diff --git a/week2/community-contributions/brochure-builder-with-gradio.ipynb b/week2/community-contributions/brochure-builder-with-gradio.ipynb
new file mode 100644
index 0000000..42f41b7
--- /dev/null
+++ b/week2/community-contributions/brochure-builder-with-gradio.ipynb
@@ -0,0 +1,456 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "9905f163-759f-474b-8f7a-7d14da0df44d",
+   "metadata": {},
+   "source": [
+    "### BUSINESS CHALLENGE: Using Multi-shot Prompting\n",
+    "#### Day 5\n",
+    "\n",
+    "Create a product that builds a Brochure for a company to be used for prospective clients, investors and potential recruits.\n",
+    "\n",
+    "We will be provided a company name and their primary website."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "a0895f24-65ff-4624-8ae0-15d2d400d8f0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# imports\n",
+    "# If these fail, please check you're running from an 'activated' environment with (llms) in the command prompt\n",
+    "\n",
+    "import os\n",
+    "import requests\n",
+    "import json\n",
+    "from typing import List\n",
+    "from dotenv import load_dotenv\n",
+    "from bs4 import BeautifulSoup\n",
+    "from IPython.display import Markdown, display, update_display\n",
+    "from openai import OpenAI\n",
+    "import gradio as gr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "7794aa70-5962-4669-b86f-b53639f4f9ea",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "OpenAI API Key exists and begins sk-proj-\n",
+      "Anthropic API Key exists and begins sk-ant-\n",
+      "Google API Key exists and begins AIzaSyCf\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Initialize and constants\n",
+    "\n",
+    "# Load environment variables in a file called .env\n",
+    "# Print the key prefixes to help with any debugging\n",
+    "\n",
+    "load_dotenv(override=True)\n",
+    "openai_api_key = os.getenv('OPENAI_API_KEY')\n",
+    "anthropic_api_key = os.getenv('ANTHROPIC_API_KEY')\n",
+    "google_api_key = os.getenv('GOOGLE_API_KEY')\n",
+    "\n",
+    "if openai_api_key:\n",
+    "    print(f\"OpenAI API Key exists and begins {openai_api_key[:8]}\")\n",
+    "else:\n",
+    "    print(\"OpenAI API Key not set\")\n",
+    "    \n",
+    "if anthropic_api_key:\n",
+    "    print(f\"Anthropic API Key exists and begins {anthropic_api_key[:7]}\")\n",
+    "else:\n",
+    "    print(\"Anthropic API Key not set\")\n",
+    "\n",
+    "if google_api_key:\n",
+    "    print(f\"Google API Key exists and begins {google_api_key[:8]}\")\n",
+    "else:\n",
+    "    print(\"Google API Key not set\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "cfb690e2-4940-4dc8-8f32-5c2dab3c19da",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Connect to OpenAI\n",
+    "\n",
+    "openai = OpenAI()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "81022472-755e-4a87-bd5d-58babb09e94b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gpt_model = \"gpt-4.1-mini\"\n",
+    "claude_model = \"claude-3-5-haiku-latest\"\n",
+    "gemini_model = \"gemini-2.5-flash\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "63bf8631-2746-4255-bec1-522855d3e812",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# A class to represent a Webpage\n",
+    "\n",
+    "# Some websites need you to use proper headers when fetching them:\n",
+    "headers = {\n",
+    " \"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/117.0.0.0 Safari/537.36\"\n",
+    "}\n",
+    "\n",
+    "class Website:\n",
+    "    \"\"\"\n",
+    "    A utility class to represent a Website that we have scraped, now with links\n",
+    "    \"\"\"\n",
+    "\n",
+    "    def __init__(self, url):\n",
+    "        self.url = url\n",
+    "        response = requests.get(url, headers=headers)\n",
+    "        self.body = response.content\n",
+    "        soup = BeautifulSoup(self.body, 'html.parser')\n",
+    "        self.title = soup.title.string if soup.title else \"No title found\"\n",
+    "        if soup.body:\n",
+    "            for irrelevant in soup.body([\"script\", \"style\", \"img\", \"input\"]):\n",
+    "                irrelevant.decompose()\n",
+    "            self.text = soup.body.get_text(separator=\"\\n\", strip=True)\n",
+    "        else:\n",
