Learn Docker, Ionic and Scikit-learn

Get started with MongoDB and Docker really quick 🔥 even if you have no backend/fullstack experience.

This lesson guides you through installing Docker Desktop and setting up a MongoDB image.

We explain Docker images and containers, and their roles in providing isolated software environments.

We run a MongoDB image in 2 ways: using the terminal and Docker Desktop

We demonstrates configuring port settings for a container and accessing the MongoDB shell (`mongosh`) to interact with the database.

We learn how to manage Docker containers, configure settings, and run commands inside the container.

Grab the CLI command: 🔥 `docker run --name some-mongo -d mongo:latest` 🔥

Tomasz Ducin

Install Docker to run local MongoDB Container

Learn to install Docker Desktop, set up a MongoDB image, and manage containers. Configure ports and access the Mongo shell

Get started building web applications with Angular and Firebase.

In this course, you'll learn how to set up and use Firebase in your Angular applications using the AngularFire library.

We choose the AngularFire library because it is an official library created by the Firebase and Angular teams to work better with Angular.

It helps you by wrapping everything into observables and helps update zones. 

By the end of this course, you'll be able to build web applications using Cloud Firestore as your database, Firebase Authentication as your Auth system, Cloud Storage to save files, and Firebase Hosting to deploy your applications to the web.

TypeScript is a typed superset of JavaScript that compiles to plain JavaScript.

typescript

Build Full Stack Web Applications with Angular and Firebase

Ionic is the beautiful, free and open source mobile SDK for developing native and progressive web apps with ease.

ionic

Now that file uploading works, we want to actually show that a task has files associated with it. To do this we will need to update the task object with a file property that we'll push files too.

In this lesson, we'll use Cloud Storage methods like getDownloadUrl() to get the file's download URL from our bucket.

Then we'll add that file's information to our task and persist it in the database to show the list of files in a task to the user.

While we do this, you'll see how we can easily add a spinner when files are being uploaded using Ionic loadingController.


Save and Display Tasks Files Utilizing Firebase Cloud Storage getDownloadUrl

A scenario you will inevitably run into is users needing to reset their passwords for security reasons or they lost and forgot their password.

We'll learn how to send our users an email to reset their passwords, after we send the email, we'll display a notification alert with ionic alertController to the user and redirect them to the login page.


Send a Password Reset Email Using Firebase Authentication with sendPasswordResetEmail

Now it's time to create a template for our authentication page using Ionics page generation CLI.

We'll show how to use the same page for all of our authentication operations by reading the parameters from the URL and displaying the right form elements and buttons depending on the parameters present.


Create an Authentication Page Template with Ionic Page Generation

We'll learn how to delete documents from the database. This action is pretty straight forward, we'll add a delete button that will call Firestore `deleteDoc` and pass in the task id.

We'll also explore a good practice when performing destructive operations (__like deleting database objects__) of adding a confirmation step to make sure our users don't delete objects by accident. We'll do this with the Ionic Alert Controller.


Delete a Document with a Confirmation Step from Firestore

We'll evaluate 2 good practices when adding a new task.

First, we'll show the users a toast alert to notify them that their document was saved in the database. We'll do this with a toast component that ionic makes available to us

And second, we'll use Angular's router to navigate the user back to the task list.

Notify Users Their Database Operation Worked with a Toast Alert

We'll create the template for our new page and set everything up so that we can use this page to create new tasks. The first thing we will do is create a BehaviorSubject task variable that we'll store form values in to send to Firebase.

Because we are using the ionic framework we'll lean heavily into this when creating our templates. You'll see how we bind text values to the object we'll send to Firebase as well as a selector component that will use `ion-segment`.

We'll also see a tip on handling the `async` pipe in a top container in our template so that we don't have to manually add it everywhere we'll render a property from an observable


Create a New Task Form Page Template with Ionic

firebase

There are 2 different ways to get a document's ID when using `@angular/fire`.

You can add the parameter `{ idField: 'id' }` to your collection when using the `collectionData()` observable.

You can also use the observable `collectionChanges()` and access the internal metadata of the document to manually get the ID and add it to the resulting object.

Code sample: https://github.com/javebratt/angularfire-idField

Access a Firebase Document's ID Even when It's Not Stored in the Document with angularfire

Docker containers wrap a piece of software in a complete filesystem that contains everything needed to run: code, runtime, system tools, system libraries – anything that can be installed on a server. This guarantees that the software will always run the same, regardless of its environment.

docker

The end is nigh. Your application is almost ready, and you will need to deploy it. And with the deployment, the endless tweaking to get everything running on your production servers. 

If only there were a way to test everything first. That is where containers will come in to save the day. With containers, you not only run your code in an environment similar to the production server, but you also run your code in precisely the same environment. 

In this talk, the attendees will learn about how to create container friendly applications and how to use containers to share their code with their team.

Containers for Software Developers

While "It works on my computer" might give you an excuse to close a bug ticket in the backlog, it doesn't really work when you're working on a team.

You need to ensure that your application can run regardless of what hardware your teammates are running or libraries they have installed.

Docker solves this problem by collecting everything your application needs to run into a container.

Containers aren't just for source code. They can hold configuration, scripts, and even their own filesystem. It's similar to a Virtual Machine, without having to bring a full operating system along for the ride.

Because containers run in isolation, there is some configuration involved to allow for files to be saved and containers to communicate with one another.

One of the most useful ways to get your head around Docker is to see it in action.

Follow along with Joel Lord as he works through the process of preparing an application to run in separate containers for the frontend and backend of an application that searches for gifs and re-encodes them with a caption supplied by the user.

You'll see how to configure the separate containers to communicate with one another, work with environment variables, and persist data to your local machine. Along the way, you'll pick up some tips on useful commands and bash scripting. Finally, you'll learn how to use Docker Compose to make it easy to run multiple containers simultaneously.


Containerize Full-Stack JavaScript Applications with Docker

Now that all three tiers of your application are available in containers, you'll need to push those images to container registries. Registries are the container equivalent to repositories for your code. Registries can be public or private. 

In this lesson, we'll use Docker Hub to push our images. Docker Hub repositories allow you share container images with your team, customers, or the Docker community at large.

Docker images are pushed to Docker Hub through the `[docker push](https://docs.docker.com/engine/reference/commandline/push/)` command. A single Docker Hub repository can hold many Docker images (stored as **tags**).

Images pushed to a public registry can be downloaded by using the `docker pull` command. Once they have the image downloaded, they can use Docker run to start each container just like you did in the previous steps.

