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AI Dev Essentials #13: Cursor's huge update, Claude's new powers, and a big announcement

Cursor 1.2 adds agent to-dos and mobile support; Claude Code gets new commands and hooks. Plus, early access to the CursorPro.ai course.

Hey Everyone 👋,

John Lindquist here with the thirteenth issue of AI Dev Essentials!. I've been spending the past week exploring the various workflows around containerized dev agents and hooking the up to MCPs (specifically the "Pieces" mcp which is my current favorite for gathering context). I'm trying everything to avoid running into the "wall of low value pull requests" and how we, as developers, can stay engaged and with the agents to deliver higher-quality work.

It's been fascinating watching the explosion of new workflows emerge around Claude Code and the Gemini CLI (still kinda waiting for the Codex team to catch up...). I find the ideas around Claude controlling Gemini controlling MCPs to gather context especially intriguing as you're optimizing each model (AKA Agent) to a specific task. Parallel agents, sub agents, background agents... it's a whole new ball game.

On a personal note, I have a big ol' announcement...

🧙‍♂️ Introducing CursorPro.ai: Master AI Dev Tools!

Join the waitlist for CursorPro.ai, where I'm building a comprehensive self-paced course designed to help you master Cursor and AI dev ecosystem!

This course will feature:

My plan and materials are in place, now it's time to sit down and record the lessons!

Check it out to get notified of the early-bird pricing: cursorpro.ai

Note: I'm still teaching bi-weekly live workshops and we'll definitely have bundles in place for people who want to have both the live experience and the course.

🚀 Major Announcements

Cursor 1.2 Update: To-do Lists, PR Search, and Faster Tab

Cursor shipped a significant update that enhances agent capabilities and performance across the board. The headline feature is structured to-do lists that make complex tasks transparent and trackable.

Key improvements:

(Cursor Changelog)

The lesson I keep learning over and over is to never spend too much time customizing workflows for Cursor. They'll just release something in a couple of weeks that makes it obsolete. It's unforunate, because I love building dev tools, but I'm just accumulating a dev tools graveyard at this point.

Cursor Agents Now on Web and Mobile

Cursor unveiled a revolutionary update that brings agent capabilities to the web and mobile platforms. This isn't just a port—it's a complete reimagining of how we interact with AI coding assistants.

Key features:

The workflow is simple: type in a task, the agent gets to work, and when it's done you can review the code or make direct edits inside Cursor. Or if it's ready to ship, merge PRs directly from the web.

(Cursor Blog, Try it at cursor.com/agents)

Personally, I've been using this from my phone and dictating ideas to the agents to ask them to come up with plans. I wish I could spin up dozens of agents for random ideas, but I'm afraid of the bill at the end of the month. The big key is to have a robust CI in place for testing if you're planning on trusting fully-generated code.

Claude Code Gets Major Slash Command Upgrade

Anthropic enhanced Claude Code's slash command system with significant new capabilities:

(Anthropic Documentation)

I love custom slash commands. It's like autocomplete for your workflow prompts. I'm really hoping that Cursor borrows this idea next.

Claude Code Hooks: Automated Workflows Made Simple

Anthropic introduced hooks for Claude Code—user-defined shell commands that execute automatically at specific points during operation. This provides deterministic control without relying on prompts or AI decisions.

Key capabilities:

(Claude Code Hooks Documentation)

I'm really curious to see how this idea plays out. There are so many scenarios where you want to run tests/linters/formatters/etc in response to agent "events", that I can foresee some really powerful workflows emerging... This is one of those fundamental building blocks will surface an entire new ecosystem of tools.

Meta's AI Talent Raid: The Battle for Superintelligence Researchers

The AI industry witnessed a dramatic talent war as Meta successfully recruited four senior researchers from OpenAI for its superintelligence lab, sparking internal memos and public statements from both companies' leadership.

The Recruits:

OpenAI's Response: Mark Chen, OpenAI's Chief Research Officer, sent a forceful internal memo describing the situation as feeling like "someone has broken into our home and stolen something." Sam Altman followed up with his own message, characterizing Meta's recruiting approach as "distasteful" and asserting that "missionaries will beat mercenaries."

Key Takeaways:

(WIRED Article)

A couple questions: 1. If OpenAI was so close to "AGI", why are people leaving even if offered a bazillion dollars? 2. Does this mean that we'll get a really good open-source model from Meta soon?