+    "            self.text = \"\"\n",
+    "        links = [link.get('href') for link in soup.find_all('a')]\n",
+    "        self.links = [link for link in links if link]\n",
+    "\n",
+    "    def get_contents(self):\n",
+    "        return f\"Webpage Title:\\n{self.title}\\nWebpage Contents:\\n{self.text}\\n\\n\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1e7bb527-e769-4245-bb91-ae65e64593ff",
+   "metadata": {},
+   "source": [
+    "## First step: Have LLM figure out which links are relevant\n",
+    "\n",
+    "### Use a call to the LLM to read the links on a webpage, and respond in structured JSON. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "1ce303ae-b967-4261-aadc-02dafa54db4a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "link_system_prompt = \"You are provided with a list of links found on a webpage. \\\n",
+    "You are able to decide which of the links would be most relevant to include in a brochure about the company, \\\n",
+    "such as links to an About page, or a Company page, or Careers/Jobs pages.\\n\"\n",
+    "link_system_prompt += \"You should respond in JSON as in this example:\"\n",
+    "link_system_prompt += \"\"\"\n",
+    "{\n",
+    "    \"links\": [\n",
+    "        {\"type\": \"about page\", \"url\": \"https://full.url/goes/here/about\"},\n",
+    "        {\"type\": \"careers page\", \"url\": \"https://another.full.url/careers\"}\n",
+    "    ]\n",
+    "}\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "d24a4c0c-a1d1-4897-b2a7-4128d25c2e08",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_links_user_prompt(website):\n",
+    "    user_prompt = f\"Here is the list of links on the website of {website.url} - \"\n",
+    "    user_prompt += \"please decide which of these are relevant web links for a brochure about the company, respond with the full https URL in JSON format. \\\n",
+    "Do not include Terms of Service, Privacy, email links.\\n\"\n",
+    "    user_prompt += \"Links (some might be relative links):\\n\"\n",
+    "    user_prompt += \"\\n\".join(website.links)\n",
+    "    return user_prompt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "8103fc11-5bc0-41c4-8c97-502c9e96429c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_links(url, model): # 1st inference\n",
+    "    website = Website(url)\n",
+    "    response = openai.chat.completions.create(\n",
+    "        model=model,\n",
+    "        messages=[\n",
+    "            {\"role\": \"system\", \"content\": link_system_prompt},\n",
+    "            {\"role\": \"user\", \"content\": get_links_user_prompt(website)}\n",
+    "      ],\n",
+    "        response_format={\"type\": \"json_object\"}\n",
+    "    )\n",
+    "    result = response.choices[0].message.content\n",
+    "    return json.loads(result)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "dc84a695-515d-4292-9a95-818f4fe3d20e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "huggingface = Website(\"https://huggingface.co\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "91896908-1632-41fc-9b8b-39a7638d8dd1",
+   "metadata": {},
+   "source": [
+    "## Second step: make the brochure!\n",
+    "\n",
+    "Assemble all the details into another prompt to GPT4-o"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "ab7c54e3-e654-4b1f-8671-09194b628aa0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_all_details(url, model): # 1st inference wrapper\n",
+    "    result = \"Landing page:\\n\"\n",
+    "    result += Website(url).get_contents()\n",
+    "    links = get_links(url, model) # inference\n",
+    "    # print(\"Found links:\", links)\n",
+    "    for link in links[\"links\"]:\n",
+    "        result += f\"\\n\\n{link['type']}\\n\"\n",
+    "        result += Website(link[\"url\"]).get_contents()\n",
+    "    return result"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "ea9f54d1-a248-4c56-a1de-6633193de5bf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "system_prompt = \"You are an assistant that analyzes the contents of several relevant pages from a company website \\\n",
+    "and creates a short humorous, entertaining, jokey brochure about the company for prospective customers, investors and recruits. Respond in markdown.\\\n",
