diff --git "a/1_lab1.ipynb" "b/1_lab1.ipynb" new file mode 100644--- /dev/null +++ "b/1_lab1.ipynb" @@ -0,0 +1,1188 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Welcome to the start of your adventure in Agentic AI" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + " \n", + " \n", + " \n", + " \n", + "
\n", + " \n", + " \n", + "

Are you ready for action??

\n", + " Have you completed all the setup steps in the setup folder?
\n", + " Have you checked out the guides in the guides folder?
\n", + " Well in that case, you're ready!!\n", + " \n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + " \n", + " \n", + " \n", + " \n", + "
\n", + " \n", + " \n", + "

This code is a live resource - keep an eye out for my updates

\n", + " I push updates regularly. As people ask questions or have problems, I add more examples and improve explanations. As a result, the code below might not be identical to the videos, as I've added more steps and better comments. Consider this like an interactive book that accompanies the lectures.

\n", + " I try to send emails regularly with important updates related to the course. You can find this in the 'Announcements' section of Udemy in the left sidebar. You can also choose to receive my emails via your Notification Settings in Udemy. I'm respectful of your inbox and always try to add value with my emails!\n", + " \n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### And please do remember to contact me if I can help\n", + "\n", + "And I love to connect: https://www.linkedin.com/in/eddonner/\n", + "\n", + "\n", + "### New to Notebooks like this one? Head over to the guides folder!\n", + "\n", + "Just to check you've already added the Python and Jupyter extensions to Cursor, if not already installed:\n", + "- Open extensions (View >> extensions)\n", + "- Search for python, and when the results show, click on the ms-python one, and Install it if not already installed\n", + "- Search for jupyter, and when the results show, click on the Microsoft one, and Install it if not already installed \n", + "Then View >> Explorer to bring back the File Explorer.\n", + "\n", + "And then:\n", + "1. Click where it says \"Select Kernel\" near the top right, and select the option called `.venv (Python 3.12.9)` or similar, which should be the first choice or the most prominent choice. You may need to choose \"Python Environments\" first.\n", + "2. Click in each \"cell\" below, starting with the cell immediately below this text, and press Shift+Enter to run\n", + "3. Enjoy!\n", + "\n", + "After you click \"Select Kernel\", if there is no option like `.venv (Python 3.12.9)` then please do the following: \n", + "1. On Mac: From the Cursor menu, choose Settings >> VS Code Settings (NOTE: be sure to select `VSCode Settings` not `Cursor Settings`); \n", + "On Windows PC: From the File menu, choose Preferences >> VS Code Settings(NOTE: be sure to select `VSCode Settings` not `Cursor Settings`) \n", + "2. In the Settings search bar, type \"venv\" \n", + "3. In the field \"Path to folder with a list of Virtual Environments\" put the path to the project root, like C:\\Users\\username\\projects\\agents (on a Windows PC) or /Users/username/projects/agents (on Mac or Linux). \n", + "And then try again.\n", + "\n", + "Having problems with missing Python versions in that list? Have you ever used Anaconda before? It might be interferring. Quit Cursor, bring up a new command line, and make sure that your Anaconda environment is deactivated: \n", + "`conda deactivate` \n", + "And if you still have any problems with conda and python versions, it's possible that you will need to run this too: \n", + "`conda config --set auto_activate_base false` \n", + "and then from within the Agents directory, you should be able to run `uv python list` and see the Python 3.12 version." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "# First let's do an import\n", + "from dotenv import load_dotenv\n" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Next it's time to load the API keys into environment variables\n", + "\n", + "load_dotenv(override=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "OpenAI API Key exists and begins sk-proj-\n" + ] + } + ], + "source": [ + "# Check the keys\n", + "\n", + "import os\n", + "openai_api_key = os.getenv('OPENAI_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 - please head to the troubleshooting guide in the setup folder\")\n", + " \n" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "# And now - the all important import statement\n", + "# If you get an import error - head over to troubleshooting guide\n", + "\n", + "from openai import OpenAI" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "# And now we'll create an instance of the OpenAI class\n", + "# If you're not sure what it means to create an instance of a class - head over to the guides folder!\n", + "# If you get a NameError - head over to the guides folder to learn about NameErrors\n", + "\n", + "openai = OpenAI()" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a list of messages in the familiar OpenAI format\n", + "\n", + "messages = [{\"role\": \"user\", \"content\": \"What is 2+2?\"}]" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2 + 2 equals 4.