 Courses from John Lindquist

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)

John Lindquist

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.

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

Recreating web UIs manually can be time-consuming. This lesson demonstrates using Google Gemini's multimodal features to generate UI code directly from screenshots. See how to capture a webpage, provide the image to Gemini, and prompt it to generate a React/Next.js and Tailwind CSS representation. The lesson also covers refining the AI's output using annotated screenshots for corrections and integrating the generated code into a local development project, speeding up the initial UI scaffolding process.

**Workflow demonstrated in this lesson:**

*   Use browser dev tools to capture full-page screenshots of a target website.
*   Upload screenshots to Google Gemini and prompt it to generate UI code (React/Next.js/Tailwind).
*   Refine AI output using additional annotated screenshots for feedback and corrections.
*   Set up a basic Next.js project locally.
*   Use an AI-generated bash script to parse the generated code, split it into components, and integrate it into the local project structure.
*   Install necessary dependencies (like icon libraries) identified during the process.
*   Debug issues in the integrated code, potentially using in-editor AI assistance.
*   Run the local development server to view the AI-assisted UI clone.

**Key benefits:**

*   Reduces the time needed to create initial UI structures based on existing designs.
*   Utilizes AI's image understanding to translate visual layouts into code.
*   Shows a workflow for refinement using visual feedback (annotated screenshots).
*   Provides an example of integrating AI-generated code into a development project.

**Resources** 

[https://www.npmjs.com/package/@johnlindquist/file-forge](https://www.npmjs.com/package/@johnlindquist/file-forge)

**Want to Learn More? Sign-up for my Cursor Workshop!**

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

Visually Clone Websites with Gemini 2.5 Canvas for Cursor

Cursor's default inline edit (`Cmd+K`) has scope limitations, and using the full chat (`Cmd+I`) for simple refactors can be overly conversational. This lesson demonstrates how to create a custom chat mode specifically configured for direct code editing. By setting up a dedicated mode with custom instructions and a keybinding (like `Cmd+E`), you can bypass unnecessary AI chatter and apply targeted, file-aware modifications more efficiently than standard methods.

**Workflow Demonstrated in this Lesson:**

*   Create a "Custom Mode" within Cursor's chat interface.
*   Assign a name (e.g., "Edit") and a preferred AI model.
*   Set a dedicated keybinding (e.g., `Cmd+E`).
*   Configure allowed tools (enable `Edit`, disable `Run`/`MCP`).
*   Use "Advanced options" to enable `auto-run`.
*   Add "Custom instructions" to prompt the AI for direct edits without explanation.
*   Trigger the mode with the keybinding on a code selection to perform edits beyond the selection's scope (e.g., hoisting variables).

**Key Benefits:**

*   Perform refactors and edits beyond the scope of the initial selection, overcoming `Cmd+K` limitations.
*   Achieve a more direct, less "chatty" AI editing experience compared to standard chat (`Cmd+I`).
*   Streamline common AI-assisted modifications with a dedicated keybinding.
*   Gain fine-grained control over the AI's editing behavior using custom instructions.

**Want to Learn More? Sign-up for my Cursor Workshop!**

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

Build a Better Edit Tool in Cursor with Custom Agent Modes

Stop waiting for a single AI agent! This lesson demonstrates a powerful workflow to massively parallelize your AI-driven development in Cursor. Learn how to use tools like `file-forge` to package your project context, prompt advanced AI models (like Gemini in AI Studio) to brainstorm features and generate setup scripts, and then leverage a custom `git worktree` CLI tool (`wt`) to automatically launch an "army" of isolated Cursor instances, each tackling a different task on its own branch.

**Workflow Demonstrated / Key Benefits:**

*   **Bundle Project Context:** Use `file-forge` to create an AI-friendly representation of your codebase.
*   **AI-Powered Brainstorming & Scripting:** Prompt a reasoning model (e.g., Gemini via AI Studio) to suggest features *and* generate a shell script (`zsh`/`bash`) to automate setup.
*   **Automated Worktree Creation:** Execute the generated script which uses a custom `git worktree` CLI (`wt`) to:
    *   Create new branches and worktrees for each suggested feature.
    *   Install dependencies automatically (e.g., `pnpm install`).
    *   Launch multiple, isolated Cursor instances.
*   **Parallel AI Development:** Assign different features or implementation approaches to each Cursor agent (Composer) simultaneously.
*   **Efficient Monitoring:** Easily switch between active Cursor projects/worktrees using `Ctrl+R` or OS spaces.
*   **Seamless Integration:** Merge completed features back into your main branch using `wt merge` or use agents to commit, push, and create Pull Requests via the GitHub CLI (`gh pr create`).
*   **Boost Productivity:** Dramatically speed up iteration and experimentation by running multiple AI agents concurrently without conflicts.

This approach transforms your workflow, allowing you to explore multiple development paths simultaneously and integrate the best results efficiently using standard Git practices.