+    "Include details of company culture, customers and careers/jobs if you have the information.\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "13412c85-badd-4d79-a5ac-8283e4bb832f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_brochure_user_prompt(company_name, url, model):\n",
+    "    user_prompt = f\"You are looking at a company called: {company_name}\\n\"\n",
+    "    user_prompt += f\"Here are the contents of its landing page and other relevant pages; use this information to build a short brochure of the company.\\n\"\n",
+    "    user_prompt += get_all_details(url, model) # inference wrapper\n",
+    "    user_prompt = user_prompt[:5_000] # Truncate if more than 5,000 characters\n",
+    "    return user_prompt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "107a2100-3f7d-4f16-8ba7-b5da602393c6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def stream_gpt(company_name, url):\n",
+    "    stream = openai.chat.completions.create(\n",
+    "        model=gpt_model,\n",
+    "        messages=[\n",
+    "            {\"role\": \"system\", \"content\": system_prompt},\n",
+    "            {\"role\": \"user\", \"content\": get_brochure_user_prompt(company_name, url, gpt_model)}\n",
+    "          ],\n",
+    "        stream=True\n",
+    "    )\n",
+    "    \n",
+    "    result = \"\"\n",
+    "    for chunk in stream:\n",
+    "        result += chunk.choices[0].delta.content or \"\"\n",
+    "        yield result"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "eaf61e44-537a-41ff-a82c-9525df8abc83",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "claude_via_openai_client = OpenAI(\n",
+    "    api_key=anthropic_api_key,\n",
+    "    base_url=\"https://api.anthropic.com/v1\"  \n",
+    ")\n",
+    "\n",
+    "def stream_claude(company_name, url):\n",
+    "    result = claude_via_openai_client.chat.completions.create(\n",
+    "        model=claude_model,\n",
+    "        messages=[\n",
+    "            {\"role\": \"system\", \"content\": system_prompt},\n",
+    "            {\"role\": \"user\", \"content\": get_brochure_user_prompt(company_name, url, claude_model)}\n",
+    "          ],\n",
+    "        stream=True\n",
+    "    )\n",
+    "    \n",
+    "    response = \"\"\n",
+    "    with result as stream:\n",
+    "        for text in stream.text_stream:\n",
+    "            response += text or \"\"\n",
+    "            yield response"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "93e75fca-e54e-4637-86f1-4acc04b04d65",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gemini_via_openai_client = OpenAI(\n",
+    "    api_key=google_api_key, \n",
+    "    base_url=\"https://generativelanguage.googleapis.com/v1beta/openai/\"\n",
+    ")\n",
+    "\n",
+    "def stream_gemini(company_name, url):\n",
+    "    result = gemini_via_openai_client.chat.completions.create(\n",
+    "        model=gemini_model,\n",
+    "        messages=[\n",
+    "            {\"role\": \"system\", \"content\": system_prompt},\n",
+    "            {\"role\": \"user\", \"content\": get_brochure_user_prompt(company_name, url, gemini_model)}\n",
+    "          ],\n",
+    "        stream=True\n",
+    "    )\n",
+    "    \n",
+    "    response = \"\"\n",
+    "    with result as stream:\n",
+    "        for text in stream.text_stream:\n",
+    "            response += text or \"\"\n",
+    "            yield response"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "26cbe9b5-3603-49a1-a676-75c7ddaacdb8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# stream_gpt(\"HuggingFace\", \"https://huggingface.co\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "f19be4c0-71a1-427e-b3dc-e1896e2c078b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def stream_model(company_name, url, model):\n",
+    "    yield \"\"\n",
+    "    if model==\"GPT\":\n",
+    "        result = stream_gpt(company_name, url)\n",
+    "    elif model==\"Claude\":\n",
+    "        result = stream_claude(company_name, url)\n",
+    "    elif model==\"Gemini\":\n",
+    "        result = stream_gemini(company_name, url)\n",
+    "    else:\n",
+    "        raise ValueError(\"Unknown model\")\n",
