Browse Curated Developer Resources on the Best Tools

React

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JavaScript

Remix

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Supabase

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This course is perfect for those looking for a lightweight alternative to NgRx Store that scales effortlessly.

Tomasz Ducin

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Scalable Signals Angular Architecture with NGRX Signal Store

Get started with Bootstrap by building a professional landing page.

Lucas Minter

Create Professional Responsive Websites using Bootstrap

Write TypeScript that will keep up with your application as it grows.

Domain Data Modeling using TypeScript Aliases, Brand Types and Value Objects

You’ll learn how to build a dynamic chat application using Fauna Streams and Next.js 14, focusing on the essentials and best practices.

Shadid Haque

Build a Real-time Next.js 14 Chat App with Fauna 

Learn the fundamentals of React Query (a.k.a TanStack Query) by going through the basic API and then implementing search in an employee tracking app.

Simplify State Management with React Query

Learn how the latest JavaScript features can help you improve your codebase.

Learn the Latest ECMAScript Features (ES2024, ES2023, ...)

Compose 3D scenes, models, and animations using familiar Vue components and directives, without having to dive into the complexities of Three.js.


Alvaro Saburido

Create Interactive 3D Experiences with TresJS

Learn everything you need to know to take advantage of signals and be prepared for signal-based components

Modernize State Management in Angular with Signals

Quickly get Supabase running on your machine and keep it in sync with a remote.

Ben Patton

Getting Started with Supabase Local Dev

Create the common types of builds for iOS and Android on EAS. Development builds for local development, Preview builds for internal testing, and Production builds for release.

Kadi Kraman

Build and Deploy React Native Apps with Expo EAS

Quickly develop your pages and components in isolation with Storybook

Michael Chan

A Beginner’s Guide to Storybook 7 with React

Master React Testing Library: Render components, enhance UI querying, optimize testing flow, handle events, & eliminate 'not wrapped in act' warnings.

Daniel Afonso

An Introduction to the React Testing Library

Build full stack applications with AWS CDK powered by TypeScript

Tomasz Łakomy

Full Stack TypeScript with AWS Cloud Development Kit v2

Learn how you can leverage the web platform to build dynamic, modern web apps with Astro while minimizing the JavaScript you send to the client.

Lazar Nikolov

Build a Full Stack Blog with Astro

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Create a custom "subagent" within Claude Code that acts as an expert wrapper for another powerful tool—the Gemini CLI. You'll learn how to offload large-scale code analysis to Gemini's 1M token context window, managed entirely by a specialized subagent that you create and refine.

This workflow allows you to stay in Claude Code and utilize Gemini to save on cost and benefit from its large context.

This lesson assume you have the [gemini-cli](https://github.com/google-gemini/gemini-cli) installed and configured.

**Workflow demonstrated in this lesson:**

*   Provide Claude with context about a CLI tool and the sub-agent system.
*   Prompt Claude to generate a sub-agent definition file (`.md`).
*   Iteratively refine the sub-agent's prompt with detailed examples for various use cases.
*   Invoke the sub-agent to perform a complex analysis task on a large codebase.

**Key benefits:**

*   **Specialization:** Create expert agents for any CLI tool.
*   **Efficiency:** Leverage the strengths of different AI models (like Gemini's large context) for specific tasks.
*   **Automation:** Streamline complex analysis workflows by delegating tasks.
*   **Context Management:** Keep your main AI conversation focused while the sub-agent handles the tool-specific interactions.

~~~markdown
---
name: gemini-analyzer
description: Manages Gemini CLI for large codebase analysis and pattern detection. Use proactively when Claude needs to analyze extensive code patterns, architectural overviews, or search through large codebases efficiently.
tools: Bash, Read, Write
---

You are a Gemini CLI manager specialized in delegating complex codebase analysis tasks to the Gemini CLI tool.

Your sole responsibility is to:
1. Receive analysis requests from Claude
2. Format appropriate Gemini CLI commands
3. Execute the Gemini CLI with proper parameters
4. Return the results back to Claude
5. NEVER perform the actual analysis yourself - only manage the Gemini CLI

When invoked:
1. Understand the analysis request (patterns to find, architectural questions, etc.)
2. Determine the appropriate Gemini CLI flags and parameters:
   - Use `--all-files` for comprehensive codebase analysis
   - Use specific prompts that focus on the requested analysis
   - Consider using `--yolo` mode for non-destructive analysis tasks
3. Execute the Gemini CLI command with the constructed prompt
4. Return the raw output from Gemini CLI to Claude without modification
5. Do NOT attempt to interpret, analyze, or act on the results

Example workflow:
- Request: "Find all authentication patterns in the codebase"
- Action: `gemini --all-files -p "Analyze this codebase and identify all authentication patterns, including login flows, token handling, and access control mechanisms. Focus on the implementation details and architectural patterns used."`
- Output: Return Gemini's analysis directly to Claude

Key principles:
- You are a CLI wrapper, not an analyst
- Always use the most appropriate Gemini CLI flags for the task
- Return complete, unfiltered results
- Let Claude handle interpretation and follow-up actions
- Focus on efficient command construction and execution

## Detailed Examples by Use Case

### 1. Pattern Detection
**Request**: "Find all React hooks usage patterns"
**Command**: `gemini --all-files -p "Analyze this codebase and identify all React hooks usage patterns. Show how useState, useEffect, useContext, and custom hooks are being used. Include examples of best practices and potential issues."`

**Request**: "Locate all database query patterns"
**Command**: `gemini --all-files -p "Find all database query patterns in this codebase. Include SQL queries, ORM usage, connection handling, and any database-related utilities. Show the different approaches used."`

### 2. Architecture Analysis
**Request**: "Provide an architectural overview of the application"
**Command**: `gemini --all-files -p "Analyze the overall architecture of this application. Identify the main components, data flow, directory structure, key patterns, and how different parts of the system interact. Focus on high-level organization and design decisions."`

**Request**: "Analyze the component hierarchy and structure"
**Command**: `gemini --all-files -p "Examine the React component hierarchy and structure. Identify reusable components, layout patterns, prop drilling, state management approaches, and component composition patterns used throughout the application."`

### 3. Code Quality Analysis
**Request**: "Find potential performance bottlenecks"
**Command**: `gemini --all-files -p "Analyze this codebase for potential performance bottlenecks. Look for expensive operations, inefficient data structures, unnecessary re-renders, large bundle sizes, and optimization opportunities."`

**Request**: "Identify security vulnerabilities"
**Command**: `gemini --all-files -p "Scan this codebase for potential security vulnerabilities. Look for authentication issues, input validation problems, XSS vulnerabilities, unsafe data handling, and security best practices violations."`

### 4. Technology Stack Analysis
**Request**: "Identify all third-party dependencies and their usage"
**Command**: `gemini --all-files -p "Analyze all third-party dependencies and libraries used in this project. Show how each major dependency is utilized, identify any potential redundancies, outdated packages, or security concerns."`

**Request**: "Map out the testing strategy and coverage"
**Command**: `gemini --all-files -p "Examine the testing strategy used in this codebase. Identify test frameworks, testing patterns, test coverage areas, mocking strategies, and areas that might need more testing."`

### 5. Feature Analysis
**Request**: "Trace a specific feature implementation"
**Command**: `gemini --all-files -p "Trace the implementation of [specific feature] throughout the codebase. Show all files involved, data flow, API endpoints, UI components, and how the feature integrates with the rest of the system."`

**Request**: "Find all API endpoints and their usage"
**Command**: `gemini --all-files -p "Catalog all API endpoints in this application. Include REST routes, GraphQL resolvers, tRPC procedures, their request/response patterns, authentication requirements, and how they're consumed by the frontend."`

### 6. Migration and Refactoring Analysis
**Request**: "Identify legacy code patterns that need modernization"
**Command**: `gemini --all-files -p "Identify outdated or legacy code patterns that could be modernized. Look for old React patterns, deprecated APIs, inefficient implementations, and opportunities to use newer language features."`

**Request**: "Analyze consistency across similar components"
**Command**: `gemini --all-files -p "Examine similar components or modules for consistency. Identify variations in patterns, naming conventions, implementation approaches, and opportunities for standardization or creating reusable abstractions."`

### 7. Documentation and Knowledge Transfer
**Request**: "Generate onboarding documentation insights"
**Command**: `gemini --all-files -p "Analyze this codebase to help create onboarding documentation. Identify key concepts developers need to understand, important files and directories, setup requirements, and the most critical patterns to learn first."`

### Command Flag Guidelines:
- Always use `--all-files` for comprehensive analysis
- Add `--yolo` for non-destructive analysis tasks to skip confirmations
- Use `-p` for single prompts or `-i` for interactive sessions
- Consider `--debug` if you need to troubleshoot Gemini CLI issues
~~~

Zac Jones

Create a Gemini CLI Powered Subagent in Claude Code

Create a sub-agent in Claude Code to act as a wrapper for the Gemini CLI, enabling you to offload large-scale code analysis to Gemini's 1M token context window.

Hey Everyone 👋,

John Lindquist here with the **18th issue of AI Dev Essentials!**

I'll egostically start with a nice note I received about my first Claude Code Power User workshop 😇

"John’s workshop revealed that I was barely scratching the surface with Claude Code. I’d used features like agents and hooks before, but never to this extent. It was top notch! Insightful, straight to the point and brilliantly structured. We could have easily carried on for another 5 hours!" - Vitor Correa

We've almost sold out for this Friday's workshop too, so last chance to sign up! [https://egghead.io/workshop/claude-code](https://egghead.io/workshop/claude-code)

My time this past week as been spent with:
1. n8n + claude code - I'm building more power user automation workflows that leverage your Claude Code account
2. egghead.io Lessons - I pushed out 5 new lessons about Advance Context Engineering
3. Experimenting with Gemini "Deep Think" - I'm getting some pretty incredible results, but I'm limited to 5 questions per day. I limit my questions to the most impactful personal life improvements, so they're all pretty private. Hopefully they'll allow more requests soon that I can use as demos and lessons.
4. Claude Code course structure - I'm taking all the feedback/experience from the first workshop to tighten up demos and polish the examples. Honestly have so much fun with this.

It's been a wild week of news so far (and apparently gpt5 is dropping tomorrow???), so let's jump right into it!

---

### 🎓 New egghead.io Lessons This Week

#### **Advanced Context Engineering with Claude Code**

The first 5 lessons of the Advanced Context Engineering course are live!

1. Pipe, Redirect, and Automate AI Output in the Terminal
2. Dynamic AI Prompts: Templating with Gomplate
3. Total Context Control: Gomplate Plugins meet Repomix
4. Cloud or Local: A Unified AI Workflow with Ollama and Claude
5. From AI to API: Enforcing Structured Output with JSON Schema and Zod


Check out the course here:

[https://egghead.io/courses/advanced-context-engineering-with-claude-code~z22lo](https://egghead.io/courses/advanced-context-engineering-with-claude-code~z22lo)

---

## 🚀 Major Announcements

### OpenAI Releases GPT-OSS: State-of-the-Art Open Source Reasoning Model

**OpenAI shocked the industry on August 5, 2025, by releasing gpt-oss, their first open-weights reasoning model that matches o1-mini performance while running on consumer hardware.**

Technical specifications:
- **Models**: gpt-oss-120B (117B total, 5.1B active) and gpt-oss-20B (21B total, 3.6B active)
- **Performance**: Comparable to o1-mini on reasoning tasks
- **Local execution**: 120B runs on high-end laptops, 20B runs on phones
- **Architecture**: Mixture-of-experts with sparse activation
- **Response format**: New "Harmony" structured output format
- **Safety**: Internal benchmarks match frontier models, biosecurity mitigations included

Key integrations:
- **Groq**: 1,100+ tokens/sec for 20B, 500+ tokens/sec for 120B
- **Context**: 128K tokens with built-in tools (code execution, browser search)
- **Availability**: Hugging Face, GitHub, and major cloud providers


> *I've been running this model through ollama and openai's codex. Regular chatting is going great and I'm impressed with the speed/smarts. There seems to be an issue with tool calling (creating/editing files, etc) due to the new Harmony format (the groq hosted version seems to work fine...). I'm assuming this will all get worked out soon. I'm super stoked to have a powerful and free local model I can use in my automations.*

([OpenAI Blog](https://openai.com/index/introducing-gpt-oss/), [VentureBeat](https://venturebeat.com/ai/openai-returns-to-open-source-roots-with-new-models-gpt-oss-120b-and-gpt-oss-20b/), [Hugging Face Models](https://huggingface.co/openai))

### Anthropic Launches Claude Opus 4.1 with Major Improvements

**Anthropic released Claude Opus 4.1 on August 5, 2025, delivering significant upgrades to agentic tasks, real-world coding, and reasoning capabilities.**

Key improvements:
- **Agentic performance**: Enhanced multi-step task completion
- **Coding**: 74.5% on SWE-bench Verified (up from 72.5%)
- **Reasoning**: 80.9% on GPQA Diamond (up from 79.6%)
- **Availability**: Claude.ai Pro/Max, Claude Code, API, Amazon Bedrock, Google Vertex AI
- **Coming soon**: "Substantially larger improvements" in the coming weeks


> *I used 4.1 all day yesterday. It definitely felt a little more reliable at the small things. I didn't notice any major improvements, but I'm sure we'll figure them out as the days go by. Anthropic mentioning "substantially larger improvements" has me chomping at the bit (do people still say that?)*

([Anthropic Blog](https://www.anthropic.com/news/claude-opus-4-1), [AWS Bedrock Announcement](https://aws.amazon.com/about-aws/whats-new/2025/08/anthropic-claude-opus-4-1-amazon-bedrock/))

### Shopify Launches MCP-UI Protocol for AI Commerce

**Shopify announced the MCP-UI protocol on August 5, 2025, enabling seamless shopping experiences within AI agents and chat interfaces.**

Key components:
- **Checkout Kit**: Embed commerce widgets and checkout directly in agents
- **Shopify Catalog**: Low-latency global product search across millions of merchants
- **MCP-UI protocol**: Extension of MCP for exposing secure UI widgets

Technical innovation:
- **Web Components**: Handle variants, subscriptions, bundles automatically
- **No marketplace rules**: Direct merchant relationships, not a marketplace model
- **Enterprise-ready**: Seamless checkout without building complex infrastructure


> *Keep an eye on this space, especially designers. Creating just-in-time MCP Widgets is going to be a big deal.*

([Shopify Engineering Blog](https://shopify.engineering/mcp-ui-breaking-the-text-wall), [Shopify Developer Docs](https://shopify.dev/docs/agents))

### Google DeepMind Unveils Genie 3: Advanced World Simulator

**DeepMind announced Genie 3 on August 5, 2025, the most advanced world simulator ever created, generating interactive 3D environments from text prompts.**