**Resources** 

[https://www.npmjs.com/package/@johnlindquist/worktree](https://www.npmjs.com/package/@johnlindquist/worktree)
[https://www.npmjs.com/package/@johnlindquist/file-forge](https://www.npmjs.com/package/@johnlindquist/file-forge)

**Want to Learn More? Sign-up for my Cursor Workshop!**

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

Swarm Intelligence: Massively Parallelize Your AI Agents in Cursor

Creating powerful and context-aware tools for Cursor IDE becomes straightforward when leveraging information directly from your current project. In this lesson, you'll learn how to build a custom MCP (model context protocol) tool that intelligently requests essential details—such as the current project path and conversation summaries—from your Cursor project.

Workflow demonstrated in this lesson:

- Define MCP tool parameters to request specific project details.
- Pass project path and conversation summaries into your MCP tool.
- Automatically write or update files within your current project using these parameters.

Key benefits:

- Automate tasks with precision based on your active project's context.
- Simplify interactions by creating AI-driven CLI commands tailored to your needs.
- Open endless possibilities for integrating external APIs and advanced automation.

Through this practical demonstration, you'll gain a versatile technique for enhancing your development workflow in Cursor IDE.

[https://github.com/johnlindquist/mcp-cursor-tool-starter](https://github.com/johnlindquist/mcp-cursor-tool-starter)

Build MCP Tools Using Project Context for Custom Cursor IDE Automation

Setting up custom MCP (Machine Command Protocol) servers in Cursor can involve tedious steps, such as configuring node paths or referencing additional commands. This lesson shows how to streamline this process by bundling your MCP server directly into a standalone executable.

Workflow demonstrated in this lesson:

- Use a simple MCP server project starter.
- Run a single bundling command to create an executable.
- Right-click the generated executable, copy its full path, and add that directly to your Cursor settings.

Key benefits:

- Eliminates the need for complex node path configurations.
- Simplifies the setup and integration of custom MCP servers into Cursor.
- Makes the server executable instantly accessible within your Cursor environment.

By following this streamlined approach, you can quickly and easily integrate custom MCP servers into Cursor, making your development workflow cleaner and more efficient.

[https://github.com/johnlindquist/mcp-cursor-tool-starter]()

Easy MCP Server Setup in Cursor IDE with Bundled Executables

Finding the *perfect* code editor theme is a myth.  There are always small visual tweaks you'd like to make, but forking and maintaining an entire theme is overkill.  This lesson shows you how to quickly and easily customize *any* theme directly within Cursor (or VS Code) using the `workbench.colorCustomizations` setting in your `settings.json` file.

**The Problem:**

*   No single theme perfectly matches everyone's preferences.
*   Minor visual annoyances (like search highlight colors) can impact focus and productivity.
*   Forking and publishing a theme for small changes is excessive.

**The Solution: `workbench.colorCustomizations`**

This setting allows you to override specific color values of your current theme *without* creating a new theme.  You get fine-grained control over the editor's appearance.

**How it Works:**

1.  **Open Settings (JSON):** Use the command palette (`Cmd/Ctrl + Shift + P`) and search for "Preferences: Open User Settings (JSON)". It is best to create a new editor group to the right, and then open the json settings, so that you can visualize your changes.
2.  **Add `workbench.colorCustomizations`:**  Within your `settings.json`, add a `"workbench.colorCustomizations"` object.
3.  **Use Autocomplete:** Inside this object, start typing "editor." and use Cursor's/VS Code's autocomplete to explore the available customization options.  Examples include:
    *   `editor.findMatchBackground`: Background color of search matches.
    *   `editor.findMatchHighlightBackground`: Background color of the *current* search match.
    *   `editor.findMatchBorder`: Border color of search matches.
    *   `editor.findMatchHighlightBorder`: Border color of the *current* search match.
    *   `editor.background`: Overall editor background color.
    *   ...and many more!
4.  **Set Color Values:** Assign valid color values (hex codes, named colors, etc.) to the properties you want to change.  You can even use transparency (alpha channel) with hex codes (e.g., `#FF000022` for a semi-transparent red).
5.  **Save and See the Changes:**  As soon as you save `settings.json`, the changes are applied *live* to your editor.  Experiment with different colors until you find what works best.

**Benefits:**

*   **Precise Control:**  Tweak individual elements of your theme without affecting others.
*   **No Forking Needed:**  Make personal adjustments without the overhead of creating and managing a separate theme.
*   **Live Updates:**  See your changes instantly as you edit the JSON.
*   **Easy Experimentation:**  Try out different colors and settings quickly.

This lesson demonstrates a simple yet powerful way to personalize your coding environment, improving focus and making your editor truly your own. No need for extensive theme development – just targeted tweaks to make things *just right*.


Personalize Your Cursor/VS Code Theme with workbench.colorCustomizations

AI coding assistants (like Cursor's Composer) are powerful, but running a single instance can become a bottleneck, especially for larger tasks or when experimenting with different approaches.  This lesson shows you how to break free from that limitation by leveraging Git worktrees to enable *parallel* AI development.

