Learn Angularjs, Gulp and Ramda

A library designed specifically for a functional programming style, one that makes it easy to create functional pipelines, one that never mutates user data.

ramda

In this lesson we'll see how to use the `without` function to remove items in a known list from an array. Then we'll generate our exclusion list and apply it to `without` using `reduce`, `min`, `max` and `converge` to detect the highest and lowest values in an array and creating a new array that excludes those values.

Discard the High and Low Values From an Array of Numbers with Ramda

Learn how you can use ramda.js to bring functional programming concepts into your JavaScript code. Ramda offers composability and immutability right out of the box, so you can leave your imperative code behind and build cleaner, more maintainable code.

Functional Programming in JavaScript with Ramda.js

Angular is a development platform for creating applications using modern web standards. Angular includes a wealth of essential features such as mobile gestures, animations, filtering, routing, data binding, security, internationalization, and beautiful UI components. It's extremely modular, lightweight, and easy to learn.

angular

When migrating AngularJS (v1.x) applications to Angular you [have different options](https://github.com/angular/ngMigration-Forum/wiki/Migration-Paths-Overview). Using Angular Elements is one of them. In this lesson we learn how to integrate an Angular Element into an AngularJS (v1.x) application. We will leverage some features release in the latest AngularJS 1.7.3 that make it fully compatible with custom elements.

Use Angular components in AngularJS applications with Angular Elements

AngularJS is an open-source JavaScript framework, maintained by Google, that assists with running single-page applications. Its goal is to augment web-based applications with model–view–controller (MVC) capability, in an effort to make both development and testing easier.

angularjs

Many times while building an AngularJS application you'll need to manipulate data, and it doesn't make sense to do it with a Controller method. Luckily you have Filters, which are particularly well suited for manipulating text within your HTML views. This handy tool uses a simple syntax to create highly reusable functionality for your apps.

Filters

Most.js is a toolkit for reactive programming. It helps you compose asynchronous operations on streams of values and events, e.g. WebSocket messages, DOM events, etc, and on time-varying values, e.g. the "current value" of an <input>, without many of the hazards of side effects and mutable shared state.

most

Unlike RxJS, Most does not include an observable-based AJAX helper utility. However, it can be very convenient to have one, because it allows you to use streams to do things like declaratively handling errors & setting up cancellation. In this lesson, we use currying & functional composition to create an Observable-based wrapper around the fetch API with Ramda & Most.

Create an Observable-Based Fetch Wrapper Using Ramda & Most

React is one of the web’s most popular frameworks for building JavaScript applications. 

If you know what you’re doing, React can drastically simplify how you build, use, and maintain code.

Whether you’re a React newbie or you’re ready for advanced techniques, you can level-up with egghead. Explore our in-depth courses, lessons, and community resources to build more powerful applications and crack open your career.

react

In this lesson we'll take a stateful React component and look at how we can refactor our `setState` calls to use an updater function and then leverage Ramda's `evolve` function to make our updater function a reusable utility that isn't tied to the React API.

Update State in React with Ramda's Evolve

In this lesson, we'll refactor a React component to use Ramda lenses to update our component state. We'll create a lens to focus on the property we want to target and use `over` to apply the existing state value to a utility function and we'll get back a new state that will be reflected in our rendered UI.

Update Component State in React With Ramda Lenses

When doing comparisons inside of functions, you end of relying heavily on the argument passed into the function. Ramda's `converge` allows you to do comparisons in a Point-Free style allowing you more flexibility with composing and constructing functions. This lesson walks through refactoring a function to Point-Free style using Ramda's Converge.

Eliminate Function Arguments (Point-Free Style) with Ramda's Converge

You can really unlock the power of ramda (and functional programming in general) when you combine functions. Counting words in a string may seem like a relatively difficult task, but ramda makes it easy by providing a `countBy` function.  This lesson walks through using the `countBy` to count words in a string.

Count Words in a String with Ramda's countBy and invert

Manually creating action items every time we need to dispatch an action eventually accumulates to a very tedious process. In this lesson, we will learn how to abstract out the creation of action items so that we consolidate their creation into a single place. This not only creates efficiencies in how we express code but also reduces code volume while simultaneously increasing stability. 