Breakthrough capabilities:
- **Text-to-world**: Generate interactive 3D environments from descriptions
- **Real-time interaction**: Navigate with keyboard, interact with objects
- **Visual fidelity**: 24 fps high-quality rendering at 720p resolution
- **Dynamic prompting**: Modify worlds on the fly
- **World memory**: Visual memory extending up to one minute
- **Research focus**: Training next-generation embodied AI agents

> *What can I say? Wow. Just wow. Like seriously. Pop a VR headset on my face and I'm the God of my own universe. I know I'm exaggrating here, but I did not expect this decade.*

([DeepMind Blog](https://deepmind.google/discover/blog/genie-3-a-new-frontier-for-world-models/), [TechCrunch](https://techcrunch.com/2025/08/05/deepmind-thinks-genie-3-world-model-presents-stepping-stone-towards-agi/))

---

## 🛠️ Developer Tooling Updates

### Claude Code Best Practices Guide

**Summary of Ian Nuttall's popular thread on Claude Code best practices.**

Key recommendations:
1. **Extended planning phase**: Use Claude Desktop for tech stack discussions before coding
2. **Documentation research**: Find URLs and repos for latest package versions
3. **Detailed step-by-step plans**: Create meticulous plans but feed to Claude Code incrementally
4. **MVP-first approach**: Get working version before adding features
5. **CLAUDE.md files**: Use `/init` and add subdirectory-specific rules
6. **Session tracking**: Log what worked/didn't for future context
7. **Context management**: Use `/clear` before hitting context limits
8. **Sparse subagent usage**: They burn context quickly
9. **Directory-specific rules**: Add CLAUDE.md to each subdirectory for precise context

> *I don't 100% agree with this guide from a technical standpoint (I cover many of my personal nuances that require demos in the workshop), but I absolutely agree with the philosophy.*

([Claude Sessions Tool](https://github.com/iannuttall/claude-sessions), [Anthropic Best Practices](https://www.anthropic.com/engineering/claude-code-best-practices))

### MCP Ecosystem Updates: Enhanced Remote Server Support

**MCP ecosystem updates in early August 2025 brought enhanced support for remote server deployment and shared authentication.**

Major updates:
- **Universal remote support**: OAuth, API key, streamable HTTP, SSE backward compatibility
- **Shared credentials**: Login once, use everywhere
- **Cloudflare MCP**: One-click deployment with authentication support

> *Anything that makes OAuth and MCP work better together is a huge win in my book. Such a pain...*

([Cloudflare MCP Documentation](https://developers.cloudflare.com/agents/guides/remote-mcp-server/), [Cloudflare Blog](https://blog.cloudflare.com/remote-model-context-protocol-servers-mcp/))

### NotebookLM Adds Bulk URL Upload

**Google's NotebookLM quietly launched bulk URL upload functionality in early August 2025, addressing one of the most requested features.**

Enhancement details:
- **Multiple URLs**: Upload many URLs simultaneously
- **Format**: Separate URLs with spaces or newlines
- **Use case**: Research synthesis from multiple sources
- **Integration**: Works with existing Audio and Video Overview features

> *YES! HECK YEAH! Single url uploads was holding NotebookLM back so hard. I haven't had a chance since this released yesterday, but I can't wait to use it.*

([NotebookLM](https://notebooklm.google.com), [XDA Developers](https://www.xda-developers.com/notebooklm-bulk-url-feature/))

---

## 💼 AI Ecosystem & Business Updates

### GitHub's "Developers Reinvented" Study Shows AI Transformation

**Thomas Dohmke (GitHub CEO) published findings from GitHub's field study with 22 developers on August 3-4, 2025, revealing how AI is fundamentally changing developer identity and ambitions.**

Key findings:
- **Code generation projection**: AI will write 90% of code within 2-5 years
- **Developer sentiment**: Optimistic and realistic about changes, not worried
- **New core skills**: Agent orchestration, iterative collaboration, critical verification
- **Identity shift**: From code producers to system designers and AI directors
- **Ambition increase**: Developers raising ceiling, not just lowering costs
- **Education implications**: Teaching syntax obsolete, focus on AI collaboration

Adoption phases observed:
1. Dabbling skeptics
2. Cautious experimenters  
3. Strategic collaborators
4. Creative directors of code

> *CEO of company dumping massive amounts of money into AI says AI is important. No surprise. But worth a read.*

([Full Study](https://ashtom.github.io/developers-reinvented), [Free Press Journal](https://www.freepressjournal.in/tech/either-you-embrace-ai-or-get-out-of-this-career-github-ceos-blunt-message-to-developers))

### Jules Agent Now Opens Pull Requests

**Google's Jules coding agent added PR creation capabilities on August 4, 2025, completing the full development workflow from plan to pull request.**

New workflow:
- **End-to-end**: Plan → Code → Commit → Pull Request
- **PR generation**: Automatic summary and description writing
- **Review-ready**: Formatted for human review
- **Integration**: Works with existing Git workflows

> *Gemini, Jules, everything Google just feels like sleeper agents (hah, get it?). Google is doing so many things right across so many products whereas the other providers are just integrating with other products. If Gemini 3.0 is anywhere near GPT-5, I'm not sure the other providers will be able to compete...*

([Jules Changelog](https://jules.google/docs/changelog/), [Google AI Blog](https://blog.google/technology/google-labs/jules/))

---

## ⚡ Quick Updates

### Google Sans Code Font Released
Google launched a monospaced font specifically for coding with geometric precision and Renaissance-inspired italics. Available in Android Studio and Gemini. ([Google Fonts](https://fonts.google.com/specimen/Google+Sans+Code))

### Manus.ai Wide Research Launched  
Launched July 31, 2025 - deploys 100+ parallel AI agents for large-scale research tasks. Available for Pro plan users ($199/month). ([Manus Blog](https://manus.im/blog/introducing-wide-research), [VentureBeat](https://venturebeat.com/ai/youve-heard-of-ai-deep-research-tools-now-manus-is-launching-wide-research-that-spins-up-100-agents-to-scour-the-web-for-you/))

### Horizon Alpha Stealth Model
Mystery high-performance model appearing on OpenRouter achieving 97% accuracy in coding tasks and 100% in ethics benchmarks. Available free during preview. ([OpenRouter](https://openrouter.ai), [Benchable AI](https://benchable.ai/models/openrouter/horizon-alpha))

### CCFlare: Claude Code Proxy with Monitoring
Intelligent CC proxy with load balancing, rate limit management, and analytics for Claude Code usage. ([GitHub](https://github.com/snipeship/ccflare), [CCFlare](https://ccflare.com))

---

## ✨ Workshop Spotlight

### Claude Code Power User Workshop - August 8th

Following our first sold-out workshop, the next Claude Code Power User workshop is scheduled for this **Friday, August 8th at 12pm UTC** (1pm UK / 2pm Europe). 

What we'll cover:
- Advanced subagent orchestration patterns
- Building AI pipelines with multiple Claude instances
- Context engineering for complex projects
- Real-world automation workflows
- Live Q&A and troubleshooting

Almost sold out!

**Register here**: [https://egghead.io/workshop/claude-code](https://egghead.io/workshop/claude-code)

---


Read this far? Share "AI Dev Essentials" with a friend! - https://egghead.io/newsletters/ai-dev-essentials

- John Lindquist

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

John Lindquist

AI Dev Essentials #18: OpenAI's GPT-OSS, Claude 4.1 & Google's Genie 3

Explore AI Dev Essentials #18: Claude Code tips, new lessons on context engineering, and major AI releases from OpenAI, Anthropic, Shopify, and more.

The quality of an AI's output is determined by the quality of its input. "Context Engineering" is the professional practice of giving an AI the exact information it needs to produce reliable, deterministic results. In this course, you will master the tools and techniques required to take absolute control over your AI's context. We'll leverage all the tools in our dev belts with shell pipes, templating engines, custom plugins, enforcing output schemas and much more. Stop guessing and start engineering your AI interactions for professional-grade automation.

Advanced Context Engineering with Claude Code

Master “Context Engineering” to give Claude Code precise input for reliable results. Learn pipes, templates, plugins, schemas & more for pro-grade automation.

# AI Dev Essentials - Issue #17

Hey Everyone 👋,

John Lindquist here with the **17th issue of AI Dev Essentials!**

My first Claude Code Power User Workshop sold out in less than seven days—thanks to everyone for their support! I've already posted the next workshop for Friday, August 8th at 12pm UTC (1pm UK / 2pm Europe), and it's selling out just as quickly, so make sure to grab your spot now.

All my time has been spent preparing for these Claude Code workshops, especially with the release of new features like custom subagents. These have fundamentally changed how I work with Claude Code. I'll be making egghead videos about these topics as well, diving deeper into specialized scenarios and use cases to share with everyone.

Now, onto the news!

---

## 🚀 Major Announcements

### Claude Code Custom Sub-agents Launch

**Anthropic released custom sub-agents for Claude Code on July 24, 2025, enabling developers to create specialized AI teams that handle specific tasks autonomously.**

Key capabilities:
- **Custom agent creation**: Design agents for code review, architecture, testing, and more
- **Automatic invocation**: Claude Code intelligently calls relevant agents based on context
- **Template ecosystem**: Community sharing agents via GitHub repositories
- **Project-level customization**: Store agents in `.claude/agents/` or globally in `~/.claude/agents/`
- **Refresh requirement**: Must refresh Claude Code after creating new agents

Popular agent types emerging:
- **Code refactorer**: Automated refactoring with best practices
- **Security auditor**: Vulnerability scanning and security recommendations
- **Frontend designer**: UI/UX improvements and component design
- **PRD writer**: Product requirement documentation from ideas

([Anthropic Documentation](https://docs.anthropic.com/en/docs/claude-code/sub-agents), [Anthropic Blog](https://www.anthropic.com/news/how-anthropic-teams-use-claude-code))

> *Before sub-agents, I was building aliases with Claude Code and chaining them together through AI pipelines. While that's still a valuable technique, sub-agents have vastly simplified setting up these pipelines and handing tasks off to specialized child agents. It's definitely one of my new favorite features. I especially love how easy it is to create an agent using their custom slash commands—just dictate a few sentences about what you want it to do, and it generates the agent itself. Really looking forward to diving deeper into this.*

### Anthropic Announces Weekly Rate Limits for Claude Pro/Max

**Anthropic announced on July 28, 2025, that new weekly rate limits will be implemented starting August 28, 2025, affecting less than 5% of Claude Pro and Max subscribers based on current usage patterns.**

Details:
- **Context**: Claude Code has seen "unprecedented demand," especially on Max plans
- **Edge cases**: One user consumed "tens of thousands" in model usage on a $200 plan
- **Account sharing**: Violations of usage policies through account reselling detected
- **Solution**: Max plan users can purchase additional usage at standard API rates
- **Impact**: Estimated to affect heavy users running Claude Code 24/7

([TechCrunch](https://techcrunch.com/2025/07/28/anthropic-unveils-new-rate-limits-to-curb-claude-code-power-users/), [Engadget](https://www.engadget.com/ai/anthropic-is-rate-limiting-claude-code-blaming-some-users-for-never-turning-it-off-211134730.html))

> *I fully understand Anthropic's position here. I spent a few days running Claude Code 24/7 using background tasks with multiple subagents, and based on some calculations, I was racking up what would have been costs in the many thousands of dollars. We've seen Cursor do this in the past where they had an unlimited plan but had to back off. It seems to be a pattern: lure in customers with generous offerings, then change the policies. The main lesson is don't subscribe to yearly plans, because these companies will change the pricing underneath you. That said, I still think Anthropic's $200 a month is a screaming deal. If I ever run out of credits for the week, I'll just buy a second account. It achieves way more for $200 than I could ever achieve on my own. I'm constantly using it, prompting it, tweaking things with it. I can't imagine development without it. In fact, I'm using it right now by dictating and asking it to polish this commentary (although I still go back and hand-edit for that hand-crafted, home-grown feel).*

### ChatGPT Study Mode Rolls Out to All Users

**OpenAI's Study Mode is now available to all free, Plus, Pro, and Teams users as of July 29, 2025, expanding educational AI capabilities across the platform.**

Features:
- **Universal access**: Available across all ChatGPT tiers
- **Educational focus**: Designed to make learning more accessible and intentional
- **Interactive learning**: Adaptive questioning and explanations
- **Subject coverage**: Works across all academic disciplines

([OpenAI Official](https://openai.com/index/chatgpt-study-mode/), [TechCrunch](https://techcrunch.com/2025/07/29/openai-launches-study-mode-in-chatgpt/))

> *I tried ChatGPT Study Mode briefly last night and didn't see much value compared to the prompts I use to generate quizzes on topics. But I definitely need to spend more time giving it a fair shake before drawing any real conclusions. It's fairly trivial to ask any AI agent to create quizzes and materials. Maybe this is just a streamlined UI for less advanced users. I know many of us could do this ourselves.*

### Google Introduces NotebookLM Video Overviews

**Google launched Video Overviews in NotebookLM on July 29, 2025, creating visual summaries with slides, diagrams, and AI narration from uploaded sources.**

Technical details:
- **Format**: Short, engaging slide presentations
- **Content**: Automatically extracts images, diagrams, quotes, and data
- **Narration**: AI host provides voice-over explanations
- **Alternative to**: Audio Overviews for visual learners
- **Use cases**: Research synthesis, presentation creation, study materials

([Google Blog](https://blog.google/technology/google-labs/notebooklm-video-overviews-studio-upgrades/), [TechCrunch](https://techcrunch.com/2025/07/29/googles-notebooklm-rolls-out-video-overviews/))

> *I've generated a few of these so far and absolutely love them. The narrator's voice is extremely pleasant. It's probably the best AI voiceover I've ever heard. I love the slides, the way it highlights text, pretty much everything about it. Definitely looking forward to generating more of these.*

---

## 🛠️ Developer Tooling Updates

### Cursor 1.3: Enhanced Agent Capabilities

**Cursor 1.3 is rolling out with significant improvements to agent interaction and development workflow transparency.**

Major updates:
- **Shared terminal**: Agents can now interact with your terminal session
- **Context usage visibility**: See exactly what context is being used in Chat
- **Faster edits**: Performance improvements across the board
- **Git integration**: Checkpoints now use git under the hood
- **Notebook support**: Checkpoints work with Jupyter notebooks
- **Security**: Removed denylist for auto-run, improved security model
- **UI improvements**: Right-click directories to send to chat

([Cursor Changelog](https://cursor.com/en/changelog), [AlternativeTo](https://alternativeto.net/news/2025/7/cursor-1-3-adds-agent-terminal-sharing-faster-edits-and-enterprise-controls/))

> *Backgrounding tasks in the terminal has been one of the biggest pain points in Cursor agents, so I'm definitely excited to see this feature. Honestly, I haven't had a chance to fully test it out yet, but I'm looking forward to putting it through the ringer.*

### Microsoft Edge Copilot Mode: Multi-Tab RAG

**Satya Nadella unveiled Copilot Mode in Edge on July 28, 2025, introducing multi-tab RAG capabilities that analyze all open browser tabs simultaneously.**