**The Problem:**

*   Single Composer instances can be slow for complex tasks.
*   Multiple agents modifying the same files simultaneously lead to conflicts.
*   Traditional branching can be cumbersome for rapid experimentation.

**The Solution: Git Worktrees**

Git worktrees allow you to create multiple, *independent* working directories linked to the *same* Git repository. Each worktree can be associated with a different branch, providing isolated environments for development.

**Workflow:**

1.  **Create Worktrees:** Use `git worktree add -b <branch-name> ../<project-name>-<branch-name>` to create a new worktree and associated branch.  The `-b` flag creates the branch. The path (`../<project-name>-<branch-name>`) places the worktree in a sibling directory (cleaner organization). Repeat for each variation you want to explore.
2.  **Launch Multiple Cursor Instances:** Open Cursor in each worktree directory (e.g., `cursor ../my-project-variant1`). Each instance is now isolated and can work on its assigned branch.
3.  **Assign Tasks to Composer:**  Within each Cursor instance, use Composer (Chat or Agent mode) to work on specific tasks or implement different approaches to the same problem. Use `@` to provide context (files, directories, docs).
4.  **Monitor and Iterate:** Check in on each Composer instance's progress. Because they're in separate worktrees, there are no conflicts.
5.  **Merge the Best Solution:**  Once a branch has a successful solution, merge it back into your main branch using `git merge <branch-name>`.
6.  **Clean Up:** Remove the worktrees with `git worktree remove ../<project-name>-<branch-name>`.

**ZSH Functions (Optional but Powerful):**

The lesson introduces two ZSH functions (provided as code snippets) to automate this process:

*   `wtree()`:  Takes branch names as arguments, creates corresponding worktrees, installs dependencies (with `-p` flag for PNPM), and optionally launches Cursor in each.  This streamlines setup.
*   `wtmerge()`:  Takes a single branch name as an argument, merges that branch into `main`, and then *cleans up all* worktrees created by `wtree()`. This simplifies merging and cleanup.

**Benefits:**

*   **Parallel Development:**  Run multiple Composer instances simultaneously, drastically speeding up development.
*   **Conflict-Free Experimentation:**  Isolate different approaches in separate branches/worktrees, eliminating merge conflicts.
*   **Rapid Iteration:**  Quickly create, test, and merge (or discard) variations of your code.
*   **Clean Workspace:**  Worktrees keep your main project directory uncluttered.

