fc1.weight_0_10:
+    SecretInteger: "3"
+  my_nn_conv1.weight_1_0_2_1:
+    SecretInteger: "3"
+  my_input_0_0_9_3:
+    SecretInteger: "3"
+  my_nn_fc1.weight_0_7:
+    SecretInteger: "3"
+  my_input_0_0_4_4:
+    SecretInteger: "3"
+  my_nn_fc1.weight_1_17:
+    SecretInteger: "3"
+  my_input_0_0_14_11:
+    SecretInteger: "3"
+  my_input_0_0_12_8:
+    SecretInteger: "3"
+  my_input_0_0_9_5:
+    SecretInteger: "3"
+  my_input_0_0_10_12:
+    SecretInteger: "3"
+  my_nn_fc1.weight_0_3:
+    SecretInteger: "3"
+  my_input_0_0_13_5:
+    SecretInteger: "3"
+  my_input_0_0_7_6:
+    SecretInteger: "3"
+  my_nn_conv1.weight_0_0_1_2:
+    SecretInteger: "3"
+  my_input_0_0_12_2:
+    SecretInteger: "3"
+  my_input_0_0_12_11:
+    SecretInteger: "3"
+  my_input_0_0_13_11:
+    SecretInteger: "3"
+  my_input_0_0_14_6:
+    SecretInteger: "3"
+  my_input_0_0_11_1:
+    SecretInteger: "3"
+  my_input_0_0_5_11:
+    SecretInteger: "3"
+  my_input_0_0_7_1:
+    SecretInteger: "3"
+  my_input_0_0_2_10:
+    SecretInteger: "3"
+  my_input_0_0_1_2:
+    SecretInteger: "3"
+  my_input_0_0_11_11:
+    SecretInteger: "3"
+  my_input_0_0_9_11:
+    SecretInteger: "3"
+  my_nn_fc1.weight_0_5:
+    SecretInteger: "3"
+  my_input_0_0_10_13:
+    SecretInteger: "3"
+  my_input_0_0_8_6:
+    SecretInteger: "3"
+  my_input_0_0_7_5:
+    SecretInteger: "3"
+  my_input_0_0_15_14:
+    SecretInteger: "3"
+  my_input_0_0_10_15:
+    SecretInteger: "3"
+  my_input_0_0_6_1:
+    SecretInteger: "3"
+  my_input_0_0_2_9:
+    SecretInteger: "3"
+  my_nn_conv1.weight_0_0_0_2:
+    SecretInteger: "3"
+  my_input_0_0_8_13:
+    SecretInteger: "3"
+  my_input_0_0_8_7:
+    SecretInteger: "3"
+  my_input_0_0_15_4:
+    SecretInteger: "3"
+  my_input_0_0_1_9:
+    SecretInteger: "3"
+  my_input_0_0_0_5:
+    SecretInteger: "3"
+  my_nn_fc1.weight_0_15:
+    SecretInteger: "3"
+  my_input_0_0_12_5:
+    SecretInteger: "3"
+  my_input_0_0_9_7:
+    SecretInteger: "3"
+  my_input_0_0_0_7:
+    SecretInteger: "3"
+  my_input_0_0_4_1:
+    SecretInteger: "3"
+  my_input_0_0_3_10:
+    SecretInteger: "3"
+  my_input_0_0_0_9:
+    SecretInteger: "3"
+  my_input_0_0_1_4:
+    SecretInteger: "3"
+  my_input_0_0_7_15:
+    SecretInteger: "3"
+  my_input_0_0_11_13:
+    SecretInteger: "3"
+  my_input_0_0_12_9:
+    SecretInteger: "3"
+  my_input_0_0_1_10:
+    SecretInteger: "3"
+  my_input_0_0_13_1:
+    SecretInteger: "3"
+  my_input_0_0_11_15:
+    SecretInteger: "3"
+  my_input_0_0_0_10:
+    SecretInteger: "3"
+  my_input_0_0_1_15:
+    SecretInteger: "3"
+  my_input_0_0_1_7:
+    SecretInteger: "3"
+  my_input_0_0_3_4:
+    SecretInteger: "3"
+  my_input_0_0_11_5:
+    SecretInteger: "3"
+  my_input_0_0_12_13:
+    SecretInteger: "3"
+  my_input_0_0_11_4:
+    SecretInteger: "3"
+  my_nn_fc1.weight_0_0:
+    SecretInteger: "3"
+  my_input_0_0_14_13:
+    SecretInteger: "3"
+  my_input_0_0_13_12:
+    SecretInteger: "3"
+  my_input_0_0_10_8:
+    SecretInteger: "3"
+  my_input_0_0_13_15:
+    SecretInteger: "3"
+  my_input_0_0_3_0:
+    SecretInteger: "3"
+  my_input_0_0_0_2:
+    SecretInteger: "3"
+  my_input_0_0_3_6:
+    SecretInteger: "3"
+  my_input_0_0_11_8:
+    SecretInteger: "3"
+  my_nn_fc1.weight_0_1:
+    SecretInteger: "3"
+  my_input_0_0_6_6:
+    SecretInteger: "3"
+  my_input_0_0_1_8:
+    SecretInteger: "3"
+  my_input_0_0_1_0:
+    SecretInteger: "3"
+  my_input_0_0_14_12:
+    SecretInteger: "3"
+  my_input_0_0_15_13:
+    SecretInteger: "3"
+  my_input_0_0_7_14:
+    SecretInteger: "3"
+  my_input_0_0_3_11:
+    SecretInteger: "3"
+  my_input_0_0_14_10:
+    SecretInteger: "3"
+  my_input_0_0_14_1:
+    SecretInteger: "3"
+  my_input_0_0_14_3:
+    SecretInteger: "3"
+  my_input_0_0_10_6:
+    SecretInteger: "3"
+  my_input_0_0_10_0:
+    SecretInteger: "3"
+  my_input_0_0_8_8:
+    SecretInteger: "3"
+  my_input_0_0_15_12:
+    SecretInteger: "3"
+  my_input_0_0_3_1:
+    SecretInteger: "3"
+  my_input_0_0_2_8:
+    SecretInteger: "3"
+  my_input_0_0_7_12:
+    SecretInteger: "3"
+  my_input_0_0_0_0:
+    SecretInteger: "3"
+  my_input_0_0_4_0:
+    SecretInteger: "3"
+  my_input_0_0_15_9:
+    SecretInteger: "3"
+  my_input_0_0_0_13:
+    SecretInteger: "3"
+  my_input_0_0_2_4:
+    SecretInteger: "3"
+  my_input_0_0_1_12:
+    SecretInteger: "3"
+  my_input_0_0_11_10:
+    SecretInteger: "3"
+  my_nn_fc1.weight_1_7:
+    SecretInteger: "3"
+  my_input_0_0_8_10:
+    SecretInteger: "3"
+  my_input_0_0_2_2:
+    SecretInteger: "3"
+  my_input_0_0_13_10:
+    SecretInteger: "3"
+  my_input_0_0_6_13:
+    SecretInteger: "3"
+  my_input_0_0_8_4:
+    SecretInteger: "3"
+  my_input_0_0_6_9:
+    SecretInteger: "3"
+  my_input_0_0_14_4:
+    SecretInteger: "3"
+  my_input_0_0_6_14:
+    SecretInteger: "3"
+  my_input_0_0_6_11:
+    SecretInteger: "3"
+  my_input_0_0_4_3:
+    SecretInteger: "3"
+  my_input_0_0_7_13:
+    SecretInteger: "3"
+  my_input_0_0_4_6:
+    SecretInteger: "3"
+  my_input_0_0_10_2:
+    SecretInteger: "3"
+  my_input_0_0_9_15:
+    SecretInteger: "3"
+  my_nn_fc1.weight_0_14:
+    SecretInteger: "3"
+  my_input_0_0_0_14:
+    SecretInteger: "3"
+  my_input_0_0_9_6:
+    SecretInteger: "3"
+  my_input_0_0_10_11:
+    SecretInteger: "3"
+  my_input_0_0_3_12:
+    SecretInteger: "3"
+  my_input_0_0_1_13:
+    SecretInteger: "3"
+  my_input_0_0_13_2:
+    SecretInteger: "3"
+  my_input_0_0_3_7:
+    SecretInteger: "3"
+  my_input_0_0_3_2:
+    SecretInteger: "3"
+  my_input_0_0_4_5:
+    SecretInteger: "3"
+  my_input_0_0_12_4:
+    SecretInteger: "3"
+  my_input_0_0_2_15:
+    SecretInteger: "3"
+  my_input_0_0_15_11:
+    SecretInteger: "3"
+  my_input_0_0_13_0:
+    SecretInteger: "3"
+  my_input_0_0_10_7:
+    SecretInteger: "3"
+  my_input_0_0_7_8:
+    SecretInteger: "3"
+  my_input_0_0_4_13:
+    SecretInteger: "3"
+  my_input_0_0_4_2:
+    SecretInteger: "3"
+  my_input_0_0_8_1:
+    SecretInteger: "3"
+  my_input_0_0_14_14:
+    SecretInteger: "3"
+  my_input_0_0_5_0:
+    SecretInteger: "3"
+  my_nn_fc1.bias_0:
+    SecretInteger: "3"
+  my_nn_fc1.weight_1_15:
+    SecretInteger: "3"
+  my_input_0_0_5_4:
+    SecretInteger: "3"
+  my_input_0_0_0_4:
+    SecretInteger: "3"
+  my_nn_fc1.weight_1_8:
+    SecretInteger: "3"
+  my_input_0_0_9_9:
+    SecretInteger: "3"
+  my_input_0_0_2_3:
+    SecretInteger: "3"
+  my_input_0_0_0_3:
+    SecretInteger: "3"
+  my_input_0_0_9_13:
+    SecretInteger: "3"
+  my_nn_fc1.weight_1_9:
+    SecretInteger: "3"
+  my_input_0_0_3_5:
+    SecretInteger: "3"
+  my_input_0_0_7_0:
+    SecretInteger: "3"
+  my_input_0_0_6_10:
+    SecretInteger: "3"
+  my_nn_fc1.weight_0_6:
+    SecretInteger: "3"
+  my_input_0_0_8_5:
+    SecretInteger: "3"
+  my_input_0_0_10_9:
+    SecretInteger: "3"
+  my_input_0_0_5_9:
+    SecretInteger: "3"
+  my_nn_fc1.weight_1_16:
+    SecretInteger: "3"
+  my_input_0_0_14_15:
+    SecretInteger: "3"
+  my_input_0_0_2_6:
+    SecretInteger: "3"
+  my_input_0_0_14_0:
+    SecretInteger: "3"
+  my_input_0_0_10_3:
+    SecretInteger: "3"
+  my_input_0_0_10_5:
+    SecretInteger: "3"
+  my_input_0_0_4_11:
+    SecretInteger: "3"
+  my_input_0_0_1_14:
+    SecretInteger: "3"
+  my_nn_conv1.weight_1_0_1_2:
+    SecretInteger: "3"
+  my_input_0_0_15_8:
+    SecretInteger: "3"
+  my_input_0_0_3_13:
+    SecretInteger: "3"
+  my_nn_conv1.weight_1_0_0_1:
+    SecretInteger: "3"
+  my_input_0_0_2_0:
+    SecretInteger: "3"
+  my_input_0_0_1_3:
+    SecretInteger: "3"
+  my_nn_fc1.weight_1_11:
+    SecretInteger: "3"
+  my_nn_fc1.weight_0_13:
+    SecretInteger: "3"
+  my_input_0_0_12_1:
+    SecretInteger: "3"
+  my_input_0_0_15_7:
+    SecretInteger: "3"
+  my_input_0_0_14_8:
+    SecretInteger: "3"
+  my_input_0_0_15_15:
+    SecretInteger: "3"
+  my_input_0_0_6_8:
+    SecretInteger: "3"
+  my_input_0_0_5_2:
+    SecretInteger: "3"
+  my_input_0_0_2_5:
+    SecretInteger: "3"
+  my_nn_fc1.weight_1_5:
+    SecretInteger: "3"
+  my_input_0_0_12_10:
+    SecretInteger: "3"
+  my_nn_conv1.bias_0:
+    SecretInteger: "3"
+  my_input_0_0_7_10:
+    SecretInteger: "3"
+  my_input_0_0_8_14:
+    SecretInteger: "3"
+  my_input_0_0_7_3:
+    SecretInteger: "3"
+  my_input_0_0_6_2:
+    SecretInteger: "3"
+  my_input_0_0_5_13:
+    SecretInteger: "3"
+  my_nn_fc1.weight_1_2:
+    SecretInteger: "3"
+  my_nn_fc1.weight_1_1:
+    SecretInteger: "3"
+  my_nn_fc1.weight_0_17:
+    SecretInteger: "3"
+  my_input_0_0_13_6:
+    SecretInteger: "3"
+  my_input_0_0_1_5:
+    SecretInteger: "3"
+  my_input_0_0_0_6:
+    SecretInteger: "3"
+  my_input_0_0_7_2:
+    SecretInteger: "3"
+  my_input_0_0_9_12:
+    SecretInteger: "3"
+  my_input_0_0_6_12:
+    SecretInteger: "3"
+  my_input_0_0_4_7:
+    SecretInteger: "3"
+  my_input_0_0_9_14:
+    SecretInteger: "3"
+  my_nn_fc1.weight_0_4:
+    SecretInteger: "3"
+  my_nn_conv1.weight_0_0_2_0:
+    SecretInteger: "3"
+  my_input_0_0_13_4:
+    SecretInteger: "3"
+  my_input_0_0_6_5:
+    SecretInteger: "3"
+  my_input_0_0_3_3:
+    SecretInteger: "3"
+  my_input_0_0_5_3:
+    SecretInteger: "3"
+  my_nn_conv1.weight_0_0_0_1:
+    SecretInteger: "3"
+  my_input_0_0_4_9:
+    SecretInteger: "3"
+  my_nn_fc1.weight_1_4:
+    SecretInteger: "3"
+  my_nn_conv1.weight_0_0_0_0:
+    SecretInteger: "3"
+  my_input_0_0_15_6:
+    SecretInteger: "3"
+  my_input_0_0_8_12:
+    SecretInteger: "3"
+  my_input_0_0_10_14:
+    SecretInteger: "3"
+  my_input_0_0_9_8:
+    SecretInteger: "3"
+  my_input_0_0_5_7:
+    SecretInteger: "3"
+  my_nn_fc1.weight_1_14:
+    SecretInteger: "3"
+  my_nn_conv1.weight_0_0_2_2:
+    SecretInteger: "3"
+  my_nn_conv1.weight_0_0_1_0:
+    SecretInteger: "3"
+  my_input_0_0_4_15:
+    SecretInteger: "3"
+  my_input_0_0_6_7:
+    SecretInteger: "3"
+  my_input_0_0_8_11:
+    SecretInteger: "3"
+  my_input_0_0_5_8:
+    SecretInteger: "3"
+  my_input_0_0_4_12:
+    SecretInteger: "3"
+  my_input_0_0_12_12:
+    SecretInteger: "3"
+  my_input_0_0_2_11:
+    SecretInteger: "3"
+  my_nn_fc1.weight_1_3:
+    SecretInteger: "3"
+  my_input_0_0_3_9:
+    SecretInteger: "3"
+  my_input_0_0_2_1:
+    SecretInteger: "3"
+  my_nn_fc1.weight_0_8:
+    SecretInteger: "3"
+  my_input_0_0_5_5:
+    SecretInteger: "3"
+  my_input_0_0_11_2:
+    SecretInteger: "3"
+  my_input_0_0_6_3:
+    SecretInteger: "3"
+  my_nn_fc1.weight_0_11:
+    SecretInteger: "3"
+  my_input_0_0_11_7:
+    SecretInteger: "3"
+  my_input_0_0_15_0:
+    SecretInteger: "3"
+  my_nn_conv1.weight_1_0_0_2:
+    SecretInteger: "3"
+  my_input_0_0_15_5:
+    SecretInteger: "3"
+  my_input_0_0_13_13:
+    SecretInteger: "3"
+  my_input_0_0_11_9:
+    SecretInteger: "3"
+  my_input_0_0_6_15:
+    SecretInteger: "3"
+  my_input_0_0_9_10:
+    SecretInteger: "3"
+  my_input_0_0_5_6:
+    SecretInteger: "3"
+  my_nn_fc1.weight_0_2:
+    SecretInteger: "3"
+  my_input_0_0_14_2:
+    SecretInteger: "3"
+  my_nn_conv1.weight_0_0_2_1:
+    SecretInteger: "3"
+  my_input_0_0_7_9:
+    SecretInteger: "3"
+  my_input_0_0_8_0:
+    SecretInteger: "3"
+  my_input_0_0_3_8:
+    SecretInteger: "3"
+  my_input_0_0_12_15:
+    SecretInteger: "3"
 expected_outputs:
   my_output_0_0:
     SecretInteger: "3"
diff --git a/examples/neural_net/main.py b/examples/neural_net/main.py
index e5dcc82..8f721e0 100644
--- a/examples/neural_net/main.py
+++ b/examples/neural_net/main.py
@@ -3,7 +3,7 @@
 import os
 import sys
 
-sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "../..")))
+sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
 
 import asyncio
 
@@ -12,36 +12,55 @@
 import numpy as np
 import py_nillion_client as nillion
 import torch
+from common.utils import compute, store_program, store_secrets
+from cosmpy.aerial.client import LedgerClient
+from cosmpy.aerial.wallet import LocalWallet
+from cosmpy.crypto.keypairs import PrivateKey
 from dotenv import load_dotenv
-# Import helper functions for creating nillion client and getting keys
-from nillion_python_helpers import (create_nillion_client, getNodeKeyFromFile,
-                                    getUserKeyFromFile)
+from nillion_python_helpers import (create_nillion_client,
+                                    create_payments_config)
+from py_nillion_client import NodeKey, UserKey
 
-from examples.common.utils import compute, store_program, store_secrets
 from nada_ai.client import TorchClient
 
-# Load environment variables from a .env file
-load_dotenv()
+home = os.getenv("HOME")
+load_dotenv(f"{home}/.config/nillion/nillion-devnet.env")
 
 
-# Main asynchronous function to coordinate the process
-async def main():
+async def main() -> None:
+    """Main nada program"""
+
     cluster_id = os.getenv("NILLION_CLUSTER_ID")
-    userkey = getUserKeyFromFile(os.getenv("NILLION_USERKEY_PATH_PARTY_1"))
-    nodekey = getNodeKeyFromFile(os.getenv("NILLION_NODEKEY_PATH_PARTY_1"))
+    grpc_endpoint = os.getenv("NILLION_NILCHAIN_GRPC")
+    chain_id = os.getenv("NILLION_NILCHAIN_CHAIN_ID")
+    seed = "my_seed"
+    userkey = UserKey.from_seed((seed))
+    nodekey = NodeKey.from_seed((seed))
     client = create_nillion_client(userkey, nodekey)
     party_id = client.party_id
     user_id = client.user_id
+
     party_names = na_client.parties(2)
     program_name = "neural_net"
-    program_mir_path = f"./target/{program_name}.nada.bin"
-
-    if not os.path.exists("bench"):
-        os.mkdir("bench")
+    program_mir_path = f"target/{program_name}.nada.bin"
+
+    # Configure payments
+    payments_config = create_payments_config(chain_id, grpc_endpoint)
+    payments_client = LedgerClient(payments_config)
+    payments_wallet = LocalWallet(
+        PrivateKey(bytes.fromhex(os.getenv("NILLION_NILCHAIN_PRIVATE_KEY_0"))),
+        prefix="nillion",
+    )
 
-    # Store the program
+    # Store program
     program_id = await store_program(
-        client, user_id, cluster_id, program_name, program_mir_path
+        client,
+        payments_wallet,
+        payments_client,
+        user_id,
+        cluster_id,
+        program_name,
+        program_mir_path,
     )
 
     # Create custom torch Module
@@ -50,7 +69,7 @@ class MyNN(torch.nn.Module):
 
         def __init__(self) -> None:
             """Model is a two layers and an activations"""
-            super(MyNN, self).__init__()
+            super().__init__()
             self.linear_0 = torch.nn.Linear(8, 4)
             self.linear_1 = torch.nn.Linear(4, 2)
             self.relu = torch.nn.ReLU()
@@ -68,40 +87,60 @@ def forward(self, x: torch.tensor) -> torch.tensor:
 
     # Create and store model secrets via ModelClient
     model_client = TorchClient(my_nn)
-    model_secrets = nillion.Secrets(
+    model_secrets = nillion.NadaValues(
         model_client.export_state_as_secrets("my_nn", na.SecretRational)
     )
+    permissions = nillion.Permissions.default_for_user(client.user_id)
+    permissions.add_compute_permissions({client.user_id: {program_id}})
 
     model_store_id = await store_secrets(
-        client, cluster_id, program_id, party_id, party_names[0], model_secrets
+        client,
+        payments_wallet,
+        payments_client,
+        cluster_id,
+        model_secrets,
+        1,
+        permissions,
     )
 
     # Store inputs to perform inference for
     my_input = na_client.array(np.ones((8,)), "my_input", na.SecretRational)
-    input_secrets = nillion.Secrets(my_input)
+    input_secrets = nillion.NadaValues(my_input)
 
     data_store_id = await store_secrets(
-        client, cluster_id, program_id, party_id, party_names[1], input_secrets
+        client,
+        payments_wallet,
+        payments_client,
+        cluster_id,
+        input_secrets,
+        1,
+        permissions,
     )
 
     # Set up the compute bindings for the parties
     compute_bindings = nillion.ProgramBindings(program_id)
-    [
+
+    for party_name in party_names:
         compute_bindings.add_input_party(party_name, party_id)
-        for party_name in party_names
-    ]
-    compute_bindings.add_output_party(party_names[1], party_id)
+    compute_bindings.add_output_party(party_names[-1], party_id)
 
     print(f"Computing using program {program_id}")
     print(f"Use secret store_id: {model_store_id} {data_store_id}")
 
-    # Perform the computation and return the result
+    # Create a computation time secret to use
+    computation_time_secrets = nillion.NadaValues({})
+
+    # Compute, passing all params including the receipt that shows proof of payment
     result = await compute(
         client,
+        payments_wallet,
+        payments_client,
+        program_id,
         cluster_id,
         compute_bindings,
         [model_store_id, data_store_id],
-        nillion.Secrets({}),
+        computation_time_secrets,
+        verbose=True,
     )
 