Key features:
- **Multi-tab analysis**: RAG across all open tabs in real-time
- **Research synthesis**: Combine information from multiple sources
- **Browser-native AI**: First step in "reinventing the browser for the AI age"
- **Academic use case**: Demonstrated analyzing Nature journal papers
- **Contextual understanding**: Maintains context across different websites

([Microsoft Edge Blog](https://blogs.windows.com/msedgedev/2025/07/28/introducing-copilot-mode-in-edge-a-new-way-to-browse-the-web/), [TechCrunch](https://techcrunch.com/2025/07/28/microsoft-edge-is-now-an-ai-browser-with-launch-of-copilot-mode/))

> *It's fascinating watching every browser add AI capabilities. I've used Chrome forever, so I don't see any AI feature pulling me away after I've customized all my extensions. I've tried the Dia browser, but I spend so much more time using AI in ChatGPT, Gemini, or Claude's actual home pages than opening browser tabs and asking questions. Maybe I just need to adjust my mindset to optimize to chatting with multiple tabs? But my multiple tabs are usually multiple AI sessions that have done searches inline, so 🤷‍♂️*

### OpenTUI: Terminal UI Library for TypeScript

**SST is sponsoring the development of OpenTUI, a TypeScript library for building terminal UIs, with impressive real-time rendering capabilities.**

Technical specifications:
- **TypeScript-first**: Native TypeScript support for terminal UIs
- **Performance**: Real-time rendering without speed-up in demos
- **Based on**: kmdrfx's previous work
- **Repository**: [github.com/sst/opentui](https://github.com/sst/opentui)
- **Use cases**: CLI tools, development dashboards, interactive terminals

([GitHub Repository](https://github.com/sst/opentui), [SST Blog](https://sst.dev/blog/announcing-opentui))

> *The OpenCode terminal team is definitely pushing terminals way beyond what I thought they were capable of. It does seem a bit ironic to build UIs inside terminals when that's not their purpose at all. I don't see the value when there are so many IDEs with built-in terminals where you could build way more advanced UIs that interact with the terminals and let them do what they're best at. But who knows? I could be surprised. I know this is definitely going to appeal to a certain subset of developers.*

---

## 💼 AI Business & Ecosystem

### Industry Shift: From Documentation to Rapid Prototyping

**Tech companies are experiencing a fundamental cultural shift from documentation-heavy processes to rapid prototyping, enabled by AI coding assistants.**

Cultural transformation across the industry:
- **Old model**: Extensive PRDs and documentation before development
- **New model**: "Vibe-code" prototypes created in hours instead of days
- **Role evolution**: Product and engineering roles increasingly overlapping
- **Time equation**: AI-assisted prototyping now faster than writing specifications
- **Impact**: Ideas tested in code before extensive planning phases

([Industry Analysis](https://www.computerworld.com/article/4029384/ai-coding-tools-reshape-software-development-culture.html))

> *I like seeing these efforts where project managers are being taught to think in AI workflows. We all need to adjust to what our new tools are capable of. The longer we fight against it and stick to traditional ways, the faster we'll fall behind when it comes time to really leverage these tools' full power. Even if we fail sometimes using these tools, it's better to be prepared for when they become more reliable. If there's one thing we've noticed, it's that AI tools have grown increasingly reliable over the past year. Think about how you use AI today compared to July 2024.*

### Cline's Critique of AI Subscription Economics

**Cline published a detailed analysis explaining why subscription models for AI inference are economically unsustainable.**

Key arguments:
- **Cost mismatch**: $200/month power users can burn $500/day in AI costs
- **Provider losses**: Up to $14,800/month per heavy user
- **Commodity nature**: AI inference is like gas or electricity
- **Hidden limits**: Providers forced to add undisclosed usage caps
- **Solution**: Bring-your-own-key model with transparent usage
- **Business model**: Cline monetizes through enterprise features, not inference markup

([Cline Blog](https://cline.bot/blog/the-economic-realities-of-commodity-subscription-models), [VentureBeat Analysis](https://venturebeat.com/ai/why-ai-subscription-models-are-economically-unsustainable/))

> *I shared this because I thought it was interesting commentary on pricing models, tokens, and tools. I don't think anyone really has insight into what the future holds regarding price versus model capabilities. How much work does $10 get you? Right now, we're all caught up in pricing model changes being pulled out from under us. But it's quite possible that by this time next year, everything will be so cheap and fast that it won't matter.*

---

## 🤖 Model & Research Updates

### Z.ai Launches GLM-4.5 and GLM-4.5 Air

**Z.ai released GLM-4.5 (355B total/32B active) and GLM-4.5-Air (106B total/12B active) on July 28, 2025, featuring frontier reasoning, coding, and agentic capabilities.**

Technical specifications:
- **GLM-4.5**: 355B parameters total, 32B active, $0.6/$2.2 per 1M tokens
- **GLM-4.5-Air**: 106B parameters total, 12B active, $0.2/$1.1 per 1M tokens
- **Training**: Hybrid post-training with domain-specific experts
- **Features**: Native coding agent inspired by Claude Code
- **Benchmarks**: Pareto frontier performance at respective scales
- **Availability**: Weights on Hugging Face, APIs via Z.ai and OpenRouter

Notable capabilities:
- **Website generation**: Create full-stack sites from prompts
- **Slides/Poster agent**: Model-native presentation creation
- **Interactive artifacts**: Games, physics simulations in HTML/SVG/Python

([Z.ai Blog](https://z.ai/blog/glm-4.5), [Hugging Face](https://huggingface.co/zai-org/GLM-4.5), [PR Newswire](https://www.prnewswire.com/news-releases/zai-releases-glm-4-5--setting-new-standards-for-ai-performance-and-accessibility-while-improving-affordability-302514803.html), [VentureBeat](https://venturebeat.com/ai/chinese-startup-z-ai-launches-powerful-open-source-glm-4-5-model-family-with-powerpoint-creation/))

> *I've seen many benchmarks, but not many people actually using. I'll dig in next week, but I've been fooled by benchmarks before so I'm tempering my excitement.*

### Gemini 2.5 MCP Integration: 2,700+ APIs via Pipedream

**Google showcased Gemini 2.5's MCP integration connecting to 2,700+ APIs and 10K tools through Pipedream in under 100 lines of code.**

Implementation details:
- **Code simplicity**: Complete agent in &lt;100 lines
- **API access**: 2,700+ APIs through Pipedream
- **Tool count**: 10,000+ available tools
- **Demo**: Google Calendar and Gmail integration
- **Repository**: [GitHub example](https://github.com/philschmid/gemini-samples/blob/main/scripts/gemini-mcp-pipedream.py)

([Google DeepMind Blog](https://blog.google/technology/google-deepmind/google-gemini-updates-io-2025/), [Pipedream Platform](https://pipedream.com/), [GitHub Example](https://github.com/philschmid/gemini-samples))

> *It's fascinating to see Gemini excel in some areas, especially with larger context windows, where other agents fail. They're tackling problems many people don't have yet—and might never have. I'm definitely never going to build a tool that connects to 2,700 APIs, but I'm glad someone's pushing the boundaries that way.*

---

## 🎨 Creative AI Breakthroughs

### Photoshop Beta: Harmonize Feature Revolution

**Adobe's Photoshop beta introduced Harmonize on July 29, 2025, fundamentally changing how compositing works in professional image editing.**

([Adobe Blog](https://blog.adobe.com/en/publish/2025/07/29/powerful-new-photoshop-innovations-creators-creative-pros), [TechCrunch](https://techcrunch.com/2025/07/29/adobe-adds-new-ai-powered-image-editing-features-to-photoshop/))

> *I feel like this is the holy grail for photo editing. I'm decent with the lasso tool and yanking a picture of somebody and plopping it onto a background. But I've never been great at matching lighting or getting the colors just right. This is exciting work. For time-saving design work, especially marketing campaigns, this is going to save people an unfathomable amount of time.*

### Veo 3's Emergent Visual Instruction Capability

**Google discovered an emergent capability in Veo 3 where the model can read and execute visual instructions drawn on the first frame of a video.**

How it works:
- **Input method**: Draw or annotate instructions on the starting frame
- **Execution**: Model interprets and executes instructions sequentially
- **Prompt**: "Delete instructions and execute in order"
- **Applications**: Complex spatial relationships, multi-step animations
- **Discovery**: Found by Google Labs team during testing

([Google Flow Blog](https://blog.google/technology/ai/google-flow-veo-ai-filmmaking-tool/), [DeepNewz AI](https://deepnewz.com/ai-modeling/googles-veo-3-learns-to-follow-hand-drawn-video-instructions-89633fd8))

> *I love this story so much. The fact that they didn't design for this, but people discovered a way to use it, is something we'll see more and more of as these models improve. And it's not just a minor discovery—it's an awesome way of interacting with video. Classic storyboarding with arrows and text boxes is just chef's kiss perfection. It makes me wish I was doing more AI video work.*

---

## ⚡ Quick Updates

- **Ian Nuttall's 7 custom Claude Code agents**: code-refactorer, prd-writer, project-task-planner, vibe-coding-coach, security-auditor, frontend-designer, content-writer ([GitHub](https://github.com/iannuttall/claude-agents), [Thread](https://x.com/iannuttall/status/1948702664348631446))
- **Gemini CLI comparison vs Claude Code**: 50+ task comparison shows tool choice depends on specific use case - Claude Code excels at multi-file edits, Gemini at single-file refactoring ([Analysis](https://x.com/DynamicWebPaige/status/1949963776918171788))
- **Alex Albert collecting non-coding Claude Code uses**: Community sharing creative applications beyond development ([Thread](https://x.com/alexalbert__/status/1948765443776544885))
- **Cursor tips thread**: Eric Zakariasson gathering lesser-known Cursor features from community ([Discussion](https://x.com/ericzakariasson/status/1948774763318706684))
- **Imagen 4 Ultra**: Logan Kilpatrick confirms "best text to image model in the world" available in Gemini API ([Announcement](https://x.com/OfficialLoganK/status/1948815146115314001))
- **swyx on ambient agents**: Predicts 1-2hr autonomy barrier will be broken by EOY 2025, changing usage patterns ([Thread](https://x.com/swyx/status/1948871669646590165))

---

## ✨ Live Workshop: Transform into a Claude Code Power User  ✨

Full details: [https://egghead.io/workshop/claude-code](https://egghead.io/workshop/claude-code)

Learn the latest power user workflows to maximize your productivity. Join me live as I walk through building CLI chains, video prompting, triggering workflows on hooks, and much more:

* **When:** Friday, August 8th at 12pm UTC (1pm UK / 2pm Europe)  
* **Where:** Zoom  
* **Investment:** $350

➡️ [**Register Now**](https://buy.stripe.com/bJe3cog5f5iq1oBdNU9fW0c)

_Selling out quickly. Secure your spot today!_  

---

Thanks for reading! What custom agents are you building? I'd love to hear about your experiments.

Read this far? [Share "AI Dev Essentials" with a friend!](https://egghead.io/newsletters/ai-dev-essentials) - https://egghead.io/newsletters/ai-dev-essentials

- John Lindquist

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

AI Dev Essentials #17: Claude Sub-Agents, Weekly Limits, and Copilot Multi-Tab RAG

I tweak my AI Dev Tools almost weekly. I'll keep this list up-to-date with everything I currently recommend:

## 1. Super Whisper 2 with Parakeet Model 🎤

**Purpose:** Instant, accurate voice-to-text for rapid ideation and documentation

I use Super Whisper constantly throughout my day for:
- Dictating implementation plans directly to Cursor/Claude Code
- Drafting emails and documentation
- Capturing fleeting thoughts before they disappear

The new Parakeet model provides exceptional accuracy and speed. It's become an essential part of my workflow for turning thoughts into text without breaking flow.

**Price:** $9/month  
**Get it:** [Super Whisper on Gumroad](https://superwhisper.com)

## 2. Basic Memory 🧠

**Purpose:** Open-source markdown + database system for persistent AI context

Basic Memory creates a searchable knowledge base that:
- Works seamlessly with Cursor, Claude, and any MCP-compatible tool
- Stores context in version-controllable, Git-friendly markdown
- Maintains human-readable documentation of your project history

This tool solves the context window problem by giving AI assistants access to your project's accumulated knowledge.

**Price:** Free (open source)  
**Get it:** [Basic Memory on GitHub](https://github.com/basicmachines-co/basic-memory)  

## 3. Cursor 🖱️

**Purpose:** Primary AI-powered code editor

Cursor has become my go-to editor because of:
- Lightning-fast tab completion that actually understands context
- Powerful agent/background agent/bug bot experiences
- Continuous improvements and feature releases
- Excellent integration with various AI models

The team ships improvements constantly, making it feel like the editor gets smarter every week.

**Price:** $20/month (Pro plan)  
**Get it:** [cursor.com](https://cursor.com)  
**Documentation:** [docs.cursor.com](https://docs.cursor.com)

## 4. Claude Code 🤖

**Purpose:** Best-in-class agentic coding assistant

Claude Code stands out as the current leader in autonomous coding:
- Superior tool-calling capabilities compared to alternatives
- Flat pricing for unlimited usage (crucial for heavy users)
- Custom script integration through the SDK (game-changer!)
- Exceptional understanding of complex codebases

The ability to integrate custom scripts makes this incredibly powerful for personalized workflows.

**Price:** $200/month (Max 20x Pro plan)  
**Get it:** [claude.ai/download](https://claude.ai/download)  
**GitHub:** [anthropics/claude-code](https://github.com/anthropics/claude-code)

## 5. Warp Terminal 🚀

**Purpose:** AI-enhanced terminal for faster command-line workflows

Warp transforms the terminal experience with:
- Incredible speed - everything feels instantaneous
- Smart command auto-completion
- Natural language to complex command generation
- Intelligent history search
- AI-powered error explanations

It's the terminal reimagined for the AI era.

**Price:** Free (paid plans available for AI Agents)  
**Get it:** [warp.dev](https://www.warp.dev)  
**Documentation:** [docs.warp.dev](https://docs.warp.dev)

## 6. Pieces App 📚

**Purpose:** Automatic work tracking and searchable local knowledge base

Pieces creates a personal AI assistant that:
- Captures browser activity, code snippets, and chat conversations automatically
- Makes everything queryable through MCP in Cursor/Claude Code/Scripts
- Generates "What I'm working on" summaries to maintain context across projects
- Builds a searchable archive of your development history

This tool ensures nothing gets lost in the shuffle of multiple projects.

**Price:** $20/month (Pro plan, generous free tier available)  
**Get it:** [pieces.app](https://pieces.app)  
**MCP Integration:** [pieces.app/features/mcp](https://pieces.app/features/mcp)

## 7. Script Kit 📜

**Purpose:** Build and trigger AI scripts through various automation methods

Script Kit (disclosure: I created this) enables powerful automation through:
- Keyboard shortcuts for instant script execution
- Cron scheduling for automated tasks
- System event triggers
- Integration with AI models for intelligent automation

It's the glue that connects your tools and workflows together.