Angular 1.x Redux: Create Reusable Actions with Action Creators

In this lesson, we are going to learn how to enable our store to work with more than one reducer in an application.

Currently, we have set up our store to work with the categories reducer exclusively, but this leaves us with a real problem, what if we want to keep track of the currently selected category within our store so that we can access that data somewhere else in the app? It is safe to say that most applications consist of many data models interacting together, and we need a way to accommodate this within the single state tree in our application.

When we initialize our app store, wouldn't it be convenient if we could take all of our reducers and combine them into one big reducer that our store knows how to work with? Well, this is exactly what the combine reducer method does in Redux. It allows us to combine reducers together and then access them as a named property on our application store.

We currently have a category reducer just hanging out here doing nothing because we have been focusing entirely on the categories reducer. We are going to combine the two reducers so that we can use the current category data within our bookmarks component.

Angular 1.x Redux: Handle Multiple Models by Combining Reducers

Invariably the question that comes up when talking about Redux is how does one handle asynchronous operations in redux. For instance, how do we hand off an operation to redux that requires a remote call to the server? Where exactly does the async part get handled? 

Redux has this concept of middleware that allows us to insert custom logic in the space between dispatching an action, and the moment it reaches the reducer. In this lesson, we are going to set up a few async operations and then use redux thunk middleware to make sure that everything makes it into the reducer properly.

The main key to thunk middleware is that it allows us to return a function instead of an action. This function can encapsulate the async operation and dispatches the appropriate action when the operation is completed.

Angular 1.x Redux: Handle Asynchronous Operations with Middleware

One of the primary advantages of using redux is that reducers are incredibly easy to test. By nature, they have a clearly defined contract of inputs and outputs, and because they are stateless, they require very little instrumentation to test in isolation. 

We will start with a few simple tests for the categories reducer and then move on to testing the CRUD functionality within the bookmarks reducer. 

Angular 1.x Redux: Test Reducers

Up to this point, we have created an effective, yet rudimentary, implementation of Redux by manually creating an application store, reducers and action creators. We have already made significant progress in simplifying state management within our application, but we have in fact only touched the surface of what Redux has to offer.

In this lesson, we are going to introduce ngRedux into our application and let it handle the rest of the heavy lifting as we work through this series. ngRedux is a project that provides angular bindings to redux to streamline integrating it into our application.

Angular 1.x Redux: Integrate ngRedux

In this lesson, we are going to learn how to integrate Redux Devtools into our Angular application.

Redux Devtools is a live-editing time travel environment for Redux. Devtools boasts a list of awesome features but my two favorite ones are that we can inspect every state and action as it happens and we can go back in time by “cancelling” actions.

This is going to be an interesting lesson because, in order for this to work, we are going to need to make something that was written for React work in Angular. For the most part, everything will play side by side with one small trick that we will pull off at the end to force an Angular digest cycle when React manipulates the application store. 

Angular 1.x Redux: Integrate Redux Devtools

One of the biggest mental shifts that developers have to take when embracing redux is shifting from mutable operations to immutable operations. The problem with mutable operations is that they create side effects that involve anything else that is referencing the thing that is being mutated.

With immutable operations within a reducer, a new object is returned for every operation – completely bypassing the possibility that an existing reference is going to be compromised. It is then up to the consumer to decide how they want to get the new data and what to do with it.

In this lesson, we are going to flesh out the CRUD functionality of our bookmarks reducer, doing it first using mutable operations and then refactoring it to use immutable operations.

Angular 1.x Redux: Avoid State Mutation with Immutable Operations

In this lesson, we are going are going to apply everything that we have learned by building out our bookmarks component to be powered by Redux. This will help reinforce all the techniques we have covered by seeing them built out  in a single, contiguous lesson.

We will walk through the steps necessary to phase out the bookmarks model with a Redux implementation that includes building and integrating the equivalent reducers and action creators to integrate into our application.

Angular 1.x Redux: Put It All Together

In this lesson, we are going to learn how to map our Angular component directly to our application store using the connect method on ngRedux. 

In Angular, it is a common technique to bind a controller property directly to the model so that our controllers remain lightweight and operate more as a pass through mechanism than anything else.