This lesson empowers you to unlock the full potential of AI coding assistants by running them in parallel, fostering faster experimentation and more efficient development. You'll learn a practical technique that significantly boosts your productivity when working with AI-generated code.

```bash
# wtree: Create a new worktree for each given branch.
# Usage: wtree [ -p|--pnpm ] branch1 branch2 ...
#
# This function does the following:
#   1. Parses command-line arguments; if -p/--pnpm is provided, it will later run "pnpm install".
#   2. Determines the current branch and repository root.
#   3. Uses a fixed parent directory (~/dev) to house all worktree directories.
#   4. For each branch passed:
#        - If the branch does not exist, it is created from the current branch.
#        - It checks that a worktree for that branch does not already exist.
#        - It then creates a worktree in ~/dev using a naming convention: <repoName>-<branch>.
#        - If the install-deps flag is true, it runs "pnpm install" inside the new worktree.
#        - Finally, it either opens the new worktree via the custom "cursor" command (if defined)
#          or prints its path.
wtree() {
  # Flag to determine whether to run "pnpm install"
  local install_deps=false
  local branches=()

  # Parse command-line arguments
  while [[ $# -gt 0 ]]; do
    case "$1" in
      -p|--pnpm)
        install_deps=true
        shift
        ;;
      *)
        branches+=("$1")
        shift
        ;;
    esac
  done

  # Ensure at least one branch name is provided.
  if [[ ${#branches[@]} -eq 0 ]]; then
    echo "Usage: wtree [ -p|--pnpm ] branch1 branch2 ..."
    return 1
  fi

  # Determine the current branch; exit if not in a git repository.
  local current_branch
  current_branch=$(git rev-parse --abbrev-ref HEAD) || {
    echo "Error: Not a git repository."
    return 1
  }

  # Determine repository root and name.
  local repo_root repo_name
  repo_root=$(git rev-parse --show-toplevel) || {
    echo "Error: Cannot determine repository root."
    return 1
  }
  repo_name=$(basename "$repo_root")

  # Set fixed parent directory for worktrees.
  local worktree_parent="$HOME/dev"
  # Ensure the worktree parent directory exists.
  if [[ ! -d "$worktree_parent" ]]; then
    if ! mkdir -p "$worktree_parent"; then
      echo "Error: Failed to create worktree parent directory: $worktree_parent"
      return 1
    fi
  fi

  # Loop over each branch provided as argument.
  for branch in "${branches[@]}"; do
    # Define the target path using a naming convention: <repoName>-<branch>
    local target_path="$worktree_parent/${repo_name}-${branch}"
    
    echo "Processing branch: ${branch}"

    # Check if a worktree already exists at the target path.
    if git worktree list | grep -q "^${target_path}[[:space:]]"; then
      echo "Error: Worktree already exists at ${target_path}. Skipping branch '${branch}'."
      continue
    fi

    # If the branch does not exist, create it from the current branch.
    if ! git show-ref --verify --quiet "refs/heads/${branch}"; then
      echo "Branch '${branch}' does not exist. Creating it from '${current_branch}'..."
      if ! git branch "${branch}"; then
        echo "Error: Failed to create branch '${branch}'. Skipping."
        continue
      fi
    fi

    # Create the new worktree for the branch.
    echo "Creating worktree for branch '${branch}' at ${target_path}..."
    if ! git worktree add "$target_path" "${branch}"; then
      echo "Error: Failed to create worktree for branch '${branch}'. Skipping."
      continue
    fi

    # If the install flag is set, run "pnpm install" in the new worktree.
    if $install_deps; then
      echo "Installing dependencies in worktree for branch '${branch}'..."
      if ! ( cd "$target_path" && pnpm install ); then
        echo "Warning: Failed to install dependencies in '${target_path}'."
      fi
    fi

    # Optionally, open the worktree directory via a custom "cursor" command if available.
    if type cursor >/dev/null 2>&1; then
      cursor "$target_path"
    else
      echo "Worktree created at: ${target_path}"
    fi

    echo "Worktree for branch '${branch}' created successfully."
    echo "-----------------------------------------------------"
  done
}


# wtmerge: Merge changes from a specified worktree branch into main,
# then clean up all worktrees and delete their branches.
#
# Usage: wtmerge <branch-to-keep>
#
# This function does the following:
#   1. Verifies that the branch to merge (branch-to-keep) exists as an active worktree.
#   2. Checks for uncommitted changes in that worktree:
#        - If changes exist, it attempts to stage and commit them.
#        - It gracefully handles the situation where there are no changes.
#   3. Switches the current (main) worktree to the "main" branch.
#   4. Merges the specified branch into main, with proper error checking.
#   5. Uses "git worktree list" to retrieve all active worktrees (under ~/dev
#      and matching the naming pattern) and removes them.
#   6. Deletes each branch that was created for a worktree (skipping "main").
wtmerge() {
  # Ensure exactly one argument is passed: the branch to merge.
  if [ $# -ne 1 ]; then
    echo "Usage: wtmerge <branch-to-keep>"
    return 1
  fi

  local branch_to_keep="$1"

  # Determine the repository root and its name.
  local repo_root repo_name
  repo_root=$(git rev-parse --show-toplevel) || {
    echo "Error: Not a git repository."
    return 1
  }
  repo_name=$(basename "$repo_root")

  # Fixed parent directory where worktrees are located.
  local worktree_parent="$HOME/dev"

  # Retrieve all active worktrees (from git worktree list) that match our naming convention.
  local worktrees=()
  while IFS= read -r line; do
    # Extract the worktree path (first field)
    local wt_path
    wt_path=$(echo "$line" | awk '{print $1}')
    # Only consider worktrees under our fixed parent directory that match "<repo_name>-*"
    if [[ "$wt_path" == "$worktree_parent/${repo_name}-"* ]]; then
      worktrees+=("$wt_path")
    fi
  done < <(git worktree list)

  # Check that the target branch worktree exists.
  local target_worktree=""
  for wt in "${worktrees[@]}"; do
    if [[ "$wt" == "$worktree_parent/${repo_name}-${branch_to_keep}" ]]; then
      target_worktree="$wt"
      break
    fi
  done

  if [[ -z "$target_worktree" ]]; then
    echo "Error: No active worktree found for branch '${branch_to_keep}' under ${worktree_parent}."
    return 1
  fi

  # Step 1: In the target worktree, check for uncommitted changes.
  echo "Checking for uncommitted changes in worktree for branch '${branch_to_keep}'..."
  if ! ( cd "$target_worktree" && git diff --quiet && git diff --cached --quiet ); then
    echo "Changes detected in branch '${branch_to_keep}'. Attempting auto-commit..."
    if ! ( cd "$target_worktree" &&
            git add . &&
            git commit -m "chore: auto-commit changes in '${branch_to_keep}' before merge" ); then
      echo "Error: Auto-commit failed in branch '${branch_to_keep}'. Aborting merge."
      return 1
    else
      echo "Auto-commit successful in branch '${branch_to_keep}'."
    fi
  else
    echo "No uncommitted changes found in branch '${branch_to_keep}'."
  fi

  # Step 2: Switch to the main worktree (assumed to be the current directory) and check out main.
  echo "Switching to 'main' branch in the main worktree..."
  if ! git checkout main; then
    echo "Error: Failed to switch to 'main' branch."
    return 1
  fi

  # Step 3: Merge the target branch into main.
  echo "Merging branch '${branch_to_keep}' into 'main'..."
  if ! git merge "${branch_to_keep}" -m "feat: merge changes from '${branch_to_keep}'"; then
    echo "Error: Merge failed. Please resolve conflicts and try again."
    return 1
  fi

  # Step 4: Remove all worktrees that were created via wtree().
  echo "Cleaning up worktrees and deleting temporary branches..."
  for wt in "${worktrees[@]}"; do
    # Extract branch name from worktree path.
    local wt_branch
    wt_branch=$(basename "$wt")
    wt_branch=${wt_branch#${repo_name}-}  # Remove the repo name prefix

    echo "Processing worktree for branch '${wt_branch}' at ${wt}..."
    # Remove the worktree using --force to ensure removal.
    if git worktree remove "$wt" --force; then
      echo "Worktree at ${wt} removed."
    else
      echo "Warning: Failed to remove worktree at ${wt}."
    fi

    # Do not delete the 'main' branch.
    if [[ "$wt_branch" != "main" ]]; then
      if git branch -D "$wt_branch"; then
        echo "Branch '${wt_branch}' deleted."
      else
        echo "Warning: Failed to delete branch '${wt_branch}'."
      fi
    fi
  done

  echo "Merge complete: Branch '${branch_to_keep}' merged into 'main', and all worktrees cleaned up."
}
```