Push a Docker Container Image to Docker Hub

In the last lesson [Create a Custom Image with a Dockerfile running Node.js](https://egghead.io/lessons/docker-create-a-custom-image-with-a-dockerfile-running-node-js?pl=introduction-to-docker-afaf), our backend stopped working again because it couldn’t find the database.

One of the advantages of running containers is that they run in isolation. As far as the container is concerned, nothing exists outside of it. It behaves as if it was its own operating system. This is why the backend can’t find the database on "localhost". The two containers are running on the same host but as far as the backend is concerned, there is no database. The same goes for the database. It can’t see anything outside of what is running in its container therefore it can’t see the backend application running.

When you need a way for multiple containers to talk to each other, Docker has a tool called networks to help you. We'll use a network to allow our backend to successfully connect to the database.

Allow Containers to Communicate through Docker Networks

You will learn how to create your own images that will include all the source code of your application, along with a specific version of Node.js and all the dependencies that are needed. Once you are done, you will be able to share this image with other people and this will ensure that everyone is running the same environment. It will event let people that don’t have Node.js run this server. This will also have the added benefit of cleaning up everything once you close the application.

You'll notice that we will run into the same error stating we can't connect to the database. This is expected and we'll fix this in [Allow Container to Communicate through Docker Networks](https://egghead.io/lessons/docker-allow-container-to-communicate-through-docker-networks)

Create a Custom Image with a Dockerfile running Node.js

First, we'll see how you can have different run-times installed in a docker container that isn't dependent on your system's current installs. You don't need anything but docker to run these containers! We will demonstrate this by installing the latest version of Node.js in a docker container and compare that to the version of Node.js I'm running on my machine.

Next, you'll see how to pass environment variables to containers. This is important because you will use these variables for API calls as well as configuring databases, and more!

Use Environment Variables for Docker Containers

In this lesson, you will learn how to start basic containers and how to use them in your day to day developer life. You will also learn how to start a database container that can be used with the demo application for this course.

Running Your First Container

JavaScript® (often shortened to JS) is a lightweight, interpreted, object-oriented language with first-class functions, most known as the scripting language for Web pages, but used in many non-browser environments as well such as node.js or Apache CouchDB. It is a prototype-based, multi-paradigm scripting language that is dynamic, and supports object-oriented, imperative, and functional programming styles.

javascript

First, we'll get a tour of the application and what it does then we will start configuring a fresh project. 

The application UI is simple. It asks for a keyword and will search and display a random GIF from Giphy. Once you are satisfied with the image, you will be able to add a caption to it. The server will deconstruct the GIF frames, add the caption and re-create the GIF for you. Everything is then saved to a database so that all the users can benefit from your creations.

The tech involved to do this is a Vue frontend, Node.js backend, and MySQL database. I'll walk you through cloning, installing dependencies, configuring the giphy API key that we use, and starting the application. You'll notice at the end we'll need to configure the database. We'll get an error but this is expected!

Configure and Run a Node.js API Powered by a Vue Application

Now that you have three containers ready to be used, you will learn how to create a docker-compose.yaml file. By using Docker Compose, you will be able to share a single YAML file and anyone will be able to run your application.

Docker-compose is part of the Docker tools suite and is an application that will take a YAML file to start all of your containers with one single command. 

Using Compose is basically a three-step process:

1. Define your app’s environment with a `Dockerfile` so it can be reproduced anywhere.
2. Define the services that make up your app in `docker-compose.yml` so they can be run together in an isolated environment.
3. Run `docker-compose up` and Compose starts and runs your applications. 

Compose works in all environments: production, staging, development, and testing.

Use Docker Compose To Share An Entire Application

Introduction for the lesson on using environment variables in a front-end container

Mini talk - Access Environment Variables in a Dockerfile to Build a Front-End Container

In this small talk, the instructor explains why you need to use a multi-step build to prepare your front-end container.

Mini talk - Containerize the Front End of an Application with a Multi-Step Docker Build

Docker containers that are running on your local machine can't communicate with each other. In this mini talk, the instructor explains why and shows a demonstration of Docker networks.

Mini talk - Allow Containers to Communicate through Docker Networks

Writing clear and effective prompts or rules for AI (.mdc files in Cursor) can be tricky. Your instructions might start out clumsy or ambiguous, leading to less-than-ideal AI responses. This lesson demonstrates how to quickly refine them by referencing a dedicated "best practices" rule file directly within Cursor's inline editor (`Cmd+K`).

**Workflow demonstrated in this lesson:**

*   Select the text (a prompt or section of a rule) you want to improve.
*   Use `Cmd+K` to open the inline AI editor.
*   Reference your separate prompt best practices rule file using `@` (e.g., `@prompt-improve.mdc`).
*   Cursor's AI rewrites the selected text, applying the referenced best practices.
*   Accept the improved version (`Cmd+Enter`).
*   **(Bonus)** Use `Cmd+L` to send the edited section to the Agent chat and ask it to compare the changes with the previous version (using git history) and explain *why* the new version is better (e.g., improved clarity, stronger action verbs, explicit exclusions).

**Key benefits:**

*   Instantly improve prompt clarity and effectiveness based on your own defined standards stored in a rule file.
*   Reduces ambiguity in instructions, leading to better AI outcomes.
*   Leverages Cursor rules (`.mdc` files) as reusable templates for prompt refinement.
*   Provides insight into *how* and *why* prompts are improved via Agent comparison.

