⭐️ Get free Postgres and start forking data with Ghost right now: https://b.link/jm-ghost-ma Hermes is an incredible AI Agent framework. With it, you can safely build AI Agents that run 24/7. Taking it once step further, you can build Hermes Agent Profiles so you can share them across your teams or create your very own fleet of agents that take information, triage it, and perform actions as needed scope to your project's needs. Resource: - hermes docs: https://hermes-agent.nousresearch.com/ - agentlink for hermes profiles: https://github.com/codingforentrepreneurs/agentlink - Daily Research Pipeline repo: https://github.com/codingforentrepreneurs/Daily-Data-Research-Pipeline - [Video] Learn to build an automated daily research pipeline https://youtu.be/blFouj5orYo ✅ Code: https://github.com/codingforentrepreneurs/JNN-Justin-News-Network ✅ Subscribe: https://cfe.sh/youtube Chapters 00:00:00 Welcome 00:01:51 Install Hermes Locally & Why 00:05:44 Configure Hermes Agent with OpenAI Codex 00:10:21 Create a new Hermes Agent Profile 00:15:03 Clone Hermes Agent Profile 00:18:50 Hermes Agent Export & Import for Backup and Recovery 00:22:11 Install Third Party Hermes Agent Profiles via Distributions 00:29:46 Create a Hermes Agent Profile Distribution 00:38:22 Building a Multi-Agent Ecosystem 00:39:35 Automated Data Pipeline Repo 00:46:05 Store API Data on Ghost hosted Postgres 00:52:18 Triage Database Skill & Runbook 01:00:48 Hermes Triage Agent Profile 01:03:19 Adding a Custom Skill to a Hermes Profile Distribution 01:07:35 Env Vars in Custom Hermes Profiles 01:17:42 Schedule Cron Jobs with Hermes 01:24:27 Triage Data with Hermes Cron 01:33:21 Duplicate Data with Ghost Forking 01:36:36 Per Agent Forked Postgres Database 01:40:32 Putting it all together 01:52:59 Thank you

Give your agents a dedicated environment with Docker Sandboxes. This environment is a "microVM" which means you can treat it as a disposable mini computer that runs on your normal computer or server. But wait, how is this different than Docker Containers? Each sandbox gets its own Docker daemon. In other words, sandboxes can _build_ container images for you (container images don't usually pull this kind of inception-level skill). Sandboxes are still based in containers but work more like a virtual machine offering better isolation and different networking policies. This means your AI agents can build containers, install packages, and modify files without touching your host system. And yup, you can use Claude Code with your subscription. Codex too. And even spin up Ollama for full open-source models. Sandboxes are fully customizable just like containers. In this series, I'll show you how to: - Install & Run Docker Sandboxes - Setup Claude Code on Docker Sandboxes - Setup Codex on Docker Sandboxes - Setup Ollama and Qwen 2.5 with OpenCode - Build a custom Sandbox - Git Worktrees with Docker Sandboxes - Read-only mode for attaching folders - And more It's easy and ready to use right now. Here are some key references: 🔗 Custom Sandbox Code: https://github.com/codingforentrepreneurs/opencode-ollama-sbx-template 🔗 Official Docs: https://docs.docker.com/ai/sandboxes/ Be sure to subscribe for more: https://cfe.sh/youtube Chapters: 00:00:00 Welcome 00:02:04 Installing Docker Sandboxes CLI on macOS 00:05:25 Your first Agent Sandbox with Claude Code 00:11:24 Your first Agent Sandbox with OpenAI Codex 00:15:43 A Sandboxed Bash Shell 00:21:34 Resume a chat session with sbx 00:24:29 Multiple Workspace Folders in a Sandbox 00:31:34 Git Worktrees in Docker Sandboxes 00:40:20 Create a Custom Sandbox with a Docker Container & sbx Templates 00:50:32 Wrap up