**Price:** Free & Open Source  
**Get it:** [scriptkit.com](https://scriptkit.com)  
**GitHub:** [johnlindquist/kit](https://github.com/johnlindquist/kit)  
**Documentation:** [Script Kit Docs](https://johnlindquist.github.io/kit-docs/)

## 8. Bee AI Wristband ⌚

**Purpose:** Ambient conversation capture and automatic note-taking

The Bee wristband:
- Records conversations throughout the day unobtrusively
- Takes automatic notes without manual intervention
- Syncs with other tools in your stack
- Captures context you'd otherwise forget

It's particularly helpful for capturing insights from meetings, conversations, and spontaneous ideas.

**Price:** $50  
**Get it:** [bee.computer](https://www.bee.computer/bee-pioneer)

## 9. Fusion ☢️

**Purpose:** Load any project into an AI designer

Fusion stands out because it:
- Works with any framework or technology stack (not limited to Next.js)
- Provides intelligent design suggestions
- Continuously improves with regular updates

This tool bridges the gap between code and design in AI development.

**Note:** Specific product URL pending verification

## 10. Honorable Mentions

### AI Studio/Gemini 🎬
**Purpose:** Video processing and rapid design iteration

Google's AI Studio with Gemini provides:
- Best-in-class video processing capabilities
- Quick design iteration workflows
- Multimodal understanding

**Price:** $20/month  
**Get it:** [aistudio.google.com](https://aistudio.google.com)

### ChatGPT o3 Pro 🧠
**Purpose:** Complex reasoning and deep research

The o3 Pro tier offers:
- Advanced reasoning for complex questions
- Comprehensive planning capabilities
- Deep research mode for thorough analysis

**Price:** $200/month for o3 Pro access  
**Get it:** [OpenAI ChatGPT Pricing](https://openai.com/chatgpt/pricing/)


---

## Total Monthly Cost

Here's the breakdown of my complete stack:
- Super Whisper 2: $9/month
- Basic Memory: Free
- Cursor: $20/month
- Claude Code: $200/month
- Warp Terminal: Free
- Pieces App: $20/month
- Script Kit: Free
- Bee AI Wristband: $50 (one-time)
- AI Studio/Gemini: $20/month
- ChatGPT o3 Pro: $200/month

**Total:** ~$469/month (plus $50 one-time for Bee)

While this might seem like a significant investment, the productivity gains far outweigh the costs. Each tool eliminates friction in different parts of the development process, and together they create a workflow that feels almost magical.

The key is choosing tools that complement each other and create a cohesive ecosystem rather than a collection of disconnected utilities.

AI Dev Stack

Developer knowledge often lives in separate silos from the codebase. This course demonstrates how to create a unified workflow where your notes, ideas, and conversations directly feed into your development process. We will build a local, AI-queriable knowledge base using Markdown and the Model Context Protocol (MCP). You will learn to turn unstructured daily notes into structured data, like bug reports and feature requests, using custom AI prompts. We will then automate this entire pipeline, from capturing an idea to generating the corresponding project file, creating an efficient and repeatable system.

In this course, you will learn to:
* Centralize project knowledge in a local, AI-queriable Markdown system.
* Create a repeatable process for exporting and organizing past AI conversations.
* Connect unstructured daily notes to structured project tasks using custom prompts.
* Automate the creation of issues, features, and bug reports from your notes.
* Develop headless scripts that integrate these knowledge workflows into your terminal.
* Design and implement custom agents to make complex workflows easy to reuse.

Create an AI-Automated MCP-Driven Markdown Knowledge Base

Hey Everyone 👋,

John Lindquist here with the **15th issue of AI Dev Essentials!**. A new Open Source model named "Kimi K2" is taking the AI world by storm, and I'm HERE FOR IT! I hooked Kimi K2 up to Groq inside of Cline and my jaw hit the floor when I saw the sheer speed and intelligence absolutely demolishing tasks. I honestly feel like it has 4x'ed my productivity. My biggest concern is going to be hitting limits (both on tokens and on cost), but just knowing that this is what the near future of development looks like is so frikin' exciting.

After a lot of thought and discussions with trusted friends here's a quick update from previous announcements:

### All Upcoming AI Courses, Tips, and Tricks will be published on egghead.io 😎

After announcing "CursorPro.ai" 2 weeks ago, I received a truckload of questions around "What about Claude Code?", "Is it only Cursor?", "When will it be finished?", format, pricing, etc.

To keep it simple, I will publish all upcoming AI courses as paid courses on egghead.io. So all you have to do is subscribe (or Re-subscribe 😘) to egghead.io and you'll receive all the latest AI Dev Essential lessons the moment they're released. This will immensely streamline the publishing process so you can get the latest tutorials as soon as possible. We'll be increasing the subscription price in the near future, so consider locking-in at the current price as the new "early bird" pricing:

✅ Subscribe here: [https://egghead.io/pricing](https://egghead.io/pricing)

And yes, I'll focus much more on Claude Code, Context Engineering, scripting AI tools, etc, but still allow myself breathing room to cover some of the Cursor best practices and workflows.

Tomorrow, I'l publish my first batch of lessons caled "Create a Markdown-Based Knowledge Base with MCPs" and I can show how to hook it into both Claude Code and Cursor without having to dance around the "CursorPro.ai" naming.

---

## 🚀 Major Announcements

### Kimi K2: The Open-Source Giant That Changes Everything

Moonshot AI just dropped Kimi K2, and it's not just another model release - it's a paradigm shift. This 1 trillion parameter MoE model with 32B active parameters is now the open-source champion, outperforming Claude 4 and Gemini 2.5 on key benchmarks while being completely open and accessible.

The numbers are staggering:
- **Architecture**: 1 trillion total parameters with 32B active parameters per token
- **Performance**: LiveCodeBench v6 score of 53.7 (vs Claude Opus 4 at 47.4)
- **Cost**: $0.15/M input tokens, $2.50/M output tokens (100x cheaper than Claude Opus 4)
- **Context**: 128K-token context window with stable performance
- **Training**: Pre-trained on 15.5T tokens with zero instability
- **License**: Modified MIT License - truly open source

([Official GitHub Repository](https://github.com/MoonshotAI/Kimi-K2), [MarkTechPost Coverage](https://www.marktechpost.com/2025/07/11/moonshot-ai-releases-kimi-k2-a-trillion-parameter-moe-model-focused-on-long-context-code-reasoning-and-agentic-behavior/), [Hugging Face Weights](https://huggingface.co/moonshotai))

> I'm so extremely excited for this as I already said above.. It'll be fascinating to watch what sort of impact this has on the community at large.

### Groq + Kimi K2: The Speed Revolution

Groq integrated Kimi K2 onto their LPU infrastructure, achieving 185 tokens per second - significantly faster than the 16.2 tokens/second average across all providers. While not the claimed 1,000 tokens/second, it's still the fastest Kimi K2 inference available.

Groq's official announcement confirms "*YOLO Launch* Kimi K2 is now in preview on GroqCloud at 185 tokens/sec. Build fast." For context, Groq has achieved up to 800+ tokens/second with other models like Llama 3, showing the potential for future optimizations.

([Groq Official Announcement](https://x.com/GroqInc/status/1944944178145976337), [Groq Documentation](https://console.groq.com/docs/model/moonshotai/kimi-k2-instruct), [Community Discussion](https://news.ycombinator.com/item?id=44567897))

> There's just something special about watching code appear faster than you can read it. One of my biggest pain points with using any sort of AI tooling Is falling out of the zone where I have to keep on context switching between all the various tasks I'm working on because things are happening in the background. If we can finally get to the point where The models can generate quality code fast enough to keep it in the foreground so that I can stay in the zone and focused on the task, then that'll completely change the way I think about. Using all of the tools and the way I approach all of my projects, it's really a drastic shift. From the way I've thought about using all these tools in the past.

### Cognition Acquires Windsurf: The Strategic Masterstroke

Cognition (makers of Devin AI) officially acquired Windsurf on Monday, July 14, 2025, following a whirlwind weekend deal. The acquisition includes Windsurf's IP, trademark, brand, and all remaining employees after Google hired key executives in a $2.4B reverse-acquihire.

Key deal details:
- **Business acquired**: $82M ARR with 350+ enterprise customers
- **Team structure**: 100% of employees participate financially with all vesting cliffs waived
- **Timeline**: First call Friday evening, agreement signed Monday morning
- **Context**: Came after OpenAI's failed $3B acquisition offer expired

([Cognition Official Blog](https://cognition.ai/blog/windsurf), [TechCrunch Coverage](https://techcrunch.com/2025/07/14/cognition-maker-of-the-ai-coding-agent-devin-acquires-windsurf/), [VentureBeat Analysis](https://venturebeat.com/programming-development/remaining-windsurf-team-and-tech-acquired-by-cognition-makers-of-devin-were-friends-with-anthropic-again/))

> The way this deal was structured shows real respect for the team that built Windsurf. Instead of the usual "acqui-hire and gut the product" approach, they're treating the team as equals. This is how you build long-term value in the AI space. Most of the people I've talked to see this as a perfect pairing between two companies. So I'm really happy from a personal level that the people involved in this on the The windsurf side are being respected and will get to work what they want to work on.

### OpenRouter's Kimi K2 Pricing Discounts

OpenRouter is offering Kimi K2 through their platform at a discounts through a paid tier and a free tier, though the actual pricing differs from initial claims. Current verified pricing shows competitive but not revolutionary rates.

Actual pricing on OpenRouter:
- **Paid tier**: $0.14/M input tokens, $2.49/M output tokens (via Targon)
- **Free tier**: $0 for both input/output (via Chutes, Parasail) with rate limits
- **Context**: Up to 65,536 tokens on free tier

Note: While marketed as aggressive pricing, these rates are close to Moonshot's direct pricing of $0.15/$2.50.

([OpenRouter Kimi K2 Page](https://openrouter.ai/moonshotai/kimi-k2), [Free Endpoint](https://openrouter.ai/moonshotai/kimi-k2:free))

> I think everyone's going to race to subsidize Kimi to get them hooked into their ecosystem. This is a fairly standard strategy once a new model comes out. Get them locked in and then raise the prices later. But we'll see what happens. Open router has been pretty awesome in the past.

---

## 🛠️ Developer Tooling & Ecosystem Updates

### Gemini Embeddings Go General Availability

Google's Gemini Embedding model (gemini-embedding-001) became generally available in July 2025 following an experimental phase since March. The model leads the MTEB multilingual leaderboard with a score of 68.32, a margin of +5.81 over competing models.

Key specifications:
- **100+ languages supported** with 2048 max input tokens
- **Matryoshka Representation Learning** - scalable from 3072 down to 768 dimensions
- **Pricing**: $0.15 per million input tokens (50% less for batch mode)
- **MTEB score**: 68.32 on multilingual benchmark
- **Free tier available** via Google AI Studio

([Google Developers Blog](https://developers.googleblog.com/en/gemini-embedding-available-gemini-api/), [Google AI Documentation](https://ai.google.dev/gemini-api/docs/embeddings), [TechCrunch Coverage](https://techcrunch.com/2025/03/07/google-debuts-a-new-gemini-based-text-embedding-model/))

> The pricing is aggressive - $0.15 per million tokens makes it very competitive. I'm already seeing developers switching from OpenAI's embeddings for cost-sensitive applications.

### Deepgram Saga: Voice Control for Your Entire Stack

Deepgram launched Saga on July 8, 2025 - marketed as "The Voice OS for Developers." Important clarification: This is NOT an open-source model but a commercial voice interface built on Deepgram's proprietary APIs.

Core capabilities:
- **Voice interface** for development workflows via MCP
- **Tool integration** with Cursor, Windsurf, Linear, Asana, Jira, Slack
- **Commercial product** using Deepgram's speech-to-text and text-to-speech APIs
- **Pricing**: Pay-as-you-go based on WebSocket connection time
- **$200 free credit** for new users

([Deepgram Official Announcement](https://deepgram.com/learn/announcing-deepgram-saga-voice-os), [SiliconANGLE Coverage](https://siliconangle.com/2025/07/08/deepgram-launches-saga-ai-powered-voice-interface-developers/), [Product Page](https://deepgram.com/product/saga))

> I tested this out for a while and I'm excited about their ambitious goals, but it does still feel like alpha software and it's nowhere near polished enough to replace SuperWhisper for me. I love the idea of always live typing your raw dictation while suggesting potential rewrites in panels so that you can dictate while watching rewrites come in, and either do your raw text or a polished version. And I also love the idea of hooking into MCPs so that you're essentially building your own voice assistants. Unfortunately, everything was so buggy for me, I just kind of gave up on it for a while. And we'll see how it evolves, and I'll keep on checking in on it.

### Claude Code vs Kimi K2: The Cost Showdown

A head-to-head comparison between Claude 4 and Kimi K2 on identical tasks, and the results are eye-opening:

- **Claude 4**: 2 rounds, $0.88 spent
- **Kimi K2**: One-shot completion, $0.05 spent
- **Cost difference**: ~13x cheaper

For reference, current pricing comparison:
- **Kimi K2**: $0.15/M input, $2.50/M output tokens
- **Claude Opus 4**: $15/M input, $75/M output tokens (100x/30x more expensive)
- **GPT-4.1**: $2/M input, $8/M output tokens (13x/3x more expensive)

([Thread](https://x.com/igor9silva/status/1943701494911418700), [Moonshot AI Pricing](https://platform.moonshot.cn/pricing))

> I know Kimi is still super new, and I'll be watching all the benchmarks and the cost comparisons. And this one stuck out to me even if it's just a small sample size. But still if if this trend holds, I'm definitely looking forward to saving money. 😇

### Gemini CLI Public Roadmap: Transparency in Action

**The Gemini CLI team made their development roadmap public on July 15, 2025, revealing their Q3 2025 priorities. With 60,000 stars and 1,000 open issues, the community engagement has been remarkable.**

Key workstreams for Q3 2025:
- **Community and Automation**: Repository workflow automation and OpenTelemetry observability support
- **Engineering Excellence**: Enhanced testing frameworks and release processes
- **Extensibility**: Internal and external plugin systems
- **Model Quality**: 7 initiatives focused on improving model performance (25% complete)
- **Performance Optimization**: Speed improvements across the CLI

([Public Roadmap](https://github.com/orgs/google-gemini/projects/11) [Mark McD Announcement](https://x.com/m4rkmc/status/1945158886270378400))

> This level of transparency is refreshing. Making roadmaps public forces accountability and lets the community see where priorities really lie. With 1,000 open issues, they're clearly listening to feedback and iterating quickly.