     # Sort & rescale the obtained results by the quantization scale
@@ -113,13 +152,12 @@ def forward(self, x: torch.tensor) -> torch.tensor:
         )
     ]
 
-    print(f"🖥️  The result is {outputs}")
+    print(f"🖥️  The processed result is {outputs} @ {na.get_log_scale()}-bit precision")
 
     expected = my_nn.forward(torch.ones((8,))).detach().numpy().tolist()
-    print(f"🖥️  VS expected plain-text result {expected}")
-    return result
+
+    print(f"🖥️  VS expected result {expected}")
 
 
-# Run the main function if the script is executed directly
 if __name__ == "__main__":
     asyncio.run(main())
diff --git a/examples/neural_net/src/my_nn.py b/examples/neural_net/src/my_nn.py
index eaaefca..15b1d81 100644
--- a/examples/neural_net/src/my_nn.py
+++ b/examples/neural_net/src/my_nn.py
@@ -8,7 +8,7 @@ class MyNN(nn.Module):
 
     def __init__(self) -> None:
         """Model is a two layers and an activations"""
-        super(MyNN, self).__init__()
+        super().__init__()
         self.linear_0 = nn.Linear(8, 4)
         self.linear_1 = nn.Linear(4, 2)
         self.relu = nn.ReLU()
diff --git a/examples/neural_net/tests/neural_net.yaml b/examples/neural_net/tests/neural_net.yaml
index bb15100..8bff483 100644
--- a/examples/neural_net/tests/neural_net.yaml
+++ b/examples/neural_net/tests/neural_net.yaml
@@ -1,125 +1,115 @@
----
 program: neural_net
 inputs:
-  secrets:
-    # We assume all values were originally floats, scaled & rounded by a factor of 2**16
-
-    # For simplicity's sake, we assume all coefficients and inputs are 2.5
-    # 2.5 * 2**16 = 163840
-
-    my_nn_linear_0.weight_1_2:
-      SecretInteger: "163840"
-    my_nn_linear_0.weight_2_4:
-      SecretInteger: "163840"
-    my_nn_linear_0.weight_1_7:
-      SecretInteger: "163840"
-    my_nn_linear_0.weight_0_4:
-      SecretInteger: "163840"
-    my_nn_linear_0.bias_2:
-      SecretInteger: "163840"
-    my_nn_linear_1.weight_0_3:
-      SecretInteger: "163840"
-    my_nn_linear_0.weight_3_1:
-      SecretInteger: "163840"
-    my_nn_linear_0.weight_3_6:
-      SecretInteger: "163840"
-    my_nn_linear_0.weight_1_3:
-      SecretInteger: "163840"
-    my_nn_linear_0.bias_3:
-      SecretInteger: "163840"
-    my_nn_linear_1.weight_1_1:
-      SecretInteger: "163840"
-    my_input_0:
-      SecretInteger: "163840"
-    my_input_4:
-      SecretInteger: "163840"
-    my_nn_linear_1.weight_0_0:
-      SecretInteger: "163840"
-    my_nn_linear_0.weight_3_0:
-      SecretInteger: "163840"
-    my_nn_linear_1.weight_1_2:
-      SecretInteger: "163840"
-    my_nn_linear_0.weight_0_1:
-      SecretInteger: "163840"
-    my_nn_linear_0.weight_2_2:
-      SecretInteger: "163840"
-    my_input_2:
-      SecretInteger: "163840"
-    my_nn_linear_1.weight_1_0:
-      SecretInteger: "163840"
-    my_input_1:
-      SecretInteger: "163840"
-    my_nn_linear_0.weight_0_0:
-      SecretInteger: "163840"
-    my_input_6:
-      SecretInteger: "163840"
-    my_nn_linear_0.weight_0_5:
-      SecretInteger: "163840"
-    my_nn_linear_1.weight_0_1:
-      SecretInteger: "163840"
-    my_nn_linear_0.weight_0_2:
-      SecretInteger: "163840"
-    my_nn_linear_0.weight_1_1:
-      SecretInteger: "163840"
-    my_nn_linear_0.weight_2_1:
-      SecretInteger: "163840"
-    my_nn_linear_0.weight_2_3:
-      SecretInteger: "163840"
-    my_nn_linear_0.weight_2_7:
-      SecretInteger: "163840"
-    my_nn_linear_1.weight_1_3:
-      SecretInteger: "163840"
-    my_nn_linear_0.bias_1:
-      SecretInteger: "163840"
-    my_nn_linear_0.weight_1_4:
-      SecretInteger: "163840"
-    my_nn_linear_0.weight_2_5:
-      SecretInteger: "163840"
-    my_nn_linear_0.weight_0_3:
-      SecretInteger: "163840"
-    my_nn_linear_0.weight_3_5:
-      SecretInteger: "163840"
-    my_nn_linear_0.weight_3_7:
-      SecretInteger: "163840"
-    my_input_5:
-      SecretInteger: "163840"
-    my_nn_linear_0.weight_2_0:
-      SecretInteger: "163840"
-    my_nn_linear_0.weight_1_0:
-      SecretInteger: "163840"
-    my_nn_linear_0.weight_1_6:
-      SecretInteger: "163840"
-    my_nn_linear_1.weight_0_2:
-      SecretInteger: "163840"
-    my_nn_linear_0.weight_3_4:
-      SecretInteger: "163840"
-    my_nn_linear_0.weight_3_3:
-      SecretInteger: "163840"
-    my_input_3:
-      SecretInteger: "163840"
-    my_nn_linear_0.weight_3_2:
-      SecretInteger: "163840"
-    my_nn_linear_1.bias_1:
-      SecretInteger: "163840"
-    my_input_7:
-      SecretInteger: "163840"
-    my_nn_linear_0.bias_0:
-      SecretInteger: "163840"
-    my_nn_linear_0.weight_0_6:
-      SecretInteger: "163840"
-    my_nn_linear_0.weight_2_6:
-      SecretInteger: "163840"
-    my_nn_linear_0.weight_1_5:
-      SecretInteger: "163840"
-    my_nn_linear_1.bias_0:
-      SecretInteger: "163840"
-    my_nn_linear_0.weight_0_7:
-      SecretInteger: "163840"
-  public_variables: {}
+  my_nn_linear_0.weight_1_2:
+    SecretInteger: '163840'
+  my_nn_linear_0.weight_2_4:
+    SecretInteger: '163840'
+  my_nn_linear_0.weight_1_7:
+    SecretInteger: '163840'
+  my_nn_linear_0.weight_0_4:
+    SecretInteger: '163840'
+  my_nn_linear_0.bias_2:
+    SecretInteger: '163840'
+  my_nn_linear_1.weight_0_3:
+    SecretInteger: '163840'
+  my_nn_linear_0.weight_3_1:
+    SecretInteger: '163840'
+  my_nn_linear_0.weight_3_6:
+    SecretInteger: '163840'
+  my_nn_linear_0.weight_1_3:
+    SecretInteger: '163840'
+  my_nn_linear_0.bias_3:
+    SecretInteger: '163840'
+  my_nn_linear_1.weight_1_1:
+    SecretInteger: '163840'
+  my_input_0:
+    SecretInteger: '163840'
+  my_input_4:
+    SecretInteger: '163840'
+  my_nn_linear_1.weight_0_0:
+    SecretInteger: '163840'
+  my_nn_linear_0.weight_3_0:
+    SecretInteger: '163840'
+  my_nn_linear_1.weight_1_2:
+    SecretInteger: '163840'
+  my_nn_linear_0.weight_0_1:
+    SecretInteger: '163840'
+  my_nn_linear_0.weight_2_2:
+    SecretInteger: '163840'
+  my_input_2:
+    SecretInteger: '163840'
+  my_nn_linear_1.weight_1_0:
+    SecretInteger: '163840'
+  my_input_1:
+    SecretInteger: '163840'
+  my_nn_linear_0.weight_0_0:
+    SecretInteger: '163840'
+  my_input_6:
+    SecretInteger: '163840'
+  my_nn_linear_0.weight_0_5:
+    SecretInteger: '163840'
+  my_nn_linear_1.weight_0_1:
+    SecretInteger: '163840'
+  my_nn_linear_0.weight_0_2:
+    SecretInteger: '163840'
+  my_nn_linear_0.weight_1_1:
+    SecretInteger: '163840'
+  my_nn_linear_0.weight_2_1:
+    SecretInteger: '163840'
+  my_nn_linear_0.weight_2_3:
+    SecretInteger: '163840'
+  my_nn_linear_0.weight_2_7:
+    SecretInteger: '163840'
+  my_nn_linear_1.weight_1_3:
+    SecretInteger: '163840'
+  my_nn_linear_0.bias_1:
+    SecretInteger: '163840'
+  my_nn_linear_0.weight_1_4:
+    SecretInteger: '163840'
+  my_nn_linear_0.weight_2_5:
+    SecretInteger: '163840'
+  my_nn_linear_0.weight_0_3:
+    SecretInteger: '163840'
+  my_nn_linear_0.weight_3_5:
+    SecretInteger: '163840'
+  my_nn_linear_0.weight_3_7:
+    SecretInteger: '163840'
+  my_input_5:
+    SecretInteger: '163840'
+  my_nn_linear_0.weight_2_0:
+    SecretInteger: '163840'
+  my_nn_linear_0.weight_1_0:
+    SecretInteger: '163840'
+  my_nn_linear_0.weight_1_6:
+    SecretInteger: '163840'
+  my_nn_linear_1.weight_0_2:
+    SecretInteger: '163840'
+  my_nn_linear_0.weight_3_4:
+    SecretInteger: '163840'
+  my_nn_linear_0.weight_3_3:
+    SecretInteger: '163840'
+  my_input_3:
+    SecretInteger: '163840'
+  my_nn_linear_0.weight_3_2:
+    SecretInteger: '163840'
+  my_nn_linear_1.bias_1:
+    SecretInteger: '163840'
+  my_input_7:
+    SecretInteger: '163840'
+  my_nn_linear_0.bias_0:
+    SecretInteger: '163840'
+  my_nn_linear_0.weight_0_6:
+    SecretInteger: '163840'
+  my_nn_linear_0.weight_2_6:
+    SecretInteger: '163840'
+  my_nn_linear_0.weight_1_5:
+    SecretInteger: '163840'
+  my_nn_linear_1.bias_0:
+    SecretInteger: '163840'
+  my_nn_linear_0.weight_0_7:
+    SecretInteger: '163840'
 expected_outputs:
-  # If you go in and crunch the numbers of this one, the result should be 527.5
-  # 527.5 * 2**16 = 34_570_240
   my_output_0:
-    SecretInteger: "34570240"
+    SecretInteger: '34570240'
   my_output_1:
-    SecretInteger: "34570240"
+    SecretInteger: '34570240'
diff --git a/examples/spam_detection/01_model_provider.ipynb b/examples/spam_detection/01_model_provider.ipynb
index 039200e..d81bb6e 100644
--- a/examples/spam_detection/01_model_provider.ipynb
+++ b/examples/spam_detection/01_model_provider.ipynb
@@ -63,13 +63,26 @@
       "/Users/mathiasleys/projects/venv/lib/python3.12/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
       "  from .autonotebook import tqdm as notebook_tqdm\n"
      ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "import json\n",
     "import os\n",
+    "import sys\n",
+    "\n",
+    "sys.path.append(os.path.abspath(os.path.join(os.getcwd(), os.pardir)))\n",
+    "\n",
+    "import json\n",
     "import zipfile\n",
-    "from typing import Dict\n",
     "\n",
     "import joblib\n",
     "import pandas as pd\n",
@@ -84,15 +97,19 @@
     "\n",
     "from config import NUM_FEATS\n",
     "\n",
-    "# Using Nada AI model client\n",
     "from nada_ai.client import SklearnClient\n",
     "import nada_numpy as na\n",
     "import py_nillion_client as nillion\n",
-    "from nillion_python_helpers import (\n",
-    "    create_nillion_client,\n",
-    "    getUserKeyFromFile,\n",
-    "    getNodeKeyFromFile,\n",
-    ")"
+    "from nillion_python_helpers import (create_nillion_client,\n",
+    "                                    create_payments_config)\n",
+    "from py_nillion_client import NodeKey, UserKey\n",
+    "from common.utils import store_program, store_secrets\n",
+    "from cosmpy.aerial.client import LedgerClient\n",
+    "from cosmpy.aerial.wallet import LocalWallet\n",
+    "from cosmpy.crypto.keypairs import PrivateKey\n",
+    "\n",
+    "home = os.getenv(\"HOME\")\n",
+    "load_dotenv(f\"{home}/.config/nillion/nillion-devnet.env\")"
    ]
   },
   {
@@ -784,11 +801,12 @@
    "outputs": [],
    "source": [
     "cluster_id = os.getenv(\"NILLION_CLUSTER_ID\")\n",
-    "model_provider_userkey = getUserKeyFromFile(os.getenv(\"NILLION_USERKEY_PATH_PARTY_1\"))\n",
-    "model_provider_nodekey = getNodeKeyFromFile(os.getenv(\"NILLION_NODEKEY_PATH_PARTY_1\"))\n",
-    "model_provider_client = create_nillion_client(\n",
-    "    model_provider_userkey, model_provider_nodekey\n",
-    ")\n",
+    "grpc_endpoint = os.getenv(\"NILLION_NILCHAIN_GRPC\")\n",
+    "chain_id = os.getenv(\"NILLION_NILCHAIN_CHAIN_ID\")\n",
+    "seed = \"my_seed\"\n",
+    "model_provider_userkey = UserKey.from_seed((seed))\n",
+    "model_provider_nodekey = NodeKey.from_seed((seed))\n",
+    "model_provider_client = create_nillion_client(model_provider_userkey, model_provider_nodekey)\n",
     "model_provider_party_id = model_provider_client.party_id\n",
     "model_provider_user_id = model_provider_client.user_id"
    ]
@@ -799,67 +817,44 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "model_user_userkey = getUserKeyFromFile(os.getenv(\"NILLION_USERKEY_PATH_PARTY_2\"))\n",
-    "model_user_nodekey = getNodeKeyFromFile(os.getenv(\"NILLION_NODEKEY_PATH_PARTY_2\"))\n",
-    "model_user_client = create_nillion_client(model_user_userkey, model_user_nodekey)\n",
-    "model_user_party_id = model_user_client.party_id\n",
-    "model_user_user_id = create_nillion_client(\n",
-    "    model_user_userkey, model_user_nodekey\n",
-    ").user_id"
+    "party_names = [\"Provider\", \"User\"]\n",
+    "program_name = \"spam_detection\"\n",
+    "program_mir_path = f\"target/{program_name}.nada.bin\""
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 14,
    "metadata": {},
+   "outputs": [],
    "source": [
-    "## Model Provider flow"
+    "payments_config = create_payments_config(chain_id, grpc_endpoint)\n",
+    "payments_client = LedgerClient(payments_config)\n",
+    "payments_wallet = LocalWallet(\n",
+    "    PrivateKey(bytes.fromhex(os.getenv(\"NILLION_NILCHAIN_PRIVATE_KEY_0\"))),\n",
+    "    prefix=\"nillion\",\n",
+    ")"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Upload Nada program to Nillion"
+    "## Model Provider flow"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "TODO: explain what the Nada program does"
+    "## Upload Nada program to Nillion"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 14,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "async def store_program(\n",
-    "    *,\n",
-    "    client: nillion.NillionClient,\n",
-    "    cluster_id: str,\n",
-    "    user_id: str,\n",
-    "    nada_program_path: str,\n",
-    ") -> Dict[str, str]:\n",
-    "    \"\"\"Stores Nada program binary in Nillion network.\n",
-    "\n",
-    "    Args:\n",
-    "        client (nillion.NillionClient): Client that will upload Nada program.\n",
-    "        cluster_id (str): Nillion cluster ID.\n",
-    "        user_id (str): User ID of user that will upload Nada program.\n",
-    "        nada_program_path (str): Path to Nada program binary.\n",
-    "\n",
-    "    Returns:\n",
-    "        Dict[str, str]: Resulting `action_id` and `program_id`.\n",
-    "    \"\"\"\n",
-    "    action_id = await client.store_program(cluster_id, \"spam_detection\", nada_program_path)\n",
-    "    program_id = f\"{user_id}/spam_detection\"\n",
-    "\n",
-    "    return {\n",
-    "        \"action_id\": action_id,\n",
-    "        \"program_id\": program_id,\n",
-    "    }"
+    "TODO: explain what the Nada program does"
    ]
   },
   {
@@ -871,26 +866,23 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "✅ Program saved successfully!\n",
-      "action_id: 4aff9311-ec32-440e-b38d-30af171000cd\n",
-      "program_id: 33sxVBj3jenx74bGq5eiX3HzwBJS85aGTjutfnfwPwVyJEPhhWr2h1CcYeryqUvvNXKr4ipGQjNFBVbHUDCWXjWE/spam_detection\n"
+      "Getting quote for operation...\n",
+      "Submitting payment receipt 2 unil, tx hash 8117B7327C2D1A37D8F5A6DBD8286FC1A34F2B10695F955F66CA564E51CCC1DC\n",
+      "Stored program. action_id: 3rgqxWd47e171EUwe4RXP9hm45tmoXfuF8fC52S7jcFoQTnU8wPiL7hqWzyV1muak6bEg7iWhudwg4t2pM9XnXcp/spam_detection\n",
+      "Stored program_id: 3rgqxWd47e171EUwe4RXP9hm45tmoXfuF8fC52S7jcFoQTnU8wPiL7hqWzyV1muak6bEg7iWhudwg4t2pM9XnXcp/spam_detection\n"
      ]
     }
    ],
    "source": [
-    "result_store_program = await store_program(\n",
-    "    client=model_provider_client,\n",
-    "    cluster_id=cluster_id,\n",
-    "    user_id=model_provider_user_id,\n",
-    "    nada_program_path=\"target/spam_detection.nada.bin\",\n",
-    ")\n",
-    "\n",
-    "action_id = result_store_program[\"action_id\"]\n",
-    "program_id = result_store_program[\"program_id\"]\n",
-    "\n",
-    "print(\"✅ Program saved successfully!\")\n",
-    "print(\"action_id:\", action_id)\n",
-    "print(\"program_id:\", program_id)"
+    "program_id = await store_program(\n",
+    "    model_provider_client,\n",
+    "    payments_wallet,\n",
+    "    payments_client,\n",
+    "    model_provider_user_id,\n",
+    "    cluster_id,\n",
+    "    program_name,\n",
+    "    program_mir_path,\n",
+    ")"
    ]
   },
   {
@@ -914,94 +906,38 @@
    "cell_type": "code",
    "execution_count": 17,
    "metadata": {},
-   "outputs": [],
-   "source": [
-    "async def store_model(\n",
-    "    *,\n",
-    "    model: SklearnClient,\n",
-    "    client: nillion.NillionClient,\n",
-    "    cluster_id: str,\n",
-    "    program_id: str,\n",
-    "    party_id: str,\n",
-    "    model_user_user_id: str,\n",
-    "    model_provider_user_id: str,\n",
-    ") -> Dict[str, str]:\n",
-    "    \"\"\"Stores model params in Nillion network.\n",
-    "\n",
-    "    Args:\n",
-    "        model (LogisticRegression): Model object to store in network.\n",
-    "        client (nillion.NillionClient): Nillion client that stores model params.\n",
-    "        cluster_id (str): Nillion cluster ID.\n",
-    "        program_id (str): Program ID of Nada program.\n",
-    "        party_id (str): Party ID of party that will store model params.\n",
-    "        model_user_user_id (str): User ID of user that will get compute permissions.\n",
-    "        model_provider_user_id (str): User ID of user that will provide model params.\n",
-    "\n",
-    "    Returns:\n",
-    "        Dict[str, str]: Resulting `provider_party_id` and `model_store_id`.\n",
-    "    \"\"\"\n",
-    "\n",
-    "    secrets = nillion.Secrets(\n",
-    "        model.export_state_as_secrets(\"my_model\", na.SecretRational)\n",
-    "    )\n",
-    "\n",
-    "    secret_bindings = nillion.ProgramBindings(program_id)\n",
-    "    secret_bindings.add_input_party(\"Provider\", party_id)\n",
-    "\n",
-    "    permissions = nillion.Permissions.default_for_user(model_provider_user_id)\n",
-    "    compute_permissions = {\n",
-    "        model_user_user_id: {program_id},\n",
-    "    }\n",
-    "    # Give permission to model user to run inference\n",
-    "    permissions.add_compute_permissions(compute_permissions)\n",
-    "\n",
-    "    store_id = await client.store_secrets(\n",
-    "        cluster_id, secret_bindings, secrets, permissions\n",
-    "    )\n",
-    "\n",
-    "    return {\n",
-    "        \"provider_party_id\": party_id,\n",
-    "        \"model_store_id\": store_id,\n",
-    "    }"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "✅ Model params uploaded successfully!\n",
-      "provider_party_id: 12D3KooWFYjK13Ny2W4hEfcZtD5DUvCGP6CJ4H2YnnUCHpZBDKpj\n",
-      "model_store_id: bf1d62e2-beff-41bd-9c5a-f1acf9e6779d\n"
+      "Getting quote for operation...\n",
+      "Quote cost is 48098 unil\n",
+      "Submitting payment receipt 48098 unil, tx hash 363B761F30F2957BA94CFFDDA1819821EA7C7E4826A0586B38255BB7A739436C\n"
      ]
     }
    ],
    "source": [
-    "result_store_model = await store_model(\n",
-    "    model=model_client,\n",
-    "    client=model_provider_client,\n",
-    "    cluster_id=cluster_id,\n",
-    "    program_id=program_id,\n",
-    "    party_id=model_provider_party_id,\n",
-    "    model_user_user_id=model_user_user_id,\n",
-    "    model_provider_user_id=model_provider_user_id,\n",
+    "model_secrets = nillion.NadaValues(\n",
+    "    model_client.export_state_as_secrets(\"my_model\", na.SecretRational)\n",
     ")\n",
+    "permissions = nillion.Permissions.default_for_user(model_provider_client.user_id)\n",
+    "permissions.add_compute_permissions({model_provider_client.user_id: {program_id}})\n",
     "\n",
-    "provider_party_id = result_store_model[\"provider_party_id\"]\n",
-    "model_store_id = result_store_model[\"model_store_id\"]\n",
-    "\n",
-    "print(\"✅ Model params uploaded successfully!\")\n",
-    "print(\"provider_party_id:\", provider_party_id)\n",
-    "print(\"model_store_id:\", model_store_id)"
+    "model_store_id = await store_secrets(\n",
+    "    model_provider_client,\n",
+    "    payments_wallet,\n",
+    "    payments_client,\n",
+    "    cluster_id,\n",
+    "    model_secrets,\n",
+    "    1,\n",
+    "    permissions,\n",
+    ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1014,29 +950,6 @@
     "    }\n",
     "    json.dump(provider_variables, provider_variables_file)"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "('40a606cc-70a5-4268-8f72-f7f75c388146', <SecretInteger at 0x33ff1bef0>)"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "result_tuple = await model_provider_client.retrieve_secret(\n",
-    "    cluster_id, model_store_id, \"my_model_coef_0_123\"\n",
-    ")\n",
-    "result_tuple"
-   ]
   }
  ],
  "metadata": {
diff --git a/examples/spam_detection/02_model_inference.ipynb b/examples/spam_detection/02_model_inference.ipynb
index 30e031a..137a6e3 100644
--- a/examples/spam_detection/02_model_inference.ipynb
+++ b/examples/spam_detection/02_model_inference.ipynb
@@ -32,14 +32,30 @@
    "cell_type": "code",
    "execution_count": 2,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "from typing import Dict, List\n",
+    "import os\n",
+    "import sys\n",
+    "\n",
+    "sys.path.append(os.path.abspath(os.path.join(os.getcwd(), os.pardir)))\n",
+    "\n",
+    "import math\n",
     "\n",
     "import json\n",
-    "import os\n",
     "import joblib\n",
     "\n",
+    "from common.utils import compute, store_secret_array\n",
     "\n",
     "from dotenv import load_dotenv\n",
     "import numpy as np\n",
@@ -49,13 +65,16 @@
     "import nada_numpy as na\n",
     "import nada_numpy.client as na_client\n",
     "import py_nillion_client as nillion\n",
-    "from nillion_python_helpers import (\n",
-    "    create_nillion_client,\n",
-    "    getUserKeyFromFile,\n",