⭐️ Get Ghost for fast free postgres right now: https://b.link/ghost-jmyt I built a pipeline to automatically grab my starred GitHub repos, extract trending repos, and the hackernews feed into a single Postgres database using Ghost and GitHub Actions. Now any AI agent I use can search anything I've ever saved or have been interested in. This video will show you exactly how. We'll fork the repo, add a custom runbook, and learn to build your own personal data pipeline. Own the algorithm! ✅ Code: https://github.com/codingforentrepreneurs/remember-me ✅ Subscribe: https://cfe.sh/youtube ⏱️ Chapters 00:00 Welcome 00:06:57 Getting Started with GitHub 00:11:00 Postgres for Agents with Ghost 00:14:53 Your First Markdown Runbook 00:18:05 Run Markdown as Code for Hacker News 00:20:20 Run Markdown as Code with an AI Coding Agent 00:26:46 GitHub Action Workflow for Hacker News Automation 00:32:14 Ghost API Key for Full Automation 00:36:50 GitHub Personal Access Token

🚀 Use Postgres for Free: https://tsdb.co/jm-pgtextsearch pg_textsearch was just open sourced. It enables BM25 to search your database.... massive upgrade for keyword search. Google uses BM25 in their search engine. Now you can too. Claude told me: "if you're already on Postgres, you can now skip the whole sync-your-data-to-Elasticsearch dance for search." (ps, how can you not love Claude). This video is all about implementing your own pg_textsearch _and_ vector search for powerful hybrid searches. 🆇 pg_textsearch went viral: https://x.com/JustinMitchel/status/2001750598329499681?s=20 ✅ Blog post: https://www.tigerdata.com/docs/use-timescale/latest/extensions/pg-textsearch 🖥️ Code: https://cfe.sh/bots/search-engine 🖥️ pg_textsearch: https://github.com/timescale/pg_textsearch 00:00:00 Welcome 00:01:00 Search Engine Bot with Python, Postgres & pg_textsearch 00:04:08 AI Agent to Build a Command Line Tool with Python 00:16:04 Storing Basic Data 00:20:32 Web Scraping to Markdown 00:25:56 Scraping with a Web Browser 00:35:53 Adding New Features 00:38:40 Fixing Code Errors with Claude 00:39:35 Create a Claude Project 00:42:08 Get Interviewed by AI Agents 00:46:02 Tracking File Changes with GitHub 00:49:44 Start building from AI Agent Interview Spec 00:54:02 Postgres on Tiger Data Cloud with pg_textsearch 01:01:42 Postgres MCP with pg-aiguide 01:13:44 Add Links, Crawl and Search 01:25:07 Vector Search 01:33:16 Hybrid Search 01:43:04 Adding a Web App 01:51:39 Reviewing the UI 01:58:12 Creating a Search Engine MCP 02:04:58 Modify CLI to run the MCP Server 02:10:49 Using the MCP Server in LLMs 02:18:41 Using our MCP in Claude Desktop 02:22:10 Thank you

🚀 Sign up for free Postgres now: https://kirr.co/j2bylk Learn to build a Django CRM from scratch in this comprehensive nearly 6-hour course. You'll integrate Google Contacts, implement time-series analytics with TimescaleDB, and use production-ready automation with modern Python tools. This project covers everything from initial setup to production-ready automation. Key Technologies 🕹️ Django - Full-stack web framework 🕹️ Tiger Cloud & TimescaleDB - Fast & time-series optimized PostgreSQL 🕹️ Google OAuth & People API - Authentication and contact syncing 🕹️ Tailwind CSS & Flowbite - Modern UI components 🕹️ Chart.js - Data visualization 🕹️ uv - Modern Python package management 🕹️ GitHub Actions - Automated workflows What You'll Learn ✅ Set up a modern Django project with uv and pre-commit ✅ Build Django models with foreign keys and relationships ✅ Implement Google OAuth authentication ✅ Create dynamic templates with Django templating system ✅ Track user events with Django signals and generic foreign keys ✅ Analyze time-series data with TimescaleDB ✅ Sync and parse data from Google People API ✅ Build custom Django management commands ✅ Visualize analytics with Chart.js ✅ Automate contact syncing with GitHub Actions ✅ Build a near-production-ready Django application ⭐️ Course Outline ⭐️ 1. Getting Started •• Project overview and setup •• Django project creation with uv •• Pre-commit hooks and automation •• Creating your first Django user 2. Django Fundamentals •• Models and database design •• Foreign key relationships •• Database migrations best practices •• Environment variable management 3. Authentication & Views •• Google Cloud setup and OAuth •• Django views and URL routing •• HTML templates and template inheritance •• Dynamic vs static content 4. Frontend & Static Files •• Managing static files with Whitenoise •• UI design with Tailwind and Flowbite •• Rendering database data in templates •• Custom Tailwind theme (optional) 5. Event Tracking System •• Event model for user activity tracking •• Generic foreign keys for flexible relationships •• Django signals for automated event creation •• Custom signals and event analytics 6. TimescaleDB Analytics •• Setting up TimescaleDB with Django •• Time-series event tracking •• Time bucket analysis and data aggregation •• Visualizing analytics with Chart.js 7. Google Contacts Integration •• Google People API setup and scopes •• Parsing Google API responses •• Contact sync service implementation •• Custom Django management commands 8. Automation with GitHub Actions •• Production-ready contact sync automation •• Scheduled workflow implementation 💻 Code: https://github.com/codingforentrepreneurs/Django-CRM Chapters: 00:00:00 Welcome 00:03:34 Final Project & Code Overview 00:07:47 Python & Django Project Setup 00:14:48 Create a Django Project with uv 00:24:22 Install pre-commit 00:29:50 Automate requirements file with uv and pre-commit 00:35:04 Creating your first Django User 00:41:09 Using Django to Store Data 00:48:28 Connecting Data with Foreign Keys 00:55:58 The Good, the bad, and the Ugly of DB Migrations 01:02:18 Handling Secrets with Python Dotenv 01:07:01 Google Cloud & Sign in with Google 01:13:53 Sign in with django-googler 01:21:28 Handling URLs with Views 01:28:43 Django View to Valid HTML 01:32:59 HTML Templates with Pure Python 01:38:36 Using Django Templates 01:44:43 Django Template Inheritance 01:50:34 Dynamic vs Static Content 01:57:16 Manage Static Files with Django 02:02:32 Django and Whitenoise for Static Files 02:07:26 Dashboard Templates & rav Install 02:18:47 Render a User's Data from Models 02:24:00 Loop Database Data in Django Templates 02:25:54 Render Contacts through Flowbite Blocks 02:30:57 Custom Tailwind Theme Build (optional) 02:36:40 Contacts Detail Page and Get Absolute Url 02:45:50 Tracking Attention with an Event Model 02:55:40 Data Ownership through Foreign Keys 03:02:31 Generic Foreign Keys for Event Tracking 03:10:08 Using Django Signals to Create Events 03:15:52 Custom Django Signal To Track & Handle Events 03:28:04 Event Analytics Basics 03:33:04 Reverse Relationships in Generic Foreign Keys 03:37:14 TimescaleDB & Time-Series Postgres 03:38:40 Create TimescaleDB & Integrate to Django 03:46:38 Timescale Model for Event Tracking 03:51:05 Counting Event Types in Time Ranges 03:58:07 Using Timescale Time Buckets to Analyze Data 04:07:15 Fill Gaps in Data with Timescale 04:15:42 Group Event Types by Time Buckets 04:22:36 Django Template Tag for Analytics Data as JSON 04:30:25 Using Chartjs to Render Time Series Analytics Data 04:41:11 New Scopes for the Google People API 04:44:44 Helper Functions for the Google API 04:55:42 Verify the Google People API Functions 05:02:19 Parse Google API Results 05:05:24 Contact Sync Service Part 1 05:11:59 Contact Sync Service Part 2 05:19:05 Load Contacts with a Custom Django Management Command 05:26:18 Automate Google Contacts Sync with GitHub 05:47:27 Thank you and 2 Challenges

Want to go deeper? Fill out this survey: https://tally.cfe.sh/chatgpt-apps Tunnels for live domains & https: https://youtu.be/Xv_d4Ta1mDE Code: https://github.com/codingforentrepreneurs/chatgpt-apps-sdk-hello-world Chapters: 00:00:00 Welcome 00:03:36 Developer Mode for ChatGPT 00:06:01 Tech Stack 00:07:35 Setup Python Environment 00:10:33 Connect ChatGPT with FastMCP 00:22:37 MCP with OpenAI Platform Chat 00:27:09 Minimal FastAPI App 00:31:27 FastAPI & FastMCP Running Together 00:37:25 Global Lifespan for FastAPI & FastMCP 00:44:30 Thank you and next steps