Using the connect method, we can accomplish the same effect by not only binding properties directly to the application store, but also binding our controller to our action creators. This allows us to drastically simplify our controllers as we are able to delete a bunch of local controller methods as they become unnecessary because of our mappings.

Angular 1.x Redux: Map State and Dispatchers to Redux

In this lesson, we are going to extend our application store with a subscribe method so that we do not have to manually query our application store every time we dispatch an action.

In our categories controller, we are dispatching a **GET_CATEGORIES** action every time we want to update the categories collection within our application store. For this change to be propagated back into our controller, we need to call **store.getState**. 

Depending on how many times we are calling **store.dispatch**, this could get very cumbersome. We will extend our application by adding a subscribe method so that state changes are automatically pushed into our controller.

Angular 1.x Redux: Subscribe to the Application Store

In this lesson, we are are going to learn how to abstract out state mutations into a reducer. In our current Eggly application, all of the state and business logic for our categories is handled within the categories model. We are going to refactor our code to entirely eliminate the need for our categories model by building out a couple of reducers and seeing how we can interact with them directly. 
The key thing to notice as we build out our reducers is that a reducer itself is completely stateless because it receives current state every single time it needs to calculate and return the new state. With a clearly defined contract of inputs and outputs, this makes testing reducers really easy which is one of the huge benefits of using this approach. 

Angular 1.x Redux: Handle State Mutations with Reducers

In this lesson, we are going to learn how to build an application store so that we can have a single place to store our application state. Reducers are great for updating state because they are stateless, but they are very bad at preserving state, which is vital when you need to reference state in more than one place.

We will build out a simple application store that will be responsible for keeping track of our categories collection that our categories controller can consume.

Angular 1.x Redux: Persist State with an Application Store

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)).

___

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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.

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_(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


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.

Typing repetitive or complex instructions for Cursor's AI agent can slow down your workflow. This lesson teaches you how to create custom slash commands (e.g., `/s term`, `/l`, `/c`) using Cursor's "Rules" feature. Learn how to map these simple shortcuts to specific AI actions, instructing the agent to use precise tools like `codebase_search`, `edit_file`, or `run_terminal_cmd` based on your command and the current conversation context.

**Workflow demonstrated in this lesson:**

*   List available AI tools (built-in and MCP) to get their exact names.
*   Navigate to Cursor Settings -> Rules.
*   Define custom rules under a heading (e.g., `## Slash Commands`).
*   Create rules mapping a slash command pattern (like `/s <term>`) to a detailed instruction specifying which tool to use and how (e.g., "Use the codebase_search tool to search for <term/>").
*   Define rules that leverage conversation context (e.g., for summarizing notes or creating commit messages).
*   Save the rules (potentially in `.cursor/rules/project.mdc` for team sharing).
*   Invoke the custom slash commands (e.g., `/s handle`, `/l`, `/c`) in the chat/agent window.
*   Observe the AI understanding the shortcut and executing the intended tool call.

**Key benefits:**

*   Creates efficient, reusable shortcuts for common AI interactions.
*   Reduces the need for typing lengthy, detailed prompts repeatedly.
*   Allows mapping simple commands to specific Cursor tool invocations.
*   Enables commands that leverage the ongoing conversation context.
*   Improves workflow speed and consistency when working with the AI agent.

Speed Up Your Agents with Cursor Slash Commands

Hey Everyone 👋,

John Lindquist here. Since I've been diving deep into AI tooling and workflows over the past year, I'm starting this weekly newsletter to share updates on what interests me and could be helpful to the community. The goal is to help everyone stay up-to-date with the latest in essentials tools for AI devs.

I'll focus on relevant community news, model evaluations for coding, notable tools like Cursor and Windsurf, and practical workflows (including tools I build myself 😇). This week kicks off Cloudflare Developer Week with major announcements around agent/MCP services that many of us will likely use soon (I'll dig in and share my thoughts next issue). There are also lots of hints about new coding models possibly coming to Cursor (though my preference remains Gemini 2.5). Personally, I'm hoping for a Gemini Flash 2.5 variant. So let's hop into the latest! For my detailed, up-to-date workflows, check out the workshop info below.

