Learn Alpine, D3 and Egghead

Mock Service Worker is an API mocking library that allows you to write client-agnostic mocks and reuse them across any frameworks, tools, and environments.



Mocking WebSocket APIs with Mock Service Worker

**Note:** This course is being actively developed and more lessons will be added in the future.

Cursor is an AI-enhnaced code editor that allows you to call upon the robots to refactor or stub out functions for you with natural language. 

In this course, John will show you workflows that will save you time and effort by utilizing the features such as its mult-line editing and context.

In particular, by giving Cursor's AI the proper context, whether that's lines of code or multiple files, you'll get better output when trying to extend or refactor logic that's present.


Bite-size video tutorials for badass web developers.

egghead

Productive Developer Workflows with AI Enhanced Cursor IDE

D3.js is a JavaScript library for manipulating documents based on data. D3 helps you bring data to life using HTML, SVG and CSS. D3’s emphasis on web standards gives you the full capabilities of modern browsers without tying yourself to a proprietary framework, combining powerful visualization components and a data-driven approach to DOM manipulation.

In this lesson, we'll clean up some of our placeholder elements. We'll then look at D3's ordinal scale to create a color scale that maps our regions to colors. We'll apply the colors based on region to our data points via the SVG `g`(group) element to apply the color to both our bars and their corresponding inline labels.

Create a Color Scale with D3's scaleOrdinal

Looking at our bars against each other with the x-axis as a guide gives us some relative information, but doesn't deliver the most precise values. In this lesson, we'll add additional labels to show the exact values of each data point in the context of the individual bars.

Clarify Data in D3 with Additional Labels and Rounded Domain Values

In this lesson, we'll see how we can add labels down the y-axis using our band scale. We'll see how to render the labels in React and how we can precisely position the labels based on values calculated by D3 and our scales.

Render a Y Axis for a Bar Chart with D3 and React

In this lesson, we'll take advantage of our `xScale` and the bottom margin to create an axis to start giving our data meaning. We'll access `ticks` from the scale and then use React to render and position the SVG `rect` and `text` elements to create the x-axis for our chart.

Render an X Axis for a Bar Chart with D3 and React

When applying scales to data elements in D3, it's common to combine the scale with an accessor function in order to apply the scale to the appropriate key on a data object. In this lesson, we'll create scaled accessors to refactor and tighten up some existing code.

Create Scaled Accessor Functions from D3 Scales

A band scale allows us to map a discrete set of values to a continuous range. In this lesson, we'll use a band scale in D3 to take the distinct "region" keys in our sample data and map those to evenly spaced and sized elements on our y-axis. We'll use this band scale to show a bar in the bar chart for each region, setting their size and position based on this scale.

Layout SVG Elements on a Y Axis with a D3 Band Scale in React

Drawing data accurately requires a consistent and precise mechanism for mapping data values to pixels on the screen. We can accomplish this mapping in D3 using scales. There are various scales available in D3, depending on your needs. In this lesson we'll using a linear scale to map a data domain of continuous values to continuous range of pixel values. We'll render a new SVG element, setting its width from our scale.

Set an SVG Element's Width Based on a D3 Linear Scale in React

When you want to add data visualizations to your web app, D3 is pretty much the accepted standard.

In this course, we'll start from scratch and build out a bar chart for some sample data using D3's utilities to wrangle the data and then we'll handle the rendering with React.

We'll start by laying out the SVG element that will hold everything, then we'll move on to creating scales that will give us the information we need to render our data as well as axes to give our data some meaning.

Along the way, we'll cover accessor functions, linear scales, band scales and finally, we'll use an ordinal scale to map data objects to colors.

This course certainly won't cover all of D3, but we'll cover enough of the fundamentals to give you a solid jumping off point for future D3 and React data visualization work.

Introduction to Create a Bar Chart with React and D3

A keyboard is the gateway to code. Your hands are the tools that manage the keyboard. Many people develop pain in their hands from repetitive stress injuries that can seriously damage their ability to code. This talk is about making your keyboard experience as gentle as possible on your hands so your hands are happier and you work more effeciently.

Save Your Hands and Save Your Time: Rethinking How to Use a Keyboard

While editing, you can make mistakes that you don't realize right away. One of these is cropping off part of a word that you didn't mean to.

If you don't notice right away, you could be convinced that you needed to undo all of your edits up to that point but that's not the case! When you ripple delete in ScreenFlow, it saves the portion of the clip that you deleted. All you have to do to recover that clip is to select the clip and drag it. Then you can make your desired edits again

Recover a Cropped Section of Video in Screenflow

Speeding up a clip can be useful for moving quickly past parts of your video that are not critical such as periods of typing or installing dependencies. In this lesson you will learn multiple ways to adjust the speed and duration of a clip.