Using a tunnel is fundamental to modern software development. This video shows you exactly how to set up the following free tunneling services: - ngrok (00:01:36) - Cloudflare Tunnels (00:09:20) - Tailscale Funnels (00:19:16) The tunnel forwards a public domain name (such as https:// mydomain.com) to a local and private development server (such https:// localhost:8000). This process is a must so that you can: - Enable HTTPs on your project - Setup and test live webhooks - Create ChatGPT Apps with Apps SDK - Test your MCP Servers outside of your local environment - Configure OAuth flows (Google Login, GitHub Login, Facebook Login, etc) - Share your project with anyone in the world - Run live tests - In some cases, you can even use to to go into Production (not covered in this video). Chapters: 00:00:00 Welcome 00:01:36 Install and Use Ngrok Tunnels 00:09:20 Setup a Cloudflare Tunnel with a Custom Domain 00:19:16 Use Tailscale Funnels for Tunneling 00:25:39 Thank you

🚀 Launch V1 of your SaaS this weekend: https://www.paracord.co/?promo=FOUNDERS75 N8n combines no-code automation pipelines with the power of full-code automation pipelines. Resources - N8n x Gmail - justautomate.co/gmail - N8n x Clerk - justautomate.co/clerk - Creating SSH Keys - cfe.sh/blog/using-ssh-creating-ssh-keys - N8n Self Hosted Docs - docs.n8n.io/hosting/ Chapters 00:00:00 Welcome 00:01:24 Demo Simple Email Sending in N8n 00:09:08 Intro 00:12:33 Send Email through Gmail 00:21:17 Use Other Node Values 00:28:00 Adding a node for Constant Values 00:32:08 Accessing Different Node Outputs 00:36:21 The if Condition Node 00:43:53 Ready for Cloud Self Hosting with N8n 00:46:33 Create a Self Hosted N8n Server 00:52:27 Custom Domain for Self Hosted N8n 01:00:08 Setup N8n account owner 01:03:37 Your First Workflow Trigger 01:08:28 Configure the Email Trigger with IMAP 01:14:49 Editing Passwords and Credentials in N8n 01:18:39 Automatically Respond to Emails with Send Email 01:26:18 if & then Conditions in Workflows 01:34:16 Gmail, Google Cloud & OAuth 01:47:03 Connect N8n with the Gmail API 01:51:25 Using the Gmail API Node in N8n 02:00:06 Loop Over Many Gmail Messages 02:07:41 Filter Messages by Gmail Label 02:13:25 Adding Labels to Help Prevent Duplicate Messages 02:20:33 Creating Drafts in Gmail 02:30:30 Google Sheets in N8n Workflows 02:43:52 Section wrapup Advanced Self Hosting & Mods with N8n 02:46:01 Customize the Dockerfile to unleash N8n 02:57:38 Syncing Python Files & Custom Python Requirements 03:09:23 npm packages for JavaScript & Using Environment Variables 03:22:54 Practical Example Combining Custom Dockerfile & Self Hosting 03:28:34 Thank you

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🚀 Sign up for Bright Data right now: https://brdta.com/cfe Automatically find and track topics you care about across Reddit posts. From camping to the latest in AI news, this course will show you how to build a powerful and resilient system in Python. The goal is of this course is to help you develop the skills you need to build a resilient data extraction platform using only a handful of tools and the latest in LLMs from Google. In addition to the new skills you'll learn, you'll also have rich data to help you better learn from what real people are experiencing all around the world. Topics: ✅ Easily download the latest Reddit conversations around topics you care about ✅ Ai-Powered Google search to extract relevant Reddit Communities (aka SERP) ✅ Build & ingest data through public webhooks (notifications that work software-to-software or app-to-app) ✅ Rapid prototype data scraping/extracting with Python & Jupyter Notebooks ✅ Use Gemini to run your Python functions based on plain english (aka Tool Calling) ✅ Store extracted data through the Django ORM and PostgreSQL ✅ Strict & structured data outputs for LLMs with Pydantic ✅ Fault-tolerant data downloads using background tasks & webhooks ✅ Configure serverless and serverfull worker managers (django-qstash & celery) ✅ and much more Resourses - My github: https://cfe.sh/github - Project Code Repo https://github.com/codingforentrepreneurs/Reddit-Content-Research-Agent - My Bright Data link - https://brdta.com/cfe (means more sign ups, more free courses) - Django QStash repo & docs https://djangoqstash.com - Django with Celery & Redit Blog Post: https://www.codingforentrepreneurs.com/blog/celery-redis-django Stack: ‣ Python ‣ Jupyter (rapid prototyping) ‣ Django (web app & automation coordinator) ‣ Postgres (database) ‣ Redis (caching & queues) ‣ Celery (background tasks) ‣ Django QStash (serverless background tasks) ‣ Bright Data Search Engine AI (SERP) ‣ Bright Data Crawl API (extract Reddit posts) ‣ LangChain (integration to Google Gemini LLM) ‣ LangGraph (easily unlock Tool Calling) ‣ Cloudflare Tunnels (public domain to your project to accept webhooks) Chapters 00:00:00 Welcome 00:03:46 Demo 00:12:03 Using Search Engine Results 00:14:16 Setup your Python Project 00:20:36 Load API Keys with Dotenv Files 00:24:26 Intro to LangChain 00:26:19 Bright Data Serp API with Python & LangChain 00:38:01 Strip Notebook Outputs for Security with pre-commit 00:42:56 Setup Google Gemini Models with LangChain 00:52:43 LLM with Structured Output 00:59:58 LLM Tool Calling The Hard Way 01:08:19 Tool Calling with LangGraph 01:23:41 Search & Format Reddit Communities via LLM and Bright Data 01:29:38 Scrape Reddit with the Bright Data Crawl API 01:41:58 Get Crawl API Snapshot Progress 01:47:00 Download Data from the Crawl API 01:54:53 Automating Data Pulls for Users 01:58:39 Install & Start the Django Project 02:02:31 Combine Django with Jupyter 02:05:23 Implement Postgres Database with Django 02:15:19 Setup Redis for Django & Caching 02:22:36 Getting Started with Celery & Django 02:33:51 Webhooks & Cloudflare Tunnels 02:36:47 Setup Cloudflare Tunnel with a Custom Domain 02:45:24 Django Qstash for Webhook-based Background Tasks 02:52:55 Bright Data to Django Model Part 1 03:02:16 Bright Data to Django Model Part 2 03:09:38 Store Bright Data Snapshots 03:17:38 Helper Functions for Scraping Events Part 1 03:24:56 Helper Functions for Scraping Events Part 2 03:32:52 Saving Snapshot Scraping Results 03:38:29 Configure Scraping as Background Tasks 03:49:48 Run Background Scraping Tasks 03:53:48 Poll Scrape Status as Background Task 04:02:04 Tracking Scrape Event Finished At Time 04:08:36 A Webhook Handler View in Django 04:16:11 Tracking Scraping Snapshots through Webhooks with Django 04:25:48 Improved Auth Key for Webhooks 04:30:42 Webhook Handler for Reddit Posts 04:38:32 Adjust Data to Scrape 04:50:47 Background Sync Snapshot Reddit Results 05:04:39 Storing Reddit Communities in Django 05:16:53 Reddit AI Agent into Django Project 05:26:04 Topic Extraction Agent 05:32:50 Fuzzy Query to Scraping 05:40:45 Auto Scrape Reddit Communities on Save 05:52:37 Scraping Workflow as a Service Function 06:00:17 Store Queries & Topics 06:09:24 Topics to Reddit Communities 06:16:41 Full Query Automation 06:23:09 Reddit Community Trackablity 06:28:19 Scheduled Background Task to Trigger Reddit Scraping 06:33:27 Django Management Command to Trigger Scraping 06:36:17 Final Query Commands 06:38:22 Thank you and next steps

Blog post & full guide: https://codingforentrepreneurs.com/blog/python-based-csv-orm

🚀 Start using Inngest for free right now: https://innge.st/yt-CE-1 Share Posts on LinkedIn through Python and Django is a very easy process. Get some API keys and send an HTTP POST Request. What's not so easy, is reliably scheduling those posts for a later date. What's more, setting up a system that is ready to share not just on LinkedIn but any other social platform that has an API to do so. So we need to solve two primary things: ‣ OAuth - Connecting LinkedIn users to our Django users ‣ Scheduling/Offloading tasks - Using just Django (thanks to Inngest), to run our tasks later The OAuth process is this: ‣ We create an "App" on LinkedIn (or Facebook or X or Discord or