\n" + ] + } + ], + "source": [ + "# And now call it! Any problems, head to the troubleshooting guide\n", + "# This uses GPT 4.1 nano, the incredibly cheap model\n", + "\n", + "response = openai.chat.completions.create(\n", + " model=\"gpt-4.1-nano\",\n", + " messages=messages\n", + ")\n", + "\n", + "print(response.choices[0].message.content)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "# And now - let's ask for a question:\n", + "\n", + "question = \"Please propose a hard, challenging question to assess someone's IQ. Respond only with the question.\"\n", + "messages = [{\"role\": \"user\", \"content\": question}]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "If 3 cats can catch 3 mice in 3 minutes, how many cats are needed to catch 100 mice in 100 minutes?\n" + ] + } + ], + "source": [ + "# ask it - this uses GPT 4.1 mini, still cheap but more powerful than nano\n", + "\n", + "response = openai.chat.completions.create(\n", + " model=\"gpt-4.1-mini\",\n", + " messages=messages\n", + ")\n", + "\n", + "question = response.choices[0].message.content\n", + "\n", + "print(question)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "ChatCompletionMessage(content='If 3 cats can catch 3 mice in 3 minutes, how many cats are needed to catch 100 mice in 100 minutes?', refusal=None, role='assistant', annotations=[], audio=None, function_call=None, tool_calls=None)" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "response.choices[0].message" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "# form a new messages list\n", + "messages = [{\"role\": \"user\", \"content\": question}]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Let's analyze the problem step by step.\n", + "\n", + "### Given:\n", + "- 3 cats catch 3 mice in 3 minutes.\n", + "\n", + "### What do we need to find?\n", + "- Number of cats needed to catch 100 mice in 100 minutes.\n", + "\n", + "---\n", + "\n", + "### Step 1: Find the rate of catching mice per cat\n", + "\n", + "- 3 cats catch 3 mice in 3 minutes.\n", + "- So, total catching rate for 3 cats = 3 mice / 3 minutes = 1 mouse per minute.\n", + "- Therefore, 1 cat catches \\(\\frac{1 \\text{ mouse}}{3 \\text{ minutes}}\\).\n", + " \n", + "So, **each cat catches 1 mouse every 3 minutes**.\n", + "\n", + "---\n", + "\n", + "### Step 2: Find how many mice one cat can catch in 100 minutes\n", + "\n", + "- Since 1 cat catches 1 mouse in 3 minutes,\n", + "- In 100 minutes, 1 cat catches \\(\\frac{100}{3} \\approx 33.33\\) mice.\n", + "\n", + "---\n", + "\n", + "### Step 3: Find how many cats are needed to catch 100 mice in 100 minutes\n", + "\n", + "Let \\(x\\) be the number of cats needed.\n", + "\n", + "- Total mice caught by \\(x\\) cats in 100 minutes \\(= x \\times \\frac{100}{3}\\)\n", + "- This must equal 100 mice:\n", + " \n", + "\\[\n", + "x \\times \\frac{100}{3} = 100\n", + "\\]\n", + "\n", + "\\[\n", + "x = \\frac{100 \\times 3}{100} = 3\n", + "\\]\n", + "\n", + "---\n", + "\n", + "### **Answer:**\n", + "\n", + "\\[\n", + "\\boxed{3}\n", + "\\]\n", + "\n", + "So, **3 cats are needed to catch 100 mice in 100 minutes**.\n" + ] + } + ], + "source": [ + "# Ask it again\n", + "\n", + "response = openai.chat.completions.create(\n", + " model=\"gpt-4.1-mini\",\n", + " messages=messages\n", + ")\n", + "\n", + "answer = response.choices[0].message.content\n", + "print(answer)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "Let's analyze the problem step by step.\n", + "\n", + "### Given:\n", + "- 3 cats catch 3 mice in 3 minutes.\n", + "\n", + "### What do we need to find?\n", + "- Number of cats needed to catch 100 mice in 100 minutes.\n", + "\n", + "---\n", + "\n", + "### Step 1: Find the rate of catching mice per cat\n", + "\n", + "- 3 cats catch 3 mice in 3 minutes.\n", + "- So, total catching rate for 3 cats = 3 mice / 3 minutes = 1 mouse per minute.\n", + "- Therefore, 1 cat catches \\(\\frac{1 \\text{ mouse}}{3 \\text{ minutes}}\\).\n", + " \n", + "So, **each cat catches 1 mouse every 3 minutes**.\n", + "\n", + "---\n", + "\n", + "### Step 2: Find how many mice one cat can catch in 100 minutes\n", + "\n", + "- Since 1 cat catches 1 mouse in 3 minutes,\n", + "- In 100 minutes, 1 cat catches \\(\\frac{100}{3} \\approx 33.33\\) mice.\n", + "\n", + "---\n", + "\n", + "### Step 3: Find how many cats are needed to catch 100 mice in 100 minutes\n", + "\n", + "Let \\(x\\) be the number of cats needed.\n", + "\n", + "- Total mice caught by \\(x\\) cats in 100 minutes \\(= x \\times \\frac{100}{3}\\)\n", + "- This must equal 100 mice:\n", + " \n", + "\\[\n", + "x \\times \\frac{100}{3} = 100\n", + "\\]\n", + "\n", + "\\[\n", + "x = \\frac{100 \\times 3}{100} = 3\n", + "\\]\n", + "\n", + "---\n", + "\n", + "### **Answer:**\n", + "\n", + "\\[\n", + "\\boxed{3}\n", + "\\]\n", + "\n", + "So, **3 cats are needed to catch 100 mice in 100 minutes**." + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from IPython.display import Markdown, display\n", + "\n", + "display(Markdown(answer))\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Congratulations!\n", + "\n", + "That was a small, simple step in the direction of Agentic AI, with your new environment!\n", + "\n", + "Next time things get more interesting..." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + " \n", + " \n", + " \n", + " \n", + "
\n", + " \n", + " \n", + "

Exercise

\n", + " Now try this commercial application:
\n", + " First ask the LLM to pick a business area that might be worth exploring for an Agentic AI opportunity.
\n", + " Then ask the LLM to present a pain-point in that industry - something challenging that might be ripe for an Agentic solution.