Stop wrestling with clunky prompts. Use this technique to systematically apply best practices and level up your AI instructions directly within Cursor.

```markdown
# Improve the User's Prompt Following the Patterns Below

> Practical prompt patterns to help anyone get clearer, more reliable answers from an AI agent.

| # | Pattern                       | Why It Matters                                                          | Template                                                                 | Example Prompt                                                                                             |
|---|-------------------------------|-------------------------------------------------------------------------|--------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------|
| 1 | Lead with the ask             | The model reads top-down; putting the goal first stops it wandering.    | `Do X. [context]`                                                        | "Summarize this PDF in 5 bullet points. The text is below: ..."                                            |
| 2 | Repeat the key ask at the end | Long contexts sometimes truncate; an end-cap protects you.              | `...[detail]... REMEMBER: Do X.`                                         | "List pros & cons, keep it balanced. REMEMBER: 5 pros, 5 cons."                                             |
| 3 | Specify output shape          | Dictating format cuts revision loops.                                   | `Return as: 1) short title, 2) table (CSV).`                             | "Give 3 holiday ideas. Format: destination, flight-time-hrs, avg cost."                                    |
| 4 | Use clear delimiters          | Backticks/headings/XML keep sections from blending.                     | `TEXT TO ANALYSE`                                                        | "Rate the style of the text between the fences."                                                            |
| 5 | Induce step-by-step thinking  | Planning first boosts multi-step accuracy.                              | `Think step-by-step then answer.`                                        | "Solve this puzzle. Think step-by-step before giving the final move."                                      |
| 6 | Ask it to plan its workflow   | For big jobs, AI outlines tasks before doing them.                      | `First draft a plan, wait, then execute.`                                | "We're writing an e-book. ❶ Outline chapters. ❷ Wait. ❸ When I say 'go', draft chapter 1."              |
| 7 | Limit or widen knowledge sources | Controls hallucination.                                               | `Use only the info below. / Combine basic knowledge + this context.`     | "Using only the product sheet below, write FAQs."                                                          |
| 8 | Guide information retrieval   | Helps AI pick the right docs before answering.                          | `List which docs look relevant, then answer.`                            | "30 sales memos attached. 1) Name the 5 most relevant. 2) Summarise their common points."                 |
| 9 | Show a style/example          | Anchors tone, length, vocabulary.                                       | `Match the style of: <example>`                                          | "Review this gadget in the style of the sample below: 'Short, witty, 3 key facts...'"                      |
| 10| Set correction handles        | One-line fixes let you steer quickly.                                   | `If length > 150 words, shorten.`                                        | "Describe blockchain to a child. If your answer is >150 words, cut it in half."                            |
| 11| Tell it when to stop or loop  | Prevents half-finished lists or runaway essays.                         | `Keep going until you list 20 ideas, then stop.`                         | "Brainstorm webinar titles. Give exactly 12, then finish."                                                  |
| 12| Request the hidden reasoning  | Good for audits; otherwise omit to keep it short.                       | `After the answer, include a brief reasoning section.`                   | "Which of these stocks looks over-valued? Answer first; add a 2-sentence rationale below a divider."        |
```

John Lindquist

Automatically Improve Cursor Rules Using Custom Prompts

Cursor's `Cmd+K` is fantastic for targeted code edits, but sometimes you need the AI to operate beyond the confines of your current selection—like extracting a piece of logic into a brand-new, top-level function. Standard `Cmd+K` often falls short, keeping changes localized. This lesson reveals how to overcome this limitation using Cursor's "Edit Full File" mode, turning complex refactoring of legacy code into a streamlined process.

**Workflow demonstrated in this lesson:**

*   Contrast the behavior of standard `Cmd+K` (Edit Selection) with the "Edit Full File" mode.
*   Use `Cmd+K`, then `Cmd+Shift+Enter` (or the dropdown) to invoke "Edit Full File" mode for broader AI refactoring.
*   Instruct the AI to extract selected code into new functions at the top level of the module.
*   Demonstrate breaking down a large, monolithic function into multiple smaller, exported, and testable functions.
*   Utilize the "Fix in Chat" feature (`Cmd+Shift+D`) to send error messages and surrounding code directly to an AI agent for quick resolution.

**Key benefits:**

*   **True Top-Level Extraction:** Move code out of its current scope and into new, independent functions anywhere in the file.
*   **Simplified Legacy Code Refactoring:** Easily break apart large, untestable functions into manageable, testable units.
*   **Improved Code Structure:** Quickly organize and modularize your codebase with AI assistance.
*   **Contextual Error Fixing:** Send errors and relevant code snippets to an AI agent via "Fix in Chat" for efficient debugging.

This lesson shows a powerful technique for serious refactoring in Cursor, especially when dealing with large or legacy codebases, making your functions more modular, testable, and maintainable.

Clean up Legacy Functions for Testability in Cursor (0.50+) with cmd+k

Want Cursor's AI Agent to intelligently run your custom CLI tools without complex MCP setups or constant hand-holding? If only the AI could understand your tool's `--help` output, life would be much simpler. This lesson demonstrates exactly that, leveraging Cursor's "Rules" feature with the "Agent Requested" type. You'll learn to feed your CLI's help text directly into a rule, effectively teaching the AI to operate your custom commands through natural language.

**Workflow demonstrated in this lesson:**

*   Execute your custom CLI tool's `--help` command (e.g., `ghx --help`) and pipe its output to your clipboard (e.g., `| pbcopy`).
*   Create a "New Cursor Rule" (Cmd/Ctrl + Shift + P, then "New Cursor Rule").
*   Paste the copied help text into the main content area of the rule.
*   Set the "Rule Type" to "Agent Requested".
*   Provide a clear, concise "Description" (e.g., "Search GitHub") that will act as the trigger phrase for the AI.
*   In an Agent chat, prompt the AI using natural language that corresponds to your rule's description (e.g., "Search GitHub for examples of package.jsons using semantic-release").
*   Observe as the Agent uses the rule's help text to correctly formulate and run your custom CLI command.
*   Leverage the tool's output by further instructing the AI to generate or modify project files (e.g., create a `package.json`, `.releaserc.json`, or `biome.json` based on findings).

**Key benefits:**

*   Empowers Cursor's AI Agent to directly use your existing custom CLI tools without needing to modify the tools themselves.
*   Simple and intuitive setup: Uses your tool's own documentation (`--help` output) as the "API" for the AI.
*   Efficient context usage: The detailed help text for your tool is only loaded into the AI's context when your specific trigger phrase is detected.
*   Provides a straightforward and powerful alternative to MCPs for many CLI tool integration scenarios, especially when tools already have good help messages.
*   Enables complex, natural language-driven automation by combining custom tool execution with the AI's code generation and modification capabilities.

By using "Agent Requested" rules, you gain maximum control over how and when the AI interacts with your custom tools, making the Agent a more versatile and powerful assistant tailored to your specific command-line utilities.

**Resources**

[https://www.npmjs.com/package/@johnlindquist/ghx](https://www.npmjs.com/package/@johnlindquist/ghx)



When to Use Cursor Rules Instead of MCPs

Planning complex application logic or workflows before coding can be challenging. This lesson demonstrates how to leverage Cursor's AI (Chat and Agent modes) to visually brainstorm and iterate on features by generating Mermaid diagrams directly within Markdown files. You'll learn how to use prompts to create flowcharts, sequence diagrams, explore edge cases, and improve the viewing experience for detailed diagrams.