whatever) ‣ We connect that "LinkedIn App" to our Python code via API Keys through Environment Variables ‣ Django & Django AllAuth are configured with those keys ‣ User logs in clicking the "Login with LinkedIn" button ‣ Django and LinkedIn talk to ensure all permissions are valid ‣ LinkedIn shares an Access Token to Django, Django stores it ‣ Through Django, our code can now post to LinkedIn via the LinkedIn API, our LinkedIn App, a User's Access Token, and an HTTP Request. The first part of this course is dedicated to enabling Django to be connected to LinkedIn users to login and share posts. The second part of this course is dedicated to building a scheduling system via Inngest. Thanks to Inngest for partnering with us on this course. Show them some love by signing up right now https://innge.st/yt-CE-1 (it's free) ⦿ Course Code: https://github.com/codingforentrepreneurs/Social-Share-Scheduler ⦿ LinkedIn Post from the course (actual post from the demo): https://www.linkedin.com/posts/justinmitchel_hello-world-this-is-a-demo-from-my-new-course-activity-7353159792107905024-tl5_?utm_source=share&utm_medium=member_desktop&rcm=ACoAAAC3efIBKP1EwXh4DtLky2-WBW3wn1xtoD8 📺 Chapters 00:00:00 Welcome 00:02:17 Demo 00:06:00 Create a Python Virtual Environment 00:10:19 Create the Django Project 00:14:32 Creating a Django Admin User 00:18:58 Django AllAuth Quickstart Setup 00:25:55 LinkedIn with Django AllAuth & OpenID Connect 00:32:36 Redirect URL & Login with LinkedIn 00:37:40 Store User Access Tokens with Django AllAuth 00:40:11 Django with Interactive Python 00:45:46 Reverse Lookup for User Token 00:52:08 Share to LinkedIn with Python Part 1 01:00:23 Share to LinkedIn with Python Part 2 01:05:21 Share to LinkedIn with Python Part 3 01:07:06 Python Helper Function for Linkedin Share 01:18:11 Store Social Posts in Database through Django 01:26:34 Using the Django Model Save Method 01:32:21 Human Readable Model Error Messages 01:37:26 Ensure User has LinkedIn OAuth Connected 01:43:05 Share to LinkedIn via Database Entry 01:44:52 Improved Validation on the Post Model 01:50:41 Prototyping User Features in the Django Admin 02:03:42 Inngest & Django 02:07:25 Inngest Development Server with Docker 02:11:32 Configure Inngest with Django 02:21:44 Invoke Django-based Inngest Function in Jupyter 02:28:03 Trigger Inngest Function via Django Model Save 02:34:30 Fields to Schedule Sharing 02:39:43 Prevent Duplicate Share Events 02:43:20 Mock Post to LinkedIn via Inngest Function 02:51:51 Schedule Inngest Events 02:57:08 Scheduling via Inngest Function Step Sleep 03:04:12 Update QuerySet to Prevent Recursive Saving 03:08:04 Background Jobs with Inngest Functions 03:14:25 Timezone as a Workflow Node 03:18:50 Using Inngest Cloud With Django 03:27:58 Thank you and next steps

🚀 Get clerk now https://go.clerk.com/YXDCdS6 Build a full stack web app in pure Python to help users discover podcast episodes. We'll build this step-by-step. Check the chapters to skip around if you want. ⦿ Code: https://github.com/codingforentrepreneurs/podcast-platform-reflex-clerk ⦿ Get Clerk Now: https://go.clerk.com/YXDCdS6 Chapters: 00:00:00 Welcome 00:01:53 Demo 00:06:43 Python Virtual Environment Setup for Reflex 00:09:24 Start Blank Project with reflex init 00:13:29 Responding to User Events 00:20:40 New Page and URL Route 00:23:18 Nested Contact Page Module 00:26:51 Reusable UI Headings 00:31:57 Static Page Layout Component 00:37:28 Creating a Contact Form 00:46:21 Root Layout 00:50:55 Using the Navbar Recipe 00:55:38 Mobile Navbar Event Redirect 00:58:28 Create a Postgres Database 01:02:34 Integrate Reflex with Postgres through Dotenv 01:07:38 Your First Database Table and Migrations 01:15:10 Storing Form Data 01:20:20 Default Value and Conditional Rendering 01:28:51 Pydantic Validation for Reflex Fields 01:35:45 The Case Against Rolling Custom Auth 01:42:45 Create a free Clerk App 01:45:10 Integrating Clerk with Reflex 01:53:16 Login, Logout