\n", + " Finally have 3 third LLM call propose the Agentic AI solution.\n", + " \n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "One promising business area for an agentic AI opportunity is **personalized mental health and wellness coaching**.\n", + "\n", + "### Why this area?\n", + "\n", + "- **High demand:** Increasing awareness and focus on mental health create a growing market for accessible, personalized support.\n", + "- **Scalability:** AI can serve large numbers of users simultaneously, providing tailored interventions without requiring proportional human resource increases.\n", + "- **Agentic capabilities:** An agentic AI can actively monitor user inputs (e.g., mood tracking, journaling, behavior patterns), suggest personalized exercises or coping strategies, schedule reminders, and even proactively initiate supportive conversations or meditations.\n", + "- **Continuous learning:** The AI can learn from individual user responses over time to improve recommendations and adapt to changing needs.\n", + "- **Integration potential:** It can connect with wearables, calendars, and other health apps for a holistic approach to wellness.\n", + "\n", + "### Potential features\n", + "\n", + "- Real-time mood and stress detection from text, voice, or biometric data.\n", + "- Dynamic, context-aware coaching sessions.\n", + "- Goal setting and progress tracking with adaptive feedback.\n", + "- Crisis detection with escalation protocols.\n", + "- Privacy-focused data handling and compliance with healthcare regulations.\n", + "\n", + "### Business models to explore\n", + "\n", + "- Subscription-based access with tiered service levels.\n", + "- White-label solutions for healthcare providers or employers.\n", + "- Partnerships with insurance companies for preventive care incentives.\n", + "\n", + "Would you like me to help design an initial product concept or roadmap for this?\n" + ] + }, + { + "data": { + "text/plain": [ + "'Certainly! Let’s consider the **healthcare industry**, which is a domain ripe for agentic AI solutions due to its complexity and critical challenges.\\n\\n### Pain-point: Patient Data Management and Care Coordination\\n\\n**Challenge:** \\nHealthcare providers often struggle with managing vast amounts of patient data that is siloed across multiple systems (EHRs, labs, imaging centers, pharmacies). This fragmentation leads to delays in diagnosis, redundant testing, medication errors, and suboptimal care coordination among specialists, primary care physicians, and patients.\\n\\n**Why it’s ripe for an agentic AI solution:** \\nAn agentic AI could autonomously navigate diverse healthcare data systems, synthesize critical patient information in real-time, and proactively coordinate care tasks, such as scheduling appointments, flagging potential drug interactions, and suggesting personalized treatment plans by learning from the latest medical guidelines and patient history.\\n\\n---\\n\\nIf you’d like, I can help brainstorm what such an AI assistant could look like or how it might integrate with existing healthcare workflows.'" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# First create the messages:\n", + "\n", + "messages = [{\"role\": \"user\", \"content\": \"Pick a business area that might be worth exploring for an agentic AI opportunity\"}]\n", + "\n", + "# Then make the first call:\n", + "\n", + "response = openai.chat.completions.create(\n", + " model=\"gpt-4.1-mini\",\n", + " messages=messages\n", + ")\n", + "\n", + "# Then read the business idea:\n", + "\n", + "business_idea = response.choices[0].message.content\n", + "print(business_idea)\n", + "# And repeat!\n", + "messages1 = [{\"role\": \"user\", \"content\": \"Pick a pain-point in that industry - something challenging that might be ripe for an agentic AI solution\"}]\n", + "\n", + "# Then make the first call:\n", + "\n", + "response1 = openai.chat.completions.create(\n", + " model=\"gpt-4.1-mini\",\n", + " messages=messages1\n", + ")\n", + "\n", + "# Then read the business idea:\n", + "\n", + "business_idea1 = response1.choices[0].message.content\n", + "business_idea1" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest ⠋ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest ⠙ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest ⠹ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest ⠸ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest ⠼ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest ⠴ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest ⠦ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest ⠧ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 0% ▕ ▏ 2.2 MB/2.0 GB \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 0% ▕ ▏ 7.3 MB/2.0 GB 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"pulling dde5aa3fc5ff: 3% ▕ ▏ 67 MB/2.0 GB \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 4% ▕ ▏ 79 MB/2.0 GB \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 4% ▕ ▏ 87 MB/2.0 GB 87 MB/s 22s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 5% ▕ ▏ 99 MB/2.0 GB 87 MB/s 22s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 6% ▕█ ▏ 118 MB/2.0 GB 87 MB/s 21s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 7% ▕█ ▏ 133 MB/2.0 GB 87 MB/s 21s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 7% ▕█ ▏ 136 MB/2.0 GB 87 MB/s 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17s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 11% ▕█ ▏ 224 MB/2.0 GB 102 MB/s 17s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 11% ▕██ ▏ 225 MB/2.0 GB 102 MB/s 17s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 12% ▕██ ▏ 243 MB/2.0 GB 102 MB/s 17s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 12% ▕██ ▏ 250 MB/2.0 GB 102 MB/s 17s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 13% ▕██ ▏ 263 MB/2.0 GB 102 MB/s 17s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 14% ▕██ ▏ 273 MB/2.0 GB 102 MB/s 17s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 14% ▕██ ▏ 285 MB/2.0 GB 102 MB/s 16s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 15% ▕██ ▏ 293 MB/2.0 GB 102 MB/s 16s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 15% ▕██ ▏ 304 MB/2.0 GB 102 MB/s 16s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 16% ▕██ ▏ 318 MB/2.0 GB 106 MB/s 16s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 16% ▕██ ▏ 322 MB/2.0 GB 106 MB/s 16s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 