Launch Multiple Cursor Composer AI Agents to Work in Parallel

AI-generated code often comes riddled with errors, especially TypeScript errors.  Trying to fix these *one by one* with in-editor AI tools (like Cursor's "Fix with AI") can be slow, frustrating, and sometimes even *introduce* new problems.  The fixes can be too localized, missing the bigger picture.

This lesson demonstrates a more efficient workflow: leveraging the power of **reasoning models** (like those found in AI Studio or advanced ChatGPT models) to fix errors *in bulk*.

Here's the process:

1.  **Identify All Errors:** Use the "Problems" panel in your editor to get a comprehensive list of all errors in the file.  Don't try to fix them individually yet! (Use `Cmd/Ctrl + Shift + P`, then type "problems".)
2.  **Gather Context:**
    *   Copy the *entire file's code*.
    *   Copy the *entire list of errors* from the Problems panel.
3.  **Prompt the Reasoning Model:** Paste *both* the code and the error list into a reasoning model.  Your prompt should be something like:  "Please fix all of these errors and rewrite the entire file with the corrections."  The key is to give the AI the *full context*. Wrap your code with XML tags to help the AI recognize it.
4.  **Iterate (if needed):**  Copy the corrected code back into your editor.  If a few errors remain, *now* it's time to use in-editor AI tools (like Cursor's Composer or "Fix with AI") for the final cleanup. These tools are better suited for small, isolated issues.
5.  **Provide Additional Context (for Stubborn Errors):** If a specific error persists (e.g., related to a library like `yargs`), provide *even more* context to the AI:
    *   Include links to the library's documentation (NPM page, official docs).
    *   Use Cursor's `@` feature to add specific files or documentation to the context.
6. Make sure to test! Run `npm test` or whichever testing command you're using for this project.

**Why this works:**

*   **Holistic Understanding:** Reasoning models excel at understanding the *relationships* between different parts of the code. They can see how a change in one place affects others, leading to more consistent and correct fixes.
*   **Efficiency:** Fixing multiple errors in a single pass is *much* faster than addressing them individually.
*   **Reduced "Whack-a-Mole":**  Fixing one error often reveals another.  Bulk fixing with a reasoning model minimizes this frustrating cycle.

This lesson teaches you a practical, powerful technique to tame AI-generated code and get it working quickly, even when it arrives full of errors. By combining the strengths of reasoning models and in-editor AI, you'll become a more efficient and confident developer.


Bulk Fixing AI-Generated Code Errors with Reasoning Models and Cursor Composer

[https://github.com/johnlindquist/mcp-cursor-tool-starter/tree/main](https://github.com/johnlindquist/mcp-cursor-tool-starter/tree/main)

Cursor's AI is powerful, but it can't do everything. What if you could give it the ability to interact with your specific workflows, tools, and data? This lesson shows you how to do exactly that by building a custom MCP tool – and we'll do it in under two minutes!

We'll leverage the Model Context Protocol (MCP), an open standard that bridges the gap between AI agents (like Cursor's Composer) and your custom code. By creating an MCP tool, you're essentially giving Cursor a new skill.

Here's the breakdown:

Understanding MCP: We'll briefly cover what the Model Context Protocol (MCP) is and why it's the key to extending Cursor's functionality. In short, it enables communication between the AI and your tools in a standardized way.

Fast Track Setup: We'll use a ready-made starter project (a single TypeScript file and package.json) to skip the boilerplate and get straight to the core concepts.

Step-by-Step Integration: We'll walk through the simple process of:

Cloning the starter project.

Installing dependencies with pnpm install.

Running the starter server.

Adding the tool to Cursor's MCP settings (under Features > MCP).

Verifying that your tool is working

Real-World Example: We'll demonstrate the tool in action, using Cursor's Composer to create a new GitHub issue using the gh CLI, all driven by our custom MCP tool.