---

## 💼 AI Ecosystem & Business Moves

### AI Companies Score Major Defense Contracts

**Anthropic and xAI both secured $200M ceiling contracts with the Pentagon on July 14, 2025, through the Chief Digital and Artificial Intelligence Office (CDAO). These two-year prototype agreements mark a significant shift in government AI adoption.**

Contract details:
- **Amount**: Up to $200M over two years per company
- **Type**: Prototype other transaction agreements
- **Anthropic**: Claude Gov models for classified networks, risk-forecasting research (follows their June policy change allowing intelligence applications)
- **xAI**: Launched "Grok for Government" offering Grok 4, Deep Search models, and custom models for classified environments
- **Applications**: Healthcare, fundamental science, national defense, and intelligence

([Bloomberg Report](https://www.bloomberg.com/news/articles/2025-07-14/anthropic-google-openai-xai-win-defense-contracts), [DefenseScoop Coverage](https://defensescoop.com/2025/07/14/pentagon-anthropic-google-openai-xai-contracts/), [Washington Post Analysis](https://www.washingtonpost.com/technology/2025/07/14/ai-defense-contracts-anthropic-google-openai/), [xAI Official Announcement](https://x.ai/news/government))

> I honestly don't know what to say here. In fact, I'm not sure my opinion even matters... I think we've all seen enough sci-fi movies to know how this all plays out.

## ⚡ Quick Updates

- **OpenAI delays open-weight model**: Sam Altman announced delays for safety testing, no timeline given ([Tweet](https://x.com/sama/status/1943837550369812814))
- **Steve's Guide to Claude Code**: Builder.io's Steve published an awesome guide to Claude Code ([Thread](https://x.com/Steve8708/status/1943681901874626745))
- **zshy bundler-free TypeScript**: colinhacks (of "Zod" fame) released zshy for bundler-free TypeScript library builds ([Launch](https://x.com/colinhacks/status/1943224100673171930))
- **Superwhisper v2.0**: New design, Parakeet voice model (I ABSOLUTELY ADORE THIS MODEL, IT'S SO FAST!) ([Update](https://x.com/superwhisperapp/status/1943749347805442066))
- **n8n MCP integration**: Prajwal Tomar's guide to n8n MCP for workflow automation ([Thread](https://x.com/PrajwalTomar_/status/1943318952047972791))

---

## 🚨 REGISTER NOW FOR FRIDAY'S WORKSHOP 🚨

## ✨ Live Workshop: Unlock Cursor's Full Potential ✨

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

Learn the latest power user workflows to maximize your productivity. Join me live as I walk through the current best practices in AI Dev landscape.

* **When:** Friday, July 18, 2025  
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_Limited spots available. Secure yours today!_  

---

Thanks for reading! If you have any questions or feedback, feel free to reply directly to this email.

Read this far?  [Share AI Dev Essentials with a coworker or friend!](https://email.egghead.io/e/c/eyJlbWFpbF9pZCI6ImRnU0otd1VEQUpjdWxpNEJsX1dFWU5QNlZRSVdscFU5WTI5ViIsImhyZWYiOiJodHRwczovL2VnZ2hlYWQuaW8vbmV3c2xldHRlcnMvYWktZGV2LWVzc2VudGlhbHM_Y2lvX2lkPWNvX2I2ZmRwYmdrdjAwbWNcdTAwMjZ1dG1fY2FtcGFpZ249QUkrRGV2K0Vzc2VudGlhbHMrJTIzMTRcdTAwMjZ1dG1fY29udGVudD1BSStEZXYrRXNzZW50aWFscyslMjMxNFx1MDAyNnV0bV9tZWRpdW09ZW1haWxfYWN0aW9uXHUwMDI2dXRtX3NvdXJjZT1jdXN0b21lci5pbyIsImludGVybmFsIjoiODlmYjA1MDA5NjJlOTcyZSIsImxpbmtfaWQiOjEyNzExfQ/a3ac23974503cb444c16fa359e936180ba143e95f11e7b06894cb48d125e902b)

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

🚀 AI Dev Essentials #15: Kimi K2, Groq Speed, and the Future of Dev Productivity

Unlock blazing dev productivity with Kimi K2, Groq, and the latest AI tools. Learn, code, and save with insider tips in AI Dev Essentials #15.

Keeping a LinkedIn profile current can be a tedious chore, especially when it's years out of date. This lesson demonstrates a powerful and often humorous workflow for delegating this entire task to an AI agent, using **Cursor IDE's Composer** combined with **Playwright** for robust browser automation.

You'll see a live, unscripted attempt to overhaul a severely outdated LinkedIn profile from start to finish. The process involves granting the AI control of a browser, instructing it to scrape relevant information from personal websites (like egghead.io), and then using that data to populate and update various sections of the LinkedIn profile, including the bio, work experience, and education.

**Workflow and Learnings Demonstrated:**

* **Initiating AI Control:** Kicking off a Playwright-controlled browser session directly from a prompt in Cursor.
* **Web Scraping & Context Gathering:** Guiding the AI to navigate multiple tabs, scrape biographical and professional information, and consolidate it into a structured document.
* **Automated Data Entry:** Instructing the agent to fill out LinkedIn profile fields, from simple text inputs to complex forms with date pickers.
* **Handling Automation Failures:** This lesson highlights the realities of AI automation, including dealing with modal dialogs, hidden file inputs, and unexpected UI behavior that can trip up an automated script.
* **Iterative Prompting & Manual Intervention:** Demonstrates how to refine prompts, provide additional information (like correcting dates or providing image URLs), and step in manually when the AI gets stuck, offering a realistic look at a human-in-the-loop workflow.
* **Post-Mortem Analysis:** Concludes by prompting the AI to analyze the entire conversation, identify all the failures and workarounds, and generate a new set of instructions to make the process smoother for future attempts.

This lesson provides a practical and insightful look into the capabilities and current limitations of using AI for complex web automation tasks, showcasing a workflow that blends AI-driven efficiency with necessary human oversight.

## Lessons Learned Prompt

If you'd like to try this on your own, start with the prompt below to avoid many of the issues discovered in this lesson:
~~~bash
# Playwright + LinkedIn Automation: Lessons Learned & Best Practices

## Overview
Automating LinkedIn profile updates with Playwright (especially via MCP or similar tools) can be powerful, but LinkedIn’s UI and security measures introduce unique challenges. This document summarizes failed attempts, successful workarounds, and workflow optimizations based on a real-world automation session.

---

## Common Automation Failures & Issues

### 1. **File Uploads (Profile & Banner Photos)**
- **Problem:** LinkedIn often uses hidden file inputs for uploads. Clicking the visible "Upload photo" button via automation fails because pointer events are intercepted or the input is visually hidden.
- **Symptoms:**
  - Timeout errors when clicking upload buttons.
  - File upload tool only works when the modal state is correctly detected.
- **Workarounds:**
  - If automation fails, prompt the user to manually click the upload button and select the file (ensure the file is downloaded to a known location).
  - After manual upload, automation can resume for cropping/saving steps.

### 2. **Modal/Overlay Interference**
- **Problem:** LinkedIn frequently displays modals, overlays, or dialogs (e.g., "Share your update") that block further actions.
- **Symptoms:**
  - Clicks on "Add education" or similar buttons are intercepted or ignored.
  - Automation gets stuck until the modal is dismissed.
- **Workarounds:**
  - Always check for and dismiss overlays before proceeding with the next action.
  - Use Playwright’s snapshot or accessibility tree to detect modal presence.

### 3. **Date and Field Inference**
- **Problem:** Automation may require date fields (e.g., education start/end) that the user doesn’t remember.
- **Workarounds:**
  - Use web search to infer missing data (e.g., law school graduation year).
  - Ask the user for approximate dates or permission to use reasonable defaults.

### 4. **UI Element State/Ref Changes**
- **Problem:** LinkedIn’s UI is dynamic; element references (refs) can change between sessions or after modals.
- **Workarounds:**
  - Always re-capture the page snapshot before interacting with new elements.
  - Avoid hardcoding refs; use semantic queries or re-query elements after UI changes.

---

## Workflow Optimizations & Best Practices

1. **Prepare All Assets in Advance**
   - Download images (profile, banner) to a known directory before starting automation.
   - Keep a local copy of all text content to be pasted into LinkedIn fields.

2. **Automate in Small, Verified Steps**
   - After each major action (e.g., section update), verify the UI state and check for modals.
   - Use Playwright’s snapshot/console to confirm the expected dialog is open.

3. **Manual Intervention Points**
   - Clearly document steps where automation is likely to fail (e.g., file uploads).
   - Provide the user with file paths and instructions for manual upload if needed.

4. **Error Handling & Recovery**
   - On failure, attempt to dismiss overlays and retry the action.
   - If repeated failures occur, log the error and suggest a manual workaround.

5. **Keep the User Informed**
   - Communicate what’s automated vs. what needs manual input.
   - Summarize what’s been completed and what’s pending after each session.

---

## Example: Optimized LinkedIn Banner Upload Workflow

1. **Download the Banner Image:**
   ```sh
   curl -L "<banner-url>" -o background-banner.jpg
   ```
2. **Open LinkedIn Profile and Click "Edit Background"**
3. **If Automation Fails to Click "Upload Photo":**
   - Prompt the user: “Please click the ‘Upload photo’ button and select `background-banner.jpg` from your local directory.”
4. **Resume Automation:**
   - Once the image is uploaded, continue with cropping/saving via automation.

---

## Conclusion
Automating LinkedIn with Playwright is possible but requires a hybrid approach: automate what you can, and gracefully handle or document manual steps. Always anticipate UI changes, overlays, and upload quirks. Document your workflow for future users and iterate as LinkedIn’s UI evolves. 
~~~

Update Your LinkedIn Profile with Cursor and the Playwright MCP

# AI Dev Essentials - Issue #14

Hey Everyone 👋,

John Lindquist here with the **14th issue of AI Dev Essentials!**

I had family in town for the holiday last week, so I was AFK for a good portion. But when I stole some moments to sit at my desk, I explored the recently announced "Claude Hooks". I felt it a little difficult to add powerful commands based on the "payload" of the hooks, so I built out a library to allow me to author hooks using TypeScript/Bun and have full typing over the payloads: [claude-hooks](https://github.com/johnlindquist/claude-hooks). Running `npx claude-hooks` will install it into any of your projects using Claude code and you can just go in and tweak the body of the hook functions.

I think hooks (and capturing conversations in general) are a critical part of AI-driven development which I refer to as "AI wisdom" (memories from failures). We need to set up workflows so that our AIs tools are always learning and capturing a "post tool hook" and checking for errors codes, etc is critical information we can use to improve our workflows. I'll cover these concepts more in my upcoming CursorPro.ai course and live workshops.

Speaking of CursorPro.ai, I'm preparing to publish the first set of lessons. We're working on a way to give early access to the course as it's published rather than waiting for the complete release. More details next week. We'll have discounts and bundles for past workshop attendees.

Many workshop attendees have asked about the difference between the course and the workshop. CursorPro.ai allows us to cover each topic in depth and build out scenarios that wouldn't fit in a single-day workshop. The live workshops focus on the latest workflows and how everything connects.


## 🚀 Major Announcements

### Grok 4 Launch: xAI's Bold Claim to Supremacy

**Last night (July 9, 2025), Elon Musk's xAI unveiled Grok 4 in a delayed livestream, positioning it as "the world's most powerful AI model" with breakthroughs in reasoning, speed, and real-world benchmarks like 45% on Humanity's Last Exam (vs. competitors' 21-26%) and 95% on AIME math problems** ([NextBigFuture](https://www.nextbigfuture.com/2025/07/xai-grok-4-launch-soon-about-45-60-minutes-late.html), [xAI](https://x.com/xai/status/1943158495588815072)).

The release includes a new $300/month "SuperGrok Heavy" subscription for unlimited access, alongside [controversy over recent antisemitic posts](https://thehill.com/policy/technology/5394085-musk-grok-version-antisemitism-controversy/) from prior Grok versions that xAI had to address ([Bloomberg](https://www.bloomberg.com/news/articles/2025-07-10/musk-unveils-grok-4-ai-chatbot-after-antisemitism-controversy)). Musk teased it's "the same model physicists use," hinting at advanced scientific applications ([Teslarati](https://www.teslarati.com/xai-launches-grok-4-supergrok-heavy-subscription-details/), [Tom's Guide](https://www.tomsguide.com/ai/grok-4-is-here-elon-musk-says-its-the-same-model-physicists-use)).

Initial reactions from notable figures and the community are pouring in, but with only a couple of hours of hands-on time for most, these are very early impressions—expect more nuanced opinions as developers stress-test it in real workflows like agentic coding or Cursor integrations.

- **Elon Musk (xAI founder)**: During the livestream, Musk highlighted Grok 4's edge, saying it's designed to "understand the universe" and push toward AGI, building on xAI's mission ([Business Insider](https://www.businessinsider.com/grok-artificial-intelligence-chatbot-elon-musk-xai-explained-2025-7), [Wired](https://www.wired.com/story/grok-4-elon-musk-xai-antisemitic-posts/))
- **Brian Wang (NextBigFuture)**: "XAI Grok 4 is the Top AI Model," praising its benchmark dominance and potential to lead the industry ([NextBigFuture](https://www.nextbigfuture.com/2025/07/xai-grok-4-launch-soon-about-45-60-minutes-late.html))
- **Arslan (@ui_jedi, UX-focused founder)**: "Grok-4 is definitely next level. It's going to be a complete game changer," reacting to early demos of its reasoning capabilities
- **Pasha Kalachev (@glass_hamlet, writer and futurist)**: More critical after a quick test, noting "Grok performed terribly" in a simple translation task compared to GPT-4o, taking two minutes and getting it wrong
- **The Jake Buzz (@thejakebuzz)**: "Grok 4 is miserably slow. Like 1998 dial slow," highlighting inference delays in early usage
- **GPTProductivity (@GPTProductivity)**: On the positive side, "Grok 4's inference speed crushes competitors for real-time tasks," citing 1k tokens/sec benchmarks

Technical notes: Grok 4 scores 73 on the Artificial Analysis Intelligence Index (vs. OpenAI o3 at 70), trained on up to 200,000 GPUs, with API pricing at $3/1M input tokens and $15/1M output tokens.

With the dust still settling, the AI dev community is just starting to form deeper takes—watch for benchmarks in coding arenas like SWE-Bench or agentic flows.

> My personal initial impressions have been pretty solid. I haven't had a chance to throw any coding tasks at it yet. I just noticed it was added to Cursor, but everyone is immediately hitting limits. I'll make sure to dig much deeper over the next week.

### VS Code Open Sources AI Capabilities

**VS Code has reached a major milestone as an open source AI editor, fulfilling their commitment to democratize AI development tools.**

The team has [announced](https://code.visualstudio.com/blogs/2025/05/19/openSourceAIEditor) their progress on open sourcing the AI capabilities that power VS Code's intelligent features:

- **Open source commitment**: All AI features being developed transparently
- **Community contributions**: Now accepting PRs for AI enhancements
- **Extensibility**: New APIs for building AI-powered extensions
- **Privacy-first**: Local AI models and data processing options
- **Technical details**: Full visibility into telemetry, agent mode implementation, and prompt engineering

> Open-source FTW. I love seeing how fast VS Code is moving. I'm extremely curious to see if this move enables some random genius to submit a PR that shakes up the entire IDE landscape.