Recent egghead.io lessons about AI tooling:

* **[Swarm Intelligence: Massively Parallelize Your AI Agents in Cursor](https://egghead.io/swarm-intelligence-massively-parallelize-your-ai-agents-in-cursor~g7u2g):** A workflow using `file-forge`, AI scripting, and a custom `git worktree` tool (`wt`) to launch parallel Cursor agents on different branches.
* **[Build a Better Edit Tool in Cursor with Custom Agent Modes](https://egghead.io/build-a-better-edit-tool-in-cursor-with-custom-agent-modes~21rr0):** Create a focused 'edit mode' in Cursor for precise AI refactors without conversational overhead, overcoming Command-K limitations.
* **[Visually Clone Websites with Gemini 2.5 Canvas for Cursor](https://egghead.io/visually-clone-websites-with-gemini-2-5-canvas-for-cursor~s5ugk):** Use Gemini 2.5 Pro's Canvas feature within Cursor to analyze and replicate website structures.

---

## Cloudflare Developer Week: Agent & Platform Updates

Cloudflare announced several updates relevant to AI application development:

* **Durable Objects Free Tier:** Lowers the barrier for experimenting with stateful serverless and the Agents SDK. ([Rita Kozlov](https://x.com/ritakozlov_/status/1909231625855983939))
* **MCP Enhancements:** BYO Auth (incl. [Auth0](https://x.com/auth0/status/1909307220816228354)) and Hibernation for MCP servers aim to improve agent security and efficiency. Exercise caution with MCP, but these are steps toward production viability.
* **Workflows GA & autoRAG:** Orchestration tool is now GA; a new autoRAG product indicates more integrated AI solutions.
* **Outerbase Acquisition:** Expect Durable Objects usability improvements following this acquisition. ([Rita Kozlov](https://x.com/ritakozlov_/status/1909232149246492887))
* **OpenNext Adapter Beta:** v1.0-beta for deploying Next.js apps on Cloudflare Pages adds caching, PPR, middleware support. ([CloudflareDev](https://x.com/CloudflareDev/status/1909598450871652398))

**Summary:** Cloudflare is actively enhancing its platform for AI/agent development, particularly reducing friction with Durable Objects and MCP.

## Llama 4 & Nemotron-Ultra Released

Meta's Llama 4 models began rolling out, with initial reports of variable quality across implementations. Meta attributes this to implementation stabilization, denying training on test sets ([Ahmad Al-Dahle](https://x.com/Ahmad_Al_Dahle/status/1909302532306092107)). Nvidia subsequently released **Llama-Nemotron-Ultra** (253B parameters), derived from Llama-405B via pruning and reasoning-focused post-training. Open weights and data are available ([Oleksii Kuchaiev](https://x.com/kuchaev/status/1909444566379573646)).

**Summary:** More powerful open models are available. Llama 4's initial rollout highlights the importance of stable implementations. Nemotron-Ultra adds a high-end specialized option.

---

## Quasar Alpha: Stealth Model on OpenRouter

OpenRouter provides early access to "Quasar Alpha," a model with limited public details but notable claims: 1M token context, benchmarks reportedly surpassing Claude 3.7 Sonnet, and >4x faster speeds. Reports suggest strong coding performance comparable to O3 Mini/DeepSeek R1 but significantly faster ([Matthew Berman's Thread](https://x.com/TheMattBerman/status/1908286423976886371)). The originating lab remains anonymous; speculation includes GPT-5, Claude 4, or Gemini 3.

**Summary:** The combination of large context, speed, and reported coding strength warrants attention for agentic workflows and large codebase analysis, despite benchmark caveats. Worth testing via OpenRouter if accessible.

---

**✨ 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 10, 2025, 9:00 AM - 2:00 PM (PDT)
* **Where:** Zoom (Live Q&A included)
* **Investment:** $149

Unlock Cursor's potential and streamline your development process.