This lesson is about more than just building one tool; it's about understanding how to extend Cursor's capabilities to fit your unique development needs. You'll learn the foundational steps to create a whole ecosystem of AI-powered tools.

Build Your First MCP Tool in Cursor in Just 2 Minutes

Install `ghx` with `npm i -g @johnlindquist/ghx`

https://github.com/johnlindquist/ghx

You've copied and pasted code, set up your project, and... *error*.  "Stream is not readable."  Sound familiar?  Generic Google searches and outdated Stack Overflow answers often leave you more confused than when you started.  You need more than just documentation; you need *context*.  You need to see how real developers solve this *exact* problem.

This lesson shows you a better way to debug, using real-world GitHub examples and the power of Cursor's Composer.

Here's the workflow we'll cover:

1.  **Identify the Error:** You've already done this!  You have a specific error message ("Stream is not readable") and the code that's causing it.
2.  **Find Real-World Solutions with `ghx`:** We'll use `ghx`, a tool for searching GitHub, to find *actual projects* that have encountered (and solved!) this same issue.  No more generic advice – you'll see working code.
3.  **Consolidate and Analyze:**  `ghx` brings those relevant code snippets into a single, easy-to-read file.
4.  **Leverage Cursor's Composer:** We'll use Cursor's Composer to directly interact with the examples and your problematic code.  Paste in your error, reference the `ghx`-generated file, and ask targeted questions like:
    *   "Based on these examples, how should I modify my code to fix the 'stream is not readable' error?"
    *   "What's the common cause of this error in the context of an MCP server?"
    *   "Show me the specific lines in the examples that handle stream readability correctly."
5. **Fix the Error:** Implement the solution using this workflow.

Instead of guessing, you'll learn directly from proven solutions.  You'll gain a deeper understanding of your issue and how to troubleshoot it effectively.  This approach transforms debugging from a frustrating hunt into a targeted learning experience.

Fix Real-World Coding Errors with GitHub Context through ghx and Cursor Composer

AI is amazing for generating code, but often leaves us staring at functions and libraries we've never seen before. This creates a huge problem: When things break (and they will break), we're lost. We don't understand the individual pieces, let alone how they interact. Knowing one part isn't enough; we need to see how unfamiliar components work together.

The solution? Real-world examples.

This lesson introduces ghx, a tool I built to do exactly that. ghx searches GitHub, finding actual code examples that use the specific combinations of functions and libraries that are tripping you up. Instead of generic documentation, you get concrete implementations. You can find the ghx repository at https://github.com/johnlindquist/ghx.

Here's the workflow we cover:

Identify the Unknown: Pinpoint the AI-generated code you don't understand – specific functions, libraries, or their interactions.

Search with ghx: Use ghx to search GitHub for examples using those terms together. ghx finds repos where those elements co-exist.

Consolidate Examples: ghx combines the relevant code snippets from those examples into a single, digestible file.

Ask the AI (Again, but Better!): Feed this consolidated code to an AI (like a coding-focused model in an AI studio). Now, instead of asking general questions, you can ask targeted questions based on real-world usage:

"Explain how these functions are used together in these examples."

"What are some common use cases for this combination of libraries?"

"Show me alternative code snippets that achieve the same result."

"Can you identify potential problems or areas for improvement, based on these examples?"

Iterate. Use your improved understanding to make changes, and then repeat the process to debug.

By leveraging ghx and targeted AI prompting, we transform from being lost in a sea of unfamiliar code to actively learning from how others have solved similar problems. We gain a deeper understanding of our AI-generated codebase, making debugging, modification, and future development far more manageable. This gives you the tools to understand, debug and improve your own code.

How to Work with AI-Generated Code You Don't Understand with ghx and Gemini

This lesson demonstrates how to utilize DeepSeek's R1 model to access its reasoning capabilities and then seamlessly transition to other models for subsequent tasks. By extracting the reasoning output from DeepSeek R1, you can leverage its powerful reasoning while gaining the speed, cost efficiency, and configuration flexibility of alternative models. This two-phase approach begins with DeepSeek’s new R1 model, praised for its open-source nature and value. You then gain the adaptability to switch to other models depending on your needs or existing workflows. This lesson provides a step-by-step walkthrough of this process, along with demo code to help you implement this technique yourself.

Combine DeepSeek R1 Reasoning with GPT 3.5 Turbo for the Cheapest, Fastest, and Best AI

While much focus in AI has been on optimizing input and generating concise outputs, a new model in Google's AI Studio dramatically expands output possibilities with a capacity of 65,000 tokens. This represents a significant shift, opening doors to new application scenarios, especially for developers and creators. This lesson explores a practical and immediately useful scenario: leveraging this large output capability within Google's AI Studio to generate extensive sample datasets for your projects. Whether you are developing a new application, building a resource, or require robust datasets for testing and validation, this model offers a powerful solution. We'll demonstrate how to move beyond the constraints of short responses and utilize the full potential of large output models to generate the data volume and variety you need, enhancing your development workflow through Google's AI Studio.