Adjust play speed in ScreenFlow

Use annotations to highlight specific areas of your screen. In this lesson you will learn how to draw annotations with ScreenFlow. We'll choose a shape and color, and then fade it in and out using a video action with easing.

Add Annotations with ScreenFlow

Zooming in to an important part of your screen can eliminate distractions and focus the viewer's attention on what's important at the time. In this lesson you will learn how to create a zoom in and out animation with easing in ScreenFlow.

Zoom in and out with ScreenFlow

Alpine.js offers you the reactive and declarative nature of big frameworks like Vue or React at a much lower cost.

You get to keep your DOM, and sprinkle in behavior as you see fit.

alpine

Welcome to this beginner level series on Alpine JS! 👋🏞

Alpine JS is a rugged, minimal framework for composing JavaScript behaviour in your markup. It brings declarative, reactive, data-driven nature of libraries like React or Vue to your HTML templates.

This is the series intro video. Let's get started!

Series Intro - Beginner Level Alpine JS

In this lesson, we build a little app that fetches dog photos from the dog.ceo API, based on a "breed" search field. We want the API call to happen again when the search value changes, and to do so we use the `$watch` property from Alpine JS, which lets us define what state property we want to watch, and what callback function to run when a change happens.

We also use the `debounce` modifier on the `x-model` property to avoid firing an API call on each keystroke.

Refetch API data when a state value changes with the `$watch` property in Alpine JS

Welcome to this beginner level series on Alpine JS! 👋🏞

Alpine JS is a rugged, minimal framework for composing JavaScript behaviour in your markup. It brings declarative, reactive, data-driven nature of libraries like React or Vue to your HTML templates.

In this series, you will learn about Alpine's handy **directives**. You'll discover how to define a component scope with `x-data`, run code on mount with `x-init`, attach event listeners with `x-on`, display data with `x-text`, decide wether or not to show an element with `x-show`, iterate through data with `x-for`, retrieve a DOM element with `x-ref`, learn how to achieve _two-way data binding_ with `x-model`, and much more.

You'll also learn how **"modifiers"** can be applied and chained to directives to provide additional functionality: control the animation transition of entering and leaving DOM elements, debounce an event listener, etc.

We'll also touch on what Alpine calls **"magic properties"**, which give you access to native browser events (`$event`), reference to DOM elements (`$refs`), and more.

You'll gradually discover Alpine's API and syntax, from building simple tabs to a mini app that fetches a dog picture based on a "breed" search field. 🐶

**Get ready to breathe some fresh air** - it's time to walk into cool, green Alpine pastures! 🏞

Sprinkle declarative, reactive behaviour on your HTML with Alpine JS

In this lesson, we see how the `x-model` directive makes achieving "two-way data binding" effortless in Alpine JS.

We see what it would take to recreate the same functionality with an `x-bind` directive for the input value, as well as an `@input` event listener which, thanks to an access to the browser's native events via the `$event` magic property, sets the state value to `$event.target.value`.

Keep a DOM input and state value in sync with the `x-model` directive in Alpine JS

In this lesson, we learn how to retrieve a DOM element via the `x-ref` directive in Alpine JS, which gives us a reference to the element it is applied on. In our case, an input field.

We can then use the `$refs` object, and reach for the ref we need to target.

This allows us to trigger the focus state of the input field, with the `.focus()` method.

Focus an input field on button click with `x-ref` and the `$refs` property in Alpine JS

In this lesson, we learn how the `x-init` directive in Alpine JS lets us run a JavaScript expression once the component has initiated.

We see the nuances that allow us to trigger that code before Alpine performs initial DOM updates, or after these updates have been made.

Focus in input field on page load with `x-init` in Alpine JS

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.

John Lindquist

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.

In this quick walkthrough, you’ll learn how to drop Nx into an existing npm or pnpm workspace and immediately benefit from project graph visualization, caching, and smart task orchestration, all without rewriting your setup.

You’ll start with a standard React monorepo containing a Vite app, a UI package, and utility functions. Then, using a single command (`nx init`), you’ll initialize Nx and see how it auto-detects your existing scripts, builds a dependency graph, and enables advanced features like affected commands and local caching.

We're also going to explore optional enhancements using Nx plugins for Vite and React. You’ll see how they help Nx infer tasks directly from your Vite config files, improve DX with the Nx Console VS Code extension, and leverage Nx plugin generators to scaffold code with a consistent structure.

What you’ll learn
- How to initialize Nx in an existing npm or pnpm monorepo
- How Nx understands your workspace structure and scripts
- How local caching and affected builds speed up development
- How optional Nx plugins improve DX without adding bloat
- How to scaffold new packages

Using Nx on an existing React and NPM workspace based monorepo

Applying Nx to an existing React monorepo to enable caching, task orchestration, and project graph analysis without changing your current setup.