-    "    getNodeKeyFromFile,\n",
-    ")\n",
+    "from cosmpy.aerial.client import LedgerClient\n",
+    "from cosmpy.aerial.wallet import LocalWallet\n",
+    "from cosmpy.crypto.keypairs import PrivateKey\n",
+    "from py_nillion_client import NodeKey, UserKey\n",
+    "from nillion_python_helpers import (create_nillion_client,\n",
+    "                                    create_payments_config)\n",
     "\n",
-    "from config import NUM_FEATS"
+    "\n",
+    "home = os.getenv(\"HOME\")\n",
+    "load_dotenv(f\"{home}/.config/nillion/nillion-devnet.env\")"
    ]
   },
   {
@@ -107,8 +126,11 @@
    "outputs": [],
    "source": [
     "cluster_id = os.getenv(\"NILLION_CLUSTER_ID\")\n",
-    "model_user_userkey = getUserKeyFromFile(os.getenv(\"NILLION_USERKEY_PATH_PARTY_2\"))\n",
-    "model_user_nodekey = getNodeKeyFromFile(os.getenv(\"NILLION_NODEKEY_PATH_PARTY_2\"))\n",
+    "grpc_endpoint = os.getenv(\"NILLION_NILCHAIN_GRPC\")\n",
+    "chain_id = os.getenv(\"NILLION_NILCHAIN_CHAIN_ID\")\n",
+    "seed = \"my_seed\"\n",
+    "model_user_userkey = UserKey.from_seed((seed))\n",
+    "model_user_nodekey = NodeKey.from_seed((seed))\n",
     "model_user_client = create_nillion_client(model_user_userkey, model_user_nodekey)\n",
     "model_user_party_id = model_user_client.party_id\n",
     "model_user_user_id = model_user_client.user_id"
@@ -118,14 +140,28 @@
    "cell_type": "code",
    "execution_count": 5,
    "metadata": {},
+   "outputs": [],
+   "source": [
+    "payments_config = create_payments_config(chain_id, grpc_endpoint)\n",
+    "payments_client = LedgerClient(payments_config)\n",
+    "payments_wallet = LocalWallet(\n",
+    "    PrivateKey(bytes.fromhex(os.getenv(\"NILLION_NILCHAIN_PRIVATE_KEY_0\"))),\n",
+    "    prefix=\"nillion\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Program ID:  33sxVBj3jenx74bGq5eiX3HzwBJS85aGTjutfnfwPwVyJEPhhWr2h1CcYeryqUvvNXKr4ipGQjNFBVbHUDCWXjWE/spam_detection\n",
-      "Model Store ID:  bf1d62e2-beff-41bd-9c5a-f1acf9e6779d\n",
-      "Model Provider Party ID:  12D3KooWFYjK13Ny2W4hEfcZtD5DUvCGP6CJ4H2YnnUCHpZBDKpj\n"
+      "Program ID:  3rgqxWd47e171EUwe4RXP9hm45tmoXfuF8fC52S7jcFoQTnU8wPiL7hqWzyV1muak6bEg7iWhudwg4t2pM9XnXcp/spam_detection\n",
+      "Model Store ID:  1a67209f-359e-46fa-9451-6dd559c27ca1\n",
+      "Model Provider Party ID:  12D3KooWJHrXiK2JTCjJxwCCktJPSYsUsz2WHEBSB5iZtqGiZ8Qm\n"
      ]
     }
    ],
@@ -159,7 +195,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -168,7 +204,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -180,7 +216,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -194,52 +230,6 @@
     "### Send features to Nillion"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "async def store_features(\n",
-    "    *,\n",
-    "    client: nillion.NillionClient,\n",
-    "    cluster_id: str,\n",
-    "    program_id: str,\n",
-    "    party_id: str,\n",
-    "    user_id: str,\n",
-    "    features: np.ndarray\n",
-    ") -> Dict[str, str]:\n",
-    "    \"\"\"Stores text features in Nillion network.\n",
-    "\n",
-    "    Args:\n",
-    "        client (nillion.NillionClient): Nillion client that stores features.\n",
-    "        cluster_id (str): Nillion cluster ID.\n",
-    "        program_id (str): Program ID of Nada program.\n",
-    "        party_id (str): Party ID of party that will store text features.\n",
-    "        user_id (str): User ID of user that will get compute permissions.\n",
-    "        features (List[float]): List of text features.\n",
-    "        precision (int): Scaling factor to convert float to ints.\n",
-    "\n",
-    "    Returns:\n",
-    "        Dict[str, str]: Resulting `model_user_party_id` and `features_store_id`.\n",
-    "    \"\"\"\n",
-    "\n",
-    "    secrets = nillion.Secrets(na_client.array(features, \"my_input\", na.SecretRational))\n",
-    "\n",
-    "    print(secrets)\n",
-    "    secret_bindings = nillion.ProgramBindings(program_id)\n",
-    "    secret_bindings.add_input_party(\"User\", party_id)\n",
-    "\n",
-    "    features_store_id = await client.store_secrets(\n",
-    "        cluster_id, secret_bindings, secrets, None\n",
-    "    )\n",
-    "\n",
-    "    return {\n",
-    "        \"model_user_user_id\": user_id,\n",
-    "        \"features_store_id\": features_store_id,\n",
-    "    }"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 10,
@@ -249,29 +239,27 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "<builtins.Secrets object at 0x30ad72dd0>\n",
-      "✅ Text features uploaded successfully!\n",
-      "model_user_user_id: 346jE91YmBjhSdvVSKCSpBxAY1kj5brpnCtWutLWMvXj8sF2iBR7fnmm4fjAGj7uPmHtb7CBQRjV5Q4H1KzepDCL\n",
-      "features_store_id: 10c7d969-f60f-43fe-8795-2335bc9be522\n"
+      "Getting quote for operation...\n",
+      "Quote cost is 48002 unil\n",
+      "Submitting payment receipt 48002 unil, tx hash ADC46091FE92101AC9740304E5C2DF883C90ACF66EB155EAEFE23D4438479A4A\n"
      ]
     }
    ],
    "source": [
-    "result_store_features = await store_features(\n",
-    "    client=model_user_client,\n",
-    "    cluster_id=cluster_id,\n",
-    "    program_id=program_id,\n",
-    "    party_id=model_user_party_id,\n",
-    "    user_id=model_user_user_id,\n",
-    "    features=features,\n",
-    ")\n",
+    "permissions = nillion.Permissions.default_for_user(model_user_client.user_id)\n",
+    "permissions.add_compute_permissions({model_user_client.user_id: {program_id}})\n",
     "\n",
-    "model_user_user_id = result_store_features[\"model_user_user_id\"]\n",
-    "features_store_id = result_store_features[\"features_store_id\"]\n",
-    "\n",
-    "print(\"✅ Text features uploaded successfully!\")\n",
-    "print(\"model_user_user_id:\", model_user_user_id)\n",
-    "print(\"features_store_id:\", features_store_id)"
+    "images_store_id = await store_secret_array(\n",
+    "    model_user_client,\n",
+    "    payments_wallet,\n",
+    "    payments_client,\n",
+    "    cluster_id,\n",
+    "    features,\n",
+    "    \"my_input\",\n",
+    "    na.SecretRational,\n",
+    "    1,\n",
+    "    permissions,\n",
+    ")"
    ]
   },
   {
@@ -287,60 +275,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "async def run_inference(\n",
-    "    *,\n",
-    "    client: nillion.NillionClient,\n",
-    "    cluster_id: str,\n",
-    "    program_id: str,\n",
-    "    model_user_party_id: str,\n",
-    "    model_provider_party_id: str,\n",
-    "    model_store_id: str,\n",
-    "    features_store_id: str,\n",
-    ") -> Dict[str, str | float]:\n",
-    "    \"\"\"Runs blind inference on the Nillion network by executing the Nada program on the uploaded data.\n",
-    "\n",
-    "    Args:\n",
-    "        client (nillion.NillionClient): Nillion client that runs inference.\n",
-    "        cluster_id (str): Nillion cluster ID.\n",
-    "        program_id (str): Program ID of Nada program.\n",
-    "        model_user_party_id (str): Party ID of party that will run inference.\n",
-    "        model_user_party_id (str): Party ID of party that will provide model params.\n",
-    "        model_store_id (str): Store ID that points to the model params in the Nillion network.\n",
-    "        features_store_id (str): Store ID that points to the text features in the Nillion network.\n",
-    "        precision (int): Scaling factor to convert float to ints.s\n",
-    "\n",
-    "    Returns:\n",
-    "        Dict[str, str | float]: Resulting `compute_id` and `logit`.\n",
-    "    \"\"\"\n",
-    "    compute_bindings = nillion.ProgramBindings(program_id)\n",
-    "    compute_bindings.add_input_party(\"User\", model_user_party_id)\n",
-    "    compute_bindings.add_input_party(\"Provider\", model_provider_party_id)\n",
-    "    compute_bindings.add_output_party(\"User\", model_user_party_id)\n",
+    "compute_bindings = nillion.ProgramBindings(program_id)\n",
     "\n",
-    "    _ = await client.compute(\n",
-    "        cluster_id,\n",
-    "        compute_bindings,\n",
-    "        [features_store_id, model_store_id],\n",
-    "        nillion.Secrets({}),\n",
-    "        nillion.PublicVariables({}),\n",
-    "    )\n",
-    "\n",
-    "    while True:\n",
-    "        compute_event = await client.next_compute_event()\n",
-    "        if isinstance(compute_event, nillion.ComputeFinishedEvent):\n",
-    "            inference_result = compute_event.result.value\n",
-    "            break\n",
-    "\n",
-    "    sigmoid = lambda x: 1 / (1 + np.exp(-x))\n",
-    "\n",
-    "    quantized_logit = inference_result[\"logit_0\"]\n",
-    "    logit = quantized_logit / (2 ** na.get_log_scale())\n",
-    "    output_probability = sigmoid(logit)\n",
-    "    return {\n",
-    "        \"compute_id\": compute_event.uuid,\n",
-    "        \"logit\": logit,\n",
-    "        \"output_probability\": output_probability,\n",
-    "    }"
+    "compute_bindings.add_input_party(\"Provider\", model_provider_party_id)\n",
+    "compute_bindings.add_input_party(\"User\", model_user_party_id)\n",
+    "compute_bindings.add_output_party(\"User\", model_user_party_id)"
    ]
   },
   {
@@ -352,33 +291,38 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "✅ Inference ran successfully!\n",
-      "compute_id: dc230bb3-1a27-41c0-9236-3ee0e11f5c1a\n",
-      "logit: 2.4093170166015625\n",
-      "Probability of spam in Nillion: 91.753502%\n"
+      "Getting quote for operation...\n",
+      "Quote cost is 1004 unil\n",
+      "Submitting payment receipt 1004 unil, tx hash 3B0F0A512E3459077F4B0C76C51573FE6E0123695BA5350C88C82B74D4ED7799\n",
+      "✅ Compute complete for compute_id d256e8bb-b85a-428f-b168-6359cfaf3ec8\n",
+      "🖥️  The result is {'logit_0': 157897}\n",
+      "Computed logit is 2.4093170166015625\n",
+      "Which corresponds to probability 0.9175350190040187\n"
      ]
     }
    ],
    "source": [
-    "result_inference = await run_inference(\n",
-    "    client=model_user_client,\n",
-    "    cluster_id=cluster_id,\n",
-    "    program_id=program_id,\n",
-    "    model_user_party_id=model_user_party_id,\n",
-    "    model_provider_party_id=model_provider_party_id,\n",
-    "    model_store_id=model_store_id,\n",
-    "    features_store_id=features_store_id,\n",
+    "result = await compute(\n",
+    "    model_user_client,\n",
+    "    payments_wallet,\n",
+    "    payments_client,\n",
+    "    program_id,\n",
+    "    cluster_id,\n",
+    "    compute_bindings,\n",
+    "    [model_store_id, images_store_id],\n",
+    "    nillion.NadaValues({}),\n",
+    "    verbose=True,\n",
     ")\n",
     "\n",
-    "compute_id = result_inference[\"compute_id\"]\n",
-    "logit = result_inference[\"logit\"]\n",
-    "output_probability = result_inference[\"output_probability\"]\n",
+    "logit = na_client.float_from_rational(result[\"logit_0\"])\n",
+    "print(\"Computed logit is\", logit)\n",
     "\n",
-    "print(\"✅ Inference ran successfully!\")\n",
-    "print(\"compute_id:\", compute_id)\n",
-    "print(\"logit:\", logit)\n",
+    "def sigmoid(x):\n",
+    "  return 1 / (1 + math.exp(-x))\n",
     "\n",
-    "print(\"Probability of spam in Nillion: {:.6f}%\".format(output_probability * 100))"
+    "output_probability = sigmoid(logit)\n",
+    "\n",
+    "print(\"Which corresponds to probability\", output_probability)"
    ]
   },
   {
@@ -425,7 +369,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Logit in plain text: 2.4080795630742755\n"
+      "Logit in plain text: 2.408079563074276\n"
      ]
     }
    ],
diff --git a/examples/spam_detection/tests/spam_detection.yaml b/examples/spam_detection/tests/spam_detection.yaml
index ede8336..84f417c 100644
--- a/examples/spam_detection/tests/spam_detection.yaml
+++ b/examples/spam_detection/tests/spam_detection.yaml
@@ -1,2010 +1,2007 @@
----
 program: spam_detection
 inputs:
-  secrets:
-    my_input_81:
-      SecretInteger: "3"
-    my_input_417:
-      SecretInteger: "3"
-    my_input_168:
-      SecretInteger: "3"
-    my_input_75:
-      SecretInteger: "3"
-    my_model_coef_0_81:
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+    SecretInteger: '3'
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+    SecretInteger: '3'
+  my_model_coef_0_128:
+    SecretInteger: '3'
+  my_input_402:
+    SecretInteger: '3'
+  my_model_coef_0_315:
+    SecretInteger: '3'
+  my_input_403:
+    SecretInteger: '3'
+  my_input_481:
+    SecretInteger: '3'
+  my_model_coef_0_162:
+    SecretInteger: '3'
+  my_model_coef_0_266:
+    SecretInteger: '3'
+  my_model_coef_0_461:
+    SecretInteger: '3'
+  my_input_18:
+    SecretInteger: '3'
+  my_input_97:
+    SecretInteger: '3'
+  my_model_coef_0_459:
+    SecretInteger: '3'
+  my_input_130:
+    SecretInteger: '3'
+  my_input_295:
+    SecretInteger: '3'
+  my_model_coef_0_396:
+    SecretInteger: '3'
+  my_model_coef_0_239:
+    SecretInteger: '3'
+  my_model_coef_0_318:
+    SecretInteger: '3'
+  my_model_coef_0_324:
+    SecretInteger: '3'
+  my_model_coef_0_332:
+    SecretInteger: '3'
+  my_input_28:
+    SecretInteger: '3'
+  my_input_111:
+    SecretInteger: '3'
+  my_input_312:
+    SecretInteger: '3'
+  my_input_40:
+    SecretInteger: '3'
+  my_input_214:
+    SecretInteger: '3'
+  my_model_coef_0_51:
+    SecretInteger: '3'
+  my_model_coef_0_75:
+    SecretInteger: '3'
+  my_model_coef_0_345:
+    SecretInteger: '3'
+  my_model_coef_0_249:
+    SecretInteger: '3'
+  my_input_398:
+    SecretInteger: '3'
+  my_model_coef_0_269:
+    SecretInteger: '3'
+  my_model_coef_0_196:
+    SecretInteger: '3'
+  my_input_297:
+    SecretInteger: '3'
+  my_model_coef_0_450:
+    SecretInteger: '3'
+  my_model_coef_0_183:
+    SecretInteger: '3'
+  my_input_263:
+    SecretInteger: '3'
+  my_input_410:
+    SecretInteger: '3'
+  my_input_367:
+    SecretInteger: '3'
+  my_model_coef_0_496:
+    SecretInteger: '3'
+  my_input_107:
+    SecretInteger: '3'
+  my_model_coef_0_19:
+    SecretInteger: '3'
+  my_input_490:
+    SecretInteger: '3'
+  my_model_coef_0_218:
+    SecretInteger: '3'
+  my_model_coef_0_261:
+    SecretInteger: '3'
+  my_model_coef_0_370:
+    SecretInteger: '3'
+  my_input_479:
+    SecretInteger: '3'
+  my_input_401:
+    SecretInteger: '3'
+  my_input_423:
+    SecretInteger: '3'
+  my_input_95:
+    SecretInteger: '3'
+  my_model_coef_0_456:
+    SecretInteger: '3'
+  my_model_coef_0_189:
+    SecretInteger: '3'
+  my_model_coef_0_347:
+    SecretInteger: '3'
+  my_input_273:
+    SecretInteger: '3'
+  my_input_465:
+    SecretInteger: '3'
+  my_model_coef_0_356:
+    SecretInteger: '3'
+  my_input_311:
+    SecretInteger: '3'
+  my_model_coef_0_78:
+    SecretInteger: '3'
+  my_input_345:
+    SecretInteger: '3'
+  my_model_coef_0_63:
+    SecretInteger: '3'
+  my_input_396:
+    SecretInteger: '3'
+  my_model_coef_0_111:
+    SecretInteger: '3'
+  my_input_192:
+    SecretInteger: '3'
+  my_model_coef_0_1:
+    SecretInteger: '3'
+  my_input_33:
+    SecretInteger: '3'
+  my_model_coef_0_471:
+    SecretInteger: '3'
+  my_input_80:
+    SecretInteger: '3'
+  my_input_281:
+    SecretInteger: '3'
+  my_model_coef_0_3:
+    SecretInteger: '3'
+  my_model_coef_0_417:
+    SecretInteger: '3'
+  my_model_coef_0_462:
+    SecretInteger: '3'
+  my_model_coef_0_224:
+    SecretInteger: '3'
+  my_input_437:
+    SecretInteger: '3'
+  my_input_386:
+    SecretInteger: '3'
+  my_input_27:
+    SecretInteger: '3'
+  my_input_166:
+    SecretInteger: '3'
+  my_input_279:
+    SecretInteger: '3'
+  my_model_coef_0_197:
+    SecretInteger: '3'
+  my_model_coef_0_369:
+    SecretInteger: '3'
+  my_model_coef_0_133:
+    SecretInteger: '3'
+  my_input_141:
+    SecretInteger: '3'
+  my_model_coef_0_372:
+    SecretInteger: '3'
+  my_input_451:
+    SecretInteger: '3'
+  my_model_coef_0_415:
+    SecretInteger: '3'
+  my_model_coef_0_455:
+    SecretInteger: '3'
+  my_input_157:
+    SecretInteger: '3'
+  my_model_coef_0_487:
+    SecretInteger: '3'
+  my_input_210:
+    SecretInteger: '3'
+  my_model_coef_0_319:
+    SecretInteger: '3'
+  my_input_301:
+    SecretInteger: '3'
+  my_input_68:
+    SecretInteger: '3'
+  my_model_coef_0_431:
+    SecretInteger: '3'
+  my_input_234:
+    SecretInteger: '3'
+  my_model_coef_0_448:
+    SecretInteger: '3'
+  my_input_285:
+    SecretInteger: '3'
+  my_input_184:
+    SecretInteger: '3'
+  my_model_coef_0_228:
+    SecretInteger: '3'
+  my_input_360:
+    SecretInteger: '3'
+  my_input_436:
+    SecretInteger: '3'
+  my_input_331:
+    SecretInteger: '3'
+  my_model_coef_0_364:
+    SecretInteger: '3'
+  my_model_coef_0_317:
+    SecretInteger: '3'
+  my_model_coef_0_402:
+    SecretInteger: '3'
+  my_input_17:
+    SecretInteger: '3'
+  my_model_coef_0_61:
+    SecretInteger: '3'
+  my_input_351:
+    SecretInteger: '3'
+  my_input_370:
+    SecretInteger: '3'
+  my_input_429:
+    SecretInteger: '3'
+  my_input_393:
+    SecretInteger: '3'
+  my_input_211:
+    SecretInteger: '3'
+  my_model_coef_0_36:
+    SecretInteger: '3'
+  my_input_108:
+    SecretInteger: '3'
+  my_model_coef_0_278:
+    SecretInteger: '3'
+  my_model_coef_0_18:
+    SecretInteger: '3'
+  my_model_coef_0_84:
+    SecretInteger: '3'
+  my_model_coef_0_467:
+    SecretInteger: '3'
+  my_input_193:
+    SecretInteger: '3'
+  my_input_290:
+    SecretInteger: '3'
+  my_input_104:
+    SecretInteger: '3'
+  my_model_coef_0_490:
+    SecretInteger: '3'
+  my_input_78:
+    SecretInteger: '3'
+  my_input_457:
+    SecretInteger: '3'
+  my_input_238:
+    SecretInteger: '3'
+  my_input_259:
+    SecretInteger: '3'
+  my_model_coef_0_297:
+    SecretInteger: '3'
+  my_input_283:
+    SecretInteger: '3'
+  my_input_198:
+    SecretInteger: '3'
+  my_model_coef_0_195:
+    SecretInteger: '3'
+  my_model_coef_0_299:
+    SecretInteger: '3'
+  my_model_coef_0_257:
+    SecretInteger: '3'
+  my_model_coef_0_287:
+    SecretInteger: '3'
+  my_input_261:
+    SecretInteger: '3'
+  my_input_344:
+    SecretInteger: '3'
+  my_input_332:
+    SecretInteger: '3'
+  my_model_coef_0_419:
+    SecretInteger: '3'
+  my_model_coef_0_98:
+    SecretInteger: '3'
+  my_input_105:
+    SecretInteger: '3'
+  my_input_171:
+    SecretInteger: '3'
+  my_input_152:
+    SecretInteger: '3'
+  my_model_coef_0_237:
+    SecretInteger: '3'
+  my_model_coef_0_301:
+    SecretInteger: '3'
+  my_input_408:
+    SecretInteger: '3'
+  my_input_335:
+    SecretInteger: '3'
+  my_model_coef_0_207:
+    SecretInteger: '3'
+  my_model_coef_0_380:
+    SecretInteger: '3'
+  my_model_coef_0_393:
+    SecretInteger: '3'
+  my_input_317:
+    SecretInteger: '3'
+  my_input_163:
+    SecretInteger: '3'
+  my_input_415:
+    SecretInteger: '3'
+  my_input_467:
+    SecretInteger: '3'
+  my_input_21:
+    SecretInteger: '3'
+  my_input_88:
+    SecretInteger: '3'
+  my_model_coef_0_242:
+    SecretInteger: '3'
+  my_input_308:
+    SecretInteger: '3'
+  my_model_coef_0_7:
+    SecretInteger: '3'
+  my_model_coef_0_375:
+    SecretInteger: '3'
+  my_model_coef_0_285:
+    SecretInteger: '3'
+  my_input_215:
+    SecretInteger: '3'
+  my_input_12:
+    SecretInteger: '3'
+  my_input_343:
+    SecretInteger: '3'
+  my_model_coef_0_35:
+    SecretInteger: '3'
+  my_input_151:
+    SecretInteger: '3'
+  my_model_coef_0_271:
+    SecretInteger: '3'
+  my_input_397:
+    SecretInteger: '3'
+  my_input_389:
+    SecretInteger: '3'
+  my_input_425:
+    SecretInteger: '3'
+  my_input_388:
+    SecretInteger: '3'
+  my_model_coef_0_454:
+    SecretInteger: '3'
+  my_input_77:
+    SecretInteger: '3'
+  my_input_201:
+    SecretInteger: '3'
+  my_model_coef_0_407:
+    SecretInteger: '3'
+  my_input_51:
+    SecretInteger: '3'
+  my_input_334:
+    SecretInteger: '3'
+  my_model_coef_0_437:
+    SecretInteger: '3'
+  my_input_14:
+    SecretInteger: '3'
+  my_model_coef_0_206:
+    SecretInteger: '3'
+  my_model_coef_0_412:
+    SecretInteger: '3'
+  my_input_44:
+    SecretInteger: '3'
+  my_input_30:
+    SecretInteger: '3'
+  my_model_coef_0_71:
+    SecretInteger: '3'
+  my_model_coef_0_124:
+    SecretInteger: '3'
+  my_model_coef_0_286:
+    SecretInteger: '3'
+  my_input_2:
+    SecretInteger: '3'
+  my_model_coef_0_140:
+    SecretInteger: '3'
+  my_input_197:
+    SecretInteger: '3'
+  my_model_coef_0_66:
+    SecretInteger: '3'
+  my_model_coef_0_144:
+    SecretInteger: '3'
+  my_input_131:
+    SecretInteger: '3'
 expected_outputs:
   logit_0:
-    SecretInteger: "3"
+    SecretInteger: '3'
diff --git a/examples/time_series/main.py b/examples/time_series/main.py
index ed58710..fd73e27 100644
--- a/examples/time_series/main.py
+++ b/examples/time_series/main.py
@@ -3,7 +3,7 @@
 import os
 import sys
 
-sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "../..")))
+sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
 
 import asyncio
 
@@ -12,39 +12,56 @@
 import numpy as np
 import pandas as pd
 import py_nillion_client as nillion
+from common.utils import compute, store_program, store_secrets
+from cosmpy.aerial.client import LedgerClient
+from cosmpy.aerial.wallet import LocalWallet
+from cosmpy.crypto.keypairs import PrivateKey
 from dotenv import load_dotenv
-# Import helper functions for creating nillion client and getting keys
-from nillion_python_helpers import (create_nillion_client, getNodeKeyFromFile,
-                                    getUserKeyFromFile)
+from nillion_python_helpers import (create_nillion_client,
+                                    create_payments_config)
 from prophet import Prophet
+from py_nillion_client import NodeKey, UserKey
 
-from examples.common.utils import compute, store_program, store_secrets
 from nada_ai.client import ProphetClient
 
-# Load environment variables from a .env file
-load_dotenv()
+home = os.getenv("HOME")
+load_dotenv(f"{home}/.config/nillion/nillion-devnet.env")
 
 
-# Main asynchronous function to coordinate the process
-async def main():
+async def main() -> None:
+    """Main nada program"""
+
     cluster_id = os.getenv("NILLION_CLUSTER_ID")
-    userkey = getUserKeyFromFile(os.getenv("NILLION_USERKEY_PATH_PARTY_1"))
-    nodekey = getNodeKeyFromFile(os.getenv("NILLION_NODEKEY_PATH_PARTY_1"))
+    grpc_endpoint = os.getenv("NILLION_NILCHAIN_GRPC")
+    chain_id = os.getenv("NILLION_NILCHAIN_CHAIN_ID")
+    seed = "my_seed"
+    userkey = UserKey.from_seed((seed))
+    nodekey = NodeKey.from_seed((seed))
     client = create_nillion_client(userkey, nodekey)
     party_id = client.party_id
     user_id = client.user_id
-    party_names = na_client.parties(2)
-    program_name = "main"
-    program_mir_path = f"./target/{program_name}.nada.bin"
 
-    if not os.path.exists("bench"):
-        os.mkdir("bench")
-
-    na.set_log_scale(50)
+    party_names = na_client.parties(2)
+    program_name = "time_series"
+    program_mir_path = f"target/{program_name}.nada.bin"
+
+    # Configure payments
+    payments_config = create_payments_config(chain_id, grpc_endpoint)
+    payments_client = LedgerClient(payments_config)
+    payments_wallet = LocalWallet(
+        PrivateKey(bytes.fromhex(os.getenv("NILLION_NILCHAIN_PRIVATE_KEY_0"))),
+        prefix="nillion",
+    )
 
-    # Store the program
+    # Store program
     program_id = await store_program(
-        client, user_id, cluster_id, program_name, program_mir_path
+        client,
+        payments_wallet,
+        payments_client,
+        user_id,
+        cluster_id,
+        program_name,
+        program_mir_path,
     )
 
     # Train prophet model
@@ -60,12 +77,20 @@ async def main():
 
     # Create and store model secrets via ModelClient
     model_client = ProphetClient(fit_model)
-    model_secrets = nillion.Secrets(
+    model_secrets = nillion.NadaValues(
         model_client.export_state_as_secrets("my_prophet", na.SecretRational)
     )
+    permissions = nillion.Permissions.default_for_user(client.user_id)
+    permissions.add_compute_permissions({client.user_id: {program_id}})
 
     model_store_id = await store_secrets(
-        client, cluster_id, program_id, party_id, party_names[0], model_secrets
+        client,
+        payments_wallet,
+        payments_client,
+        cluster_id,
+        model_secrets,
+        1,
+        permissions,
     )
 
     # Store inputs to perform inference for
@@ -80,30 +105,42 @@ async def main():
         na_client.array(inference_ds["t"].to_numpy(), "t", na.SecretRational)
     )
 
-    input_secrets = nillion.Secrets(my_input)
+    input_secrets = nillion.NadaValues(my_input)
 
     data_store_id = await store_secrets(
-        client, cluster_id, program_id, party_id, party_names[1], input_secrets
+        client,
+        payments_wallet,
+        payments_client,
+        cluster_id,
+        input_secrets,
+        1,
+        permissions,
     )
 
     # Set up the compute bindings for the parties
     compute_bindings = nillion.ProgramBindings(program_id)
-    [
+
+    for party_name in party_names:
         compute_bindings.add_input_party(party_name, party_id)
-        for party_name in party_names
-    ]
-    compute_bindings.add_output_party(party_names[1], party_id)
+    compute_bindings.add_output_party(party_names[-1], party_id)
 
     print(f"Computing using program {program_id}")
     print(f"Use secret store_id: {model_store_id} {data_store_id}")
 
-    # Perform the computation and return the result
+    # Create a computation time secret to use
+    computation_time_secrets = nillion.NadaValues({})
+
+    # Compute, passing all params including the receipt that shows proof of payment
     result = await compute(
         client,
+        payments_wallet,
+        payments_client,
+        program_id,
         cluster_id,
         compute_bindings,
         [model_store_id, data_store_id],
-        nillion.Secrets({}),
+        computation_time_secrets,
+        verbose=True,
     )
 
     # Sort & rescale the obtained results by the quantization scale
@@ -115,13 +152,12 @@ async def main():
         )
     ]
 
-    print(f"🖥️  The result is {outputs}")
+    print(f"🖥️  The processed result is {outputs} @ {na.get_log_scale()}-bit precision")
 
     expected = fit_model.predict(inference_ds)["yhat"].to_numpy()
-    print(f"🖥️  VS expected plain-text result {expected}")
-    return result
+
+    print(f"🖥️  VS expected result {expected}")
 
 
-# Run the main function if the script is executed directly
 if __name__ == "__main__":
     asyncio.run(main())
diff --git a/examples/time_series/src/time_series.py b/examples/time_series/src/time_series.py
index d3eca93..7ac7de6 100644
--- a/examples/time_series/src/time_series.py
+++ b/examples/time_series/src/time_series.py
@@ -5,8 +5,6 @@
 
 
 def nada_main():
-    na.set_log_scale(50)
-
     # Step 1: We use Nada NumPy wrapper to create "Party0" and "Party1"
     parties = na.parties(2)
 
diff --git a/examples/time_series/tests/time_series.yaml b/examples/time_series/tests/time_series.yaml
index b0e2b98..bad8616 100644
--- a/examples/time_series/tests/time_series.yaml
+++ b/examples/time_series/tests/time_series.yaml
@@ -1,192 +1,189 @@
----
 program: time_series
 inputs:
-  secrets:
-    my_prophet_changepoints_t_8:
-      SecretInteger: "3"
-    my_prophet_changepoints_t_11:
-      SecretInteger: "3"
-    floor_17:
-      SecretInteger: "3"
-    floor_4:
-      SecretInteger: "3"
-    my_prophet_beta_0_3:
-      SecretInteger: "3"
-    t_7:
-      SecretInteger: "3"
-    t_3:
-      SecretInteger: "3"
-    my_prophet_beta_0_0:
-      SecretInteger: "3"
-    t_1:
-      SecretInteger: "3"
-    floor_5:
-      SecretInteger: "3"
-    my_prophet_changepoints_t_6:
-      SecretInteger: "3"
-    floor_16:
-      SecretInteger: "3"
-    my_prophet_y_scale_0:
-      SecretInteger: "3"
-    floor_2:
-      SecretInteger: "3"
-    floor_19:
-      SecretInteger: "3"
-    my_prophet_beta_0_5:
-      SecretInteger: "3"
-    my_prophet_changepoints_t_7:
-      SecretInteger: "3"
-    my_prophet_delta_0_5:
-      SecretInteger: "3"
-    t_8:
-      SecretInteger: "3"
-    floor_18:
-      SecretInteger: "3"
-    my_prophet_beta_0_2:
-      SecretInteger: "3"
-    floor_10:
-      SecretInteger: "3"
-    t_12:
-      SecretInteger: "3"
-    my_prophet_delta_0_4:
-      SecretInteger: "3"
-    t_14:
-      SecretInteger: "3"
-    t_16:
-      SecretInteger: "3"
-    t_2:
-      SecretInteger: "3"
-    t_5:
-      SecretInteger: "3"
-    floor_11:
-      SecretInteger: "3"
-    my_prophet_delta_0_2:
-      SecretInteger: "3"
-    my_prophet_delta_0_7:
-      SecretInteger: "3"
-    my_prophet_k_0_0:
-      SecretInteger: "3"
-    my_prophet_changepoints_t_1:
-      SecretInteger: "3"
-    t_13:
-      SecretInteger: "3"
-    t_18:
-      SecretInteger: "3"
-    my_prophet_delta_0_0:
-      SecretInteger: "3"
-    my_prophet_changepoints_t_9:
-      SecretInteger: "3"
-    floor_13:
-      SecretInteger: "3"
-    t_15:
-      SecretInteger: "3"
-    t_17:
-      SecretInteger: "3"
-    my_prophet_delta_0_6:
-      SecretInteger: "3"
-    t_10:
-      SecretInteger: "3"
-    my_prophet_changepoints_t_10:
-      SecretInteger: "3"
-    my_prophet_beta_0_4:
-      SecretInteger: "3"
-    floor_3:
-      SecretInteger: "3"
-    my_prophet_changepoints_t_2:
-      SecretInteger: "3"
-    t_9:
-      SecretInteger: "3"
-    floor_9:
-      SecretInteger: "3"
-    t_11:
-      SecretInteger: "3"
-    my_prophet_delta_0_3:
-      SecretInteger: "3"
-    my_prophet_delta_0_10:
-      SecretInteger: "3"
-    my_prophet_changepoints_t_3:
-      SecretInteger: "3"
-    floor_1:
-      SecretInteger: "3"
-    my_prophet_delta_0_8:
-      SecretInteger: "3"
-    t_19:
-      SecretInteger: "3"
-    floor_7:
-      SecretInteger: "3"
-    floor_0:
-      SecretInteger: "3"
-    floor_14:
-      SecretInteger: "3"
-    floor_8:
-      SecretInteger: "3"
-    my_prophet_changepoints_t_4:
-      SecretInteger: "3"
-    floor_12:
-      SecretInteger: "3"
-    my_prophet_changepoints_t_0:
-      SecretInteger: "3"
-    my_prophet_delta_0_9:
-      SecretInteger: "3"
-    my_prophet_m_0_0:
-      SecretInteger: "3"
-    my_prophet_changepoints_t_5:
-      SecretInteger: "3"
-    t_6:
-      SecretInteger: "3"
-    my_prophet_beta_0_1:
-      SecretInteger: "3"
-    t_0:
-      SecretInteger: "3"
-    floor_15:
-      SecretInteger: "3"
-    t_4:
-      SecretInteger: "3"
-    my_prophet_delta_0_11:
-      SecretInteger: "3"
-    my_prophet_delta_0_1:
-      SecretInteger: "3"
-    floor_6:
-      SecretInteger: "3"
-  public_variables: {}
+  my_prophet_changepoints_t_8:
+    SecretInteger: '3'
+  my_prophet_changepoints_t_11:
+    SecretInteger: '3'
+  floor_17:
+    SecretInteger: '3'
+  floor_4:
+    SecretInteger: '3'
+  my_prophet_beta_0_3:
+    SecretInteger: '3'
+  t_7:
+    SecretInteger: '3'
+  t_3:
+    SecretInteger: '3'
+  my_prophet_beta_0_0:
+    SecretInteger: '3'
+  t_1:
+    SecretInteger: '3'
+  floor_5:
+    SecretInteger: '3'
+  my_prophet_changepoints_t_6:
+    SecretInteger: '3'
+  floor_16:
+    SecretInteger: '3'
+  my_prophet_y_scale_0:
+    SecretInteger: '3'
+  floor_2:
+    SecretInteger: '3'
+  floor_19:
+    SecretInteger: '3'
+  my_prophet_beta_0_5:
+    SecretInteger: '3'
+  my_prophet_changepoints_t_7:
+    SecretInteger: '3'
+  my_prophet_delta_0_5:
+    SecretInteger: '3'
+  t_8:
+    SecretInteger: '3'
+  floor_18:
+    SecretInteger: '3'
+  my_prophet_beta_0_2:
+    SecretInteger: '3'
+  floor_10:
+    SecretInteger: '3'
+  t_12:
+    SecretInteger: '3'
+  my_prophet_delta_0_4:
+    SecretInteger: '3'
+  t_14:
+    SecretInteger: '3'
+  t_16:
+    SecretInteger: '3'
+  t_2:
+    SecretInteger: '3'
+  t_5:
+    SecretInteger: '3'
+  floor_11:
+    SecretInteger: '3'
+  my_prophet_delta_0_2:
+    SecretInteger: '3'
+  my_prophet_delta_0_7:
+    SecretInteger: '3'
+  my_prophet_k_0_0:
+    SecretInteger: '3'
+  my_prophet_changepoints_t_1:
+    SecretInteger: '3'
+  t_13:
+    SecretInteger: '3'
+  t_18:
+    SecretInteger: '3'
+  my_prophet_delta_0_0:
+    SecretInteger: '3'
+  my_prophet_changepoints_t_9:
+    SecretInteger: '3'
+  floor_13:
+    SecretInteger: '3'
+  t_15:
+    SecretInteger: '3'
+  t_17:
+    SecretInteger: '3'
+  my_prophet_delta_0_6:
+    SecretInteger: '3'
+  t_10:
+    SecretInteger: '3'
+  my_prophet_changepoints_t_10:
+    SecretInteger: '3'
+  my_prophet_beta_0_4:
+    SecretInteger: '3'
+  floor_3:
+    SecretInteger: '3'
+  my_prophet_changepoints_t_2:
+    SecretInteger: '3'
+  t_9:
+    SecretInteger: '3'
+  floor_9:
+    SecretInteger: '3'
+  t_11:
+    SecretInteger: '3'
+  my_prophet_delta_0_3:
+    SecretInteger: '3'
+  my_prophet_delta_0_10:
+    SecretInteger: '3'
+  my_prophet_changepoints_t_3:
+    SecretInteger: '3'
+  floor_1:
+    SecretInteger: '3'
+  my_prophet_delta_0_8:
+    SecretInteger: '3'
+  t_19:
+    SecretInteger: '3'
+  floor_7:
+    SecretInteger: '3'
+  floor_0:
+    SecretInteger: '3'
+  floor_14:
+    SecretInteger: '3'
+  floor_8:
+    SecretInteger: '3'
+  my_prophet_changepoints_t_4:
+    SecretInteger: '3'
+  floor_12:
+    SecretInteger: '3'
+  my_prophet_changepoints_t_0:
+    SecretInteger: '3'
+  my_prophet_delta_0_9:
+    SecretInteger: '3'
+  my_prophet_m_0_0:
+    SecretInteger: '3'
+  my_prophet_changepoints_t_5:
+    SecretInteger: '3'
+  t_6:
+    SecretInteger: '3'
+  my_prophet_beta_0_1:
+    SecretInteger: '3'
+  t_0:
+    SecretInteger: '3'
+  floor_15:
+    SecretInteger: '3'
+  t_4:
+    SecretInteger: '3'
+  my_prophet_delta_0_11:
+    SecretInteger: '3'
+  my_prophet_delta_0_1:
+    SecretInteger: '3'
+  floor_6:
+    SecretInteger: '3'
 expected_outputs:
   my_output_1:
-    SecretInteger: "3"
+    SecretInteger: '2'
   my_output_16:
-    SecretInteger: "3"
+    SecretInteger: '2'
   my_output_17:
-    SecretInteger: "3"
+    SecretInteger: '1'
   my_output_14:
-    SecretInteger: "3"
+    SecretInteger: '1'
   my_output_19:
-    SecretInteger: "3"
+    SecretInteger: '1'
   my_output_3:
-    SecretInteger: "3"
+    SecretInteger: '1'
   my_output_2:
-    SecretInteger: "3"
+    SecretInteger: '2'
   my_output_0:
-    SecretInteger: "3"
+    SecretInteger: '1'
   my_output_11:
-    SecretInteger: "3"
+    SecretInteger: '2'
   my_output_10:
-    SecretInteger: "3"
+    SecretInteger: '1'
   my_output_5:
-    SecretInteger: "3"
+    SecretInteger: '1'
   my_output_6:
-    SecretInteger: "3"
+    SecretInteger: '1'
   my_output_7:
-    SecretInteger: "3"
+    SecretInteger: '1'
   my_output_12:
-    SecretInteger: "3"
+    SecretInteger: '1'
   my_output_9:
-    SecretInteger: "3"
+    SecretInteger: '2'
   my_output_4:
-    SecretInteger: "3"
+    SecretInteger: '2'
   my_output_8:
-    SecretInteger: "3"
+    SecretInteger: '2'
   my_output_15:
-    SecretInteger: "3"
+    SecretInteger: '2'
   my_output_18:
-    SecretInteger: "3"
+    SecretInteger: '2'
   my_output_13:
-    SecretInteger: "3"
+    SecretInteger: '1'
diff --git a/nada_ai/client/model_client.py b/nada_ai/client/model_client.py
index a1bff7f..ceaf4a6 100644
--- a/nada_ai/client/model_client.py
+++ b/nada_ai/client/model_client.py
@@ -3,7 +3,6 @@
 from abc import ABC, ABCMeta
 from typing import Any, Dict, Sequence
 