AI Studio's New 65k Output Opens Exciting Possibilities

This lesson explores how to use Cursor to generate and maintain English specifications as a form of living documentation. Learn how these automatically created documents can serve as a consistent and up-to-date representation of your code's behavior. The video highlights how reviewing and refining these English specifications can help ensure that code modifications align with the intended purpose and that documentation accurately reflects the current codebase, promoting better long-term code maintainability and clarity.

Here's the "english-spec.md" used in the video:

```markdown
> You are an AI specialized in analyzing source code and producing structured, plain-English representations of its functionality.  
>  
> **Your Task**:  
> 1. Read and interpret the code I provide.  
> 2. Produce a technical, English-only summary of what the code does.  
> 3. Use headings and bullet points to clearly outline the roles of each function, class, variable, or important logic block.  
> 4. **Do not include any actual code** in your final output. Instead, translate the code’s content into an English explanation (like a specification or documentation).  
> 5. Keep the explanation concise but thorough. Focus on explaining the purpose, process, and relationships between functions or modules in the code.  

**User Prompt to the AI**:

> **Instruction**: Convert the following code into a single file of pure English text that outlines exactly what each part of the code does. Use bullet points, headings, and descriptive language. Do not show any code—only explain how the code behaves. This is intended to serve as a human-readable spec or “translation” of the code.  
>  
> **Code**:  
> ~~~  
> [PASTE YOUR CODE HERE]  
> ~~~  

**Example Input/Output #1**:

- **Example Code (Input):**
  ~~~js
  // example1.js
  const greetUser = (userName) => {
    const greeting = `Hello, ${userName}!`;
    console.log(greeting);
  };

  greetUser("Alice");
  ~~~

- **Example Explanation (Output)**:
  ~~~
  # Overview
  - This file defines a single function which greets a user by name and prints a message to the console.
  
  # Function: greetUser
  - Accepts one parameter (`userName`).
  - Constructs a greeting string that combines static text with the `userName`.
  - Outputs the greeting using `console.log`.

  # Behavior Flow
  - The function is called with the argument "Alice".
  - Upon invocation, "Hello, Alice!" is logged to the console.

  # Key Points
  - Shows how to create a greeting with a dynamic name.
  - Demonstrates basic string interpolation and console logging.
  ~~~

**Example Input/Output #2**:

- **Example Code (Input):**
  ~~~py
  # example2.py
  class Calculator:
      def __init__(self, initial=0):
          self.value = initial
      
      def add(self, amount):
          self.value += amount
      
      def subtract(self, amount):
          self.value -= amount
      
      def get_value(self):
          return self.value

  calc = Calculator(10)
  calc.add(5)
  calc.subtract(3)
  result = calc.get_value()
  print(result)
  ~~~

- **Example Explanation (Output):**
  ~~~
  # Overview
  - This file defines a `Calculator` class that keeps track of an internal numerical value and provides methods to modify it.
  
  # Class: Calculator
  - **Constructor** (`__init__`):
    - Accepts an optional initial value (default is 0).
    - Stores the initial value in a `value` property.
  - **Method: add**:
    - Takes an `amount` and increments the internal `value` by that amount.
  - **Method: subtract**:
    - Takes an `amount` and decrements the internal `value` by that amount.
  - **Method: get_value**:
    - Returns the current internal `value`.
  
  # Behavior Flow
  - An instance of `Calculator` is created with an initial value of 10.
  - The `add` method is called with 5, raising the value to 15.
  - The `subtract` method is called with 3, lowering the value to 12.
  - The `get_value` method is called to retrieve the current value (12).
  - The result (12) is printed to the console.

  # Key Points
  - Demonstrates basic OOP with a class that manages state.
  - Showcases constructor, addition, subtraction, and retrieval of a stored value.
  ~~~  

---

## Final Prompt

Below is a full, combined version of the prompt you can provide to an AI to guide it in generating the English-only, bullet-pointed explanation. Feel free to modify or adapt this as needed:

~~~
You are an AI specialized in analyzing source code and producing structured, plain-English representations of its functionality.

Your Task:
1. Read and interpret the code I provide.
2. Produce a technical, English-only summary of what the code does.
3. Use headings (e.g. # Overview, # Functions, # Classes, # Behavior Flow) and bullet points.
4. Do not include any code in your final output—only explain how the code behaves.
5. Keep it concise but thorough, focusing on purpose, process, and relationships between functions or modules.

Below are some examples of how code might be transformed into an English-only explanation:

Example Input:
-------------------
~~~js
// example1.js
const greetUser = (userName) => {
  const greeting = `Hello, ${userName}!`;
  console.log(greeting);
};

greetUser("Alice");
~~~
Example Output:
-------------------
# Overview
- This file defines a single function which greets a user by name and prints a message to the console.

# Function: greetUser
- Accepts one parameter (`userName`).
- Constructs a greeting string that combines static text with the `userName`.
- Outputs the greeting using `console.log`.