and Sign Up with Reflex and Clerk 01:57:04 User-based Welcome Message 01:59:46 Store Clerk User Data in the Database 02:06:33 Dedicated Pages for Auth & Logout 02:12:13 User and Non User Layout 02:15:11 Wrap Reflex Clerk API Everywhere 02:18:14 Reflex User Dashboard 02:26:58 Podcast Search Form with the iTunes Search API 02:35:38 Looping Data for Tables 02:39:31 Rendering Table Rows Based on Reflex State 02:46:05 Search Results Row Display 02:51:01 Audio and Video Playback 02:56:21 Podcast Episode Schema with Pydantic 03:03:30 Podcast Episode Model 03:08:14 User Did Interact Event 03:14:04 Update or Create Podcast Episode 03:23:18 User Like Button Database Model 03:33:58 Like Toggle with Database 03:43:52 User Favorites Page 03:56:12 Ordering Favorites by Like Created At 04:00:14 Discovery Page 04:07:15 Thank you and next steps

Complete Guide to Build and Deploy an AI Agent with Docker Containers and Python If you've ever thought about learning Docker for development and production, this is the course for you. 🔗 References and Links - Course Code: https://github.com/codingforentrepreneurs/build-deploy-ai-agent-python-docker - Python Gmail Inbox Code: https://github.com/codingforentrepreneurs/gmail-inbox-reader-python - Docker Desktop: https://www.docker.com/products/docker-desktop/ - Docker Hub: https://hub.docker.com/ - Docker Model Runner: https://docs.docker.com/ai/model-runner/ - Railway: https://kirr.co/wxfp4a - DigitalOcean Promo: https://do.co/cfe-youtube - FastAPI: https://fastapi.tiangolo.com/ - SQLModel: sqlmodel.tiangolo.com/ - LangGraph: https://www.langchain.com/langgraph - FastAPI Analytics Code: https://github.com/codingforentrepreneurs/analytics-api - FastAPI Analytics Course: https://www.youtube.com/watch?v=tiBeLLv5GJo&feature=youtu.be (in-depth SQLModel and Time Series) - Google Gmail App Password: https://myaccount.google.com/apppasswords 👉 Topics Covered: ✅ Docker Fundamentals ✅ Create a custom Dockerfile ✅ Pushing to Docker Hub ✅ Using Docker Compose ✅ Running Static HTML with Docker ✅ How to use Public Open Source Docker Containers (postgres, redis, etc) ✅ Build Custom Docker Images ✅ Unlock Docker Compose Watch Mode for effective Development ✅ Benefits of the dockerignore file ✅ Mount and persist data with volumes in containers ✅ Hello World with Docker and FastAPI ✅ Unlock Deployment options ✅ Deploy to Railway with your Dockerfile (same as digitalocean) ✅ Deploy to DigitalOcean with your Dockerfile (same as railway) ✅ Integrate Postgres to FastAPI through Docker ✅ Inject runtime environment variables with env files ✅ Run open-source models directly with Docker Model Runner via Docker Desktop ✅ Learn how to nest API routes with FastAPI ✅ Integrate LangChain with FastAPI ✅ Use and manually run tools with LangChain-based AI Models ✅ Use LangGraph to use and run tools automatically ✅ Build a multi-agent system with LangGraph Supervisor ✅ Send emails through Gmail and Python's standard library ✅ Read email inbox with Gmail and Python ✅ Test production-level API calls from Docker, FastAPI, OpenAI, and LangGraph Chapters: 00:00:00 Welcome 00:02:47 Demo & Software Overview 00:06:23 Docker at a Glance 00:10:13 Install Docker 00:14:45 Docker Run 00:21:57 Build your First Docker Container 00:27:26 Publish on Docker Hub 00:33:05 Accessing Python Web Servers in Docker 00:37:08 Your First Docker Compose Service 00:45:26 Build Custom Image with Docker Compose 00:47:49 Configure Dockerfile to Render HTML 00:54:35 Copy Local Files to Docker Container 01:00:38 Moving the Dockerfile and Build Context 01:04:19 Volumes for Containers during Development 01:08:51 Hello World with Docker and FastAPI 01:23:31 Docker Compose Watch Mode 01:28:15 Docker Ignore File and Volumes 01:35:19 Injecting Passwords and Secrets at Runtime 01:46:16 Basics of Pushing Code to GitHub 01:56:10 The Process of Deploying with Containers 01:59:12 Deploy Docker & FastAPI to Railway 02:11:42 Deploy Docker & FastAPI to