➡️ [**Read More about the Workshop**](https://egghead.io/workshop/cursor)
➡️ [**Register Now ($149)**](https://buy.stripe.com/dR67tT4P17Xa8CYfZ1)

*(Team training also available)*

---

## Quick Links

* **Nightwhisper Buzz:** Highly-rated coding model "Nightwhisper" (rumored Gemini 2.5 variant?) left LMSys Arena; release may be imminent. ([ʟᴇɢɪᴛ Tweet](https://x.com/legit_api/status/1908264443827351913), [AiBattle Tweet](https://x.com/AiBattle_/status/1907519430961025476))
* **AI SDK Speech-to-Text:** Vercel AI SDK added a `transcribe` function. ([AI SDK Tweet](https://x.com/aisdk/status/1909603418639155460))
* **Gemini 2.5 Pro Updates:** Supports YouTube URLs as input in AI Studio (transcribe, summarize, analyze); reported improvements reducing stop issues/displaying thinking tokens. ([Jean Gatt Tweet](https://x.com/jean__gatt/status/1909256747518226829), [Eric Zakariasson Tweet](https://x.com/ericzakariasson/status/1908619515023548880))
* **Baby Cursor Project:** Cursor's Ryo Lu is building a rapid prototyping environment for IDE ideas. ([Ryo Lu Tweet](https://x.com/ryolu_/status/1908328050154233863))
* **(Rumor) Qwen3 Soon?:** Hints suggest Alibaba's Qwen3 model release is near. ([Elaina Tweet](https://x.com/Elaina43114880/status/1909572798944841859))

---

If you have any feedback or any 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 #1: Cloudflare agent updates, Llama 4/Nemotron/Quasar models, Cursor workshop

Cursor's AI Agent has the abilitly to invoke MCP tools, but they lack direct access to your project. This lesson shows how to build custom MCP (Model Context Protocol) server tools that bridge this gap. Learn to define tool parameters using Zod schemas and the crucial `.describe()` method to explicitly request necessary context from the Cursor environment *before* your tool runs. See how the AI gathers this information (like Git status or version via terminal commands, or web search results) and passes it to your tool, enabling powerful, context-aware automation.

**Workflow demonstrated in this lesson:**

*   Scaffold a basic MCP server using a Cloudflare template (`pnpm create cloudflare`).
*   Run the MCP server locally and configure it in Cursor settings.
*   Define MCP tools (e.g., `status`) within the server code (`index.ts`).
*   Specify required context for tools using Zod schema parameters and `.describe()` (e.g., requesting "The status of the git repository").
*   Interact with the Cursor Agent using natural language (e.g., "Please check the status").
*   Observe the Agent's thought process: identifying the need for specific context based on the `.describe()` text.
*   See the Agent automatically using its *own* tools (like `run_terminal_cmd` or `web_search`) to gather the requested context *before* calling your MCP tool.
*   Watch the Agent call your MCP tool, passing the gathered local context (Git status, version, etc.) as arguments.
*   Receive the final response from the Agent, incorporating the information provided by your local MCP tool.

**Key benefits:**

*   Allows Cursor's AI to access and reason about information from your local machine (Git status, versions, file contents, etc.).
*   Enables the creation of AI tools that directly interact with or report on your local development environment.
*   Demonstrates the power of using `.describe()` in Zod schemas to guide the AI in gathering necessary context automatically.
*   Unlocks advanced automation possibilities by combining AI reasoning with local machine access.

Create a Custom MCP that Calls Cursor Tools

Vimium's Vomnibar is a "command palette"-like search tool for your open tabs, browser history, and bookmarks. We can search through and jump to any tab open across browser windows. We can also do a generalized search with results that also include browser history and bookmarks which can either be opened in the current tab or a new tab. If you're anything like me, you open tons of tabs and visit hundreds of webpages over the course of a week. The vomnibar puts all of that at your fingertips.

Josh Branchaud

Open Tabs, Browser History, and Bookmarks with the Vomnibar

Vimium's Vomnibar is a "command palette"-like search tool for your open tabs, browser history, and bookmarks.

Another aspect of Vimium's configurability is customizable key mappings. We can add new mappings and override existing ones. If there is a certain Vimium feature you want to make easier to trigger, adjust the key mapping to suit your needs.

Configure Custom Vimium Key Mappings

Another aspect of Vimium's configurability is customizable key mappings. We can add new mappings and override existing ones.