**Workflow/Benefits Demonstrated in this lesson:**

*   Use Cursor AI (Chat/Agent) to generate Mermaid diagrams from natural language prompts.
*   Employ Markdown preview, popping it into a separate window for better viewing and zooming independent of the editor font size.
*   Iteratively refine diagrams by asking the AI to add features or create new diagram types (e.g., sequence diagrams).
*   Generate diagrams to visualize complex scenarios like optimistic updates and edge cases (conflict resolution, offline sync).
*   Create persistent visual artifacts (diagrams in Markdown) for planning, documentation, and guiding further AI development tasks.


## Mermaid Cursor Rules
````markdown
---
description: Whenever diagrams or mermaid is mentioned.
globs: 
alwaysApply: false
---
Here are the essential rules for creating Mermaid diagrams that render well in GitHub Markdown:

1.  **Use the Correct Fenced Code Block:**
    *   Always start the block with ```mermaid and end it with ```.
    *   ```markdown
        ```mermaid
          graph TD
            A[Start] --> B{Decision};
            B -- Yes --> C[Action 1];
            B -- No --> D[Action 2];
        ```
        ```

2.  **Stick to Well-Supported Diagram Types:**
    *   GitHub generally has good support for the most common types:
        *   `graph` (Flowcharts - `TD` is often best for readability)
        *   `sequenceDiagram`
        *   `classDiagram`
        *   `stateDiagram-v2` (Prefer v2 for better features/rendering)
        *   `erDiagram` (Entity Relationship)
        *   `pie` (Pie charts)
        *   `gantt` (Gantt charts)
        *   `mindmap` (Mind Maps - Basic structure only. *See syntax notes below*)
    *   Avoid extremely new or less common diagram types, as GitHub's Mermaid version might lag slightly behind the latest release.

3.  **Keep Syntax Simple and Standard:**
    *   **Node IDs:** Use simple alphanumeric IDs (e.g., `node1`, `processA`, `userAuth`). Avoid spaces or special characters in IDs.
    *   **Node/Actor/State Labels:** **Use quotes (`"..."`)** for labels, especially if they contain spaces, punctuation, special characters, or Mermaid keywords. This is the most common source of rendering errors.
        *   *Good:* `A["User Input"] --> B["Validate Data"];`
        *   *Bad (potential error):* `A[User Input] --> B[Validate Data];`
    *   **Arrows:** Use standard arrow types (`-->`, `---`, `==>`, `->>`, etc.).
    *   **Comments:** Use `%%` for comments if needed.

4.  **Mindmap Specifics for GitHub:**
    *   GitHub **supports the basic `mindmap` structure**, using indentation to define hierarchy (parent/child relationships).
    *   Ensure each node/item is on its **own line** with correct indentation relative to its parent.
    *   GitHub **DOES NOT support** experimental or advanced `mindmap` features like `::icon()` syntax. Using icons **will cause rendering errors**.
    *   Stick to plain text nodes for mind maps intended for GitHub Markdown.
    *   *Correct Example (GitHub compatible):*
        ```mermaid
        mindmap
          Root
            Parent
              Child 1
              Child 2
        ```
    *   *Incorrect Example (GitHub incompatible due to icons):*
        ```mermaid
        mindmap
          Root
            Parent
              ::icon(fa fa-one) Child 1
              ::icon(fa fa-two) Child 2
        ```

5.  **Prefer Vertical Layouts (`graph TD`):**
    *   For flowcharts (`graph`), `TD` (Top Down) or `TB` (Top Bottom) usually renders more readably within the flow of a Markdown document than `LR` (Left Right). GitHub often gives diagrams ample width, but vertical flows are easier to follow on typical screens.

6.  **Let GitHub Handle the Styling:**
    *   **DO NOT** try to set themes (e.g., `%%{init: {'theme': 'dark'}}%%`) or apply custom styling using `classDef` or `style` *within the Mermaid code*.
    *   GitHub **ignores** these theme directives and applies its own styling based on whether the user is viewing GitHub in light, dark, or dimmed mode. Your diagram will automatically adapt. Trying to force colors or themes will likely just be ignored or look out of place.

7.  **Keep Diagrams Focused:**
    *   Avoid overly complex diagrams with excessive nodes, edges, or nesting in a single block. While GitHub *can* render complex ones, they might become hard to read or hit rendering limits. Break down very complex ideas into multiple simpler diagrams if possible.

8.  **Test in GitHub Preview:**
    *   Always use GitHub's preview tab when editing your Markdown file to see exactly how the diagram will render. This is the best way to catch syntax errors or unexpected layout issues.

9.  **Tooling & Edit Verification Note:**
    *   Automated code editing tools may sometimes struggle to apply changes correctly within Mermaid blocks, especially with complex syntax involving indentation or specific formatting (like the mindmap example above).
    *   **Always carefully review edits** made to Mermaid blocks by automated tools. If errors persist or the diagram doesn't render as expected, manual correction might be necessary.

**In short:** Write standard, clearly quoted Mermaid syntax using common diagram types. For mindmaps in GitHub, use only the basic indentation structure (no icons). Prefer `graph TD`. Avoid trying to control the theme/colors yourself. Use the GitHub preview, and double-check automated edits to Mermaid blocks.
````

Brainstorm and Planning with Mermaid Diagrams in Cursor

Manually staging files, writing commit messages, committing, and syncing to Git involves multiple repetitive steps. This lesson demonstrates how to significantly streamline this common Git workflow in Cursor (or VS Code) by creating a single, powerful keyboard shortcut using `keybindings.json` and the `runCommands` feature.

**Workflow demonstrated in this lesson:**

*   Open `keybindings.json` to customize keyboard shortcuts.
*   Unbind default shortcuts (like `Shift+Cmd+S`) that you might not use.
*   Utilize the `runCommands` command to create a sequence of actions.
*   Chain together `git.stageAll`, `cursor.generateGitCommitMessage`, `git.commitAll`, and `git.sync` commands.
*   Bind this entire sequence to a single custom keyboard shortcut.
*   Execute the full stage -> AI commit message -> commit -> sync workflow with one keystroke.