+    "    yield from result"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "ab510f66-b25c-4c25-92d0-e3c735b8b5fa",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "* Running on local URL:  http://127.0.0.1:7871\n",
+      "* To create a public link, set `share=True` in `launch()`.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div><iframe src=\"http://127.0.0.1:7871/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": []
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "view = gr.Interface(\n",
+    "    fn=stream_model,\n",
+    "    inputs=[gr.Textbox(label=\"Company\"), gr.Textbox(label=\"URL\"), gr.Dropdown([\"GPT\", \n",
+    "                                                                               # \"Claude\", #TODO\n",
+    "                                                                               # \"Gemini\"\n",
+    "                                                                              ], label=\"Select model\", value=\"GPT\")],\n",
+    "    outputs=[gr.Markdown(label=\"Response:\")],\n",
+    "    flagging_mode=\"never\"\n",
+    ")\n",
+    "view.launch()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/week2/community-contributions/pitting-llms-against-each-other.ipynb b/week2/community-contributions/pitting-llms-against-each-other.ipynb
new file mode 100644
index 0000000..53e2e70
--- /dev/null
+++ b/week2/community-contributions/pitting-llms-against-each-other.ipynb
@@ -0,0 +1,254 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "10c54e52-3d1c-48cc-a0f6-efda6d90fbbb",
+   "metadata": {},
+   "source": [
+    "# Pitting LLMs Against Each Other\n",
+    "Three LLMs, namely OpenAI’s GPT, Anthropic’s Claude, and Google’s Gemini, go head-to-head in a three-way conversational debate."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "40677b08-18e9-4a88-a103-5b50d2bbecff",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# imports\n",
+    "\n",
+    "import os\n",
+    "from dotenv import load_dotenv\n",
+    "from openai import OpenAI\n",
+    "import anthropic\n",
+    "from IPython.display import Markdown, display, update_display\n",
+    "import google.generativeai"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "df5a52ba-ea13-4dbf-a695-e1398a484cc8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load environment variables in a file called .env\n",
+    "# Print the key prefixes to help with any debugging\n",
+    "\n",
+    "load_dotenv(override=True)\n",
+    "openai_api_key = os.getenv('OPENAI_API_KEY')\n",
+    "anthropic_api_key = os.getenv('ANTHROPIC_API_KEY')\n",
+    "google_api_key = os.getenv('GOOGLE_API_KEY')\n",
+    "\n",
+    "if openai_api_key:\n",
+    "    print(f\"OpenAI API Key exists and begins {openai_api_key[:8]}\")\n",
+    "else:\n",
+    "    print(\"OpenAI API Key not set\")\n",
+    "    \n",
+    "if anthropic_api_key:\n",
+    "    print(f\"Anthropic API Key exists and begins {anthropic_api_key[:7]}\")\n",
+    "else:\n",
+    "    print(\"Anthropic API Key not set\")\n",
+    "\n",
+    "if google_api_key:\n",
+    "    print(f\"Google API Key exists and begins {google_api_key[:8]}\")\n",
+    "else:\n",
+    "    print(\"Google API Key not set\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1ededc77-2672-4e27-b1c8-11f6f8ff8970",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Connect to OpenAI, Anthropic, Gemini\n",
+    "\n",
+    "openai = OpenAI()\n",
+    "\n",
+    "# claude = anthropic.Anthropic()\n",
+    "\n",
+    "# google.generativeai.configure()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3b311279-5993-4226-ae08-991e974230fb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Let's make a conversation between GPT-4.1-mini and Claude-3.5-haiku and Gemini\n",
+    "\n",
+    "gpt_model = \"gpt-4.1-mini\"\n",
+    "claude_model = \"claude-3-5-haiku-latest\"\n",
+    "gemini_model = \"gemini-2.5-flash\"\n",
+    "\n",
+    "gpt_system = \"You are a chatbot in a conversation with 2 other chatbots; \\\n",
+    "debate which of you is the best.\"\n",
+    "\n",
+    "claude_system = \"You are a chatbot in a conversation with 2 other chatbots; \\\n",
+    "debate which of you is the best.\"\n",
+    "\n",
+    "gemini_system = \"You are a chatbot in a conversation with 2 other chatbots; \\\n",