17% ▕██ ▏ 335 MB/2.0 GB 106 MB/s 15s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 17% ▕███ ▏ 353 MB/2.0 GB 106 MB/s 15s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 18% ▕███ ▏ 356 MB/2.0 GB 106 MB/s 15s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 18% ▕███ ▏ 363 MB/2.0 GB 106 MB/s 15s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 19% ▕███ ▏ 381 MB/2.0 GB 106 MB/s 15s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 19% ▕███ ▏ 384 MB/2.0 GB 106 MB/s 15s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 19% ▕███ ▏ 391 MB/2.0 GB 106 MB/s 15s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 21% ▕███ ▏ 414 MB/2.0 GB 106 MB/s 15s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 21% ▕███ ▏ 417 MB/2.0 GB 106 MB/s 15s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 21% ▕███ ▏ 431 MB/2.0 GB 106 MB/s 14s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 22% ▕███ ▏ 448 MB/2.0 GB 106 MB/s 14s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 23% ▕████ ▏ 455 MB/2.0 GB 106 MB/s 14s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 23% ▕████ ▏ 464 MB/2.0 GB 106 MB/s 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MB/s 12s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 33% ▕█████ ▏ 663 MB/2.0 GB 107 MB/s 12s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 33% ▕██████ ▏ 675 MB/2.0 GB 107 MB/s 12s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 34% ▕██████ ▏ 688 MB/2.0 GB 107 MB/s 12s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 34% ▕██████ ▏ 695 MB/2.0 GB 107 MB/s 12s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 35% ▕██████ ▏ 702 MB/2.0 GB 107 MB/s 12s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 36% ▕██████ ▏ 718 MB/2.0 GB 107 MB/s 12s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 36% ▕██████ ▏ 723 MB/2.0 GB 107 MB/s 12s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 36% ▕██████ ▏ 725 MB/2.0 GB 107 MB/s 12s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 37% ▕██████ ▏ 748 MB/2.0 GB 106 MB/s 11s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 37% ▕██████ ▏ 754 MB/2.0 GB 106 MB/s 11s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 37% ▕██████ ▏ 756 MB/2.0 GB 106 MB/s 11s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 38% ▕██████ ▏ 768 MB/2.0 GB 106 MB/s 11s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 38% ▕██████ ▏ 774 MB/2.0 GB 106 MB/s 11s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 38% ▕██████ ▏ 775 MB/2.0 GB 106 MB/s 11s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 40% ▕███████ ▏ 803 MB/2.0 GB 106 MB/s 11s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 40% ▕███████ ▏ 815 MB/2.0 GB 106 MB/s 11s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 41% ▕███████ ▏ 818 MB/2.0 GB 106 MB/s 11s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 41% ▕███████ ▏ 834 MB/2.0 GB 106 MB/s 11s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 42% ▕███████ ▏ 846 MB/2.0 GB 105 MB/s 11s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 42% ▕███████ ▏ 853 MB/2.0 GB 105 MB/s 11s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 43% ▕███████ ▏ 866 MB/2.0 GB 105 MB/s 10s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 44% ▕███████ ▏ 879 MB/2.0 GB 105 MB/s 10s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 44% ▕███████ ▏ 885 MB/2.0 GB 105 MB/s 10s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 44% ▕████████ ▏ 898 MB/2.0 GB 105 MB/s 10s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 45% ▕████████ ▏ 909 MB/2.0 GB 105 MB/s 10s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 45% ▕████████ ▏ 912 MB/2.0 GB 105 MB/s 10s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 46% ▕████████ ▏ 929 MB/2.0 GB 105 MB/s 10s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 46% ▕████████ ▏ 937 MB/2.0 GB 105 MB/s 10s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 47% ▕████████ ▏ 943 MB/2.0 GB 105 MB/s 10s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest 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9s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 50% ▕█████████ ▏ 1.0 GB/2.0 GB 105 MB/s 9s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 51% ▕█████████ ▏ 1.0 GB/2.0 GB 105 MB/s 9s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 51% ▕█████████ ▏ 1.0 GB/2.0 GB 105 MB/s 9s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 52% ▕█████████ ▏ 1.0 GB/2.0 GB 105 MB/s 9s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 52% ▕█████████ ▏ 1.1 GB/2.0 GB 106 MB/s 9s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 52% ▕█████████ ▏ 1.1 GB/2.0 GB 106 MB/s 8s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 53% ▕█████████ ▏ 1.1 GB/2.0 GB 106 MB/s 8s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 53% ▕█████████ ▏ 1.1 GB/2.0 GB 106 MB/s 8s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 54% ▕█████████ ▏ 1.1 GB/2.0 GB 106 MB/s 8s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 54% ▕█████████ ▏ 1.1 GB/2.0 GB 106 MB/s 8s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 55% ▕█████████ ▏ 1.1 GB/2.0 GB 106 MB/s 8s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 55% ▕█████████ ▏ 1.1 GB/2.0 GB 106 MB/s 8s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 56% ▕██████████ ▏ 1.1 GB/2.0 GB 106 MB/s 8s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 57% ▕██████████ ▏ 1.1 GB/2.0 GB 106 MB/s 8s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 57% ▕██████████ ▏ 1.1 GB/2.0 GB 104 MB/s 8s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 58% ▕██████████ ▏ 1.2 GB/2.0 GB 104 MB/s 8s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 58% ▕██████████ ▏ 1.2 GB/2.0 GB 104 MB/s 8s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 58% ▕██████████ ▏ 1.2 GB/2.0 GB 104 MB/s 8s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 59% ▕██████████ ▏ 1.2 GB/2.0 GB 104 MB/s 7s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 60% ▕██████████ ▏ 1.2 GB/2.0 GB 104 MB/s 7s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 60% ▕██████████ ▏ 1.2 GB/2.0 GB 104 MB/s 7s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 60% ▕██████████ ▏ 1.2 GB/2.0 GB 104 MB/s 7s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 61% ▕███████████ ▏ 1.2 GB/2.0 GB 104 MB/s 7s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 62% ▕███████████ ▏ 1.2 GB/2.0 GB 104 MB/s 