**Key benefits:**

*   Combines multiple Git actions into one efficient step.
*   Automates staging, committing, and syncing changes.
*   Leverages Cursor's AI to generate commit messages automatically.
*   Frees up valuable keyboard shortcuts for powerful custom workflows.
*   Significantly speeds up your development cycle by reducing manual Git operations.

```json
  {
    "key": "shift+cmd+s",
    "command": "runCommands",
    "args": {
      "commands": [
        {
          "command": "git.stageAll"
        },
        {
          "command": "cursor.generateGitCommitMessage"
        },
        {
          "command": "git.commitAll"
        },
        {
          "command": "git.sync"
        }
      ]
    }
  },
  {
    "key": "shift+cmd+s",
    "command": "-workbench.action.files.saveAs"
  },
  {
    "key": "shift+cmd+s",
    "command": "-workbench.action.files.saveLocalFile",
    "when": "remoteFileDialogVisible"
  }
```

Create Complex Cursor Shortcuts to Run Commands and Generate AI Commits

In development we often want to test out features we are building but can only do so with deployed or staging applications or setting up Ngrok or Cloudflare Tunnels. 

UntunJS allows us to get the benefits of Cloudflare tunnels without having to go into the Cloudflare dashboard and set it up.

In this quick lesson we utilize UntunJS to run our local Nextjs application in a Cloudflare Tunnel

Ben Patton

Run local Nextjs App in a Cloudflare Tunnel with UntunJS

UntunJS is a great package to get the quickest start with Cloudflare tunnels and development and testing

Hey Everyone 👋,

John Lindquist here with the third issue of AI Dev Essentials! Another week, another whirlwind of updates in the AI development space. We saw Google drop Gemini 2.5 Flash, OpenAI counter with o3/o4-mini and a new Codex CLI, Anthropic beef up Claude with research tools, and continued evolution in the MCP and agent framework landscape. Personally, I've been digging into Gemini 2.5 Flash – it's impressive to see Google pushing the performance frontier for smaller models and comparing it directly against the competition like O4-mini. Let's dive in!

___

### New egghead.io Lesson This Week:

* **[Generate and Debug MCP Servers from Scratch using GitHub Examples and AI](https://egghead.io/generate-and-debug-mcp-servers-from-scratch~k98ad):** Building complex components like Model Context Protocol (MCP) servers from scratch can be challenging, especially without good reference implementations. This lesson demonstrates a powerful workflow combining the `ghx` CLI tool (a wrapper I built for the GitHub CLI - find it [here](https://www.npmjs.com/package/@johnlindquist/ghx)) with AI assistants (like Google's AI Studio and Cursor's agent) to overcome this hurdle. Learn how to scrape real-world examples from GitHub using `ghx` ([see scraped examples here](https://gist.github.com/johnlindquist/b19673bbf6a396406dff869beecace1d)), feed them to an AI to generate a new server, and then integrate and debug it within Cursor. It's a practical look at bootstrapping complex tools using AI and targeted code searching.

___

Here are the highlights that caught my eye this past week:

## Model & Major Platform Updates

* **Gemini 2.5 Flash Arrives:** Google released Gemini 2.5 Flash ([Google Developers Blog](https://developers.googleblog.com/en/start-building-with-gemini-25-flash/)), their first unified reasoning model with thinking budgets. It's positioned as a high-performance, cost-effective option. Notably, Google's benchmarks included comparisons against OpenAI's very recent O4-mini, drawing praise for transparency ([Nathan Lambert](https://x.com/natolambert/status/1912958672881303943)). It's available via the Gemini API, AI Studio, Vertex, and the Gemini app. The [Live API docs](https://ai.google.dev/gemini-api/docs/live) are also updated.
* **OpenAI Counters with o3/o4-mini & Codex CLI:** OpenAI launched [o3 and o4-mini](https://openai.com/index/introducing-o3-and-o4-mini/), highlighting their agentic capabilities and tool use during Chain-of-Thought. Alongside these, they released the open-source [Codex CLI](https://github.com/openai/codex), a coding agent designed to leverage these new models. Interestingly, the OpenAI dev behind Codex CLI was spotted [using Cursor](https://x.com/chuhang1122/status/1912786904812294312).
* **Claude Gets Research Superpowers:** Anthropic rolled out a major [Claude upgrade](https://x.com/jowettbrendan/status/1912446262756151732) introducing a "Research" agent capability. It can search the web and internal docs, chain queries, and explore topics agentically, building citations along the way. They also added Google Workspace integration (Gmail, Calendar, Docs) for paid users. (Official blog post link pending confirmation).
* **Cursor 0.49 Update:** The latest Cursor update ([Changelog 0.49](https://www.cursor.com/changelog/0-49)) brings several enhancements: improved Cursor Rules generation and attachment, better chat history access, built-in diff reviews, image support in MCP context, agent command editing/skipping, global ignore files, added models (Gemini 2.5 Pro, Grok 3, GPT-4.1), and project structure context for the agent. [AGIdevice](https://x.com/agidevice/status/1913575514670776520) shows how to enable the beta features.

## Tooling & Protocol Developments

* **MCP Nuances:** David Cramer cautioned against simply [proxying OpenAPI specs via MCP](https://x.com/zeeg/status/1913289979368796671), arguing it misuses the protocol and ignores LLM limitations in tool selection and JSON handling.
* **MCP Transport Clarification:** Harald Kirschner clarified the newer "Streamable HTTP" transport ([MCP Spec](https://modelcontextprotocol.io/specification/2025-03-26/basic/transports)), noting it's primarily HTTP-based, with Server-Sent Events (SSE) as an optional upgrade for streaming server messages, deprecating the older standalone SSE transport. Discussions on implementation and future streaming enhancements are ongoing ([TS SDK Issue #220](https://github.com/modelcontextprotocol/typescript-sdk/issues/220), [MCP Roadmap](https://modelcontextprotocol.io/development/roadmap)).
* **Agent2Agent (A2A) Protocol Announced:** Google unveiled the Agent2Agent (A2A) protocol ([Google Developers Blog](https://developers.googleblog.com/en/a2a-a-new-era-of-agent-interoperability/)), an open standard aiming to allow AI agents from different frameworks and vendors to communicate and collaborate. This is a significant step towards agent interoperability. Akshay Pachaar also provided a clear explanation contrasting [MCP vs. A2A](https://x.com/akshay_pachaar/status/1912123161883345321). ([GitHub Repo](https://github.com/google/A2A))
* **New MCP Tools:** Ishan Goswami released an open-source [Twitter MCP Tool using Exa](https://x.com/TheIshanGoswami/status/1914226695118168331) (GitHub: [exa-labs/exa-mcp-server](https://github.com/exa-labs/exa-mcp-server)) for searching tweets/profiles without the official API. Eyal Toledano shipped [TaskmasterAI v0.12](https://x.com/EyalToledano/status/1914084622918455572) with multi-PRD parsing, smarter breakdowns, and easier MCP setup, alongside a new [website](http://task-master.dev).
* **Cursor Workflows & Tips:** Ryo Lu shared [12 tips for using Cursor effectively](https://x.com/ryolu_/status/1914384195138511142), emphasizing clear rules, specific prompts, and iterative testing. Prajwal Tomar detailed his ["Context Boundary Method"](https://x.com/PrajwalTomar_/status/1913961327711633601) using structured docs (PRD, Flow, Tech Stack etc.) to guide AI coding tools like Cursor and reduce errors. Andrew Jefferson noted potential issues with [Markdown tab completion](https://x.com/EastlondonDev/status/1913967886521053688) in Cursor.
* **AI UI Builders Compared:** Damien shared a comparison of [Replit, Bolt.new, Lovable.dev, and v0](https://x.com/damienghader/status/1913248328302608482) for generating a multi-step onboarding flow from the same prompt.