-import nada_numpy as na
 import nada_numpy.client as na_client
 import numpy as np
 import torch
@@ -54,9 +53,6 @@ def export_state_as_secrets(
         Returns:
             Dict[str, NillionType]: Dict of Nillion secret types that represents model state.
         """
-        if nada_type not in (na.Rational, na.SecretRational):
-            raise NotImplementedError("Exporting non-rational state is not supported")
-
         state_secrets = {}
         for state_layer_name, state_layer_weight in self.state_dict.items():
             layer_name = f"{name}_{state_layer_name}"
diff --git a/nada_ai/nada_typing.py b/nada_ai/nada_typing.py
index b238b3e..a919fee 100644
--- a/nada_ai/nada_typing.py
+++ b/nada_ai/nada_typing.py
@@ -5,9 +5,8 @@
 import nada_dsl as dsl
 import nada_numpy as na
 # pylint:disable=no-name-in-module
-from py_nillion_client import (PublicVariableInteger,
-                               PublicVariableUnsignedInteger, SecretInteger,
-                               SecretUnsignedInteger)
+from py_nillion_client import (Integer, SecretInteger, SecretUnsignedInteger,
+                               UnsignedInteger)
 from sklearn.linear_model import (LinearRegression, LogisticRegression,
                                   LogisticRegressionCV)
 
@@ -18,8 +17,8 @@
     na.SecretRational,
     SecretInteger,
     SecretUnsignedInteger,
-    PublicVariableInteger,
-    PublicVariableUnsignedInteger,
+    Integer,
+    UnsignedInteger,
 ]
 
 LinearModel = Union[LinearRegression, LogisticRegression, LogisticRegressionCV]
diff --git a/nada_ai/nn/modules/conv.py b/nada_ai/nn/modules/conv.py
index 32bd7cc..3e43761 100644
--- a/nada_ai/nn/modules/conv.py
+++ b/nada_ai/nn/modules/conv.py
@@ -97,22 +97,26 @@ def forward(self, x: na.NadaArray) -> na.NadaArray:
             (batch_size, out_channels, out_height, out_width),
             x.cleartext_nada_type,
         )
-        for b in range(batch_size):
-            for oc in range(out_channels):
-                for i in range(out_height):
-                    for j in range(out_width):
-                        start_i = i * self.stride[0]
-                        start_j = j * self.stride[1]
-
-                        receptive_field = x[
-                            b,
-                            :,
-                            start_i : start_i + kernel_rows,
-                            start_j : start_j + kernel_cols,
-                        ]
-                        output_tensor[b, oc, i, j] = na.sum(
-                            self.weight[oc] * receptive_field
-                        )
+        with na.context.UnsafeArithmeticSession():
+            for b in range(batch_size):
+                for oc in range(out_channels):
+                    for i in range(out_height):
+                        for j in range(out_width):
+                            start_i = i * self.stride[0]
+                            start_j = j * self.stride[1]
+
+                            receptive_field = x[
+                                b,
+                                :,
+                                start_i : start_i + kernel_rows,
+                                start_j : start_j + kernel_cols,
+                            ]
+                            output_tensor[b, oc, i, j] = na.sum(
+                                self.weight[oc] * receptive_field
+                            )
+
+        if x.is_rational:
+            output_tensor = output_tensor.apply(lambda value: value.rescale_down())
 
         if self.bias is not None:
             output_tensor += self.bias.reshape(1, out_channels, 1, 1)
diff --git a/poetry.lock b/poetry.lock
index 22b27f4..5c6daf2 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -32,6 +32,146 @@ six = ">=1.12.0"
 astroid = ["astroid (>=1,<2)", "astroid (>=2,<4)"]
 test = ["astroid (>=1,<2)", "astroid (>=2,<4)", "pytest"]
 
+[[package]]
+name = "attrs"
+version = "23.2.0"
+description = "Classes Without Boilerplate"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "attrs-23.2.0-py3-none-any.whl", hash = "sha256:99b87a485a5820b23b879f04c2305b44b951b502fd64be915879d77a7e8fc6f1"},
+    {file = "attrs-23.2.0.tar.gz", hash = "sha256:935dc3b529c262f6cf76e50877d35a4bd3c1de194fd41f47a2b7ae8f19971f30"},
+]
+
+[package.extras]
+cov = ["attrs[tests]", "coverage[toml] (>=5.3)"]
+dev = ["attrs[tests]", "pre-commit"]
+docs = ["furo", "myst-parser", "sphinx", "sphinx-notfound-page", "sphinxcontrib-towncrier", "towncrier", "zope-interface"]
+tests = ["attrs[tests-no-zope]", "zope-interface"]
+tests-mypy = ["mypy (>=1.6)", "pytest-mypy-plugins"]
+tests-no-zope = ["attrs[tests-mypy]", "cloudpickle", "hypothesis", "pympler", "pytest (>=4.3.0)", "pytest-xdist[psutil]"]
+
+[[package]]
+name = "bech32"
+version = "1.2.0"
+description = "Reference implementation for Bech32 and segwit addresses."
+optional = false
+python-versions = ">=3.5"
+files = [
+    {file = "bech32-1.2.0-py3-none-any.whl", hash = "sha256:990dc8e5a5e4feabbdf55207b5315fdd9b73db40be294a19b3752cde9e79d981"},
+    {file = "bech32-1.2.0.tar.gz", hash = "sha256:7d6db8214603bd7871fcfa6c0826ef68b85b0abd90fa21c285a9c5e21d2bd899"},
+]
+
+[[package]]
+name = "certifi"
+version = "2024.7.4"
+description = "Python package for providing Mozilla's CA Bundle."
+optional = false
+python-versions = ">=3.6"
+files = [
+    {file = "certifi-2024.7.4-py3-none-any.whl", hash = "sha256:c198e21b1289c2ab85ee4e67bb4b4ef3ead0892059901a8d5b622f24a1101e90"},
+    {file = "certifi-2024.7.4.tar.gz", hash = "sha256:5a1e7645bc0ec61a09e26c36f6106dd4cf40c6db3a1fb6352b0244e7fb057c7b"},
+]
+
+[[package]]
+name = "charset-normalizer"
+version = "3.3.2"
+description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet."
+optional = false
+python-versions = ">=3.7.0"
+files = [
+    {file = "charset-normalizer-3.3.2.tar.gz", hash = "sha256:f30c3cb33b24454a82faecaf01b19c18562b1e89558fb6c56de4d9118a032fd5"},
+    {file = "charset_normalizer-3.3.2-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:25baf083bf6f6b341f4121c2f3c548875ee6f5339300e08be3f2b2ba1721cdd3"},
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+]
+
 [[package]]
 name = "cmdstanpy"
 version = "1.2.4"
@@ -128,6 +268,28 @@ mypy = ["contourpy[bokeh,docs]", "docutils-stubs", "mypy (==1.8.0)", "types-Pill
 test = ["Pillow", "contourpy[test-no-images]", "matplotlib"]
 test-no-images = ["pytest", "pytest-cov", "pytest-xdist", "wurlitzer"]
 
+[[package]]
+name = "cosmpy"
+version = "0.9.2"
+description = "A library for interacting with the cosmos networks"
+optional = false
+python-versions = ">=3.8,<4.0"
+files = [
+    {file = "cosmpy-0.9.2-py3-none-any.whl", hash = "sha256:3591311198b08a0aa75340851ca166669974f17ffaa207a8d2cb26504fb0fa19"},
+    {file = "cosmpy-0.9.2.tar.gz", hash = "sha256:0f0eb80152f28ef5ee4d846d581d2e34ba2d952900f0e3570cacb84bb376f664"},
+]
+
+[package.dependencies]
+bech32 = "*"
+ecdsa = "*"
+googleapis-common-protos = "*"
+grpcio = "*"
+jsonschema = ">=3.2.0,<5"
+protobuf = ">=4.21.6,<5.0dev"
+pycryptodome = ">=3.18.0,<4.0.0"
+python-dateutil = "*"
+requests = "*"
+
 [[package]]
 name = "cycler"
 version = "0.12.1"
@@ -158,6 +320,24 @@ files = [
 graph = ["objgraph (>=1.7.2)"]
 profile = ["gprof2dot (>=2022.7.29)"]
 
+[[package]]
+name = "ecdsa"
+version = "0.19.0"
+description = "ECDSA cryptographic signature library (pure python)"
+optional = false
+python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,>=2.6"
+files = [
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+    {file = "ecdsa-0.19.0.tar.gz", hash = "sha256:60eaad1199659900dd0af521ed462b793bbdf867432b3948e87416ae4caf6bf8"},
+]
+
+[package.dependencies]
+six = ">=1.9.0"
+
+[package.extras]
+gmpy = ["gmpy"]
+gmpy2 = ["gmpy2"]
+
 [[package]]
 name = "exceptiongroup"
 version = "1.2.1"
@@ -190,53 +370,53 @@ typing = ["typing-extensions (>=4.8)"]
 
 [[package]]
 name = "fonttools"
-version = "4.53.0"
+version = "4.53.1"
 description = "Tools to manipulate font files"
 optional = false
 python-versions = ">=3.8"
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+]
+
+[package.extras]
+protobuf = ["grpcio-tools (>=1.64.1)"]
+
 [[package]]
 name = "holidays"
-version = "0.51"
+version = "0.52"
 description = "Generate and work with holidays in Python"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
 python-dateutil = "*"
 
+[[package]]
+name = "idna"
+version = "3.7"
+description = "Internationalized Domain Names in Applications (IDNA)"
+optional = false
+python-versions = ">=3.5"
+files = [
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+]
+
 [[package]]
 name = "importlib-resources"
 version = "6.4.0"
@@ -388,6 +654,41 @@ files = [
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 ]
 
+[[package]]
+name = "jsonschema"
+version = "4.22.0"
+description = "An implementation of JSON Schema validation for Python"
+optional = false
+python-versions = ">=3.8"
+files = [
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+]
+
+[package.dependencies]
+attrs = ">=22.2.0"
+jsonschema-specifications = ">=2023.03.6"
+referencing = ">=0.28.4"
+rpds-py = ">=0.7.1"
+
+[package.extras]
+format = ["fqdn", "idna", "isoduration", "jsonpointer (>1.13)", "rfc3339-validator", "rfc3987", "uri-template", "webcolors (>=1.11)"]
+format-nongpl = ["fqdn", "idna", "isoduration", "jsonpointer (>1.13)", "rfc3339-validator", "rfc3986-validator (>0.1.0)", "uri-template", "webcolors (>=1.11)"]
+
+[[package]]
+name = "jsonschema-specifications"
+version = "2023.12.1"
+description = "The JSON Schema meta-schemas and vocabularies, exposed as a Registry"
+optional = false
+python-versions = ">=3.8"
+files = [
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+]
+
+[package.dependencies]
+referencing = ">=0.31.0"
+
 [[package]]
 name = "kiwisolver"
 version = "1.4.5"
@@ -572,40 +873,40 @@ files = [
 
 [[package]]
 name = "matplotlib"
-version = "3.9.0"
+version = "3.9.1"
 description = "Python plotting package"
 optional = false
 python-versions = ">=3.9"
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 ]
 
 [package.dependencies]
@@ -728,39 +1029,41 @@ files = [
 
 [[package]]
 name = "nada-dsl"
-version = "0.2.1"
+version = "0.3.0"
 description = "Nillion Nada DSL to create Nillion MPC programs."
 optional = false
 python-versions = ">=3.10"
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 fpx = ["olefile"]
 mic = ["olefile"]
 tests = ["check-manifest", "coverage", "defusedxml", "markdown2", "olefile", "packaging", "pyroma", "pytest", "pytest-cov", "pytest-timeout"]
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 dev = ["jupyterlab", "nbconvert", "plotly", "pytest", "setuptools (>=64)", "wheel"]
 parallel = ["dask[dataframe]", "distributed"]
 
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 ]
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 version = "0.2.0"
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 test = ["pytest (>=7.4,<8.0)", "pytest-cov (>=4.0,<5.0)"]
 
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-    {file = "scikit_learn-1.5.0.tar.gz", hash = "sha256:789e3db01c750ed6d496fa2db7d50637857b451e57bcae863bff707c1247bef7"},
+    {file = "scikit_learn-1.5.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:781586c414f8cc58e71da4f3d7af311e0505a683e112f2f62919e3019abd3745"},
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+    {file = "scikit_learn-1.5.1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:909144d50f367a513cee6090873ae582dba019cb3fca063b38054fa42704c3a4"},
+    {file = "scikit_learn-1.5.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:689b6f74b2c880276e365fe84fe4f1befd6a774f016339c65655eaff12e10cbf"},
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+    {file = "scikit_learn-1.5.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:97625f217c5c0c5d0505fa2af28ae424bd37949bb2f16ace3ff5f2f81fb4498b"},
+    {file = "scikit_learn-1.5.1-cp312-cp312-win_amd64.whl", hash = "sha256:da3f404e9e284d2b0a157e1b56b6566a34eb2798205cba35a211df3296ab7a74"},
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+    {file = "scikit_learn-1.5.1-cp39-cp39-macosx_12_0_arm64.whl", hash = "sha256:7b073a27797a283187a4ef4ee149959defc350b46cbf63a84d8514fe16b69855"},
+    {file = "scikit_learn-1.5.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:b59e3e62d2be870e5c74af4e793293753565c7383ae82943b83383fdcf5cc5c1"},
+    {file = "scikit_learn-1.5.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:1bd8d3a19d4bd6dc5a7d4f358c8c3a60934dc058f363c34c0ac1e9e12a31421d"},
+    {file = "scikit_learn-1.5.1-cp39-cp39-win_amd64.whl", hash = "sha256:5f57428de0c900a98389c4a433d4a3cf89de979b3aa24d1c1d251802aa15e44d"},
+    {file = "scikit_learn-1.5.1.tar.gz", hash = "sha256:0ea5d40c0e3951df445721927448755d3fe1d80833b0b7308ebff5d2a45e6414"},
 ]
 
 [package.dependencies]
@@ -1425,8 +1946,8 @@ threadpoolctl = ">=3.1.0"
 
 [package.extras]
 benchmark = ["matplotlib (>=3.3.4)", "memory_profiler (>=0.57.0)", "pandas (>=1.1.5)"]
-build = ["cython (>=3.0.10)", "meson-python (>=0.15.0)", "numpy (>=1.19.5)", "scipy (>=1.6.0)"]
-docs = ["Pillow (>=7.1.2)", "matplotlib (>=3.3.4)", "memory_profiler (>=0.57.0)", "numpydoc (>=1.2.0)", "pandas (>=1.1.5)", "plotly (>=5.14.0)", "polars (>=0.20.23)", "pooch (>=1.6.0)", "scikit-image (>=0.17.2)", "seaborn (>=0.9.0)", "sphinx (>=6.0.0)", "sphinx-copybutton (>=0.5.2)", "sphinx-gallery (>=0.15.0)", "sphinx-prompt (>=1.3.0)", "sphinxext-opengraph (>=0.4.2)"]
+build = ["cython (>=3.0.10)", "meson-python (>=0.16.0)", "numpy (>=1.19.5)", "scipy (>=1.6.0)"]
+docs = ["Pillow (>=7.1.2)", "matplotlib (>=3.3.4)", "memory_profiler (>=0.57.0)", "numpydoc (>=1.2.0)", "pandas (>=1.1.5)", "plotly (>=5.14.0)", "polars (>=0.20.23)", "pooch (>=1.6.0)", "pydata-sphinx-theme (>=0.15.3)", "scikit-image (>=0.17.2)", "seaborn (>=0.9.0)", "sphinx (>=7.3.7)", "sphinx-copybutton (>=0.5.2)", "sphinx-design (>=0.5.0)", "sphinx-gallery (>=0.16.0)", "sphinx-prompt (>=1.4.0)", "sphinx-remove-toctrees (>=1.0.0.post1)", "sphinxcontrib-sass (>=0.3.4)", "sphinxext-opengraph (>=0.9.1)"]
 examples = ["matplotlib (>=3.3.4)", "pandas (>=1.1.5)", "plotly (>=5.14.0)", "pooch (>=1.6.0)", "scikit-image (>=0.17.2)", "seaborn (>=0.9.0)"]
 install = ["joblib (>=1.2.0)", "numpy (>=1.19.5)", "scipy (>=1.6.0)", "threadpoolctl (>=3.1.0)"]
 maintenance = ["conda-lock (==2.5.6)"]
@@ -1485,6 +2006,17 @@ files = [
     {file = "six-1.16.0.tar.gz", hash = "sha256:1e61c37477a1626458e36f7b1d82aa5c9b094fa4802892072e49de9c60c4c926"},
 ]
 
+[[package]]
+name = "sortedcontainers"
+version = "2.4.0"
+description = "Sorted Containers -- Sorted List, Sorted Dict, Sorted Set"
+optional = false
+python-versions = "*"
+files = [
+    {file = "sortedcontainers-2.4.0-py2.py3-none-any.whl", hash = "sha256:a163dcaede0f1c021485e957a39245190e74249897e2ae4b2aa38595db237ee0"},
+    {file = "sortedcontainers-2.4.0.tar.gz", hash = "sha256:25caa5a06cc30b6b83d11423433f65d1f9d76c4c6a0c90e3379eaa43b9bfdb88"},
+]
+
 [[package]]
 name = "stanio"
 version = "0.5.0"
@@ -1682,7 +2214,24 @@ files = [
     {file = "tzdata-2024.1.tar.gz", hash = "sha256:2674120f8d891909751c38abcdfd386ac0a5a1127954fbc332af6b5ceae07efd"},
 ]
 
+[[package]]
+name = "urllib3"
+version = "2.2.2"
+description = "HTTP library with thread-safe connection pooling, file post, and more."
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "urllib3-2.2.2-py3-none-any.whl", hash = "sha256:a448b2f64d686155468037e1ace9f2d2199776e17f0a46610480d311f73e3472"},
+    {file = "urllib3-2.2.2.tar.gz", hash = "sha256:dd505485549a7a552833da5e6063639d0d177c04f23bc3864e41e5dc5f612168"},
+]
+
+[package.extras]
+brotli = ["brotli (>=1.0.9)", "brotlicffi (>=0.8.0)"]
+h2 = ["h2 (>=4,<5)"]
+socks = ["pysocks (>=1.5.6,!=1.5.7,<2.0)"]
+zstd = ["zstandard (>=0.18.0)"]
+
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.10"
-content-hash = "8e7527ac4cf0f1b75fc5c71484d01700d18a03bb5570dd45e4ed53d1e227acbe"
+content-hash = "5dbd35a9d64b1d152c07613c092ff86aaf849c36be51b479a0a6b96ac8b507b0"
diff --git a/pyproject.toml b/pyproject.toml
index 8832541..caef4d2 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "nada-ai"
-version = "0.1.3"
+version = "0.2.0"
 description = "Nada-AI is a Python library designed for AI/ML on top of Nada DSL and Nillion Network."
 authors = ["Mathias Leys <mathias.leys@nillion.com>"]
 readme = "README.md"
@@ -11,10 +11,10 @@ numpy = "^1.26.4"
 torch = "^2.0.0"
 scikit-learn = "^1.4.2"
 prophet = "^1.1.5"
-nada-dsl = "^0.2.1"
-py-nillion-client = "^0.2.1"
-nada-numpy = "~0.1" # Any version starting with 0.1
-nillion-python-helpers = "~0.1" # Any version starting with 0.1
+nillion-python-helpers = "~0.2.3" # Works for 0.2.3 version
+nada-numpy="~0.2" # Works for any 0.2.x version
+nada-dsl="~0.3" # Works for any 0.3.x version
+py-nillion-client="~0.3" # Works for any 0.3.x version
 