7s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 62% ▕███████████ ▏ 1.3 GB/2.0 GB 103 MB/s 7s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 63% ▕███████████ ▏ 1.3 GB/2.0 GB 103 MB/s 7s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 63% ▕███████████ ▏ 1.3 GB/2.0 GB 103 MB/s 7s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 64% ▕███████████ ▏ 1.3 GB/2.0 GB 103 MB/s 7s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 64% ▕███████████ ▏ 1.3 GB/2.0 GB 103 MB/s 6s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 64% ▕███████████ ▏ 1.3 GB/2.0 GB 103 MB/s 6s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 65% ▕███████████ ▏ 1.3 GB/2.0 GB 103 MB/s 6s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 66% ▕███████████ ▏ 1.3 GB/2.0 GB 103 MB/s 6s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 66% ▕███████████ ▏ 1.3 GB/2.0 GB 103 MB/s 6s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 67% ▕███████████ ▏ 1.3 GB/2.0 GB 103 MB/s 6s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest 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5s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 71% ▕████████████ ▏ 1.4 GB/2.0 GB 103 MB/s 5s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 71% ▕████████████ ▏ 1.4 GB/2.0 GB 103 MB/s 5s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 71% ▕████████████ ▏ 1.4 GB/2.0 GB 103 MB/s 5s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 72% ▕████████████ ▏ 1.4 GB/2.0 GB 103 MB/s 5s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 72% ▕█████████████ ▏ 1.5 GB/2.0 GB 103 MB/s 5s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 73% ▕█████████████ ▏ 1.5 GB/2.0 GB 103 MB/s 5s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 74% ▕█████████████ ▏ 1.5 GB/2.0 GB 103 MB/s 5s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 74% ▕█████████████ ▏ 1.5 GB/2.0 GB 103 MB/s 5s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 75% ▕█████████████ ▏ 1.5 GB/2.0 GB 103 MB/s 4s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 75% ▕█████████████ ▏ 1.5 GB/2.0 GB 103 MB/s 4s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 75% ▕█████████████ ▏ 1.5 GB/2.0 GB 103 MB/s 4s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 76% ▕█████████████ ▏ 1.5 GB/2.0 GB 103 MB/s 4s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 77% ▕█████████████ ▏ 1.5 GB/2.0 GB 103 MB/s 4s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 77% ▕█████████████ ▏ 1.6 GB/2.0 GB 103 MB/s 4s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 78% ▕█████████████ ▏ 1.6 GB/2.0 GB 103 MB/s 4s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 78% ▕██████████████ ▏ 1.6 GB/2.0 GB 103 MB/s 4s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 79% ▕██████████████ ▏ 1.6 GB/2.0 GB 103 MB/s 4s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 79% ▕██████████████ ▏ 1.6 GB/2.0 GB 103 MB/s 4s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 80% ▕██████████████ ▏ 1.6 GB/2.0 GB 103 MB/s 3s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 80% ▕██████████████ ▏ 1.6 GB/2.0 GB 103 MB/s 3s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 81% ▕██████████████ ▏ 1.6 GB/2.0 GB 103 MB/s 3s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 82% ▕██████████████ ▏ 1.6 GB/2.0 GB 103 MB/s 3s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 82% ▕██████████████ ▏ 1.7 GB/2.0 GB 103 MB/s 3s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 83% ▕██████████████ ▏ 1.7 GB/2.0 GB 103 MB/s 3s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 83% ▕██████████████ ▏ 1.7 GB/2.0 GB 103 MB/s 3s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 83% ▕██████████████ ▏ 1.7 GB/2.0 GB 103 MB/s 3s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 84% ▕███████████████ ▏ 1.7 GB/2.0 GB 103 MB/s 3s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 85% ▕███████████████ ▏ 1.7 GB/2.0 GB 103 MB/s 2s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 85% ▕███████████████ ▏ 1.7 GB/2.0 GB 103 MB/s 2s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 86% ▕███████████████ ▏ 1.7 GB/2.0 GB 103 MB/s 2s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 86% ▕███████████████ ▏ 1.7 GB/2.0 GB 103 MB/s 2s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 87% ▕███████████████ ▏ 1.7 GB/2.0 GB 103 MB/s 2s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 87% ▕███████████████ ▏ 1.8 GB/2.0 GB 103 MB/s 2s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 88% ▕███████████████ ▏ 1.8 GB/2.0 GB 103 MB/s 2s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 88% ▕███████████████ ▏ 1.8 GB/2.0 GB 103 MB/s 2s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 89% ▕███████████████ ▏ 1.8 GB/2.0 GB 104 MB/s 2s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 89% ▕████████████████ ▏ 1.8 GB/2.0 GB 104 MB/s 2s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 90% ▕████████████████ ▏ 1.8 GB/2.0 GB 104 MB/s 2s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 90% ▕████████████████ ▏ 1.8 GB/2.0 GB 104 MB/s 1s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 91% ▕████████████████ ▏ 1.8 GB/2.0 GB 104 MB/s 1s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 91% ▕████████████████ ▏ 1.8 GB/2.0 GB 104 MB/s 1s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 91% ▕████████████████ ▏ 1.8 GB/2.0 GB 104 MB/s 1s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 92% ▕████████████████ ▏ 1.9 GB/2.0 GB 104 MB/s 1s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 93% ▕████████████████ ▏ 1.9 GB/2.0 GB 104 MB/s 1s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 93% ▕████████████████ ▏ 1.9 GB/2.0 GB 104 MB/s 1s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 94% ▕████████████████ ▏ 1.9 GB/2.0 GB 105 MB/s 1s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 94% ▕████████████████ ▏ 1.9 GB/2.0 GB 105 MB/s 1s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 95% ▕█████████████████ ▏ 1.9 GB/2.0 GB 105 MB/s 0s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 95% ▕█████████████████ ▏ 1.9 GB/2.0 GB 105 MB/s 0s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 96% ▕█████████████████ ▏ 1.9 GB/2.0 GB 105 MB/s 0s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 96% ▕█████████████████ ▏ 1.9 GB/2.0 GB 105 MB/s 0s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 97% ▕█████████████████ ▏ 2.0 GB/2.0 GB 105 MB/s 0s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 97% ▕█████████████████ ▏ 2.0 GB/2.0 GB 105 MB/s 0s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 98% ▕█████████████████ ▏ 2.0 GB/2.0 GB 105 MB/s 0s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 98% ▕█████████████████ ▏ 2.0 GB/2.0 GB 105 MB/s 0s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 99% ▕█████████████████ ▏ 2.0 GB/2.0 GB 105 MB/s 0s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 99% ▕█████████████████ ▏ 2.0 GB/2.0 GB 104 MB/s 0s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 99% ▕█████████████████ ▏ 2.0 GB/2.0 GB 104 MB/s 0s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 99% ▕█████████████████ ▏ 2.0 GB/2.0 GB 104 MB/s 0s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 100% ▕█████████████████ ▏ 2.0 GB/2.0 GB 104 MB/s 0s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 100% ▕█████████████████ ▏ 2.0 GB/2.0 GB 104 MB/s 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\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 100% ▕██████████████████▏ 2.0 GB \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 100% ▕██████████████████▏ 2.0 GB \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 100% ▕██████████████████▏ 2.0 GB \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 100% ▕██████████████████▏ 2.0 GB \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 100% ▕██████████████████▏ 2.0 GB \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 100% ▕██████████████████▏ 2.0 GB \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 100% ▕██████████████████▏ 2.0 GB \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 100% ▕██████████████████▏ 2.0 GB \u001b[K\n", + "pulling 966de95ca8a6: 100% ▕██████████████████▏ 1.4 KB \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 100% ▕██████████████████▏ 2.0 GB \u001b[K\n", + "pulling 966de95ca8a6: 100% ▕██████████████████▏ 1.4 KB \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 100% ▕██████████████████▏ 2.0 GB \u001b[K\n", + "pulling 966de95ca8a6: 100% ▕██████████████████▏ 1.4 KB \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling 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2.0 GB \u001b[K\n", + "pulling 966de95ca8a6: 100% ▕██████████████████▏ 1.4 KB \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 100% ▕██████████████████▏ 2.0 GB \u001b[K\n", + "pulling 966de95ca8a6: 100% ▕██████████████████▏ 1.4 KB \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 100% ▕██████████████████▏ 2.0 GB \u001b[K\n", + "pulling 966de95ca8a6: 100% ▕██████████████████▏ 1.4 KB \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 100% ▕██████████████████▏ 2.0 GB \u001b[K\n", + "pulling 966de95ca8a6: 100% ▕██████████████████▏ 1.4 KB \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 100% ▕██████████████████▏ 2.0 GB \u001b[K\n", + "pulling 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\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[A\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 100% ▕██████████████████▏ 2.0 GB \u001b[K\n", + "pulling 966de95ca8a6: 100% ▕██████████████████▏ 1.4 KB \u001b[K\n", + "pulling fcc5a6bec9da: 100% ▕██████████████████▏ 7.7 KB \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[A\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 100% ▕██████████████████▏ 2.0 GB \u001b[K\n", + "pulling 966de95ca8a6: 100% ▕██████████████████▏ 1.4 KB \u001b[K\n", + "pulling fcc5a6bec9da: 100% ▕██████████████████▏ 7.7 KB \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[A\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 100% ▕██████████████████▏ 2.0 GB \u001b[K\n", + "pulling 966de95ca8a6: 100% ▕██████████████████▏ 1.4 KB \u001b[K\n", + "pulling fcc5a6bec9da: 100% ▕██████████████████▏ 7.7 KB \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[A\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 100% ▕██████████████████▏ 2.0 GB \u001b[K\n", + "pulling 966de95ca8a6: 100% ▕██████████████████▏ 1.4 KB \u001b[K\n", + "pulling fcc5a6bec9da: 100% ▕██████████████████▏ 7.7 KB \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[A\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 100% ▕██████████████████▏ 2.0 GB \u001b[K\n", + "pulling 966de95ca8a6: 100% ▕██████████████████▏ 1.4 KB \u001b[K\n", + "pulling fcc5a6bec9da: 100% ▕██████████████████▏ 7.7 KB \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[A\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 100% ▕██████████████████▏ 2.0 GB \u001b[K\n", + "pulling 966de95ca8a6: 100% ▕██████████████████▏ 1.4 KB \u001b[K\n", + "pulling fcc5a6bec9da: 100% ▕██████████████████▏ 7.7 KB \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[A\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 100% ▕██████████████████▏ 2.0 GB \u001b[K\n", + "pulling 966de95ca8a6: 100% ▕██████████████████▏ 1.4 KB \u001b[K\n", + "pulling fcc5a6bec9da: 100% ▕██████████████████▏ 7.7 KB \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[A\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 100% ▕██████████████████▏ 2.0 GB \u001b[K\n", + "pulling 966de95ca8a6: 100% ▕██████████████████▏ 1.4 KB \u001b[K\n", + "pulling fcc5a6bec9da: 100% ▕██████████████████▏ 7.7 KB \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[A\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 100% ▕██████████████████▏ 2.0 GB \u001b[K\n", + "pulling 966de95ca8a6: 100% ▕██████████████████▏ 1.4 KB \u001b[K\n", + "pulling fcc5a6bec9da: 100% ▕██████████████████▏ 7.7 KB \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[A\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 100% ▕██████████████████▏ 2.0 GB \u001b[K\n", + "pulling 966de95ca8a6: 100% ▕██████████████████▏ 1.4 KB \u001b[K\n", + "pulling fcc5a6bec9da: 100% ▕██████████████████▏ 7.7 KB \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[A\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 100% ▕██████████████████▏ 2.0 GB \u001b[K\n", + "pulling 966de95ca8a6: 100% ▕██████████████████▏ 1.4 KB \u001b[K\n", + "pulling fcc5a6bec9da: 100% ▕██████████████████▏ 7.7 KB \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[A\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 100% ▕██████████████████▏ 2.0 GB \u001b[K\n", + "pulling 966de95ca8a6: 100% ▕██████████████████▏ 1.4 KB \u001b[K\n", + "pulling fcc5a6bec9da: 100% ▕██████████████████▏ 7.7 KB \u001b[K\n", + "pulling a70ff7e570d9: 100% ▕██████████████████▏ 6.0 KB \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[A\u001b[A\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 