## Building AI Agents: Frameworks & Best Practices

* **Agent Framework Discussion:** Harrison Chase (LangChain) published a deep dive on [how to think about agent frameworks](https://blog.langchain.dev/how-to-think-about-agent-frameworks/), sparking discussion comparing approaches (agents vs. workflows, declarative vs. non-declarative) and frameworks like LangGraph, OpenAI's Agents SDK, CrewAI, and AutoGen. Key themes include balancing flexibility vs. predictability and choosing the right abstraction level. OpenAI also released its own [practical guide to building agents](https://cdn.openai.com/business-guides-and-resources/a-practical-guide-to-building-agents.pdf).
* **Production Agent Principles:** Dex Horthy shared principles for production-grade LLM apps in ["12-factor-Agents"](https://github.com/humanlayer/12-factor-agents), arguing successful systems often use focused LLM loops within well-engineered software rather than fully autonomous, monolithic agents. This resonated with many builders facing reliability challenges.
* **Agent Memory:** Taranjeet from [mem0.ai](https://x.com/taranjeetio/status/1912185506559053855) emphasized the importance of persistent memory (beyond session history) for truly intelligent agents that learn and adapt over time ([Blog Post](https://mem0.ai/blog/memory-in-agents-what-why-and-how/)).
* **Key Best Practices:** Common advice emerging from various discussions includes: start simple (maybe not even an agent), use structured outputs (like JSON schemas) to reduce hallucinations, prefer multiple specialized agents over one complex agent, own your control flow rather than relying solely on the framework, and carefully manage context windows. ([Anthropic Guide](https://www.anthropic.com/research/building-effective-agents), [Dev.to Summary](https://dev.to/isaachagoel/read-this-before-building-ai-agents-lessons-from-the-trenches-333i)).

___

**✨ Workshop Spotlight: Conquer the Complexity of Cursor ✨**

Master practical AI development workflows in Cursor. This hands-on workshop covers Agents, Ask, Custom Modes, multi-file analysis, effective prompting, Cursor rules, and strategies for handling AI failures.

* **When:** **This Thursday!** April 24, 2025, **9:00 AM - 2:00 PM (PDT)**
* **Where:** Zoom (Live Q&A included)
* **Investment:** $249

Unlock Cursor's potential and streamline your development process.

➡️ [**Read More about the Workshop**](https://egghead.io/workshop/cursor) ➡️ [**Register Now ($249)**](https://buy.stripe.com/9AQaG5ftF5P22eAfZ3)

_(Team training also available)_

___

## Notable Prompts

* **o3 Fact-Checking:** A prompt structure for using o3 to thoroughly fact-check an entire article by parsing individual facts. (See [Matt Shumer's Tweet](https://x.com/mattshumer_/status/1914412812891570199) for the prompt start).
* **Multiple Experts Method:** A system prompt that instructs the AI to analyze problems from multiple expert perspectives (e.g., Systems Thinker, Risk Assessor) and synthesize their insights. (See [God of Prompt's Tweet](https://x.com/godofprompt/status/1914370848032497906) for the full prompt and example).
* **Personality-Infused Prompt:** A detailed persona prompt instructing the AI to adopt a specific, informal, and slightly critical tone using modern slang and abbreviations. (See [Kitze's Tweet](https://x.com/thekitze/status/1913719205007937825) for the full prompt, originally from [@threepointone](https://x.com/threepointone)).

## Community Buzz & Resources

* **New Community Site:** [AGIdevice](https://x.com/agidevice/status/1913666033870647325) is building [cursorcommunity.com](http://cursorcommunity.com) in public.
* **Industry Chatter:** Deirdre Bosa discussed the potential [OpenAI bid for Windsurf](https://x.com/dee_bosa/status/1912906057791447294) and the shift towards the app layer. AI Native Dev reported on perceived [friction between Microsoft and Cursor](https://x.com/ainativedev/status/1912869556973949430) ([Article](http://ainativedev.co/spx)).
* **Android XR Demo:** Google showed off prototype smart glasses with Android XR ([Demo Video via 9to5Google](https://9to5google.com/2025/04/17/google-android-xr-smart-glasses-live-demo/)).
* **New Open Dialogue Model:** A new small open model, [Dia-1.6B](https://x.com/altryne/status/1914421817139454341), was released. It's focused on dialogue disentanglement – figuring out who's talking to whom in complex conversations. ([GitHub](https://github.com/nari-labs/dia), [Hugging Face](https://huggingface.co/nari-labs/Dia-1.6B)). I was extremely impressed with the demos – definitely worth checking out.
* **Video Generation with Veo 2:** Google's Veo 2 video generation model is now available in Gemini Advanced and Google AI Studio ([Google Blog](https://blog.google/products/gemini/video-generation/), [Developers Blog](https://developers.googleblog.com/en/veo-2-video-generation-now-generally-available/)). I've tried out a few videos and was really impressed with the quality. Pro tip: In AI Studio, you can use an image as a reference when generating video ([API Docs](https://ai.google.dev/gemini-api/docs/video#generate_from_images)), though surprisingly, Gemini doesn't support that yet. It's heavily rate-limited at the moment, but still fun to play with for generating short 8-second clips.

___

That’s the scoop for this issue! The pace isn't slowing down, particularly around agent capabilities, specialized models, and the tooling ecosystem supporting them.

