diff --git a/OS/01/assistant/assistant.py b/OS/01/assistant/assistant.py
index 8ec1ee3..681776b 100644
--- a/OS/01/assistant/assistant.py
+++ b/OS/01/assistant/assistant.py
@@ -6,6 +6,7 @@ Exposes a ws endpoint called /user. Things from there go into the queue. We also
 In a while loop we watch the queue and handle it.
 """
 
+import ast
 import json
 import time
 import queue
@@ -31,9 +32,9 @@ to_user = queue.Queue()
 to_assistant = queue.Queue()
 
 # This is so we only say() full sentences
-accumulated_text = ""
 def is_full_sentence(text):
     return text.endswith(('.', '!', '?'))
+
 def split_into_sentences(text):
     return re.split(r'(?<=[.!?])\s+', text)
 
@@ -49,13 +50,13 @@ async def websocket_endpoint(websocket: WebSocket):
     while True:
         data = await websocket.receive_json()
         to_assistant.put(data)
-        if not to_user.empty():
+        while not to_user.empty():
             message = to_user.get()
             await websocket.send_json(message)
 
-audio_file = bytearray()
 
 def queue_listener():
+    audio_file = bytearray()
     while True:
         # Check 10x a second for new messages
         while to_assistant.empty():
@@ -65,7 +66,7 @@ def queue_listener():
         # Hold the audio in a buffer. If it's ready (we got end flag, stt it)
         if message["type"] == "audio":
             if "content" in message:
-                audio_file.extend(message["content"])
+                audio_file.extend(bytes(ast.literal_eval(message["content"])))
             if "end" in message:
                 content = stt(audio_file, message["format"])
                 audio_file = bytearray()
@@ -122,4 +123,4 @@ queue_thread.start()
 
 # Run the FastAPI app
 if __name__ == "__main__":
-    uvicorn.run(app, host="0.0.0.0", port=8000)
\ No newline at end of file
+    uvicorn.run(app, host="0.0.0.0", port=8000)
diff --git a/OS/01/assistant/create_interpreter.py b/OS/01/assistant/create_interpreter.py
index df4197a..413d125 100644
--- a/OS/01/assistant/create_interpreter.py
+++ b/OS/01/assistant/create_interpreter.py
@@ -55,7 +55,7 @@ Remember: You can run Python code. Be very concise. Ensure that you actually run
     interpreter.llm.api_key = os.getenv('OPENAI_API_KEY')
     interpreter.llm.model = "gpt-4"
     interpreter.auto_run = True
-    interpreter.force_task_completion = True
+    interpreter.force_task_completion = False
 
 
     ### MISC SETTINGS
@@ -94,12 +94,12 @@ Remember: You can run Python code. Be very concise. Ensure that you actually run
             data = {"language": "python", "code": code}
 
             # Send the data to the /run endpoint
-            response = requests.post("http://localhost:8000/run", json=data, stream=True)
+            response = requests.post("http://localhost:9000/run", json=data, stream=True)
 
             # Stream the response
-            for line in response.iter_lines():
-                if line:  # filter out keep-alive new lines
-                    yield json.loads(line)
+            for chunk in response.iter_content(chunk_size=100000000):
+                if chunk:  # filter out keep-alive new lines
+                    yield json.loads(chunk.decode())
 
         def stop(self):
             """Stops the code."""
diff --git a/OS/01/assistant/tts.py b/OS/01/assistant/tts.py
index 1a1f55c..a71d530 100644
--- a/OS/01/assistant/tts.py
+++ b/OS/01/assistant/tts.py
@@ -3,4 +3,4 @@ Defines a function which takes text and returns a path to an audio file.
 """
 
 def tts(text):
-    return path_to_audio
\ No newline at end of file
+    return []
diff --git a/OS/01/user/record.py b/OS/01/user/record.py
index 5d18c50..d9b4120 100644
--- a/OS/01/user/record.py
+++ b/OS/01/user/record.py
@@ -65,7 +65,7 @@ async def start_recording():
             with open(file_path, 'rb') as audio_file:
                 byte_chunk = audio_file.read(ws_chunk_size)
                 while byte_chunk:
-                    await websocket.send({"role": "user", "type": "audio", "format": "audio/wav", "content": byte_chunk})
+                    await websocket.send(json.dumps({"role": "user", "type": "audio", "format": "audio/wav", "content": str(byte_chunk)}))
                     byte_chunk = audio_file.read(ws_chunk_size)
         finally:
             os.remove(file_path)