DigitalOcean App Platform 02:21:22 Compose with Postgres & Docker-Managed Volumes 02:30:29 Integrate Postgres with FastAPI and SQLModel 02:38:51 Your First Database Model 02:43:25 Nested API Route in FastAPI 02:46:31 Create Chat Message Route 02:54:03 Create & List Data with Curl 02:59:52 Deploy with Timestamp Field 03:11:59 Docker Model Runner 03:18:31 LangChain with Docker Model Runner 03:26:02 Structured Output with LangChain 03:29:59 LangChain with FastAPI and SQLModel 03:37:43 Deploy LangChain with DigitalOcean and OpenAI 03:42:07 Sending Email with Python and FastAPI 03:51:14 View Emails in Gmail Inbox with Python 03:58:29 Tools for Ai Agents with LangChain 04:05:31 Executing Tool Calls with LangChain 04:17:24 LangGraph Agent and Tool Calling 04:23:51 LangGraph Research Agent 04:26:52 Connecting Multiple Agents with LangGraph Supervisor 04:34:22 Runnable Config with LangChain Tools 04:39:15 LangGraph Supervisor as FastAPI Endpoint 04:44:26 Deploy LangGraph Supervisor 04:47:46 Thank you & Next Steps

🚀 Sign up for Permit: https://io.permit.io/langraph-permit Django has the data. LangGraph coordinates the Ai Agents. Permit gives us the guardrails to ensure data is safe. In this course, I'll take you step-by-step so you can build Ai Agents with Django, LangGraph, and Permit so you can: ✅ Save user-data with minimal overhead ✅ Talk to the data you or your users care about ✅ Integrate third-party rest API services ✅ Turn any Python functions into tools an Ai Agent can run with queries like 'what are my recent documents?' or 'What new movies are out?' ✅ Chat with Django User data through standard Django queries and without the need for vector embeddings (although you *can* use them) ✅ Easily switch LLMs to upgrade the effectiveness of your agents ✅ Leverage RBAC (role-based access control) within any Django or Python project ✅ Create a Super(visor) Agent that controls other agents ✅ Lock down access to what a User can or cannot do ✅ Add guardrails to ensure an Agent can't do anything it shouldn't (e.g. create, update, read, search, list, share, or delete any data) ✅ and more. Topics covered: ➕ Integrating Django with LangGraph for building Ai Agents (it's crazy easy) ➕ Django ORM fundamentals ➕ Django Model design basics with database syncing (migrations) ➕ Django Users & Permission Fundamentals ➕ Creating LangChain tools for LangGraph agents ➕ LangGraph Supervisor Agents ➕ Permit.io RBAC for powerful and granular control over user and Ai Agent access ➕ Multi-agent integration ➕ Django + Jupyter integration for rapid prototyping ➕ LangGraph-based lookups to your Django database Thanks to Permit for partnering with me on this course. Sign up for Permit right now: https://permit.io Code: https://github.com/codingforentrepreneurs/django-ai-agent Chapters: 00:00:00 Welcome 00:02:53 Demo 00:12:34 Create your Django Project 00:17:18 Designing a Database Table in Django 00:27:22 Add New Database Tables with Django ORM 00:35:09 Changing Database Tables with Django Migrations 00:44:40 Django Users and Admin 00:49:56 Interactive Django with Notebooks 00:53:47 Creating and Saving Data in Django 01:04:34 A Django Staff User 01:10:30 Built-in Django Permissions 01:20:17 Django Lookups as Ai Agent Tools 01:33:37 Configure LLM Model with Django 01:43:42 A Django-based LangGraph Agent 01:55:58 Adding Memory to the Agent 02:00:55 Django CRUD with LangGraph Agents 02:15:26 API Client for Ai Movie Discovery Agent 02:25:31 An API-driven Ai Agent 02:36:35 Multi Agent Supervisor with LangGraph 02:45:58 A Case for Guardrails 02:50:38 Crafting Resources & Roles 02:59:05 Integrate Permit.io with our Django Ai Agent 03:06:04 Syncing Django Users to Permit 03:12:06 Get or Create Permit Resources 03:18:36 Creating Roles for Resources 03:20:48 Assigning Roles to Users with Permit.io 03:26:20 Implement RBAC Guardrails for the Ai Agent 03:37:00 Django Instance Level Permissions with Permit.io 03:46:53 Thank you and next steps