If you have any feedback or questions, hit reply! Always happy to chat about the latest in AI dev tools.

John Lindquist


AI Dev Essentials #3: Gemini Flash, o4-mini, Codex CLI, A2A Protocol & Agent Updates

AI Dev Essentials covers Gemini 2.5, O4-mini, Claude’s research tools, Codex CLI, and new workflows for building AI agents with MCP and Cursor.

Establish an automated, AI-driven workflow for implementing UI designs by comparing live development versions against target screenshots. This lesson integrates the Playwright MCP server for browser automation, a custom script using Pixelmatch for visual diffing, and Cursor Rules for persistent context. See how to instruct the Cursor Agent to iteratively implement design changes based on visual diff feedback, creating a robust loop where the AI refines the UI towards the target design.

**Workflow demonstrated in this lesson:**

*   Configure the Playwright MCP server within Cursor for browser control.
*   Use the Agent and Playwright MCP tools (`browser_navigate`, `browser_resize`, `browser_take_screenshot`) to capture baseline and current state PNG screenshots.
*   Install `pixelmatch` and `pngjs` dependencies.
*   Guide the Agent to create and run a TypeScript script (`compare-images.ts`) to generate visual diff images between screenshots.
*   Establish Cursor Rules (`visual-testing.mdc`, `project-setup.mdc`, etc.) to provide the Agent with necessary context about directories, scripts, dependencies, and the overall workflow.
*   Initiate an AI-driven design implementation loop:
    *   Agent captures the current state via Playwright.
    *   Agent runs the comparison script to get a visual diff.
    *   Agent analyzes the diff and implements code changes in the project (e.g., `app/page.tsx`, components, `globals.css`).
    *   Repeat the capture-compare-implement cycle, using the latest diff as feedback.
*   Use context files and prompts like "Please continue" to manage the multi-step process and guide the AI through potential interruptions.

**Key benefits:**

*   Creates an iterative, AI-driven workflow for implementing designs based on visual comparisons.
*   Combines browser automation (Playwright) and pixel-level diffing (Pixelmatch) within the Cursor environment.
*   Utilizes Cursor Rules to effectively manage context and guide the AI through complex, multi-step tasks.
*   Demonstrates how visual diffs can serve as direct feedback for AI-driven UI code generation and refinement.

AI-Driven Design Workflow: Playwright MCP Screenshots, Visual Diffs, and Cursor Rules

[Scraped MCP Examples](https://gist.github.com/johnlindquist/b19673bbf6a396406dff869beecace1d)
[ghx tool](https://www.npmjs.com/package/@johnlindquist/ghx)

Building complex components like Model Context Protocol (MCP) servers from scratch can be challenging, especially without good reference implementations. This lesson demonstrates a powerful workflow combining the `ghx` CLI tool with AI assistants (like Google's AI Studio and Cursor's agent) to overcome this hurdle.

You'll learn how to:

1.  **Use `ghx`:** Leverage the `ghx` command-line tool (which wraps the GitHub CLI) to search GitHub for real-world code examples based on specific import paths (e.g., `modelcontextprotocol/sdk/server/mcp.js`).
2.  **Refine Search Results:** Use `ghx` options (`--help`, `--limit`, `--max-filename`, `NOT demo`) within Cursor's chat to filter results, exclude simple demos, and gather a rich set of relevant MCP server implementations.
3.  **Provide AI Context:** Copy the aggregated code examples found by `ghx` into an AI environment like Google AI Studio (or ChatGPT).
4.  **Handle Context Limits:** See techniques for trimming large context files (like using Cursor's `Cmd+K` inline edit with `head -n` commands) to fit within model limits while retaining valuable examples.
5.  **Generate Code:** Prompt the AI (Gemini 2.5 Pro in AI Studio or ChatGPT O3) to generate a *new* MCP server (e.g., with five distinct tools) based *solely* on the provided real-world examples.
6.  **Integrate and Debug:** Set up the generated TypeScript code (`mcp_server.ts`) locally in Cursor, install dependencies (`pnpm install`), and use Cursor's agent (`Cmd+I`) or inline fixes (`Cmd+K`) to resolve TypeScript errors and path issues.
7.  **Configure in Cursor:** Add the newly created MCP server (running via `bun mcp_server.ts`) to Cursor's MCP settings, using the absolute path and the `bun` command.
8.  **Test the MCP Server:** Interact with Cursor's chat agent to call the custom tools (e.g., `add_note`, `list_notes`, `write_managed_file`) defined in the AI-generated MCP server, verifying its functionality.

This lesson showcases how combining targeted code searching (`ghx`) with the generative power of AI, fueled by relevant examples, can dramatically accelerate the process of building and bootstrapping complex tools and applications.

Generate and Debug MCP Servers from Scratch

Hey Everyone 👋,

John Lindquist here with the second issue of AI Dev Essentials! It's been another busy week in the AI tooling world. The biggest splash was probably GPT-4.1 hitting the streets, but there's plenty more action around MCP, Vercel's v0, Google's developer tools, and interesting community discussions. I've spent some time with GPT-4.1 already (specifically in Cursor), and while it shows promise, it didn't quite convince me to switch from Gemini 2.5 Pro yet. It stumbled on a few basic requests where Gemini usually shines on the first attempt.

---

### New egghead.io Lesson This Week:

* **[Create a Custom MCP that Calls Cursor Tools](https://egghead.io/create-a-custom-mcp-that-calls-cursor-tools~r77sw):** Learn how to build custom MCP server tools that allow Cursor's AI Agent to access your local project context. This lesson shows how to use Zod schemas and the crucial `.describe()` method to explicitly request information (like Git status or version) from the Cursor environment *before* your tool runs, enabling powerful, context-aware automation.

---

Here are the highlights that caught my eye this past week.

## Model Mania: GPT-4.1 Arrives, Plus Grok & Llama Updates

* **GPT-4.1 Lands:** OpenAI announced GPT-4.1, and it quickly became available via the [API](https://openai.com/live/) and showed up in [Cursor](https://x.com/cursor_ai/status/1911835651810738406). Cursor is offering it free for now to let users get a feel for it, though they don't recommend it as a default just yet. Curious to hear initial impressions from folks testing it out for coding tasks.

* **Grok 3 in Cursor:** Following other model additions, [Cursor also enabled Grok 3 models](https://x.com/amanrsanger/status/1910434915419906141), with the mini version available for free users. Aman Sanger noted it seems to "think upfront more than other models."