100% ▕██████████████████▏ 2.0 GB \u001b[K\n", + "pulling 966de95ca8a6: 100% ▕██████████████████▏ 1.4 KB \u001b[K\n", + "pulling fcc5a6bec9da: 100% ▕██████████████████▏ 7.7 KB \u001b[K\n", + "pulling a70ff7e570d9: 100% ▕██████████████████▏ 6.0 KB \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[A\u001b[A\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 100% ▕██████████████████▏ 2.0 GB \u001b[K\n", + "pulling 966de95ca8a6: 100% ▕██████████████████▏ 1.4 KB \u001b[K\n", + "pulling fcc5a6bec9da: 100% ▕██████████████████▏ 7.7 KB \u001b[K\n", + "pulling a70ff7e570d9: 100% ▕██████████████████▏ 6.0 KB \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[A\u001b[A\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 100% 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"pulling 966de95ca8a6: 100% ▕██████████████████▏ 1.4 KB \u001b[K\n", + "pulling fcc5a6bec9da: 100% ▕██████████████████▏ 7.7 KB \u001b[K\n", + "pulling a70ff7e570d9: 100% ▕██████████████████▏ 6.0 KB \u001b[K\n", + "pulling 56bb8bd477a5: 100% ▕██████████████████▏ 96 B \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 100% ▕██████████████████▏ 2.0 GB \u001b[K\n", + "pulling 966de95ca8a6: 100% ▕██████████████████▏ 1.4 KB \u001b[K\n", + "pulling fcc5a6bec9da: 100% ▕██████████████████▏ 7.7 KB \u001b[K\n", + "pulling a70ff7e570d9: 100% ▕██████████████████▏ 6.0 KB \u001b[K\n", + "pulling 56bb8bd477a5: 100% ▕██████████████████▏ 96 B \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 100% ▕██████████████████▏ 2.0 GB \u001b[K\n", + "pulling 966de95ca8a6: 100% ▕██████████████████▏ 1.4 KB \u001b[K\n", + "pulling fcc5a6bec9da: 100% ▕██████████████████▏ 7.7 KB \u001b[K\n", + "pulling a70ff7e570d9: 100% ▕██████████████████▏ 6.0 KB \u001b[K\n", + "pulling 56bb8bd477a5: 100% ▕██████████████████▏ 96 B \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 100% ▕██████████████████▏ 2.0 GB \u001b[K\n", + "pulling 966de95ca8a6: 100% ▕██████████████████▏ 1.4 KB \u001b[K\n", + "pulling fcc5a6bec9da: 100% ▕██████████████████▏ 7.7 KB \u001b[K\n", + "pulling a70ff7e570d9: 100% ▕██████████████████▏ 6.0 KB \u001b[K\n", + "pulling 56bb8bd477a5: 100% ▕██████████████████▏ 96 B \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 100% ▕██████████████████▏ 2.0 GB \u001b[K\n", + "pulling 966de95ca8a6: 100% ▕██████████████████▏ 1.4 KB \u001b[K\n", + "pulling fcc5a6bec9da: 100% ▕██████████████████▏ 7.7 KB \u001b[K\n", + "pulling a70ff7e570d9: 100% ▕██████████████████▏ 6.0 KB \u001b[K\n", + "pulling 56bb8bd477a5: 100% ▕████████████████��█▏ 96 B \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 100% ▕██████████████████▏ 2.0 GB \u001b[K\n", + "pulling 966de95ca8a6: 100% ▕██████████████████▏ 1.4 KB \u001b[K\n", + "pulling fcc5a6bec9da: 100% ▕██████████████████▏ 7.7 KB \u001b[K\n", + "pulling a70ff7e570d9: 100% ▕██████████████████▏ 6.0 KB \u001b[K\n", + "pulling 56bb8bd477a5: 100% ▕██████████████████▏ 96 B \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 100% ▕██████████████████▏ 2.0 GB \u001b[K\n", + "pulling 966de95ca8a6: 100% ▕██████████████████▏ 1.4 KB \u001b[K\n", + "pulling fcc5a6bec9da: 100% ▕██████████████████▏ 7.7 KB \u001b[K\n", + "pulling a70ff7e570d9: 100% ▕██████████████████▏ 6.0 KB \u001b[K\n", + "pulling 56bb8bd477a5: 100% ▕██████████████████▏ 96 B \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 100% ▕██████████████████▏ 2.0 GB \u001b[K\n", + "pulling 966de95ca8a6: 100% ▕██████████████████▏ 1.4 KB \u001b[K\n", + "pulling fcc5a6bec9da: 100% ▕██████████████████▏ 7.7 KB \u001b[K\n", + "pulling a70ff7e570d9: 100% ▕██████████████████▏ 6.0 KB \u001b[K\n", + "pulling 56bb8bd477a5: 100% ▕██████████████████▏ 96 B \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 100% ▕██████████████████▏ 2.0 GB \u001b[K\n", + "pulling 966de95ca8a6: 100% ▕██████████████████▏ 1.4 KB \u001b[K\n", + "pulling fcc5a6bec9da: 100% ▕██████████████████▏ 7.7 KB \u001b[K\n", + "pulling a70ff7e570d9: 100% ▕██████████████████▏ 6.0 KB \u001b[K\n", + "pulling 56bb8bd477a5: 100% ▕██████████████████▏ 96 B \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 100% ▕██████████████████▏ 2.0 GB \u001b[K\n", + "pulling 966de95ca8a6: 100% ▕██████████████████▏ 1.4 KB \u001b[K\n", + "pulling fcc5a6bec9da: 100% ▕██████████████████▏ 7.7 KB \u001b[K\n", + "pulling a70ff7e570d9: 100% ▕██████████████████▏ 6.0 KB \u001b[K\n", + "pulling 56bb8bd477a5: 100% ▕██████████████████▏ 96 B \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 100% ▕██████████████████▏ 2.0 GB \u001b[K\n", + "pulling 966de95ca8a6: 100% ▕██████████████████▏ 1.4 KB \u001b[K\n", + "pulling fcc5a6bec9da: 100% ▕██████████████████▏ 7.7 KB \u001b[K\n", + "pulling a70ff7e570d9: 100% ▕██████████████████▏ 6.0 KB \u001b[K\n", + "pulling 56bb8bd477a5: 100% ▕██████████████████▏ 96 B \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 100% ▕██████████████████▏ 2.0 GB \u001b[K\n", + "pulling 966de95ca8a6: 100% ▕██████████████████▏ 1.4 KB \u001b[K\n", + "pulling fcc5a6bec9da: 100% ▕██████████████████▏ 7.7 KB \u001b[K\n", + "pulling a70ff7e570d9: 100% ▕██████████████████▏ 6.0 KB \u001b[K\n", + "pulling 56bb8bd477a5: 100% ▕██████████████████▏ 96 B \u001b[K\n", + "pulling 34bb5ab01051: 100% ▕██████████████████▏ 561 B \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[1Gpulling manifest \u001b[K\n", + "pulling dde5aa3fc5ff: 100% ▕██████████████████▏ 2.0 GB \u001b[K\n", + "pulling 966de95ca8a6: 100% ▕██████████████████▏ 1.4 KB \u001b[K\n", + "pulling fcc5a6bec9da: 100% ▕██████████████████▏ 7.7 KB \u001b[K\n", + "pulling a70ff7e570d9: 100% ▕██████████████████▏ 6.0 KB \u001b[K\n", + 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"cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "One business area that could be worth exploring for an agile AI opportunity is:\n", + "\n", + "**Healthcare Management and Clinical Decision Support**\n", + "\n", + "The healthcare sector is increasingly digital, with the need to analyze large amounts of patient data, medical records, and various diagnostic outputs. An AI system that can analyze these data sets, provide insights, and make recommendations for clinical decision-making could be highly valuable.\n", + "\n", + "Here are some potential features and use cases:\n", + "\n", + "1. **Predictive Analytics**: Develop an AI model that can identify high-risk patients, predict patient outcomes, and provide personalized treatment plans.