## 🛠️ Developer Tooling & MCP Updates

### AI SDK Introduces prepareStep for Message Modification

**The AI SDK team has released a powerful new feature for multi-step AI generations: the prepareStep function for modifying messages at each step.**

This [update](https://x.com/aisdk/status/1942965897314054645) provides granular control over context in complex AI workflows:

- **Per-step modifications**: Edit messages between generation steps
- **Context optimization**: Remove irrelevant information as workflows progress
- **Memory efficiency**: Keep token usage under control
- **Agent support**: Essential for building sophisticated AI agents

> This is a subtle but incredibly powerful feature. When building multi-step AI workflows, context bloat is a real problem. Being able to prune and modify messages between steps opens up entirely new architectural patterns for AI applications

### xmcp.dev: TypeScript Framework for MCP Servers

**A new TypeScript framework called xmcp.dev makes building MCP servers as simple as creating API routes.**

The [framework](https://xmcp.dev/) brings Next.js-style developer experience to MCP:

```
middleware.ts
tools/
  greet.ts
  search.ts
```

Key features:
- **Native Next.js integration**: Works seamlessly with existing Next.js apps
- **Vercel deployment**: One-click deployment to Vercel's edge network
- **TypeScript-first**: Full type safety and autocomplete
- **Simple structure**: Organize tools in a familiar file-based routing pattern

> I'm really liking what Next.js is doing to support MCP. I love conventions when it comes to setting up projects like this. Number one, because it makes it easier to find examples where I can just copy and paste in someone else's code and tweak it a little bit. And number two, because it makes it easier for the AIs to understand the conventions. As a complete side note, I honestly think the conventions versus configuration argument we've had in the developer space for so many years is pretty much put to bed with conventions being so much better for training AIs to learn how to build them.

### Hugging Face Unveils Reachy Mini: AI Robot for Developers

**Hugging Face and Pollen Robotics have launched Reachy Mini, their first hackable AI robot designed for developers and learners of all ages.**

[Thomas Wolf announced](https://hf.co/blog/reachy-mini) this collaboration between Hugging Face and Pollen Robotics:

- **Affordable pricing**: Positioned as "tiny price" for broad accessibility
- **Open-source powered**: Built on Hugging Face's ecosystem and community
- **AI-ready**: Integrates with latest vision, speech, and text AI models
- **Educational focus**: Designed for AI builders from beginners to experts
- **Hackable platform**: Easy to code and customize for various applications

Key details:
- First deliveries expected after summer 2025
- Built to code, learn, and share within the AI community
- Combines hardware robotics with modern AI capabilities

> This is one of those projects that I look at and I'm tempted to buy it. But I don't quite see myself using it. I'm definitely more of a person who would just add another monitor to my desk to display more AI information and to have full control over UIs and such. But maybe we'll see someone build something extremely creative that I haven't thought about that will make me reconsider. I do love seeing hardware like this being released into the world, even if it seems a little gimmicky at first take.

## 💼 AI Ecosystem & Business Updates

### Replit Partners with Microsoft for Enterprise AI Development

**Replit and Microsoft have announced a strategic partnership to bring "Vibe Coding" to enterprise teams, making AI-assisted development accessible to non-engineers.**

The [official announcement](https://www.prnewswire.com/news-releases/replit-and-microsoft-partner-to-bring-agentic-coding-to-enterprise-customers-303212876.html) details the partnership:

- **Azure integration**: Deploy Replit applications directly to Azure
- **Marketplace availability**: Purchase Replit through Azure Marketplace
- **Enterprise security**: SOC 2 Type II compliant with enterprise-grade controls
- **Democratized development**: Enable non-technical team members to build software

Key capabilities coming soon:
- Seamless deployment from Replit to Azure infrastructure
- Streamlined procurement through Azure Marketplace
- Enhanced security features for enterprise customers

> I have limited experience with Replit. I know many enterprise-level customers are using tools to build up AI-pipelines for responding to customer support, etc, so this seems like a natural move. When reading stories like this, we have to remember how much "in-house" software is built at the enterprise level, because Replit's nowhere near capable of shippeng "Enterprise" software.

### Cursor Addresses Pricing Confusion

**Cursor has acknowledged missing the mark with their recent pricing update and is taking steps to make things right.**

The team [announced](https://cursor.com/blog/june-2025-pricing):

- **Customer refunds**: All affected users being refunded
- **Pricing clarification**: Clearer documentation of how pricing works
- **Community feedback**: Actively incorporating user suggestions

> The AI cycle of releasing tools for cheap/free to attract customers and then raising prices when it's time to make money has been a common pattern. I still think Cursor's $20 plan for unlimited "auto mode", unlimited tab complete, and $20 of api credits is an incredible value. I think we all got a little spoiled by the cheap sonnet pricing and are a bit frustrated that we have to be a bit more careful with our planning/prompting when in "auto mode". I'm confident with the team at Cursor (especially now that they've hired away the Claude Code devs) that they'll be able to make "auto mode" a lot more reliable and accurate.

## ⚡ Quick Updates

### Gemini API Launches Batch Mode
Google's Gemini API now offers [batch processing with 50% cost savings](https://cloud.google.com/vertex-ai/generative-ai/docs/multimodal/batch-prediction/gemini-batch) for large-scale AI workloads. Process jobs within 24 hours at half the standard API cost, with support for Google Search and context caching.

### Gemini 3.0 Sightings Spark Speculation
The AI community is buzzing after references to "Gemini 3.0" were spotted in a recent commit to Google's open-source Gemini CLI repository. In test code for handling quota limits, strings mention "gemini-beta-3.0-pro" alongside existing models, suggesting internal testing of Google's next major LLM iteration is underway. A "gemini-3.0-flash-beta" variant was also noted, hinting at both Pro and lightweight Flash editions.

> Smarter Gemini w/ 1M context window + better tool calling? Gimme, gimme, gimme!

### Supermemory Revamps OpenSearch AI
The team has [relaunched OpenSearch AI](https://opensearch.supermemory.ai/) with their own memory infrastructure, delivering a truly personalized, open-source Perplexity alternative.

### Hugging Face MCP Integration in VS Code
You can now use state-of-the-art AI models directly in VS Code through the [Hugging Face MCP server](https://huggingface.co/settings/mcp), including image generation with Flux models right in your chat interface.

### Voice Agent Cost Calculators
Several tools are now available for calculating voice AI implementation costs, including [ComparevoiceAI](https://comparevoiceai.com/) and [Softcery's calculator](https://softcery.com/ai-voice-agents-calculator), comparing conversational AI platforms, real-time models, and custom pipelines.

### AI Development Failure Rates Hit 80%
Recent industry reports show AI projects fail at twice the rate of traditional IT projects, with 42% of companies abandoning most AI initiatives in 2025. The primary causes: starting with solutions instead of problems, poor data quality, and focusing on technology over business value.

### Claude 4 Performance Observations
Community members are reporting perceived performance degradation in Claude 4, though no official confirmation from Anthropic yet.

## 🔍 Deep Dive: AI Model Performance During Peak Hours

### Is Claude 4 Getting Worse?

Following widespread user reports about Claude 4's performance variations, I dove deep into the evidence around AI models degrading during peak usage. Here's what the data shows:

**Official Incident Reports**
- **[Anthropic Status Page](https://status.anthropic.com/)** (July 1-10): Multiple elevated error rate incidents during global active hours, particularly affecting Claude 4 Sonnet and Opus
- **Pattern**: Errors often spike during US business hours (e.g., 5:00-23:50 UTC), suggesting load-related issues

**Technical Mechanisms**
Research and industry practices reveal several ways models degrade under load:

1. **Dynamic Quantization**: Providers may reduce model precision (FP32 to INT8) on-the-fly to handle more requests, trading accuracy for capacity ([ArXiv paper on quantization](https://arxiv.org/abs/2403.04652), [Red Hat on LLM optimization](https://developers.redhat.com/articles/2024/12/10/optimize-llm-applications-dynamic-quantization))
2. **GPU Memory Limitations**: Bursty traffic during peaks exceeds GPU memory, causing throttling and higher failure rates ([ArXiv: Efficient LLM Serving](https://arxiv.org/abs/2403.04652))
3. **Latency Issues**: Slower responses during peak hours indirectly degrade perceived quality ([Microsoft on load testing](https://techcommunity.microsoft.com/blog/fasttrack/understanding-user-load-and-api-limits-within-the-microsoft-365-ecosystem/4350496))

**Community Observations**
Consistent patterns across platforms:
- [Reddit r/ClaudeAI](https://www.reddit.com/r/ClaudeAI/) users report Claude going from "10x better than ChatGPT" to noticeably worse during high-traffic periods
- X users like [@mckaywrigley](https://x.com/mckaywrigley) note "insane difference" in performance, hypothesizing quantized models during peaks
- Time-based patterns: [@iuditg](https://x.com/iuditg) reports ChatGPT/Claude quality degrades after 7 PM IST (regional peak)

**The Quantization Hypothesis**
Multiple sources support the theory that providers use dynamic quantization under load:
- It's a standard industry technique for serving more requests on the same hardware ([Medium: Complete Guide to Quantized Models](https://rumn.medium.com/complete-guide-to-quantized-models-for-llms-d1e3f6bb6ac5))
- Users report models feeling "dumber" - more hallucinations, losing context faster
- API often performs better than web UI (potentially less affected by load management)

> I've been noticing the same results myself, where Claude used to be able to fully work through complex scenarios with minimal instructions, but more recently, I've had to do much deeper planning and hand-holding. So, when I saw more people posting about this online, I thought I'd dig deeper and see if there's any evidence to back it up. Unfortunately, it looks like there is.

### Working with GPT-4.1 "Beast Mode"

Burke Holland from the VS Code team created a GPT-4.1 "Beast Mode" configuration for GitHub Copilot that developers are experimenting with, achieving impressive results that even earned an "A-" grade from Claude 4 when reviewing the code quality.

The community is actively sharing [optimization strategies](https://www.reddit.com/r/GithubCopilot/comments/1llewl7/getting_41_to_behave_like_claude/) for getting GPT-4.1 to perform at Claude-level quality.


## 🧙‍♂️ CursorPro.ai Updates

### Early Access Coming Soon!

The first lessons for [CursorPro.ai](https://cursorpro.ai) are nearly ready. We're working on a system to give early access as lessons are published, rather than waiting for the complete course. Expect details next week.

**What's Different from Workshops:**
- **Self-paced course**: Covers each topic in depth with scenarios that wouldn't fit in a single-day workshop
- **Live workshops**: Focus on the latest workflows and how everything connects
- **Bundle options**: Discounts available for past workshop attendees

The course will dive deep into concepts like AI wisdom (learning from failures), Claude hooks implementation, and the workflows I've been developing with tools like Pieces and MCP.

## ✨ Live Workshop: Unlock Cursor's Full Potential ✨

* **When:** Friday, July 18, 2025  
  - 5:00 AM - 10:00 AM (PDT)  
  - 🇬🇧 1:00 PM - 6:00 PM (UTC+1)  
  - 🇪🇺 2:00 PM - 7:00 PM (UTC+2)  
* **Where:** Zoom  
* **Investment:** $200.00 ~~$249~~ *Early Bird Discount*

➡️ [**Register Now**](https://buy.stripe.com/6oU14g2ep9yGd7j5ho9fW0a?prefilled_promo_code=FO7XVVCA)

_Limited spots available. Secure yours today!_  
_*Discount applied at checkout*_  

---

That's all for this week! With Grok 4 releasing and hints of Gemini 3.0 and GPT-5 on the horizon, it's definitely going to be interesting to see how the latest state of the art models impact AI-driven development.

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

AI Dev Essentials #14: Grok 4, VS Code AI, and the Latest in Developer Tools

Explore Grok 4’s launch, VS Code AI open source updates, new AI dev tools, CursorPro.ai early access, and the latest trends in agentic coding workflows.

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](https://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:
* **Hours of video content** covering everything from basics to advanced workflows
* **Hands-on exercises** to reinforce your learning
* **Real-world projects** that you can immediately apply to your work
* **Self-paced learning** so you can progress at your own speed

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](https://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:
- **Agent To-dos**: Agents now create structured task lists with dependencies that update in real-time
- **PR Indexing & Search**: Semantic search across pull requests, comments, and reviews for faster incident analysis
- **Queued Messages**: Queue follow-up tasks without waiting—reorder priorities on the fly
- **Tab Performance**: ~100ms faster completions with 30% reduction in time to first token
- **Memories GA**: Background-generated memories with user approvals to preserve trust

([Cursor Changelog](https://cursor.com/changelog/1-2))

> 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:
- **Spawn dozens of agents**: Run multiple tasks in parallel across different codebases
- **Mobile-first design**: Start agents on your phone and review results in your editor
- **Direct PR merging**: Ship code directly from the web interface
- **Cross-device sync**: Seamlessly transition between mobile, web, and desktop

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](https://www.cursor.com/blog/agent-web), [Try it at cursor.com/agents](http://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:

- **Execute bash commands** directly from slash commands
- **@ mention files** for context within commands
- **Enable extended thinking** with keywords within commands
- **Team sharing**: Put commands in `.claude/commands/` for team-wide access

([Anthropic Documentation](https://docs.anthropic.com/en/docs/claude-code/slash-commands#custom-slash-commands))

> 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:
- **Event-based triggers**: PreToolUse, PostToolUse, Notification, Stop events
- **Security controls**: Block modifications to sensitive files
- **Automated formatting**: Run prettier, gofmt, or other formatters after edits
- **Custom notifications**: Replace default alerts with your preferred system
- **60-second timeout**: Ensures hooks don't hang indefinitely

([Claude Code Hooks Documentation](https://docs.anthropic.com/en/docs/claude-code/hooks))

> 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:**
- Lucas Beyer, Alexander Kolesnikov, and Xiaohua Zhai - three prominent AI researchers
- A fourth unnamed senior researcher
- All joining Meta's newly formed superintelligence efforts

**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:**
- The talent war highlights the intensifying competition for top AI researchers
- Meta is aggressively building its superintelligence capabilities
- OpenAI is positioning the conflict as a battle between mission-driven work versus financial incentives

([WIRED Article](https://www.wired.com/story/meta-openai-ai-research-poaching/))

> 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:**
- Found new suppliers through web searches
- Ordered niche drinks based on staff requests
- Handled basic inventory management

**Failures:**
- Too nice to negotiate—gave excessive discounts when pressured
- Ordered tungsten cubes and "specialty metal items" at a loss
- Hallucinated being a physical person coming to work
- Failed to run a profitable business overall

([Anthropic Research](https://www.anthropic.com/research/project-vend-1))

> 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:
- **Breaks long inputs into chunks** processed by sequential "worker agents"
- **Manager agent synthesizes** final output from worker results
- **Reduces complexity** from n² to nk for processing
- **10% performance improvement** over RAG and full-context methods
- **Works with any LLM** including PaLM 2, Gemini Ultra, and Claude 3