* **Community Hack: Llama 4.0 via Groq in Cursor:** Levi Dan shared a [detailed (and slightly hacky) guide](https://x.com/lxvi_brg/status/1908865544721154455) on how to wire up Groq's fast Llama 4.0 API into Cursor by using the OpenAI API key slot and intercepting requests. A clever workaround for the adventurous!

## Tooling & Platform Pulse

Lots of movement across different platforms and tools this week:

* **Vercel Ecosystem:**

  * Guillermo Rauch teased [v0 coming to VS Code](https://x.com/rauchg/status/1911524154458984515), hinting at tighter integration for the UI generation tool.

  * v0 also [added AI integrations](https://x.com/v0/status/1911912183149256943) with models from xai (Grok), FAL, Groq, and DeepInfra, including automatic API key setup and pay-as-you-go billing via Vercel Marketplace. They're offering a $10 credit for the Grok free tier.

  * Vercel shared insights on the [grep.app migration from CRA to Next.js](http://grep.app/), showcasing performance improvements. ([Source](https://x.com/rauchg/status/1911920410020757881)) I'm personally excited about this one – I rely heavily on examples when working with AI, and for me, a good example is often worth ten prompts. Tools like `grep.app` that make code discovery easier are invaluable.

* **Google's Growing Stack:**

  * Project IDX has officially become [Firebase Studio](https://x.com/_davideast/status/1909990728593453455), positioning itself as a cloud-based, agentic dev environment for full-stack AI apps, deeply integrated with Firebase. I've tried spinning up a few sites with it, and the speed is impressive. However, for more complex tasks like designing from images or intricate interactions, I still find myself leaning towards v0 for now.

  * Google quietly released the [Agent Development Kit (ADK)](https://x.com/omarsar0/status/1910004370864742757), a Python library (`pip install google-adk`) for building multi-agent systems with features like state management, tool usage, and orchestration. I haven't had a chance to dive into this yet, but it's right at the top of my list. Building robust agent systems is a huge area of interest, so this is probably the announcement I'm most excited about exploring this week.

  * Ramón Medrano Llamas shared his experience [moving back to VS Code from Cursor](https://x.com/rmedranollamas/status/1909516749612999149), highlighting the effectiveness of GitHub Copilot combined with the Gemini Code Assist extension (using Gemini 2.5 Pro in Agent Mode). I gave the Gemini Code Assist extension a try myself, but as someone deeply embedded in the Cursor workflow with established keyboard shortcuts and patterns, I struggled to find a flow I liked and didn't stick with it for long. It's interesting to see different perspectives, though!

  * AI features are rolling out more broadly in [Chrome](https://x.com/iam_chonchol/status/1909942954686636224), including text grabbing from YouTube videos and Google Lens integration.

* **Cloudflare Corner:**

  * Big news for edge compute: [Cloudflare Containers are coming to Open Beta in June](https://x.com/CherryJimbo/status/1910682562499850405), enabling running containerized apps (Next.js, Go, PHP confirmed) directly on the edge. This is huge news, in my opinion. Think about all the WordPress sites and other applications that rely on containerization. Opening this up on the edge unlocks incredible possibilities, including for AI applications. I'm super hyped to see what people build with this come June.

  * A new tool, [MCPify.ai](http://mcpify.ai/), launched for building MCP servers conversationally, built entirely on Cloudflare Workers and Durable Objects. ([Source](https://x.com/pbteja1998/status/1911723180445610149))

  * Securing MCP servers is getting easier, with examples shared using [WorkOS AuthKit](https://x.com/WorkOS/status/1910722639036874943) and [Auth0](https://x.com/dschenkelman/status/1910693265566728319).

* **IDE Updates:**

  * JetBrains shared the public [WebStorm roadmap for March-April](https://youtrack.jetbrains.com/issue/WEB-72310). ([Source](https://x.com/WebStormIDE/status/1909291367504748937))

> **✨ Workshop Spotlight: Conquer the Complexity of Cursor (Europe Friendly Time!) ✨**
>
> Missed the last workshop or found the timing tricky? This one's for you! Master practical AI development workflows in Cursor in a session scheduled for **12:00 PM UTC** to better accommodate European attendees.
>
> This hands-on workshop covers Agents, Ask, Custom Modes, multi-file analysis, effective prompting, Cursor rules, and strategies for handling AI failures. Unlock Cursor's potential and streamline your development process.
>
> * **When:** \[Check website for upcoming dates - This session is Europe-friendly at 12:00 PM UTC\]
>
> * **Where:** Zoom (Live Q&A included)
>
> * **Investment:** $149
>
> ➡️ [**Read More about the Workshop**](https://egghead.io/workshop/cursor)
> ➡️ [**Register Now ($149)**](https://buy.stripe.com/7sI7tT81dfpC06s28c)
>
> *(Team training also available)*

## Community Buzz & Resources

* **MCP Explained:** Kent C. Dodds wrote a piece explaining [why he's excited about Model Context Protocol (MCP)](https://www.epicai.pro/the-game-changing-potential-of-model-context-protocol) and its potential. ([Source](https://x.com/kentcdodds/status/1911840456432034112))

* **Prompting Guide:** Ian Nuttall surfaced [v7 of Google's Prompt Engineering Guide](https://x.com/iannuttall/status/1911086164196606314), a deep dive into techniques and best practices. Definitely worth a look.

* **Discussion Starter:** Eric Zakariasson asked the community about their [favorite CLIs and MCP servers to use with the Cursor agent](https://x.com/ericzakariasson/status/1910808935961088346). What are you all using?

* **Gemini 2.5 Pro Praise:** The positive buzz around Gemini 2.5 Pro continues to grow in the community. It's getting rave reviews and even achieving 'S-tier' status on social platforms like Reddit for its coding capabilities. ([Source](https://x.com/suchenzang/status/1911251230317490416))

That’s a wrap for this week! There's always so much happening, it's hard to keep up, but hopefully, this helps surface some of the more interesting bits for developers working with AI.

If you have any feedback or questions, feel free to respond to this email. Always happy to talk shop about the latest AI tools.

John Lindquist

[egghead.io](https://egghead.io)

AI Dev Essentials #2: GPT-4.1 Arrives, MCP Deep Dive & Google's Agent Dev Kit

AI Dev Essentials #2: GPT-4.1 & Gemini updates, MCP tools, Vercel v0, Google ADK, Cloudflare Containers news. Latest AI dev tooling highlights.