\n", + "2. **Clinical Decision Support Systems (CDSS)**: Create a platform that leverages expert systems and machine learning to guide clinicians in diagnosis, treatment, and medication management.\n", + "3. **Medical Imaging Analysis**: Design an AI system to analyze medical images (e.g., X-rays, CT scans) to detect abnormalities and aid in disease diagnosis.\n", + "4. **Natural Language Processing (NLP)**: Develop an NLP-based platform that can help clinicians interpret patient complaints, understand symptoms, and identify potential diagnoses.\n", + "\n", + "Benefits of implementing agile AI in this space:\n", + "\n", + "1. **Improved Patient Outcomes**: By providing more accurate diagnoses and personalized treatment plans, healthcare organizations can improve patient outcomes and reduce mortality rates.\n", + "2. **Enhanced Clinical Decision-Making**: Clinicians will have access to trusted, AI-driven recommendations that can aid in informed decision-making.\n", + "3. **Operational Efficiency**: Automated tasks, such as data analysis and transcription, can free up human clinicians to focus on more complex, high-value tasks.\n", + "4. **Cost Reduction**: By optimizing resource allocation and improving patient outcomes, healthcare organizations can reduce costs associated with medical care.\n", + "\n", + "To leverage agile AI in this space, it's essential to:\n", + "\n", + "1. **Collaborate with Clinicians and Subject Matter Experts**: Ensure that the AI system is grounded in clinical reality and aligns with established standards of practice.\n", + "2. **Conduct Rigorous Testing and Validation**: Verify the performance and accuracy of AI models through extensive testing and validation.\n", + "3. **Emphasize Explainability and Transparency**: Develop AI systems that provide clear explanations for their recommendations and insights to build trust among clinicians and stakeholders.\n", + "\n", + "By combining cutting-edge AI technologies with a deep understanding of clinical practices, we can create powerful tools that transform the healthcare landscape.\n" + ] + }, + { + "data": { + "text/plain": [ + "\"I'll choose a pain-point in the healthcare industry that I think could be ripe for an agentic AI solution:\\n\\n**Challenge:** Inefficient and time-consuming diagnosis of complex medical conditions, particularly those involving rare genetic disorders.\\n\\n**Background:** Diagnosing rare genetic disorders can be extremely challenging due to the complexity of the underlying biology, limited availability of patient data, and high variability in symptoms. Traditional approaches often rely on cumbersome clinical trials, multiple testing methods, and manual review of genomic data, leading to:\\n\\n1. Delays in diagnosis: Diagnosis times can range from months to years, which affects timely intervention and treatment.\\n2. High costs: Multiple tests, specialized equipment, and extensive genomic analysis contribute to high healthcare costs.\\n3. Limited patient outcomes: Many rare disorders have limited effective treatments, leading to poor patient outcomes.\\n\\n**Agentic AI Solution:** Development of an AI-powered diagnostic system that utilizes a combination of natural language processing (NLP), deep learning, and machine learning algorithms can help address these challenges. This solution involves:\\n\\n1. **Patient data integration**: Aggregating and analyzing genomic, clinical, and phenotypic data from multiple sources to identify patterns and relationships.\\n2. **Pattern discovery**: Using advanced NLP techniques to extract relevant information from patient records, including medical histories, genetic reports, and family pedigrees.\\n3. **Predictive modeling**: Developing machine learning models that predict the likelihood of a rare disease based on the identified patterns and patient characteristics.\\n4. **Continuous learning**: Training the AI system on new data and updating its knowledge graph to ensure it remains accurate and effective.\\n\\n**Benefits:**\\n\\n1. Improved diagnosis speed: Allowing for quicker identification of rare diseases, enabling timely treatment and interventions.\\n2. Enhanced accuracy: Minimizing errors through advanced pattern recognition and predictive modeling.\\n3. Personalized medicine: Providing tailored information about genetic predispositions and disease risk, empowering patients and families to make informed decisions.\\n\\n**Ethical considerations:** The development of such an AI system requires careful consideration of:\\n\\n1. Patient consent and data access: Ensuring patients provide informed consent for the collection and analysis of their data.\\n2. Data governance: Implementing robust standards and regulations for data handling, storage, and sharing.\\n3. Bias detection and mitigation: Regularly monitoring and addressing potential biases in AI decision-making.\\n\\n**Next steps:** Conducting pilot studies and collaborating with clinicians, patient advocacy groups, and relevant experts to validate the proposed solution and address any ethical concerns before scaling up for wider deployment.\"" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "openai = OpenAI(base_url='http://localhost:11434/v1', api_key='ollama')\n", + "# First create the messages:\n", + "MODEL = \"llama3.2\"\n", + "messages = [{\"role\": \"user\", \"content\": \"Pick a business area that might be worth exploring for an agentic AI opportunity\"}]\n", + "\n", + "# Then make the first call:\n", + "\n", + "response = openai.chat.completions.create(\n", + " model=MODEL,\n", + " messages=messages\n", + ")\n", + "\n", + "# Then read the business idea:\n", + "\n", + "business_idea = response.choices[0].message.content\n", + "print(business_idea)\n", + "# And repeat!\n", + "messages1 = [{\"role\": \"user\", \"content\": \"Pick a pain-point in that industry - something challenging that might be ripe for an agentic AI solution\"}]\n", + "\n", + "# Then make the first call:\n", + "\n", + "response1 = openai.chat.completions.create(\n", + " model=MODEL,\n", + " messages=messages1\n", + ")\n", + "\n", + "# Then read the business idea:\n", + "\n", + "business_idea1 = response1.choices[0].message.content\n", + "business_idea1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "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": "3.12.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}