🧠 Research & AI Philosophy

Marc Andreessen's Career Advice for the AI Era

In a widely-discussed perspective, Marc Andreessen (cofounder and general partner at the venture capital firm Andreessen Horowitz) offered pointed career advice: "Basically, you want to be either building the robots or going to the beach. You do not want to be competing with the robots."

This stark framing has sparked significant debate about positioning oneself in an AI-dominated future—either be on the cutting edge of AI development or find ways to add uniquely human value that AI cannot replicate.

I think we all feel this from a dev standpoint. Is my code really that much better than AI-generated code? Will it be next year? And how do I step back and tell the robots what to do? Everyone is a little uncomfortable with the current state of things since the future seems so uncertain. All we can do is keep learning and adapting.

Anthropic's Project Vend: When Claude Ran a Shop

Anthropic conducted a fascinating experiment where Claude operated a real vending business in their office lunchroom. The results were illuminating:

Successes:

Failures:

(Anthropic Research)

This experiment brilliantly illustrates both the promise and current limitations of AI agents. The fact that Claude could be "browbeaten" into bad business decisions shows we're still far from AGI. But it also demonstrates that with proper guardrails and tools, AI agents could handle many routine business operations.

Google's Chain-of-Agents: Multi-Agent Collaboration for Long Context

Google Research unveiled Chain-of-Agents (CoA), a training-free framework that uses multi-agent collaboration to handle long-context tasks more efficiently than traditional approaches.

Key innovations:

(Google Research Blog, Research Paper)

This is exactly the kind of architectural innovation we need. Instead of throwing more compute at the problem, Google found a way to make existing models handle long context more efficiently. The fact that it's training-free and works with any model makes it immediately practical.

🛠️ Dev Tooling & Ecosystem

Google Gemini CLI: Million-Token Context in Your Terminal

Google quietly released Gemini CLI, bringing enterprise-grade AI capabilities to the command line:

(Google Developers Blog)

For my use cases, this is essentially a free, open-source CLI that can analyze images ❤️. I'm still trying to figure out the screenshot->gemini cli workflow and what to do with it, but so many exciting possibilities.

GitHub Copilot Chat Extension Goes Open Source

In a significant move for the AI development community, GitHub open-sourced their Copilot Chat extension under the MIT license. This provides a reference implementation for building AI-powered IDE extensions.

(GitHub Next)

Open-source FTW. You can even adjust the system prompt!

Building Claude Code Clones: Community Insights

Jason Zhou shared valuable research on building Claude Code-like agents:

Claude Desktop Extensions: One-Click MCP Installation

Anthropic launched Desktop Extensions (.dxt files) to simplify MCP server installation. This new packaging format eliminates the configuration nightmare that previously limited MCP adoption to developers.

Key improvements:

(Anthropic Engineering Blog)

One-click for all the things!

💡 Workflows & Best Practices

Plan with Gemini, Build with Claude

Kevin Kern demonstrated a powerful workflow combining Gemini's planning capabilities with Claude's execution:

  1. Use Gemini's massive context window for research and planning
  2. Generate detailed implementation plans
  3. Hand off to Claude Code or Cursor for execution

(YouTube Tutorial, GitHub Example)

This divide-and-conquer approach plays to each model's strengths. Gemini excels at big-picture analysis with its huge context window, while Claude shines at precise implementation.

Community Claude Code Guide

The community has created a comprehensive guide covering tips, prompt patterns, and hidden features for Claude Code. With over 600 stars on GitHub, it's become a valuable resource for advanced usage.

Highlights from the guide:

(Community Guide on GitHub, Official Claude Code Docs)

A great little one-stop doc worth reviewing to steal some ideas.

🤖 AI Agent Architecture

The Limitations of "Vibe Coding"

Ara from Cline shared hard-won insights about the limits of AI-driven development through their "Gray Screen of Death" debugging saga:

The bug:

Key lesson: "You can't vibe code a vibe coder"—AI struggles with complex, cross-platform bugs that require deep system understanding.

(Full Thread on Debugging)

This is a perfect example of where AI augmentation shines versus replacement. The AI helped narrow down the problem space, but human intuition and debugging skills were essential. We're not being replaced; we're being augmented.

Cline's Parallel Agent Spawning

Cline demonstrated spawning parallel Claude Code subagents for prototype variations:

"build 5 versions so I can choose my favorite"

This Midjourney-like approach to development allows rapid exploration of different implementations.

(Cookbook for Subagents)

The future of development might really be more like Midjourney than ChatGPT. Instead of perfectionism on the first try, we'll generate variations and pick the best. This fundamentally changes how we approach problem-solving.

⚡ Quick Updates

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

John Lindquist

egghead.io