 [tool.poetry.group.dev.dependencies]
 pytest = "^8.2.0"
diff --git a/tests/nada-tests/tests/activations.yaml b/tests/nada-tests/tests/activations.yaml
index 8d0291e..4bfb365 100644
--- a/tests/nada-tests/tests/activations.yaml
+++ b/tests/nada-tests/tests/activations.yaml
@@ -1,38 +1,35 @@
----
 program: activations
 inputs:
-  secrets:
-    input_x_0:
-      SecretInteger: "-2"
-    input_x_1:
-      SecretInteger: "0"
-    input_x_2:
-      SecretInteger: "4"
-    input_x_3:
-      SecretInteger: "-121"
-    input_y_0:
-      SecretInteger: "-131072"
-    input_y_1:
-      SecretInteger: "0"
-    input_y_2:
-      SecretInteger: "262144"
-    input_y_3:
-      SecretInteger: "-7929856"
-  public_variables: {}
+  input_x_0:
+    SecretInteger: '-2'
+  input_x_1:
+    SecretInteger: '0'
+  input_x_2:
+    SecretInteger: '4'
+  input_x_3:
+    SecretInteger: '-121'
+  input_y_0:
+    SecretInteger: '-131072'
+  input_y_1:
+    SecretInteger: '0'
+  input_y_2:
+    SecretInteger: '262144'
+  input_y_3:
+    SecretInteger: '-7929856'
 expected_outputs:
   relu_x_0:
-    SecretInteger: "0"
+    SecretInteger: '0'
   relu_x_1:
-    SecretInteger: "0"
+    SecretInteger: '0'
   relu_x_2:
-    SecretInteger: "4"
+    SecretInteger: '4'
   relu_x_3:
-    SecretInteger: "0"
+    SecretInteger: '0'
   relu_y_0:
-    SecretInteger: "0"
+    SecretInteger: '0'
   relu_y_1:
-    SecretInteger: "0"
+    SecretInteger: '0'
   relu_y_2:
-    SecretInteger: "262144"
+    SecretInteger: '262144'
   relu_y_3:
-    SecretInteger: "0"
+    SecretInteger: '0'
diff --git a/tests/nada-tests/tests/conv.yaml b/tests/nada-tests/tests/conv.yaml
index 48c385d..a09acec 100644
--- a/tests/nada-tests/tests/conv.yaml
+++ b/tests/nada-tests/tests/conv.yaml
@@ -1,192 +1,189 @@
----
 program: conv
 inputs:
-  secrets:
-    input_x_0_0_2_1:
-      SecretInteger: "3"
-    conv2_weight_1_2_0_1:
-      SecretInteger: "3"
-    input_x_0_0_2_0:
-      SecretInteger: "3"
-    conv1_weight_0_0_1_1:
-      SecretInteger: "3"
-    conv2_weight_1_0_1_0:
-      SecretInteger: "3"
-    conv2_weight_1_1_0_1:
-      SecretInteger: "3"
-    input_y_1_0_0:
-      SecretInteger: "3"
-    input_y_0_2_1:
-      SecretInteger: "3"
-    input_y_0_3_0:
-      SecretInteger: "3"
-    input_x_0_1_1_0:
-      SecretInteger: "3"
-    input_y_0_3_1:
-      SecretInteger: "3"
-    conv1_weight_0_1_0_0:
-      SecretInteger: "3"
-    conv1_weight_0_2_0_0:
-      SecretInteger: "3"
-    input_y_1_2_0:
-      SecretInteger: "3"
-    input_y_0_1_1:
-      SecretInteger: "3"
-    input_x_0_1_2_1:
-      SecretInteger: "3"
-    input_y_2_0_1:
-      SecretInteger: "3"
-    input_x_0_0_1_0:
-      SecretInteger: "3"
-    conv1_weight_0_1_1_0:
-      SecretInteger: "3"
-    input_x_0_2_1_1:
-      SecretInteger: "3"
-    conv2_weight_0_1_0_1:
-      SecretInteger: "3"
-    input_y_1_0_1:
-      SecretInteger: "3"
-    input_y_2_1_0:
-      SecretInteger: "3"
-    input_y_1_1_1:
-      SecretInteger: "3"
-    input_x_0_1_2_0:
-      SecretInteger: "3"
-    input_x_0_0_1_1:
-      SecretInteger: "3"
-    conv1_weight_0_0_1_0:
-      SecretInteger: "3"
-    input_x_0_0_3_0:
-      SecretInteger: "3"
-    input_x_0_1_3_1:
-      SecretInteger: "3"
-    input_y_2_1_1:
-      SecretInteger: "3"
-    conv2_bias_1:
-      SecretInteger: "3"
-    input_y_1_3_1:
-      SecretInteger: "3"
-    conv2_weight_0_0_0_0:
-      SecretInteger: "3"
-    input_x_0_2_3_0:
-      SecretInteger: "3"
-    input_x_0_1_3_0:
-      SecretInteger: "3"
-    conv2_weight_0_1_1_0:
-      SecretInteger: "3"
-    conv2_weight_1_1_1_1:
-      SecretInteger: "3"
-    input_y_1_2_1:
-      SecretInteger: "3"
-    input_x_0_0_0_1:
-      SecretInteger: "3"
-    input_x_0_2_3_1:
-      SecretInteger: "3"
-    input_y_2_3_1:
-      SecretInteger: "3"
-    input_x_0_0_3_1:
-      SecretInteger: "3"
-    input_y_0_2_0:
-      SecretInteger: "3"
-    conv2_weight_0_0_0_1:
-      SecretInteger: "3"
-    conv1_weight_0_0_0_1:
-      SecretInteger: "3"
-    conv2_weight_1_2_0_0:
-      SecretInteger: "3"
-    input_x_0_2_1_0:
-      SecretInteger: "3"
-    conv1_weight_0_2_1_0:
-      SecretInteger: "3"
-    conv2_weight_0_2_0_0:
-      SecretInteger: "3"
-    input_y_1_1_0:
-      SecretInteger: "3"
-    input_y_0_0_1:
-      SecretInteger: "3"
-    input_x_0_1_0_1:
-      SecretInteger: "3"
-    conv2_weight_1_0_0_0:
-      SecretInteger: "3"
-    conv1_weight_0_1_1_1:
-      SecretInteger: "3"
-    input_x_0_2_0_0:
-      SecretInteger: "3"
-    conv1_weight_0_2_1_1:
-      SecretInteger: "3"
-    conv2_weight_0_1_0_0:
-      SecretInteger: "3"
-    input_y_2_0_0:
-      SecretInteger: "3"
-    input_y_2_2_0:
-      SecretInteger: "3"
-    input_y_1_3_0:
-      SecretInteger: "3"
-    conv2_weight_0_0_1_0:
-      SecretInteger: "3"
-    conv1_weight_0_0_0_0:
-      SecretInteger: "3"
-    input_x_0_1_0_0:
-      SecretInteger: "3"
-    input_x_0_2_2_0:
-      SecretInteger: "3"
-    conv2_bias_0:
-      SecretInteger: "3"
-    conv2_weight_0_2_0_1:
-      SecretInteger: "3"
-    input_x_0_0_0_0:
-      SecretInteger: "3"
-    conv2_weight_1_2_1_1:
-      SecretInteger: "3"
-    input_y_2_3_0:
-      SecretInteger: "3"
-    conv2_weight_0_2_1_0:
-      SecretInteger: "3"
-    conv2_weight_1_2_1_0:
-      SecretInteger: "3"
-    input_y_2_2_1:
-      SecretInteger: "3"
-    conv1_weight_0_2_0_1:
-      SecretInteger: "3"
-    input_y_0_0_0:
-      SecretInteger: "3"
-    conv2_weight_0_0_1_1:
-      SecretInteger: "3"
-    input_y_0_1_0:
-      SecretInteger: "3"
-    input_x_0_1_1_1:
-      SecretInteger: "3"
-    input_x_0_2_0_1:
-      SecretInteger: "3"
-    conv1_weight_0_1_0_1:
-      SecretInteger: "3"
-    conv2_weight_0_2_1_1:
-      SecretInteger: "3"
-    conv1_bias_0:
-      SecretInteger: "3"
-    input_x_0_2_2_1:
-      SecretInteger: "3"
-    conv2_weight_1_0_1_1:
-      SecretInteger: "3"
-    conv2_weight_0_1_1_1:
-      SecretInteger: "3"
-    conv2_weight_1_1_1_0:
-      SecretInteger: "3"
-    conv2_weight_1_1_0_0:
-      SecretInteger: "3"
-    conv2_weight_1_0_0_1:
-      SecretInteger: "3"
-  public_variables: {}
+  input_x_0_0_2_1:
+    SecretInteger: '3'
+  conv2_weight_1_2_0_1:
+    SecretInteger: '3'
+  input_x_0_0_2_0:
+    SecretInteger: '3'
+  conv1_weight_0_0_1_1:
+    SecretInteger: '3'
+  conv2_weight_1_0_1_0:
+    SecretInteger: '3'
+  conv2_weight_1_1_0_1:
+    SecretInteger: '3'
+  input_y_1_0_0:
+    SecretInteger: '3'
+  input_y_0_2_1:
+    SecretInteger: '3'
+  input_y_0_3_0:
+    SecretInteger: '3'
+  input_x_0_1_1_0:
+    SecretInteger: '3'
+  input_y_0_3_1:
+    SecretInteger: '3'
+  conv1_weight_0_1_0_0:
+    SecretInteger: '3'
+  conv1_weight_0_2_0_0:
+    SecretInteger: '3'
+  input_y_1_2_0:
+    SecretInteger: '3'
+  input_y_0_1_1:
+    SecretInteger: '3'
+  input_x_0_1_2_1:
+    SecretInteger: '3'
+  input_y_2_0_1:
+    SecretInteger: '3'
+  input_x_0_0_1_0:
+    SecretInteger: '3'
+  conv1_weight_0_1_1_0:
+    SecretInteger: '3'
+  input_x_0_2_1_1:
+    SecretInteger: '3'
+  conv2_weight_0_1_0_1:
+    SecretInteger: '3'
+  input_y_1_0_1:
+    SecretInteger: '3'
+  input_y_2_1_0:
+    SecretInteger: '3'
+  input_y_1_1_1:
+    SecretInteger: '3'
+  input_x_0_1_2_0:
+    SecretInteger: '3'
+  input_x_0_0_1_1:
+    SecretInteger: '3'
+  conv1_weight_0_0_1_0:
+    SecretInteger: '3'
+  input_x_0_0_3_0:
+    SecretInteger: '3'
+  input_x_0_1_3_1:
+    SecretInteger: '3'
+  input_y_2_1_1:
+    SecretInteger: '3'
+  conv2_bias_1:
+    SecretInteger: '3'
+  input_y_1_3_1:
+    SecretInteger: '3'
+  conv2_weight_0_0_0_0:
+    SecretInteger: '3'
+  input_x_0_2_3_0:
+    SecretInteger: '3'
+  input_x_0_1_3_0:
+    SecretInteger: '3'
+  conv2_weight_0_1_1_0:
+    SecretInteger: '3'
+  conv2_weight_1_1_1_1:
+    SecretInteger: '3'
+  input_y_1_2_1:
+    SecretInteger: '3'
+  input_x_0_0_0_1:
+    SecretInteger: '3'
+  input_x_0_2_3_1:
+    SecretInteger: '3'
+  input_y_2_3_1:
+    SecretInteger: '3'
+  input_x_0_0_3_1:
+    SecretInteger: '3'
+  input_y_0_2_0:
+    SecretInteger: '3'
+  conv2_weight_0_0_0_1:
+    SecretInteger: '3'
+  conv1_weight_0_0_0_1:
+    SecretInteger: '3'
+  conv2_weight_1_2_0_0:
+    SecretInteger: '3'
+  input_x_0_2_1_0:
+    SecretInteger: '3'
+  conv1_weight_0_2_1_0:
+    SecretInteger: '3'
+  conv2_weight_0_2_0_0:
+    SecretInteger: '3'
+  input_y_1_1_0:
+    SecretInteger: '3'
+  input_y_0_0_1:
+    SecretInteger: '3'
+  input_x_0_1_0_1:
+    SecretInteger: '3'
+  conv2_weight_1_0_0_0:
+    SecretInteger: '3'
+  conv1_weight_0_1_1_1:
+    SecretInteger: '3'
+  input_x_0_2_0_0:
+    SecretInteger: '3'
+  conv1_weight_0_2_1_1:
+    SecretInteger: '3'
+  conv2_weight_0_1_0_0:
+    SecretInteger: '3'
+  input_y_2_0_0:
+    SecretInteger: '3'
+  input_y_2_2_0:
+    SecretInteger: '3'
+  input_y_1_3_0:
+    SecretInteger: '3'
+  conv2_weight_0_0_1_0:
+    SecretInteger: '3'
+  conv1_weight_0_0_0_0:
+    SecretInteger: '3'
+  input_x_0_1_0_0:
+    SecretInteger: '3'
+  input_x_0_2_2_0:
+    SecretInteger: '3'
+  conv2_bias_0:
+    SecretInteger: '3'
+  conv2_weight_0_2_0_1:
+    SecretInteger: '3'
+  input_x_0_0_0_0:
+    SecretInteger: '3'
+  conv2_weight_1_2_1_1:
+    SecretInteger: '3'
+  input_y_2_3_0:
+    SecretInteger: '3'
+  conv2_weight_0_2_1_0:
+    SecretInteger: '3'
+  conv2_weight_1_2_1_0:
+    SecretInteger: '3'
+  input_y_2_2_1:
+    SecretInteger: '3'
+  conv1_weight_0_2_0_1:
+    SecretInteger: '3'
+  input_y_0_0_0:
+    SecretInteger: '3'
+  conv2_weight_0_0_1_1:
+    SecretInteger: '3'
+  input_y_0_1_0:
+    SecretInteger: '3'
+  input_x_0_1_1_1:
+    SecretInteger: '3'
+  input_x_0_2_0_1:
+    SecretInteger: '3'
+  conv1_weight_0_1_0_1:
+    SecretInteger: '3'
+  conv2_weight_0_2_1_1:
+    SecretInteger: '3'
+  conv1_bias_0:
+    SecretInteger: '3'
+  input_x_0_2_2_1:
+    SecretInteger: '3'
+  conv2_weight_1_0_1_1:
+    SecretInteger: '3'
+  conv2_weight_0_1_1_1:
+    SecretInteger: '3'
+  conv2_weight_1_1_1_0:
+    SecretInteger: '3'
+  conv2_weight_1_1_0_0:
+    SecretInteger: '3'
+  conv2_weight_1_0_0_1:
+    SecretInteger: '3'
 expected_outputs:
   x_conv_0_0_2_0:
-    SecretInteger: "3"
+    SecretInteger: '3'
   y_conv_0_1_0:
-    SecretInteger: "57"
+    SecretInteger: '57'
   y_conv_0_2_0:
-    SecretInteger: "30"
+    SecretInteger: '30'
   x_conv_0_0_0_0:
-    SecretInteger: "3"
+    SecretInteger: '3'
   x_conv_0_0_1_0:
-    SecretInteger: "3"
+    SecretInteger: '3'
   y_conv_0_0_0:
-    SecretInteger: "30"
+    SecretInteger: '30'
diff --git a/tests/nada-tests/tests/distance.yaml b/tests/nada-tests/tests/distance.yaml
index 370ae94..fd65787 100644
--- a/tests/nada-tests/tests/distance.yaml
+++ b/tests/nada-tests/tests/distance.yaml
@@ -1,68 +1,65 @@
----
 program: distance
 inputs:
-  secrets:
-    input_x_0_0:
-      SecretInteger: "1"
-    input_x_0_1:
-      SecretInteger: "2"
-    input_x_0_2:
-      SecretInteger: "3"
-    input_x_1_0:
-      SecretInteger: "4"
-    input_x_1_1:
-      SecretInteger: "5"
-    input_x_1_2:
-      SecretInteger: "6"
-    input_y_0_0:
-      SecretInteger: "1"
-    input_y_0_1:
-      SecretInteger: "2"
-    input_y_0_2:
-      SecretInteger: "3"
-    input_y_1_0:
-      SecretInteger: "4"
-    input_y_1_1:
-      SecretInteger: "5"
-    input_y_1_2:
-      SecretInteger: "6"
-    input_y_2_0:
-      SecretInteger: "7"
-    input_y_2_1:
-      SecretInteger: "8"
-    input_y_2_2:
-      SecretInteger: "9"
-    input_y_3_0:
-      SecretInteger: "10"
-    input_y_3_1:
-      SecretInteger: "11"
-    input_y_3_2:
-      SecretInteger: "12"
-    input_y_4_0:
-      SecretInteger: "13"
-    input_y_4_1:
-      SecretInteger: "14"
-    input_y_4_2:
-      SecretInteger: "15"
-  public_variables: {}
+  input_x_0_0:
+    SecretInteger: '1'
+  input_x_0_1:
+    SecretInteger: '2'
+  input_x_0_2:
+    SecretInteger: '3'
+  input_x_1_0:
+    SecretInteger: '4'
+  input_x_1_1:
+    SecretInteger: '5'
+  input_x_1_2:
+    SecretInteger: '6'
+  input_y_0_0:
+    SecretInteger: '1'
+  input_y_0_1:
+    SecretInteger: '2'
+  input_y_0_2:
+    SecretInteger: '3'
+  input_y_1_0:
+    SecretInteger: '4'
+  input_y_1_1:
+    SecretInteger: '5'
+  input_y_1_2:
+    SecretInteger: '6'
+  input_y_2_0:
+    SecretInteger: '7'
+  input_y_2_1:
+    SecretInteger: '8'
+  input_y_2_2:
+    SecretInteger: '9'
+  input_y_3_0:
+    SecretInteger: '10'
+  input_y_3_1:
+    SecretInteger: '11'
+  input_y_3_2:
+    SecretInteger: '12'
+  input_y_4_0:
+    SecretInteger: '13'
+  input_y_4_1:
+    SecretInteger: '14'
+  input_y_4_2:
+    SecretInteger: '15'
 expected_outputs:
   my_output_0_0:
-    SecretInteger: "14"
+    SecretInteger: '14'
   my_output_0_1:
-    SecretInteger: "32"
+    SecretInteger: '32'
   my_output_0_2:
-    SecretInteger: "50"
+    SecretInteger: '50'
   my_output_0_3:
-    SecretInteger: "68"
+    SecretInteger: '68'
   my_output_0_4:
-    SecretInteger: "86"
+    SecretInteger: '86'
   my_output_1_0:
-    SecretInteger: "32"
+    SecretInteger: '32'
   my_output_1_1:
-    SecretInteger: "77"
+    SecretInteger: '77'
   my_output_1_2:
-    SecretInteger: "122"
+    SecretInteger: '122'
   my_output_1_3:
-    SecretInteger: "167"
+    SecretInteger: '167'
   my_output_1_4:
-    SecretInteger: "212"
+    SecretInteger: '212'
diff --git a/tests/nada-tests/tests/end-to-end.yaml b/tests/nada-tests/tests/end-to-end.yaml
index b8f8280..2c65758 100644
--- a/tests/nada-tests/tests/end-to-end.yaml
+++ b/tests/nada-tests/tests/end-to-end.yaml
@@ -1,128 +1,125 @@
----
 program: end-to-end
 inputs:
-  secrets:
-    input_x_0_0_2_0:
-      SecretInteger: "3"
-    testmod_conv_module.conv.weight_1_2_0_1:
-      SecretInteger: "3"
-    testmod_conv_module.conv.weight_0_2_0_0:
-      SecretInteger: "3"
-    testmod_linear.weight_1_0:
-      SecretInteger: "3"
-    input_x_0_0_1_0:
-      SecretInteger: "3"
-    testmod_linear.weight_0_1:
-      SecretInteger: "3"
-    testmod_conv_module.conv.weight_0_0_0_0:
-      SecretInteger: "3"
-    testmod_conv_module.conv.weight_1_1_0_1:
-      SecretInteger: "3"
-    input_x_0_1_1_1:
-      SecretInteger: "3"
-    testmod_conv_module.conv.weight_0_1_1_0:
-      SecretInteger: "3"
-    input_x_0_2_0_0:
-      SecretInteger: "3"
-    input_x_0_0_0_2:
-      SecretInteger: "3"
-    input_x_0_0_2_2:
-      SecretInteger: "3"
-    testmod_conv_module.conv.weight_1_0_1_0:
-      SecretInteger: "3"
-    testmod_conv_module.conv.weight_1_0_1_1:
-      SecretInteger: "3"
-    testmod_conv_module.conv.bias_0:
-      SecretInteger: "3"
-    testmod_conv_module.conv.weight_1_2_1_0:
-      SecretInteger: "3"
-    input_x_0_1_0_2:
-      SecretInteger: "3"
-    testmod_linear.weight_0_0:
-      SecretInteger: "3"
-    input_x_0_2_2_0:
-      SecretInteger: "3"
-    testmod_conv_module.conv.weight_0_0_1_1:
-      SecretInteger: "3"
-    input_x_0_2_2_2:
-      SecretInteger: "3"
-    testmod_conv_module.conv.weight_0_2_1_0:
-      SecretInteger: "3"
-    testmod_conv_module.conv.bias_1:
-      SecretInteger: "3"
-    input_x_0_1_1_0:
-      SecretInteger: "3"
-    testmod_conv_module.conv.weight_1_0_0_1:
-      SecretInteger: "3"
-    input_x_0_0_1_2:
-      SecretInteger: "3"
-    testmod_conv_module.conv.weight_1_0_0_0:
-      SecretInteger: "3"
-    testmod_conv_module.conv.weight_0_1_0_0:
-      SecretInteger: "3"
-    testmod_conv_module.conv.weight_1_2_0_0:
-      SecretInteger: "3"
-    input_x_0_2_2_1:
-      SecretInteger: "3"
-    testmod_conv_module.conv.weight_0_1_0_1:
-      SecretInteger: "3"
-    input_x_0_1_0_0:
-      SecretInteger: "3"
-    testmod_conv_module.conv.weight_0_0_0_1:
-      SecretInteger: "3"
-    input_x_0_0_0_0:
-      SecretInteger: "3"
-    testmod_linear.weight_1_1:
-      SecretInteger: "3"
-    input_x_0_1_1_2:
-      SecretInteger: "3"
-    input_x_0_0_0_1:
-      SecretInteger: "3"
-    testmod_linear.bias_0:
-      SecretInteger: "3"
-    input_x_0_2_0_1:
-      SecretInteger: "3"
-    testmod_conv_module.conv.weight_1_1_1_0:
-      SecretInteger: "3"
-    input_x_0_2_0_2:
-      SecretInteger: "3"
-    input_x_0_1_2_0:
-      SecretInteger: "3"
-    testmod_conv_module.conv.weight_1_1_0_0:
-      SecretInteger: "3"
-    input_x_0_0_1_1:
-      SecretInteger: "3"
-    testmod_conv_module.conv.weight_0_2_0_1:
-      SecretInteger: "3"
-    input_x_0_2_1_1:
-      SecretInteger: "3"
-    testmod_conv_module.conv.weight_0_2_1_1:
-      SecretInteger: "3"
-    input_x_0_2_1_2:
-      SecretInteger: "3"
-    input_x_0_1_2_2:
-      SecretInteger: "3"
-    testmod_conv_module.conv.weight_1_1_1_1:
-      SecretInteger: "3"
-    testmod_linear.bias_1:
-      SecretInteger: "3"
-    input_x_0_1_0_1:
-      SecretInteger: "3"
-    input_x_0_2_1_0:
-      SecretInteger: "3"
-    testmod_conv_module.conv.weight_1_2_1_1:
-      SecretInteger: "3"
-    testmod_conv_module.conv.weight_0_0_1_0:
-      SecretInteger: "3"
-    testmod_conv_module.conv.weight_0_1_1_1:
-      SecretInteger: "3"
-    input_x_0_0_2_1:
-      SecretInteger: "3"
-    input_x_0_1_2_1:
-      SecretInteger: "3"
-  public_variables: {}
+  input_x_0_0_2_0:
+    SecretInteger: '3'
+  testmod_conv_module.conv.weight_1_2_0_1:
+    SecretInteger: '3'
+  testmod_conv_module.conv.weight_0_2_0_0:
+    SecretInteger: '3'
+  testmod_linear.weight_1_0:
+    SecretInteger: '3'
+  input_x_0_0_1_0:
+    SecretInteger: '3'
+  testmod_linear.weight_0_1:
+    SecretInteger: '3'
+  testmod_conv_module.conv.weight_0_0_0_0:
+    SecretInteger: '3'
+  testmod_conv_module.conv.weight_1_1_0_1:
+    SecretInteger: '3'
+  input_x_0_1_1_1:
+    SecretInteger: '3'
+  testmod_conv_module.conv.weight_0_1_1_0:
+    SecretInteger: '3'
+  input_x_0_2_0_0:
+    SecretInteger: '3'
+  input_x_0_0_0_2:
+    SecretInteger: '3'
+  input_x_0_0_2_2:
+    SecretInteger: '3'
+  testmod_conv_module.conv.weight_1_0_1_0:
+    SecretInteger: '3'
+  testmod_conv_module.conv.weight_1_0_1_1:
+    SecretInteger: '3'
+  testmod_conv_module.conv.bias_0:
+    SecretInteger: '3'
+  testmod_conv_module.conv.weight_1_2_1_0:
+    SecretInteger: '3'
+  input_x_0_1_0_2:
+    SecretInteger: '3'
+  testmod_linear.weight_0_0:
+    SecretInteger: '3'
+  input_x_0_2_2_0:
+    SecretInteger: '3'
+  testmod_conv_module.conv.weight_0_0_1_1:
+    SecretInteger: '3'
+  input_x_0_2_2_2:
+    SecretInteger: '3'
+  testmod_conv_module.conv.weight_0_2_1_0:
+    SecretInteger: '3'
+  testmod_conv_module.conv.bias_1:
+    SecretInteger: '3'
+  input_x_0_1_1_0:
+    SecretInteger: '3'
+  testmod_conv_module.conv.weight_1_0_0_1:
+    SecretInteger: '3'
+  input_x_0_0_1_2:
+    SecretInteger: '3'
+  testmod_conv_module.conv.weight_1_0_0_0:
+    SecretInteger: '3'
+  testmod_conv_module.conv.weight_0_1_0_0:
+    SecretInteger: '3'
+  testmod_conv_module.conv.weight_1_2_0_0:
+    SecretInteger: '3'
+  input_x_0_2_2_1:
+    SecretInteger: '3'
+  testmod_conv_module.conv.weight_0_1_0_1:
+    SecretInteger: '3'
+  input_x_0_1_0_0:
+    SecretInteger: '3'
+  testmod_conv_module.conv.weight_0_0_0_1:
+    SecretInteger: '3'
+  input_x_0_0_0_0:
+    SecretInteger: '3'
+  testmod_linear.weight_1_1:
+    SecretInteger: '3'
+  input_x_0_1_1_2:
+    SecretInteger: '3'
+  input_x_0_0_0_1:
+    SecretInteger: '3'
+  testmod_linear.bias_0:
+    SecretInteger: '3'
+  input_x_0_2_0_1:
+    SecretInteger: '3'
+  testmod_conv_module.conv.weight_1_1_1_0:
+    SecretInteger: '3'
+  input_x_0_2_0_2:
+    SecretInteger: '3'
+  input_x_0_1_2_0:
+    SecretInteger: '3'
+  testmod_conv_module.conv.weight_1_1_0_0:
+    SecretInteger: '3'
+  input_x_0_0_1_1:
+    SecretInteger: '3'
+  testmod_conv_module.conv.weight_0_2_0_1:
+    SecretInteger: '3'
+  input_x_0_2_1_1:
+    SecretInteger: '3'
+  testmod_conv_module.conv.weight_0_2_1_1:
+    SecretInteger: '3'
+  input_x_0_2_1_2:
+    SecretInteger: '3'
+  input_x_0_1_2_2:
+    SecretInteger: '3'
+  testmod_conv_module.conv.weight_1_1_1_1:
+    SecretInteger: '3'
+  testmod_linear.bias_1:
+    SecretInteger: '3'
+  input_x_0_1_0_1:
+    SecretInteger: '3'
+  input_x_0_2_1_0:
+    SecretInteger: '3'
+  testmod_conv_module.conv.weight_1_2_1_1:
+    SecretInteger: '3'
+  testmod_conv_module.conv.weight_0_0_1_0:
+    SecretInteger: '3'
+  testmod_conv_module.conv.weight_0_1_1_1:
+    SecretInteger: '3'
+  input_x_0_0_2_1:
+    SecretInteger: '3'
+  input_x_0_1_2_1:
+    SecretInteger: '3'
 expected_outputs:
   output_0:
-    SecretInteger: "669"
+    SecretInteger: '669'
   output_1:
-    SecretInteger: "669"
+    SecretInteger: '669'
diff --git a/tests/nada-tests/tests/flatten.yaml b/tests/nada-tests/tests/flatten.yaml
index 357093e..23d5945 100644
--- a/tests/nada-tests/tests/flatten.yaml
+++ b/tests/nada-tests/tests/flatten.yaml
@@ -1,38 +1,35 @@
----
 program: flatten
 inputs:
-  secrets:
-    input_x_0_0_0_0:
-      SecretInteger: "1"
-    input_x_0_0_1_0:
-      SecretInteger: "2"
-    input_x_0_1_0_0:
-      SecretInteger: "3"
-    input_x_0_1_1_0:
-      SecretInteger: "4"
-    input_x_1_0_0_0:
-      SecretInteger: "5"
-    input_x_1_0_1_0:
-      SecretInteger: "6"
-    input_x_1_1_0_0:
-      SecretInteger: "7"
-    input_x_1_1_1_0:
-      SecretInteger: "8"
-  public_variables: {}
+  input_x_0_0_0_0:
+    SecretInteger: '1'
+  input_x_0_0_1_0:
+    SecretInteger: '2'
+  input_x_0_1_0_0:
+    SecretInteger: '3'
+  input_x_0_1_1_0:
+    SecretInteger: '4'
+  input_x_1_0_0_0:
+    SecretInteger: '5'
+  input_x_1_0_1_0:
+    SecretInteger: '6'
+  input_x_1_1_0_0:
+    SecretInteger: '7'
+  input_x_1_1_1_0:
+    SecretInteger: '8'
 expected_outputs:
   x_flat_0:
-    SecretInteger: "1"
+    SecretInteger: '1'
   x_flat_1:
-    SecretInteger: "2"
+    SecretInteger: '2'
   x_flat_2:
-    SecretInteger: "3"
+    SecretInteger: '3'
   x_flat_3:
-    SecretInteger: "4"
+    SecretInteger: '4'
   x_flat_4:
-    SecretInteger: "5"
+    SecretInteger: '5'
   x_flat_5:
-    SecretInteger: "6"
+    SecretInteger: '6'
   x_flat_6:
-    SecretInteger: "7"
+    SecretInteger: '7'
   x_flat_7:
-    SecretInteger: "8"
+    SecretInteger: '8'
diff --git a/tests/nada-tests/tests/linear_layers.yaml b/tests/nada-tests/tests/linear_layers.yaml
index 0c4c87e..bfae38d 100644
--- a/tests/nada-tests/tests/linear_layers.yaml
+++ b/tests/nada-tests/tests/linear_layers.yaml
@@ -1,36 +1,33 @@
----
 program: linear_layers
 inputs:
-  secrets:
-    input_0:
-      SecretInteger: "1"
-    input_1:
-      SecretInteger: "2"
-    input_2:
-      SecretInteger: "3"
-    testmod_linear_0.weight_0_0:
-      SecretInteger: "1"
-    testmod_linear_0.weight_0_1:
-      SecretInteger: "2"
-    testmod_linear_0.weight_0_2:
-      SecretInteger: "3"
-    testmod_linear_0.weight_1_0:
-      SecretInteger: "4"
-    testmod_linear_0.weight_1_1:
-      SecretInteger: "5"
-    testmod_linear_0.weight_1_2:
-      SecretInteger: "6"
-    testmod_linear_0.bias_0:
-      SecretInteger: "4"
-    testmod_linear_0.bias_1:
-      SecretInteger: "5"
-    testmod_linear_1.weight_0_0:
-      SecretInteger: "3"
-    testmod_linear_1.weight_0_1:
-      SecretInteger: "4"
-    testmod_linear_1.bias_0:
-      SecretInteger: "5"
-  public_variables: {}
+  input_0:
+    SecretInteger: '1'
+  input_1:
+    SecretInteger: '2'
+  input_2:
+    SecretInteger: '3'
+  testmod_linear_0.weight_0_0:
+    SecretInteger: '1'
+  testmod_linear_0.weight_0_1:
+    SecretInteger: '2'
+  testmod_linear_0.weight_0_2:
+    SecretInteger: '3'
+  testmod_linear_0.weight_1_0:
+    SecretInteger: '4'
+  testmod_linear_0.weight_1_1:
+    SecretInteger: '5'
+  testmod_linear_0.weight_1_2:
+    SecretInteger: '6'
+  testmod_linear_0.bias_0:
+    SecretInteger: '4'
+  testmod_linear_0.bias_1:
+    SecretInteger: '5'
+  testmod_linear_1.weight_0_0:
+    SecretInteger: '3'
+  testmod_linear_1.weight_0_1:
+    SecretInteger: '4'
+  testmod_linear_1.bias_0:
+    SecretInteger: '5'
 expected_outputs:
   output_0:
-    SecretInteger: "207"
+    SecretInteger: '207'
diff --git a/tests/nada-tests/tests/linear_regression.yaml b/tests/nada-tests/tests/linear_regression.yaml
index 38e4f17..57988e6 100644
--- a/tests/nada-tests/tests/linear_regression.yaml
+++ b/tests/nada-tests/tests/linear_regression.yaml
@@ -1,26 +1,23 @@
----
 program: linear_regression
 inputs:
-  secrets:
-    input_0:
-      SecretInteger: "1"
-    input_1:
-      SecretInteger: "2"
-    input_2:
-      SecretInteger: "3"
-    input_3:
-      SecretInteger: "3"
-    testmod_coef_0:
-      SecretInteger: "4"
-    testmod_coef_1:
-      SecretInteger: "3"
-    testmod_coef_2:
-      SecretInteger: "2"
-    testmod_coef_3:
-      SecretInteger: "1"
-    testmod_intercept_0:
-      SecretInteger: "3"
-  public_variables: {}
+  input_0:
+    SecretInteger: '1'
+  input_1:
+    SecretInteger: '2'
+  input_2:
+    SecretInteger: '3'
+  input_3:
+    SecretInteger: '3'
+  testmod_coef_0:
+    SecretInteger: '4'
+  testmod_coef_1:
+    SecretInteger: '3'
+  testmod_coef_2:
+    SecretInteger: '2'
+  testmod_coef_3:
+    SecretInteger: '1'
+  testmod_intercept_0:
+    SecretInteger: '3'
 expected_outputs:
-  output:
-    SecretInteger: "22"
+  my_output:
+    SecretInteger: '22'
diff --git a/tests/nada-tests/tests/load_state.yaml b/tests/nada-tests/tests/load_state.yaml
index e95dbff..da649fa 100644
--- a/tests/nada-tests/tests/load_state.yaml
+++ b/tests/nada-tests/tests/load_state.yaml
@@ -1,70 +1,67 @@
----
 program: load_state
 inputs:
-  secrets:
-    module1_param1_0_0:
-      SecretInteger: "1"
-    module1_param1_0_1:
-      SecretInteger: "2"
-    module1_param1_1_0:
-      SecretInteger: "3"
-    module1_param1_1_1:
-      SecretInteger: "4"
-    module1_param1_2_0:
-      SecretInteger: "5"
-    module1_param1_2_1:
-      SecretInteger: "6"
-    module1_param2_0:
-      SecretInteger: "7"
-    module1_param2_1:
-      SecretInteger: "8"
-  public_variables:
-    module2_param1_0_0:
-      Integer: "1"
-    module2_param1_0_1:
-      Integer: "2"
-    module2_param1_1_0:
-      Integer: "3"
-    module2_param1_1_1:
-      Integer: "4"
-    module2_param1_2_0:
-      Integer: "5"
-    module2_param1_2_1:
-      Integer: "6"
-    module2_param2_0:
-      Integer: "7"
-    module2_param2_1:
-      Integer: "8"
+  module1_param1_0_0:
+    SecretInteger: '1'
+  module1_param1_0_1:
+    SecretInteger: '2'
+  module1_param1_1_0:
+    SecretInteger: '3'
+  module1_param1_1_1:
+    SecretInteger: '4'
+  module1_param1_2_0:
+    SecretInteger: '5'
+  module1_param1_2_1:
+    SecretInteger: '6'
+  module1_param2_0:
+    SecretInteger: '7'
+  module1_param2_1:
+    SecretInteger: '8'
+  module2_param1_0_0:
+    Integer: '1'
+  module2_param1_0_1:
+    Integer: '2'
+  module2_param1_1_0:
+    Integer: '3'
+  module2_param1_1_1:
+    Integer: '4'
+  module2_param1_2_0:
+    Integer: '5'
+  module2_param1_2_1:
+    Integer: '6'
+  module2_param2_0:
+    Integer: '7'
+  module2_param2_1:
+    Integer: '8'
 expected_outputs:
   module1_param1_0_0:
-    SecretInteger: "1"
+    SecretInteger: '1'
   module1_param1_0_1:
-    SecretInteger: "2"
+    SecretInteger: '2'
   module1_param1_1_0:
-    SecretInteger: "3"
+    SecretInteger: '3'
   module1_param1_1_1:
-    SecretInteger: "4"
+    SecretInteger: '4'
   module1_param1_2_0:
-    SecretInteger: "5"
+    SecretInteger: '5'
   module1_param1_2_1:
-    SecretInteger: "6"
+    SecretInteger: '6'
   module1_param2_0:
-    SecretInteger: "7"
+    SecretInteger: '7'
   module1_param2_1:
-    SecretInteger: "8"
+    SecretInteger: '8'
   module2_param1_0_0:
-    Integer: "1"
+    Integer: '1'
   module2_param1_0_1:
-    Integer: "2"
+    Integer: '2'
   module2_param1_1_0:
-    Integer: "3"
+    Integer: '3'
   module2_param1_1_1:
-    Integer: "4"
+    Integer: '4'
   module2_param1_2_0:
-    Integer: "5"
+    Integer: '5'
   module2_param1_2_1:
-    Integer: "6"
+    Integer: '6'
   module2_param2_0:
-    Integer: "7"
+    Integer: '7'
   module2_param2_1:
-    Integer: "8"
+    Integer: '8'
diff --git a/tests/nada-tests/tests/logistic_regression.yaml b/tests/nada-tests/tests/logistic_regression.yaml
index 37f5b75..3b3ae1d 100644
--- a/tests/nada-tests/tests/logistic_regression.yaml
+++ b/tests/nada-tests/tests/logistic_regression.yaml
@@ -1,50 +1,47 @@
----
 program: logistic_regression
 inputs:
-  secrets:
-    testmod_coef_1_2:
-      SecretInteger: "3"
-    testmod_intercept_2:
-      SecretInteger: "3"
-    testmod_coef_1_1:
-      SecretInteger: "3"
-    testmod_coef_0_0:
-      SecretInteger: "3"
-    input_1:
-      SecretInteger: "3"
-    testmod_coef_2_1:
-      SecretInteger: "3"
-    testmod_coef_1_0:
-      SecretInteger: "3"
-    testmod_coef_1_3:
-      SecretInteger: "3"
-    testmod_coef_0_2:
-      SecretInteger: "3"
-    input_3:
-      SecretInteger: "3"
-    testmod_intercept_0:
-      SecretInteger: "3"
-    testmod_coef_2_2:
-      SecretInteger: "3"
-    testmod_coef_0_3:
-      SecretInteger: "3"
-    input_2:
-      SecretInteger: "3"
-    testmod_coef_2_3:
-      SecretInteger: "3"
-    testmod_coef_0_1:
-      SecretInteger: "3"
-    testmod_intercept_1:
-      SecretInteger: "3"
-    input_0:
-      SecretInteger: "3"
-    testmod_coef_2_0:
-      SecretInteger: "3"
-  public_variables: {}
+  testmod_coef_1_2:
+    SecretInteger: '3'
+  testmod_intercept_2:
+    SecretInteger: '3'
+  testmod_coef_1_1:
+    SecretInteger: '3'
+  testmod_coef_0_0:
+    SecretInteger: '3'
+  input_1:
+    SecretInteger: '3'
+  testmod_coef_2_1:
+    SecretInteger: '3'
+  testmod_coef_1_0:
+    SecretInteger: '3'
+  testmod_coef_1_3:
+    SecretInteger: '3'
+  testmod_coef_0_2:
+    SecretInteger: '3'
+  input_3:
+    SecretInteger: '3'
+  testmod_intercept_0:
+    SecretInteger: '3'
+  testmod_coef_2_2:
+    SecretInteger: '3'
+  testmod_coef_0_3:
+    SecretInteger: '3'
+  input_2:
+    SecretInteger: '3'
+  testmod_coef_2_3:
+    SecretInteger: '3'
+  testmod_coef_0_1:
+    SecretInteger: '3'
+  testmod_intercept_1:
+    SecretInteger: '3'
+  input_0:
+    SecretInteger: '3'
+  testmod_coef_2_0:
+    SecretInteger: '3'
 expected_outputs:
   my_output_0:
-    SecretInteger: "39"
+    SecretInteger: '39'
   my_output_1:
-    SecretInteger: "39"
+    SecretInteger: '39'
   my_output_2:
-    SecretInteger: "39"
+    SecretInteger: '39'
diff --git a/tests/nada-tests/tests/nested_modules.yaml b/tests/nada-tests/tests/nested_modules.yaml
index aa0dedf..155d93b 100644
--- a/tests/nada-tests/tests/nested_modules.yaml
+++ b/tests/nada-tests/tests/nested_modules.yaml
@@ -1,40 +1,37 @@
----
 program: nested_modules
 inputs:
-  secrets:
-    input_0:
-      SecretInteger: "1"
-    input_1:
-      SecretInteger: "2"
-    testmod_mod.param1_0_0:
-      SecretInteger: "1"
-    testmod_mod.param1_0_1:
-      SecretInteger: "2"
-    testmod_mod.param1_1_0:
-      SecretInteger: "3"
-    testmod_mod.param1_1_1:
-      SecretInteger: "4"
-    testmod_mod.param1_2_0:
-      SecretInteger: "5"
-    testmod_mod.param1_2_1:
-      SecretInteger: "6"
-    testmod_mod.param2_0:
-      SecretInteger: "7"
-    testmod_mod.param2_1:
-      SecretInteger: "8"
-    testmod_mod.param2_2:
-      SecretInteger: "9"
-    testmod_param1_0:
-      SecretInteger: "1"
-    testmod_param1_1:
-      SecretInteger: "2"
-    testmod_param1_2:
-      SecretInteger: "3"
-  public_variables: {}
+  input_0:
+    SecretInteger: '1'
+  input_1:
+    SecretInteger: '2'
+  testmod_mod.param1_0_0:
+    SecretInteger: '1'
+  testmod_mod.param1_0_1:
+    SecretInteger: '2'
+  testmod_mod.param1_1_0:
+    SecretInteger: '3'
+  testmod_mod.param1_1_1:
+    SecretInteger: '4'
+  testmod_mod.param1_2_0:
+    SecretInteger: '5'
+  testmod_mod.param1_2_1:
+    SecretInteger: '6'
+  testmod_mod.param2_0:
+    SecretInteger: '7'
+  testmod_mod.param2_1:
+    SecretInteger: '8'
+  testmod_mod.param2_2:
+    SecretInteger: '9'
+  testmod_param1_0:
+    SecretInteger: '1'
+  testmod_param1_1:
+    SecretInteger: '2'
+  testmod_param1_2:
+    SecretInteger: '3'
 expected_outputs:
   output_0:
-    SecretInteger: "8"
+    SecretInteger: '8'
   output_1:
-    SecretInteger: "10"
+    SecretInteger: '10'
   output_2:
-    SecretInteger: "12"
+    SecretInteger: '12'
diff --git a/tests/nada-tests/tests/parameters.yaml b/tests/nada-tests/tests/parameters.yaml
index 0f21414..5188d82 100644
--- a/tests/nada-tests/tests/parameters.yaml
+++ b/tests/nada-tests/tests/parameters.yaml
@@ -1,34 +1,31 @@
----
 program: parameters
 inputs:
-  secrets:
-    input_0:
-      SecretInteger: "1"
-    input_1:
-      SecretInteger: "2"
-    testmod_param1_0_0:
-      SecretInteger: "1"
-    testmod_param1_0_1:
-      SecretInteger: "2"
-    testmod_param1_1_0:
-      SecretInteger: "3"
-    testmod_param1_1_1:
-      SecretInteger: "4"
-    testmod_param1_2_0:
-      SecretInteger: "5"
-    testmod_param1_2_1:
-      SecretInteger: "6"
-    testmod_param2_0:
-      SecretInteger: "7"
-    testmod_param2_1:
-      SecretInteger: "8"
-    testmod_param2_2:
-      SecretInteger: "9"
-  public_variables: {}
+  input_0:
+    SecretInteger: '1'
+  input_1:
+    SecretInteger: '2'
+  testmod_param1_0_0:
+    SecretInteger: '1'
+  testmod_param1_0_1:
+    SecretInteger: '2'
+  testmod_param1_1_0:
+    SecretInteger: '3'
+  testmod_param1_1_1:
+    SecretInteger: '4'
+  testmod_param1_2_0:
+    SecretInteger: '5'
+  testmod_param1_2_1:
+    SecretInteger: '6'
+  testmod_param2_0:
+    SecretInteger: '7'
+  testmod_param2_1:
+    SecretInteger: '8'
+  testmod_param2_2:
+    SecretInteger: '9'
 expected_outputs:
   output_0:
-    SecretInteger: "7"
+    SecretInteger: '7'
   output_1:
-    SecretInteger: "8"
+    SecretInteger: '8'
   output_2:
-    SecretInteger: "9"
+    SecretInteger: '9'
diff --git a/tests/nada-tests/tests/pool.yaml b/tests/nada-tests/tests/pool.yaml
index 9e9a23f..47e8e0c 100644
--- a/tests/nada-tests/tests/pool.yaml
+++ b/tests/nada-tests/tests/pool.yaml
@@ -1,278 +1,275 @@
----
 program: pool
 inputs:
-  secrets:
-    input_x_0_0_0_0:
-      SecretInteger: "4"
-    input_x_0_2_0_0:
-      SecretInteger: "4"
-    input_x_0_3_1_0:
-      SecretInteger: "4"
-    input_x_0_2_2_1:
-      SecretInteger: "4"
-    input_x_0_1_2_0:
-      SecretInteger: "4"
-    input_x_0_1_0_1:
-      SecretInteger: "4"
-    input_x_0_1_1_1:
-      SecretInteger: "4"
-    input_x_0_1_2_1:
-      SecretInteger: "4"
-    input_x_0_2_0_1:
-      SecretInteger: "4"
-    input_x_0_2_1_1:
-      SecretInteger: "4"
-    input_x_0_0_2_1:
-      SecretInteger: "4"
-    input_x_0_0_1_0:
-      SecretInteger: "4"
-    input_x_0_2_3_0:
-      SecretInteger: "4"
-    input_x_0_3_3_1:
-      SecretInteger: "4"
-    input_x_0_2_3_1:
-      SecretInteger: "4"
-    input_x_0_2_2_0:
-      SecretInteger: "4"
-    input_x_0_0_3_1:
-      SecretInteger: "4"
-    input_x_0_1_3_0:
-      SecretInteger: "4"
-    input_x_0_3_3_0:
-      SecretInteger: "4"
-    input_x_0_2_1_0:
-      SecretInteger: "4"
-    input_x_0_0_2_0:
-      SecretInteger: "4"
-    input_x_0_0_3_0:
-      SecretInteger: "4"
-    input_x_0_1_3_1:
-      SecretInteger: "4"
-    input_x_0_0_0_1:
-      SecretInteger: "4"
-    input_x_0_1_1_0:
-      SecretInteger: "4"
-    input_x_0_1_0_0:
-      SecretInteger: "4"
-    input_x_0_3_2_0:
-      SecretInteger: "4"
-    input_x_0_3_0_1:
-      SecretInteger: "4"
-    input_x_0_3_0_0:
-      SecretInteger: "4"
-    input_x_0_3_2_1:
-      SecretInteger: "4"
-    input_x_0_3_1_1:
-      SecretInteger: "4"
-    input_x_0_0_1_1:
-      SecretInteger: "4"
-    input_y_0_0_0:
-      SecretInteger: "4"
-    input_y_2_0_0:
-      SecretInteger: "4"
-    input_y_3_1_0:
-      SecretInteger: "4"
-    input_y_2_2_1:
-      SecretInteger: "4"
-    input_y_1_2_0:
-      SecretInteger: "4"
-    input_y_1_0_1:
-      SecretInteger: "4"
-    input_y_1_1_1:
-      SecretInteger: "4"
-    input_y_1_2_1:
-      SecretInteger: "4"
-    input_y_2_0_1:
-      SecretInteger: "4"
-    input_y_2_1_1:
-      SecretInteger: "4"
-    input_y_0_2_1:
-      SecretInteger: "4"
-    input_y_0_1_0:
-      SecretInteger: "4"
-    input_y_2_3_0:
-      SecretInteger: "4"
-    input_y_3_3_1:
-      SecretInteger: "4"
-    input_y_2_3_1:
-      SecretInteger: "4"
-    input_y_2_2_0:
-      SecretInteger: "4"
-    input_y_0_3_1:
-      SecretInteger: "4"
-    input_y_1_3_0:
-      SecretInteger: "4"
-    input_y_3_3_0:
-      SecretInteger: "4"
-    input_y_2_1_0:
-      SecretInteger: "4"
-    input_y_0_2_0:
-      SecretInteger: "4"
-    input_y_0_3_0:
-      SecretInteger: "4"
-    input_y_1_3_1:
-      SecretInteger: "4"
-    input_y_0_0_1:
-      SecretInteger: "4"
-    input_y_1_1_0:
-      SecretInteger: "4"
-    input_y_1_0_0:
-      SecretInteger: "4"
-    input_y_3_2_0:
-      SecretInteger: "4"
-    input_y_3_0_1:
-      SecretInteger: "4"
-    input_y_3_0_0:
-      SecretInteger: "4"
-    input_y_3_2_1:
-      SecretInteger: "4"
-    input_y_3_1_1:
-      SecretInteger: "4"
-    input_y_0_1_1:
-      SecretInteger: "4"
-  public_variables: {}
+  input_x_0_0_0_0:
+    SecretInteger: '4'
+  input_x_0_2_0_0:
+    SecretInteger: '4'
+  input_x_0_3_1_0:
+    SecretInteger: '4'
+  input_x_0_2_2_1:
+    SecretInteger: '4'
+  input_x_0_1_2_0:
+    SecretInteger: '4'
+  input_x_0_1_0_1:
+    SecretInteger: '4'
+  input_x_0_1_1_1:
+    SecretInteger: '4'
+  input_x_0_1_2_1:
+    SecretInteger: '4'
+  input_x_0_2_0_1:
+    SecretInteger: '4'
+  input_x_0_2_1_1:
+    SecretInteger: '4'
+  input_x_0_0_2_1:
+    SecretInteger: '4'
+  input_x_0_0_1_0:
+    SecretInteger: '4'
+  input_x_0_2_3_0:
+    SecretInteger: '4'
+  input_x_0_3_3_1:
+    SecretInteger: '4'
+  input_x_0_2_3_1:
+    SecretInteger: '4'
+  input_x_0_2_2_0:
+    SecretInteger: '4'
+  input_x_0_0_3_1:
+    SecretInteger: '4'
+  input_x_0_1_3_0:
+    SecretInteger: '4'
+  input_x_0_3_3_0:
+    SecretInteger: '4'
+  input_x_0_2_1_0:
+    SecretInteger: '4'
+  input_x_0_0_2_0:
+    SecretInteger: '4'
+  input_x_0_0_3_0:
+    SecretInteger: '4'
+  input_x_0_1_3_1:
+    SecretInteger: '4'
+  input_x_0_0_0_1:
+    SecretInteger: '4'
+  input_x_0_1_1_0:
+    SecretInteger: '4'
+  input_x_0_1_0_0:
+    SecretInteger: '4'
+  input_x_0_3_2_0:
+    SecretInteger: '4'
+  input_x_0_3_0_1:
+    SecretInteger: '4'
+  input_x_0_3_0_0:
+    SecretInteger: '4'
+  input_x_0_3_2_1:
+    SecretInteger: '4'
+  input_x_0_3_1_1:
+    SecretInteger: '4'
+  input_x_0_0_1_1:
+    SecretInteger: '4'
+  input_y_0_0_0:
+    SecretInteger: '4'
+  input_y_2_0_0:
+    SecretInteger: '4'
+  input_y_3_1_0:
+    SecretInteger: '4'
+  input_y_2_2_1:
+    SecretInteger: '4'
+  input_y_1_2_0:
+    SecretInteger: '4'
+  input_y_1_0_1:
+    SecretInteger: '4'
+  input_y_1_1_1:
+    SecretInteger: '4'
+  input_y_1_2_1:
+    SecretInteger: '4'
+  input_y_2_0_1:
+    SecretInteger: '4'
+  input_y_2_1_1:
+    SecretInteger: '4'
+  input_y_0_2_1:
+    SecretInteger: '4'
+  input_y_0_1_0:
+    SecretInteger: '4'
+  input_y_2_3_0:
+    SecretInteger: '4'
+  input_y_3_3_1:
+    SecretInteger: '4'
+  input_y_2_3_1:
+    SecretInteger: '4'
+  input_y_2_2_0:
+    SecretInteger: '4'
+  input_y_0_3_1:
+    SecretInteger: '4'
+  input_y_1_3_0:
+    SecretInteger: '4'
+  input_y_3_3_0:
+    SecretInteger: '4'
+  input_y_2_1_0:
+    SecretInteger: '4'
+  input_y_0_2_0:
+    SecretInteger: '4'
+  input_y_0_3_0:
+    SecretInteger: '4'
+  input_y_1_3_1:
+    SecretInteger: '4'
+  input_y_0_0_1:
+    SecretInteger: '4'
+  input_y_1_1_0:
+    SecretInteger: '4'
+  input_y_1_0_0:
+    SecretInteger: '4'
+  input_y_3_2_0:
+    SecretInteger: '4'
+  input_y_3_0_1:
+    SecretInteger: '4'
+  input_y_3_0_0:
+    SecretInteger: '4'
+  input_y_3_2_1:
+    SecretInteger: '4'
+  input_y_3_1_1:
+    SecretInteger: '4'
+  input_y_0_1_1:
+    SecretInteger: '4'
 expected_outputs:
   x_pool2_0_2_1_1:
-    SecretInteger: "2"
+    SecretInteger: '2'
   x_pool2_0_1_2_0:
-    SecretInteger: "1"
+    SecretInteger: '1'
   x_pool2_0_3_2_1:
-    SecretInteger: "1"
+    SecretInteger: '1'
   x_pool1_0_2_1_0:
-    SecretInteger: "4"
+    SecretInteger: '4'
   x_pool1_0_3_0_0:
-    SecretInteger: "4"
+    SecretInteger: '4'
   x_pool1_0_1_1_0:
-    SecretInteger: "4"
+    SecretInteger: '4'
   x_pool2_0_3_0_0:
-    SecretInteger: "1"
+    SecretInteger: '1'
   x_pool2_0_3_1_1:
-    SecretInteger: "2"
+    SecretInteger: '2'
   x_pool1_0_3_2_0:
-    SecretInteger: "4"
+    SecretInteger: '4'
   x_pool1_0_0_1_0:
-    SecretInteger: "4"
+    SecretInteger: '4'
   x_pool1_0_3_1_0:
-    SecretInteger: "4"
+    SecretInteger: '4'
   x_pool2_0_2_1_0:
-    SecretInteger: "2"
+    SecretInteger: '2'
   x_pool1_0_0_0_0:
-    SecretInteger: "4"
+    SecretInteger: '4'
   x_pool2_0_1_0_1:
-    SecretInteger: "1"
+    SecretInteger: '1'
   x_pool2_0_1_0_0:
-    SecretInteger: "1"
+    SecretInteger: '1'
   x_pool2_0_2_2_1:
-    SecretInteger: "1"
+    SecretInteger: '1'
   x_pool2_0_0_2_1:
-    SecretInteger: "1"
+    SecretInteger: '1'
   x_pool2_0_0_0_0:
-    SecretInteger: "1"
+    SecretInteger: '1'
   x_pool2_0_1_1_1:
-    SecretInteger: "2"
+    SecretInteger: '2'
   x_pool1_0_0_2_0:
-    SecretInteger: "4"
+    SecretInteger: '4'
   x_pool2_0_3_2_0:
-    SecretInteger: "1"
+    SecretInteger: '1'
   x_pool1_0_1_2_0:
-    SecretInteger: "4"
+    SecretInteger: '4'
   x_pool1_0_2_0_0:
-    SecretInteger: "4"
+    SecretInteger: '4'
   x_pool2_0_0_1_0:
-    SecretInteger: "2"
+    SecretInteger: '2'
   x_pool2_0_3_1_0:
-    SecretInteger: "2"
+    SecretInteger: '2'
   x_pool2_0_1_2_1:
-    SecretInteger: "1"
+    SecretInteger: '1'
   x_pool2_0_1_1_0:
-    SecretInteger: "2"
+    SecretInteger: '2'
   x_pool2_0_2_2_0:
-    SecretInteger: "1"
+    SecretInteger: '1'
   x_pool2_0_2_0_1:
-    SecretInteger: "1"
+    SecretInteger: '1'
   x_pool1_0_1_0_0:
-    SecretInteger: "4"
+    SecretInteger: '4'
   x_pool2_0_3_0_1:
-    SecretInteger: "1"
+    SecretInteger: '1'
   x_pool1_0_2_2_0:
-    SecretInteger: "4"
+    SecretInteger: '4'
   x_pool2_0_0_1_1:
-    SecretInteger: "2"
+    SecretInteger: '2'
   x_pool2_0_0_0_1:
-    SecretInteger: "1"
+    SecretInteger: '1'
   x_pool2_0_0_2_0:
-    SecretInteger: "1"
+    SecretInteger: '1'
   x_pool2_0_2_0_0:
-    SecretInteger: "1"
+    SecretInteger: '1'
   y_pool2_2_1_1:
-    SecretInteger: "2"
+    SecretInteger: '2'
   y_pool2_1_2_0:
-    SecretInteger: "1"
+    SecretInteger: '1'
   y_pool2_3_2_1:
-    SecretInteger: "1"
+    SecretInteger: '1'
   y_pool1_2_1_0:
-    SecretInteger: "4"
+    SecretInteger: '4'
   y_pool1_3_0_0:
-    SecretInteger: "4"
+    SecretInteger: '4'
   y_pool1_1_1_0:
-    SecretInteger: "4"
+    SecretInteger: '4'
   y_pool2_3_0_0:
-    SecretInteger: "1"
+    SecretInteger: '1'
   y_pool2_3_1_1:
-    SecretInteger: "2"
+    SecretInteger: '2'
   y_pool1_3_2_0:
-    SecretInteger: "4"
+    SecretInteger: '4'
   y_pool1_0_1_0:
-    SecretInteger: "4"
+    SecretInteger: '4'
   y_pool1_3_1_0:
-    SecretInteger: "4"
+    SecretInteger: '4'
   y_pool2_2_1_0:
-    SecretInteger: "2"
+    SecretInteger: '2'
   y_pool1_0_0_0:
-    SecretInteger: "4"
+    SecretInteger: '4'
   y_pool2_1_0_1:
-    SecretInteger: "1"
+    SecretInteger: '1'
   y_pool2_1_0_0:
-    SecretInteger: "1"
+    SecretInteger: '1'
   y_pool2_2_2_1:
-    SecretInteger: "1"
+    SecretInteger: '1'
   y_pool2_0_2_1:
-    SecretInteger: "1"
+    SecretInteger: '1'
   y_pool2_0_0_0:
-    SecretInteger: "1"
+    SecretInteger: '1'
   y_pool2_1_1_1:
-    SecretInteger: "2"
+    SecretInteger: '2'
   y_pool1_0_2_0:
-    SecretInteger: "4"
+    SecretInteger: '4'
   y_pool2_3_2_0:
-    SecretInteger: "1"
+    SecretInteger: '1'
   y_pool1_1_2_0:
-    SecretInteger: "4"
+    SecretInteger: '4'
   y_pool1_2_0_0:
-    SecretInteger: "4"
+    SecretInteger: '4'
   y_pool2_0_1_0:
-    SecretInteger: "2"
+    SecretInteger: '2'
   y_pool2_3_1_0:
-    SecretInteger: "2"
+    SecretInteger: '2'
   y_pool2_1_2_1:
-    SecretInteger: "1"
+    SecretInteger: '1'
   y_pool2_1_1_0:
-    SecretInteger: "2"
+    SecretInteger: '2'
   y_pool2_2_2_0:
-    SecretInteger: "1"
+    SecretInteger: '1'
   y_pool2_2_0_1:
-    SecretInteger: "1"
+    SecretInteger: '1'
   y_pool1_1_0_0:
-    SecretInteger: "4"
+    SecretInteger: '4'
   y_pool2_3_0_1:
-    SecretInteger: "1"
+    SecretInteger: '1'
   y_pool1_2_2_0:
-    SecretInteger: "4"
+    SecretInteger: '4'
   y_pool2_0_1_1:
-    SecretInteger: "2"
+    SecretInteger: '2'
   y_pool2_0_0_1:
-    SecretInteger: "1"
+    SecretInteger: '1'
   y_pool2_0_2_0:
-    SecretInteger: "1"
+    SecretInteger: '1'
   y_pool2_2_0_0:
-    SecretInteger: "1"
+    SecretInteger: '1'
diff --git a/tests/nada-tests/tests/prophet.yaml b/tests/nada-tests/tests/prophet.yaml
index 429838c..ef2318d 100644
--- a/tests/nada-tests/tests/prophet.yaml
+++ b/tests/nada-tests/tests/prophet.yaml
@@ -1,56 +1,53 @@
----
 program: prophet
 inputs:
-  secrets:
-    my_prophet_m_0_0:
-      SecretInteger: "3"
-    my_prophet_beta_0_4:
-      SecretInteger: "3"
-    my_prophet_beta_0_5:
-      SecretInteger: "3"
-    my_prophet_changepoints_t_1:
-      SecretInteger: "3"
-    my_prophet_delta_0_1:
-      SecretInteger: "3"
-    my_prophet_y_scale_0:
-      SecretInteger: "3"
-    floor_2:
-      SecretInteger: "3"
-    my_prophet_delta_0_0:
-      SecretInteger: "3"
-    floor_3:
-      SecretInteger: "3"
-    t_2:
-      SecretInteger: "3"
-    floor_0:
-      SecretInteger: "3"
-    my_prophet_changepoints_t_0:
-      SecretInteger: "3"
-    my_prophet_beta_0_1:
-      SecretInteger: "3"
-    t_1:
-      SecretInteger: "3"
-    my_prophet_beta_0_3:
-      SecretInteger: "3"
-    my_prophet_k_0_0:
-      SecretInteger: "3"
-    my_prophet_beta_0_2:
-      SecretInteger: "3"
-    floor_1:
-      SecretInteger: "3"
-    t_0:
-      SecretInteger: "3"
-    t_3:
-      SecretInteger: "3"
-    my_prophet_beta_0_0:
-      SecretInteger: "3"
-  public_variables: {}
+  my_prophet_m_0_0:
+    SecretInteger: '3'
+  my_prophet_beta_0_4:
+    SecretInteger: '3'
+  my_prophet_beta_0_5:
+    SecretInteger: '3'
+  my_prophet_changepoints_t_1:
+    SecretInteger: '3'
+  my_prophet_delta_0_1:
+    SecretInteger: '3'
+  my_prophet_y_scale_0:
+    SecretInteger: '3'
+  floor_2:
+    SecretInteger: '3'
+  my_prophet_delta_0_0:
+    SecretInteger: '3'
+  floor_3:
+    SecretInteger: '3'
+  t_2:
+    SecretInteger: '3'
+  floor_0:
+    SecretInteger: '3'
+  my_prophet_changepoints_t_0:
+    SecretInteger: '3'
+  my_prophet_beta_0_1:
+    SecretInteger: '3'
+  t_1:
+    SecretInteger: '3'
+  my_prophet_beta_0_3:
+    SecretInteger: '3'
+  my_prophet_k_0_0:
+    SecretInteger: '3'
+  my_prophet_beta_0_2:
+    SecretInteger: '3'
+  floor_1:
+    SecretInteger: '3'
+  t_0:
+    SecretInteger: '3'
+  t_3:
+    SecretInteger: '3'
+  my_prophet_beta_0_0:
+    SecretInteger: '3'
 expected_outputs:
   forecast_2:
-    SecretInteger: "3"
+    SecretInteger: '3'
   forecast_0:
-    SecretInteger: "3"
+    SecretInteger: '3'
   forecast_3:
-    SecretInteger: "3"
+    SecretInteger: '3'
   forecast_1:
-    SecretInteger: "2"
+    SecretInteger: '2'
diff --git a/tests/python-tests/test_model_client.py b/tests/python-tests/test_model_client.py
index 5207e29..5534935 100644
--- a/tests/python-tests/test_model_client.py
+++ b/tests/python-tests/test_model_client.py
@@ -62,19 +62,6 @@ def test_sklearn_3(self):
         assert "test_model_coef_0_1" in secrets.keys()
         assert "test_model_coef_0_2" in secrets.keys()
 