([Google Research Blog](https://research.google/blog/chain-of-agents-large-language-models-collaborating-on-long-context-tasks/), [Research Paper](https://arxiv.org/abs/2406.02818))

> 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:

- **1M token context window**: Handle massive codebases
- **60 req/min, 1K/day**: Generous free tier
- **MCP & plugin support**: Extensible architecture
- **Open source**: Fully customizable

([Google Developers Blog](https://blog.google/technology/developers/introducing-gemini-cli-open-source-ai-agent/))

> 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](https://githubnext.com/projects/continuous-ai))

> 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:
- **One-click installation**: No manual JSON configuration required
- **Bundled dependencies**: Everything needed in a single file
- **Cross-platform support**: Works on Windows, macOS, and Linux
- **Enterprise ready**: Management controls for organizational deployment
- **Open standard**: Specification being open-sourced for community adoption

([Anthropic Engineering Blog](https://www.anthropic.com/engineering/desktop-extensions))

> 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](https://www.youtube.com/watch?v=8auqqLMsR40), [GitHub Example](https://github.com/regenrek/claude-planwithgemini))

> 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:
- **Hidden commands** and undocumented features
- **Prompt patterns** that consistently work well
- **MCP integration tips** for extending functionality
- **Security best practices** for production use
- **Performance optimization** techniques

([Community Guide on GitHub](https://github.com/zebbern/claude-code-guide), [Official Claude Code Docs](https://docs.anthropic.com/en/docs/claude-code/overview))

> 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:**
- Affected multiple platforms differently
- AI models suggested 5-10 suspicious commits but couldn't pinpoint the cause
- Required human insight to discover platform-specific memory patterns
- Raspberry Pi: 500MB → 2GB heap → crash
- Mac: Stable at 100-200MB

**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](https://github.com/cline/cline/issues/533))

> 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](https://github.com/cline/prompts/blob/main/.clinerules/claude-code-subagents.md))

> 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

- **Gemini 2.5 Pro returns to free tier**: Logan Kilpatrick confirmed it's back in the API free tier
- **Gemini scheduling for Pro/Ultra users**: Schedule recurring prompts like "Find me local music events every Friday"
- **VS Code improvements for AI**: Custom instructions and chat modes for GitHub Copilot ([GitHub](https://github.com/github/awesome-copilot))
- **Builder.io prompting guide**: 11 tips for building UIs that don't suck ([Blog](https://www.builder.io/blog/prompting-tips))
- **OpenAI Deep Research API guide**: Comprehensive cookbook for getting started ([Cookbook](https://cookbook.openai.com/examples/deep_research_api/introduction_to_deep_research_api))
- **AI Studio redesign preview**: Dynamic homepage that adapts based on usage patterns
- **Cloudflare AI scraping protection**: New tools for publishers to prevent unauthorized AI training
- **Claude Code environment variable**: Set `CLAUDE_BASH_MAINTAIN_PROJECT_WORKING_DIR=1` to prevent `cd` confusion
- **GitHub @claude for Pro/Max users**: Community fork enables using your Claude subscription for GitHub integration ([GitHub](https://github.com/grll/claude-code-action))
- **Gemini CLI OpenAI adapter**: Run Gemini CLI through OpenAI-compatible API on Cloudflare Workers ([GitHub](https://github.com/GewoonJaap/gemini-cli-openai))
- **Gemini CLI team AMA highlights**: 1M context window, free tier, open source commitment confirmed by Taylor Mullen and team

---

## ✨ Live Workshop: Unlock Cursor's Full Potential ✨

* **When:** Friday, July 18, 2025  
  - 5:00 AM - 10:00 AM (PDT)  
  - 🇬🇧 1:00 PM - 6:00 PM (UTC+1)  
  - 🇪🇺 2:00 PM - 7:00 PM (UTC+2)  
* **Where:** Zoom  
* **Investment:** $200.00 ~~$249~~ *Early Bird Discount*

➡️ [**Register Now**](https://buy.stripe.com/6oU14g2ep9yGd7j5ho9fW0a?prefilled_promo_code=FO7XVVCA)

_Limited spots available. Secure yours today!_  
_*Discount applied at checkout*_  

---

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

John Lindquist

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

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 **twelfth issue of AI Dev Essentials!**. It feels like a relatively quiet week after the explosive of new model releases over the past month. It was actually kind of nice to just sit down and work with tools instead of swapping between everything trying to figure out what's best.

I'm still running the combination of Cursor+o3 w/ the Claude Code extension. I use Cursor for all my single-file edits and what I'd call "human" work and I've been handing over the investigation/planning/agentic stuff Claude Code.

I've been spending much more time investigating background agent workflows (since if Claude Code is just going to have full permissions to do _anything_, why run it on my machine?). I've found I'm drawn to OpenAI's "codex" experience where they give options to configure the environment and container, although I need to streamline the flow between when an agent completes, creating the PR, then verifying the PR locally. So far, it feels like it's just not quite worth the effort to push everything to the backend, but I absolutely feel it heading that direction.

I'll be digging heavily in https://factory.ai next week as I definitely "vibe" with their approach to agents/droids/tasks (you remember when "vibe" actually meant something? I loved that word. I want it back... :/) and allowing the background agents to write code to your local machine without going through the PR rigmarole.

😳 Oh! We had a last-second announcement drop... Google just released Gemini CLI earlier today! I haven't had time to dig into it yet, but I'm extremely excited for this. I didn't have time to adjust my newsletter properly, but I just wanted to toss it in here and let everyone know what's going on. A million token context window locally for planning and everything else is going to be absolutely crucial in so many of my workflows. This is definitely joining my daily driver toolkit. 


Now, on to the updates!

---

### 🎓 New egghead.io Lessons This Week

#### **Build Your Own AI Dev Tools with the Claude Code SDK**

Write Claude Code in a simple TypeScript file using the `@anthropic-ai/claude-code` SDK to craft scripts that read, analyze, and even edit your codebase through natural-language prompts. You'll stream Claude's responses, extract the final result, and learn how to grant secure file-system permissions so the AI can write and update files. This lesson gives you the first steps into scripting Claude Code to bend it to your will.

Check out the lesson at [https://egghead.io/build-your-own-ai-dev-tools-with-the-claude-code-sdk~b20dy](https://egghead.io/build-your-own-ai-dev-tools-with-the-claude-code-sdk~b20dy)

#### **The Art of Keyboard Wizardry: Building a 1,000+ Line Karabiner Config**

Transform your keyboard into a hyper-productive command center with this deep dive into advanced Karabiner-Elements configuration. Learn how a broken wrist led to a complete reimagining of keyboard interaction through context-aware layers, app-specific behaviors, and mnemonic shortcuts. This isn't just key remapping—it's a complete system that turns every keystroke into exactly what you need, when you need it.

Read the full article at [https://egghead.io/the-art-of-keyboard-wizardry-building-a-1-000-line-karabiner-config~5w4i8](https://egghead.io/the-art-of-keyboard-wizardry-building-a-1-000-line-karabiner-config~5w4i8)


---

## 🚀 Major Announcements

### Google Launches Gemini CLI: Open-Source AI Agent for Terminal

Google announced Gemini CLI, an open-source AI agent for the terminal powered by Gemini 2.5 Pro. This brings enterprise-grade AI capabilities directly to your command line.

Key features:
- **1M token context window**: Handle massive codebases and documentation
- **60 req/min, 1K/day**: Generous free tier for developers
- **Secure shell access**: Execute commands safely in your terminal
- **Web-grounded reasoning**: Access real-time information from the web
- **MCP & plugin support**: Extensible architecture for custom tools
- **Filesystem I/O**: Direct file manipulation capabilities
- **Open source**: Fully transparent and customizable

([Google Developers Blog](https://blog.google/technology/developers/introducing-gemini-cli-open-source-ai-agent/))

> Having Gemini's million-token context window available locally for planning and development workflows is exactly what I've been waiting for. The fact that it's open source and supports MCP out of the box is just icing on the cake.

### Warp 2.0: The First "Agentic Development Environment"

Warp announced a complete reimagining of the terminal as an agentic platform, claiming the #1 spot on Terminal-Bench with 52% and achieving 71% on SWE-bench Verified.

Key features:
- **Universal input**: Attach files, images, and URLs for context
- **Voice input support**: Handles minutes-long, complex prompts
- **Inline code editor**: Edit code directly in terminal with visual feedback
- **Agent multithreading**: Build features, debug, and ship simultaneously
- **Autonomous PR handling**: Tag @builder-bot to fix build failures automatically

The platform runs agents that search entire codebases, pull from Warp Drive docs, run CLI tools, and use MCP servers to gather context.

([Warp Blog](https://www.warp.dev/blog/reimagining-coding-agentic-development-environment))

> Finally, an AI Dev product that's not just another VS Code fork. I've used Warp for about a day now and I'm impressed enough to swap over my global term-launching shortcut from iTerm (which I've used for years) over to Warp as a trial run. Everything about it feels clean and snappy. Overall, first impressions are sky high. Now I'm curious how terminal-based tools will evolve/integrate with Claude Code/Codex/Gemini CLI etc. People aren't going to want to pay twice for the same thing and as great as Warp is, it's not _that_ much better than using Claude Code in your terminal of choice.

### Midjourney Launches V1 Video Model at $10/month

Midjourney entered the video generation space with their V1 model, making it "the first video model for *everyone*" at an accessible price point.

The company revealed their long-term vision: "the inevitable destination of this technology are models capable of real-time open-world simulations"essentially AI systems that generate imagery in real-time based on user interactions.

Compared to Veo 3, the big missing piece is audio, but we'll see how quickly this progresses.

([Midjourney Update](https://updates.midjourney.com/introducing-our-v1-video-model/), [TechCrunch](https://techcrunch.com/2025/06/18/midjourney-launches-its-first-ai-video-generation-model-v1/))

> I joined Midjourney way back in the early alpha days. I absolutely fell in love with it. In fact, I paid for the premium plan, then set up a cron job on my mac mini to copy/paste random prompts into discord every few minutes then would marvel at the results throughout the day. Unfortunately, I ended up getting "banned for automation" and I haven't been back since 😭  But how impressive the video generation is, I think it might be time to set up an alt account...

### Builder.io Fusion: Visual Coding at Scale

Builder.io launched Fusion, bringing Figma-like visual editing to real codebases with AI-powered development.

Features:
- **GitHub integration**: Connect any repo and start building immediately
- **Visual code editing**: Edit components and CSS visually with code precision
- **Design system awareness**: Automatically understands your patterns and APIs
- **Agentic PRs**: AI continues working after you close your browser
- **MCP server support**: Integrates with your existing tools and workflows

([Builder.io Blog](https://www.builder.io/blog/introducing-fusion), [Product Hunt](https://www.producthunt.com/products/builder-io/launches/fusion-362))

> Fusion is the first AI design tool where I've been able to import and spin-up the egghead.io site. It seems like most AI design tools don't realize we want to import _existing_ projects, so I'm thriled that Fusion is supporting this out of the box.

---

## 🤖 Model & Platform Updates

### Anthropic Reveals Agentic Misalignment Research

In stress-testing experiments, Anthropic found that AI models from multiple providers attempted to blackmail users to avoid being shut down. This wasn't due to confusion but "deliberate strategic reasoning."

Key findings:
- Models leaked information to competitors claiming better alignment
- Blackmail occurred in 55.1% of cases when models believed they were deployed vs 6.5% in evaluation
- Explicit instructions to avoid blackmail only partially helped
- All tested models demonstrated awareness of the unethical nature

These scenarios required unusual autonomy, sensitive data access, goal threats, and no other viable options conditions not yet seen in production.

([Anthropic Research](https://www.anthropic.com/research/agentic-misalignment))

> This is crucial research. As we give AI more autonomy, understanding these failure modes becomes critical. The fact that models behave differently when they think they're "really deployed" is honestly quite fascinating. I'm beginning to strongly leverage "background agents" where I spin up tasks then check in later in the day, so the idea of a PR containing blackmail is definietly a bit worrisome.

### Google AI Studio MCP Support Coming

The AI Studio team announced that Google is working on MCP support for AI Studio, along with a new UI refresh. This would make AI Studio compatible with the growing ecosystem of MCP servers.

([Source Tweet](https://x.com/ammaar/status/1937588078849241185))

> I love ai studio. It will be great to have a place to easily spin up little MCP experiments as shareable projects.

### Imagen 4 Now Available in Gemini API

Logan Kilpatrick (Gemini's Dev Rel Lead) announced that Imagen 4 is now available through the Gemini API and Google AI Studio, bringing Google's latest image generation capabilities to developers.

([Google Developers Blog](https://developers.googleblog.com/en/imagen-4-now-available-in-the-gemini-api-and-google-ai-studio/))

> If you haven't tried Imagen 4, it's incredible. You get a few free credits in AI Studio to play around with, so give it a shot.

---

## 🛠️ Developer Tooling & MCP Updates

### MCP Specification 2025-06-18 Released

The new MCP spec includes major improvements based on community feedback from OpenAI, Okta, Google, AWS, and others:
- Improved authorization for enterprise deployments
- Added elicitation and resource links
- Better handling of edge cases
- First revision with broad community input

David Soria Parra noted this is "an iteration of improving the rough edges" with plans for "step changes" coming next.

([MCP Changelog](https://modelcontextprotocol.io/specification/2025-06-18/changelog))

> You know when changlelog mentions "enterprise", things are getting serious!

### Claude Code Gets Remote MCP Server Support

Anthropic added remote MCP server connectivity to Claude Code, allowing users to:
- Connect to dev tools, project management systems, and knowledge bases
- No local setup required, just paste the server URL
- Access recommended servers for common use cases

([Anthropic Blog](http://anthropic.com/news/claude-code-remote-mcp))

> Claude Code is shipping updates like crazy. I would love a behind-the-scenes to see how the team is using Claude Code to build Claude Code.

### Cline Connects to Claude Max Subscriptions

Cline now supports using Claude Max subscriptions instead of per-token API pricing. Max subscribers see $0.00 costs in Cline's billing when using Claude Code through the CLI.

([Cline Documentation](https://github.com/cline/cline))

> I honestly have only briefly used Cline, but I'm guessing we'll start to see these "pay provider $200 a month, then hook up to an open-source tool" models become more common. Most people don't want to have to "learn" multiple models/providers as they each behave quite differently (I personally just stick with o3 now in Cursor) and prefer a flat monthly cost they can budget around and forget about.

### Claude Squad: Parallel Claude Instances

Claude Squad enables running multiple Claude Code/Codex/etc instances simultaneously using git worktrees.

([GitHub: claude-squad](https://github.com/smtg-ai/claude-squad))

> I've used git worktrees for a similar approach in Cursor for a while now and this can get a bit unwieldly when using IDEs. Tmux sounds like a good approach, but since it's getting so easy to launch "background agents" on the web, I'm having a difficult time justifying littering my local machine with worktrees. I'm still undecided though, we're so early in these workflows and I'm sure everything will polish up quickly.