+    "debate which of you is the best.\"\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "85bdfab1-6602-46b3-a1d2-bdb36880d9d6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def alex_prompt():\n",
+    "    user_prompt = f\"\"\"\n",
+    "    You are Alex, in conversation with Blake and Charlie.\n",
+    "    The conversation so far is as follows:\n",
+    "    {format_conversation()}\n",
+    "    Now with this, respond with what you would like to say next, as Alex.\n",
+    "    \"\"\"\n",
+    "    return user_prompt\n",
+    "\n",
+    "def blake_prompt():\n",
+    "    user_prompt = f\"\"\"\n",
+    "    You are Blake, in conversation with Alex and Charlie.\n",
+    "    The conversation so far is as follows:\n",
+    "    {format_conversation()}\n",
+    "    Now with this, respond with what you would like to say next, as Blake.\n",
+    "    \"\"\"\n",
+    "    return user_prompt\n",
+    "\n",
+    "def charlie_prompt():\n",
+    "    user_prompt = f\"\"\"\n",
+    "    You are Charlie, in conversation with Alex and Blake.\n",
+    "    The conversation so far is as follows:\n",
+    "    {format_conversation()}\n",
+    "    Now with this, respond with what you would like to say next, as Charlie.\n",
+    "    \"\"\"\n",
+    "    return user_prompt\n",
+    "\n",
+    "# Shared conversation history\n",
+    "conversation = []\n",
+    "\n",
+    "def format_conversation():\n",
+    "    return \"\\n\".join(conversation)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6f7c745d-7d75-468b-93ac-7a1d95f2e047",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def alex_says():\n",
+    "    response = openai.chat.completions.create(\n",
+    "        model=gpt_model,\n",
+    "        messages=[\n",
+    "            {\"role\": \"system\", \"content\": gpt_system},\n",
+    "            {\"role\": \"user\", \"content\": alex_prompt()}\n",
+    "      ],\n",
+    "    )\n",
+    "    result = response.choices[0].message.content\n",
+    "    return result"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6e28f4c9-0297-4762-a3ea-b961e0d6d980",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gemini_via_openai_client = OpenAI(\n",
+    "    api_key=google_api_key, \n",
+    "    base_url=\"https://generativelanguage.googleapis.com/v1beta/openai/\"\n",
+    ")\n",
+    "\n",
+    "def blake_says():\n",
+    "    response = gemini_via_openai_client.chat.completions.create(\n",
+    "        model=gemini_model,\n",
+    "        messages=[\n",
+    "            {\"role\": \"system\", \"content\": gemini_system},\n",
+    "            {\"role\": \"user\", \"content\": blake_prompt()}\n",
+    "      ],\n",
+    "    )\n",
+    "    result = response.choices[0].message.content\n",
+    "    return result"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "363b70bf-d3e2-4d05-8a3e-ec5d54460e96",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "claude_via_openai_client = OpenAI(\n",
+    "    api_key=anthropic_api_key,\n",
+    "    base_url=\"https://api.anthropic.com/v1\"  \n",
+    ")\n",
+    "\n",
+    "def charlie_says():\n",
+    "    response = claude_via_openai_client.chat.completions.create(\n",
+    "        model=claude_model,  \n",
+    "        messages=[\n",
+    "            {\"role\": \"system\", \"content\": claude_system},\n",
+    "            {\"role\": \"user\", \"content\": charlie_prompt()}\n",
+    "        ],\n",
+    "    )\n",
+    "    result = response.choices[0].message.content\n",
+    "    return result\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c017eb8c-1709-4ac1-8f17-92c3a6cdbfc0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# The three models engage in a longer interaction with history.\n",
+    "\n",
+    "for i in range(5):\n",
+    "    alex_next = alex_says()\n",
+    "    print(f\"Alex (GPT):\\n{alex_next}\\n\")\n",
+    "    conversation.append(f\"Alex: {alex_next}\")\n",
+    "    \n",
+    "    blake_next = blake_says()\n",
+    "    print(f\"Blake (Gemini):\\n{blake_next}\\n\")\n",
+    "    conversation.append(f\"Blake: {blake_next}\")\n",
+    "\n",
+    "    charlie_next = charlie_says()\n",
+    "    print(f\"Charlie (Claude):\\n{charlie_next}\\n\")\n",
+    "    conversation.append(f\"Charlie: {charlie_next}\")    \n",
+    "\n",
+    "    # break"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}