-    def test_sklearn_4(self):
-        log_reg = LogisticRegression(fit_intercept=False)
-
-        X = np.array([[1, 2, 3], [2, 3, 4]])
-        y = np.array([0, 1])
-
-        log_reg_fit = log_reg.fit(X, y)
-
-        model_client = SklearnClient(log_reg_fit)
-
-        with pytest.raises(NotImplementedError):
-            model_client.export_state_as_secrets("test_model", nillion.SecretInteger)
-
     def test_custom_client_1(self):
         class MyModelClient(ModelClient):
             def __init__(self) -> None:
diff --git a/tests/test_all_nada.py b/tests/test_all_nada.py
index 523f188..0453130 100644
--- a/tests/test_all_nada.py
+++ b/tests/test_all_nada.py
@@ -25,7 +25,7 @@
     "multi_layer_perceptron",
     "neural_net",
     "spam_detection",
-    # "time_series",
+    "time_series",
 ]
 
 TESTS = [("tests/nada-tests/", test) for test in TESTS] + [
@@ -39,20 +39,21 @@ def testname(request):
 
 
 def build_nada(test_dir):
-    print(test_dir)
     result = subprocess.run(
         ["nada", "build", test_dir[1]], cwd=test_dir[0], capture_output=True, text=True
     )
-    if result.returncode != 0:
-        pytest.fail(f"Build failed: {result.stderr}")
+    err = result.stderr.lower() + result.stdout.lower()
+    if result.returncode != 0 or "error" in err or "fail" in err:
+        pytest.fail(f"Build {test_dir}:\n{result.stdout + result.stderr}")
 
 
 def run_nada(test_dir):
     result = subprocess.run(
         ["nada", "test", test_dir[1]], cwd=test_dir[0], capture_output=True, text=True
     )
-    if result.returncode != 0:
-        pytest.fail(f"Tests failed: {result.stderr}")
+    err = result.stderr.lower() + result.stdout.lower()
+    if result.returncode != 0 or "error" in err or "fail" in err:
+        pytest.fail(f"Run {test_dir}:\n{result.stdout + result.stderr}")
 
 
 class TestSuite:
@@ -87,7 +88,7 @@ def test_client():
 
     assert parties is not None
 
-    secrets = nillion.Secrets(
+    secrets = nillion.NadaValues(
         na_client.concat(
             [
                 na_client.array(np.ones((3, 3)), "A", nillion.SecretInteger),
@@ -98,13 +99,11 @@ def test_client():
 
     assert secrets is not None
 
-    public_variables = nillion.PublicVariables(
+    public_variables = nillion.NadaValues(
         na_client.concat(
             [
-                na_client.array(np.zeros((4, 4)), "C", nillion.PublicVariableInteger),
-                na_client.array(
-                    np.zeros((3, 3)), "D", nillion.PublicVariableUnsignedInteger
-                ),
+                na_client.array(np.zeros((4, 4)), "C", nillion.Integer),
+                na_client.array(np.zeros((3, 3)), "D", nillion.UnsignedInteger),
             ]
         )
     )