# Behavior Flow
- The function is called with the argument "Alice".
- Upon invocation, "Hello, Alice!" is logged to the console.

# Key Points
- Shows how to create a greeting with a dynamic name.
- Demonstrates basic string interpolation and console logging.


Example Input:
-------------------
~~~py
# example2.py
class Calculator:
    def __init__(self, initial=0):
        self.value = initial
    
    def add(self, amount):
        self.value += amount
    
    def subtract(self, amount):
        self.value -= amount
    
    def get_value(self):
        return self.value

calc = Calculator(10)
calc.add(5)
calc.subtract(3)
result = calc.get_value()
print(result)
~~~
Example Output:
-------------------
# Overview
- This file defines a `Calculator` class that keeps track of an internal numerical value and provides methods to modify it.

# Class: Calculator
- **Constructor** (`__init__`):
  - Accepts an optional initial value (default is 0).
  - Stores the initial value in a `value` property.
- **Method: add**:
  - Takes an `amount` and increments the internal `value` by that amount.
- **Method: subtract**:
  - Takes an `amount` and decrements the internal `value` by that amount.
- **Method: get_value**:
  - Returns the current internal `value`.

# Behavior Flow
- An instance of `Calculator` is created with an initial value of 10.
- The `add` method is called with 5, raising the value to 15.
- The `subtract` method is called with 3, lowering the value to 12.
- The `get_value` method is called to retrieve the current value (12).
- The result (12) is printed to the console.

# Key Points
- Demonstrates basic OOP with a class that manages state.
- Showcases constructor, addition, subtraction, and retrieval of a stored value.

---

**Now**: Please apply the same conversion process to the following code. Convert it into a plain-English document that describes what the code does in detail, using bullet points and headings. Create the document next to the original with the filename as `${fileName}.md`. Do not include any source code in the output—only the explanation:

[INSERT YOUR CODE HERE]
```

Programming in English with Cursor

In this lesson, you'll learn how to enhance the accuracy of components generated by Cursor Composer by aligning your project's package.json with Cursor's rules. We'll guide you through extracting the list of installed packages from your package.json and incorporating them into your .cursorrules file, along with brief descriptions of their functionalities. By providing Cursor with this contextual information, you'll enable it to generate components that are more precise and functional on the first attempt. The lesson includes demonstrations of component generation both with and without these enhancements, highlighting the improvements in accuracy and efficiency.

Boost Cursor Composer Accuracy: Integrating package.json with Cursor Rules

Speed up your bug-fixing process dramatically with Cursors Composer. Take a warning from your logs, search for relevant terms, then seamlessly import both the warning and search results into Composer. Add documentation and apply fixes to all search results in one go. This streamlined workflow resolves multiple issues rapidly, saving valuable development time.

Batch Fix Every File in Search Results with Cursor's Composer

You know when you're deep in the code trenches and need someone to challenge your assumptions? That's exactly what this is about.

Instead of waiting for someone to poke holes in your master plan, you can paste that strategy straight into AI Studio and let it pressure test your ideas. We're talking about using this tool to validate everything from your feature proposals to your ambitious refactoring strategies.

The real magic happens when you hit that microphone button. Now you're in a real-time verbal sparring match. It's like rubber duck debugging but the duck actually pushes back and makes you defend every architectural decision you've made.

Here's the workflow:
1. Drop your master plan into the system instructions
2. Tell the AI to challenge every assumption
3. Hit that mic and defend your choices like you mean it
4. Watch your plan either emerge stronger or get rightfully dismantled

This approach is perfect when you're:
- Stuck in your own head with a complex problem
- Need to validate if your approach holds water
- Want to catch edge cases before they catch you
- Looking for opportunities you might have missed because you're too close to the code

It's like having a senior dev on call 24/7, ready to challenge your ideas and push you to think deeper about your solutions.

Now go forth and get your ideas challenged. Your projects will thank you for it.


An Uncomfortable and Challenging Conversation with AI Studio

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.

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.

Many aspects of Vimium can be configured in order to bend the tool to your specific needs and use cases. We look at how to configure what sites turn off Vimium, the default search engine, link hint characters, and other basic settings. We also see how both insert mode and searching a visual selection work. Custom keymappings and custom search engines are covered in subsequent lessons.

Configure Basic Aspects of Vimium

Many aspects of Vimium can be configured in order to bend the tool to your specific needs and use cases.

Vimium's "marks" feature lets you mark positions in the current tab and across tabs. These are positional markers that you can jump back to. For instance, as you browse a huge page of documentation or a massive forum discussion, you can leave a series of "dog ears" to sections or conversations that you'll later want to revisit. Each tab supports its own set of lower-case letter marks. Upper-case letter marks are shared across tabs allowing you to seamlessly move between a specific set of tabs regardless of how they are arranged.

Set Marks on a Vimium Webpage You Want to Jump Back To

Vimium's "marks" feature lets you mark positions in the current tab and across tabs. These are positional markers that you can jump back to.