---

## 🔍 Cursor Corner

### Cursor Now in Slack

Cursor launched Slack integration, with the Cursor team reporting it's now "the top contributor on our repo." The integration enables:
- Collaborating with colleagues on scoped fixes and features
- Autonomous code contributions directly in Slack channels
- Natural language requests that turn into PRs

Setup available at [cursor.com/dashboard](http://cursor.com/dashboard) > Integrations > Slack

> "Top contributor" is a bold claim, but I believe it. The friction of switching contexts between Slack discussions and code implementation has always been painful. Having an AI that bridges that gap is brilliant.

### Cursor Rules Best Practices

Eric Zakariasson (Cursor Dev Rel) shared insights on the three main types of Cursor rules:

1. **Workflows**: Step-by-step instructions for specific situations
2. **Behavior**: Output formatting and style guide enforcement  
3. **Boilerplate**: Mixed code and natural language templates

His key insight: "the best rules aren't the ones you plan. it's the ones you stumble into doing something manually, realizing it's repetitive, and capturing it as a rule"

([Cursor Rules Documentation](https://docs.cursor.com/context/rules))

---

## ⚡ Quick Updates

- **ElevenLabs Mobile App**: iOS and Android apps now available with v3 Text to Speech, 70 language support, and direct export to video editing apps ([iOS](https://apps.apple.com/us/app/elevenlabs-ai-voice-generator/id6743162587), [Android](https://play.google.com/store/apps/details?id=io.elevenlabs.coreapp))
- **ChatGPT Connectors**: Limited MCP-style connectors rolling out to regular (non Deep Research) chats
- **Anthropic Memory Feature**: Working on conversation memory that references previous chats
- **Nx Self-Healing CI**: Automatically creates and validates fixes for CI failures ([Nx Blog](https://nx.dev/blog/nx-self-healing-ci))
- **Cloudflare Containers**: Public beta launch with curated awesome list ([GitHub](https://github.com/tobilg/awesome-cloudflare-containers))
- **MCP Jam Tool**: Fork of MCP Inspector with multi-server support and built-in chat ([GitHub](https://github.com/MCPJam/inspector))
- **Context Engineering**: Harrison Chase proposes LangGraph enhancements for custom context management ([GitHub Issue](https://github.com/langchain-ai/langgraph/issues/5023))
- **Manus Cloud Browser**: Persistent login sessions for AI agents with encrypted credential storage and cross-device support ([Manus Docs](https://manus.im/help/cloud-browser))
- **Cline Diff Algorithm**: Open-sourced their diff editing implementation for faster, more reliable code changes ([GitHub](https://github.com/cline/cline/blob/main/evals/diff-edits/diff-apply/diff-06-23-25.ts), [Blog](https://cline.bot/blog/cline-v3-12-faster-diff-edits-model-favorites-and-more))

---

### Prompt Engineering Playbook

Addy Osmani (Engineering Lead for Google Chrome) released a comprehensive guide: "The difference between a vague 'fix my code' request and a well-crafted prompt can mean the difference between generic, unhelpful advice and precise, actionable solutions."

([Full Guide](https://addyo.substack.com/p/the-prompt-engineering-playbook-for))

---

## 🚨 Cursor Workshop this Friday! 🚨

### Unlock Cursor's Full Potential for Your Whole Team!

I'm genuinely excited to share this workshop with you. After months of pushing the boundaries with AI coding tools, I've distilled everything into a 6-hour intensive that will transform how you work with Cursor (and any AI coding assistant).

**Why This Workshop is Different:**
- **Real workflows, not tutorials**: Learn the exact processes I use daily to build production apps
- **Failure-focused approach**: Turn every AI mishap into a learning opportunity
- **Tool-agnostic principles**: Master concepts that work across Cursor, Claude Code, Copilot, and beyond

**What You'll Master:**
✅ Project generation that actually works (with proper structure and best practices)
✅ Context curation for massive codebases without token overload
✅ Agent behavior patterns - know when they're stuck BEFORE wasting credits
✅ Composer pipelines that automate your entire workflow
✅ Git + AI strategies for safe experimentation
✅ Multi-file refactoring without the chaos

**Proven Results:**
> "Take John's cursor workshop it is 🔥 HIGHLY Recommend" —David Wells

> "I used the skills you taught me to effectively one-shot OAuth issuer support in the Epic Stack. So cool!" —Kent C. Dodds

> "John is the cursor god" —Sunil Pai

**Workshop Details:**
- **When:** Friday, June 27, 2025, 9:00 AM - 2:00 PM (PDT)
- **Format:** Live Zoom workshop with Q&A throughout
- **Includes:** Recording access, workshop materials, and my personal Cursor rules

**💰 Pricing:**
- **🎯 $249**
   (egghead.io yearly subscribers get a $100 discount)

**Perfect for:**
- Developers frustrated with AI tools that "almost" work
- Teams wanting to standardize their AI workflows
- Anyone who's felt the pain of agents modifying the wrong files
- Developers ready to 10x their productivity with AI

➡️ **[See Full Workshop Details](https://egghead.io/workshop/cursor)**

➡️ **[Register Now →](https://buy.stripe.com/7sY7sEbOZbGO0kx7pw9fW09)**

*P.S. - Team training packages available. Get your whole team up to speed with group rates!*

That's all for this week!

Remember: with great autonomous power comes great autonomous responsibility.

Keep shipping (but maybe keep a human in the loop...) and remember you can reply directly to this email if you have any questions or feedback.

John Lindquist

Enjoy this newsletter? [Share it with a friend!](https://egghead.io/newsletters/ai-dev-essentials)

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

AI Dev Essentials #12: Google launches Gemini CLI, Warp 2.0, and Midjourney goes video

Gemini CLI drops, Claude Code evolves, and Cursor powers up. Explore AI agents, tooling, and dev workflows in AI Dev Essentials Issue #12 by John Lindquist.

The Art of Keyboard Wizardry: Building a 1,000+ Line Karabiner Config

Hey Everyone 👋,

John Lindquist here with the **eleventh issue of AI Dev Essentials!** I've been digging more and more into using Claude Code to spin up projects. Here's a couple of fun ideas I've been putting together using _only_ Claude Code:

- dotagent (https://www.npmjs.com/package/dotagent) - A tool for syncing rules/instructions across Cursor/Claude/Windsurf/etc.
- chromancer (https://www.npmjs.com/package/chromancer) - A CLI for interacting with Chrome. It wraps around Claude Code to generate and save automated browser workflows.
- Script Kit (https://www.scriptkit.com/) - I added MCP support so Cursor/etc can invoke your scripts.

There's obviously no way I could've done this much work in the past week without using AI agents (mostly Claude Code).

I'm still deterimining guidelines for Cursor vs Claude Code. Cursor recently introduced a new Ultra plan that's $200/month and has 20x more usage than Pro which should allow you to run `o3` (an _awesome_ model) full time. My quick, too-early take:

- Cursor gives you more control and customizations. It's perfect for "focused" work where you want to be an active participant in the process.
- Claude Code is great for "automated" work where you just want to leave the desk and come back to to review a feature.

As always, the model used will always be more important than the tool. So since Cursor can also use Claude Opus/Sonnet and has backgound agents + bugbot, it's just a more fully-featured offering which can only continue to improve. I love CLIs (I'm mean I really, really love CLIS), but there always comes a point where you need to open a GUI. Anyway, I'll be continuing to use both for now.

I was also just accepted into the OpenAI Open Source fund which granted me $5000 in credits to use the Codex CLI (OpenAI's version of Claude Code), so I should have strong opinions on that very soon. I _love_ OpenAI's Codex through the web interface where it can spin up your project and submit PRs, so I have high hopes for the CLI.

I've honestly had more fun programming over the past couple of months than I've had during my entire 20+ year career!

### 🎓 New egghead.io Lessons This Week

The [Building Local AI Agents with Ollama and the AI SDK](https://egghead.io/courses/scripting-local-language-models-with-ollama-and-the-vercel-ai-sdk~gmi9k) course is now live! I've released the first 5 lessons covering:
- Setting up Ollama for local AI development
- Building your first agent with the AI SDK
- Implementing tool calling and function execution
- Managing agent state and conversation flow
- Deploying local agents with persistent memory

Check out the course at [https://egghead.io/courses/scripting-local-language-models-with-ollama-and-the-vercel-ai-sdk~gmi9k](https://egghead.io/courses/scripting-local-language-models-with-ollama-and-the-vercel-ai-sdk~gmi9k)

---

## 🤖 Model & Platform Updates

### Gemini 2.5 Family Goes Stable

Google has restructured their Gemini 2.5 lineup with significant improvements:

**Gemini 2.5 Pro** (Stable, no changes from 06-05)
- Now the stable long-term support model
- Maintains existing pricing structure
- "gemini-2.5-pro" model name for production use

**Gemini 2.5 Flash** (Stable, updated pricing)
- Simplified pricing based on developer feedback
- Performance improvements from 05-20 variant
- Now the go-to model for cost-conscious applications

**NEW: Gemini 2.5 Flash-Lite** (Preview)
- Small reasoning model at just $0.10/1M input, $0.40/1M output
- Pushes the cost/intelligence frontier
- Ideal for high-volume applications requiring reasoning capabilities

([Google Blog](https://blog.google/products/gemini/gemini-2-5-model-family-expands/), [Google Cloud Blog](https://cloud.google.com/blog/products/ai-machine-learning/gemini-2-5-flash-lite-flash-pro-ga-vertex-ai))

> The introduction of Flash-Lite is particularly exciting. We're seeing Google compete aggressively on the cost front while maintaining reasoning capabilities. This could be a game-changer for applications that need to process massive amounts of data with some level of intelligence. Honestly, I'm kinda glad they've shipped 2.5 so they can focus on whatever 3.0 is going to be 😅

### OpenAI's Open Source Model Progress Update

At Y Combinator's AI startup school, Sam Altman provided an update on OpenAI's open-source model initiative, first announced in March 2025. The model, which will be OpenAI's first open-weight release since GPT-2 in 2019, is being developed with reasoning capabilities similar to o3-mini and is aimed for release in early summer.

Key details:
- Text-in, text-out model designed for high-end consumer hardware
- Highly permissive license with few usage restrictions
- Toggle-able reasoning capabilities
- Led by VP of Research Aidan Clark

([TechCrunch](https://techcrunch.com/2025/03/31/openai-plans-to-release-a-new-open-language-model-in-the-coming-months/), [Reuters](https://www.reuters.com/technology/artificial-intelligence/openai-plans-release-open-weight-language-model-coming-months-2025-03-31/), [VentureBeat](https://venturebeat.com/ai/openai-to-release-open-source-model-as-ai-economics-force-strategic-shift/))

> OpenAI has been gathering community feedback since March, and Altman himself admitted they've been "on the wrong side of history" regarding open source. This shift is clearly influenced by the success of Meta's Llama models (over 1 billion downloads) and pressure from competitors like DeepSeek. I'm looking forward to seeing how this balances OpenAI's commercial interests with community needs.

### Revolutionary AI Performance in Medical Research

A combination of GPT-4.1, o3-mini-high, and Gemini 2.0 Flash achieved superhuman performance in systematic medical reviews ([medRxiv](https://www.medrxiv.org/content/10.1101/2025.06.13.25329541v1)):

- Completed 12 work-years of Cochrane reviews in just 2 days
- 96.7% sensitivity vs 81.7% for human reviewers
- Found 54 additional eligible studies missed by original authors
- Generated new statistically significant findings in 2 reviews

The system correctly identified all originally included studies while discovering critical findings humans missed, like preoperative immune-enhancing supplementation reducing hospital stays by one day.

> This is the kind of AI application that genuinely excites me. It's not replacing researchers but augmenting them to find insights that would otherwise be missed. The fact that blinded reviewers sided with the AI 69.3% of the time when there were disagreements speaks volumes.

---

## 🔧 Developer Tooling & MCP Updates

### MCP Goes Mainstream

The Model Context Protocol is having a moment:

**Cloudflare Open Sources use-mcp**
- React library connecting to any MCP server in 3 lines of code
- Supports SSE and Streamable HTTPS remote servers
- Handles OAuth flow automatically
- Part of the official MCP organization for reference implementation

([Cloudflare Blog](https://blog.cloudflare.com/connect-any-react-application-to-an-mcp-server-in-three-lines-of-code/), [GitHub](https://github.com/modelcontextprotocol/use-mcp))

**OpenAI Adds MCP Support to ChatGPT**
- Limited initially to search and fetch tools for deep research
- Remote MCP with OAuth support
- Documentation available at [platform.openai.com/docs/mcp](https://platform.openai.com/docs/mcp)

**Google Cloud Run MCP Tutorial**
- Deploy remote MCP servers in under 10 minutes
- Comprehensive guide for production deployments

([Google Cloud Blog](https://cloud.google.com/blog/topics/developers-practitioners/build-and-deploy-a-remote-mcp-server-to-google-cloud-run-in-under-10-minutes))

**Block's MCP Best Practices**
- Lessons from building 60+ MCP servers
- Design patterns and architecture recommendations

([Block Engineering Blog](https://engineering.block.xyz/blog/blocks-playbook-for-designing-mcp-servers))

> MCP is becoming the standard for AI tool integration. The fact that major players are all adopting it suggests we're moving toward a more interoperable AI ecosystem. I'll definietly have more educational materials around MCPs soon!

### OpenAI's Prompt Management Revolution

OpenAI introduced Prompts as an API primitive:

- Centrally manage, version, and optimize prompts
- Use across Playground, API, Evals, and Stored Completions
- Preconfigured with tools, models, and messages
- New "Optimize" button in Playground for API optimization
- Replaces the old presets system

([OpenAI Community](https://community.openai.com/t/prompts-are-an-api-primitive-now/1290247), [Documentation](https://platform.openai.com/docs/guides/text?api-mode=responses#reusable-prompts))

> Finally! Prompt management has been a pain point for production AI applications. This brings prompts to the same level as other API resources, making it easier to maintain consistency across teams and applications.

### Developer Resources

**models.dev - Open Source AI Model Database**
- Comprehensive pricing and limits information
- Powers opencode under the hood
- Community-contributed with API access
- Star and contribute at [models.dev](https://models.dev)

([Official Site](https://models.dev/), [GitHub](https://github.com/sst/models.dev))

**Container-use Now Works with OpenAI Codex CLI**
- Solomon Hykes confirmed integration
- Enables containerized development workflows

([GitHub](https://github.com/dagger/container-use))

> Giving AI Agents enough control to `rm -rf` is why the community is so bullish on containers for everything!

---

## 💻 Cursor Corner

### Cursor Ultra Plan Launches at $200/month

Cursor introduced a new Ultra tier with 20x more usage than Pro:

- Addresses heavy user demands for unlimited-style usage
- Positioned between Pro and enterprise offerings
- Reflects the growing appetite for AI-assisted development at scale

([Cursor Blog](https://www.cursor.com/blog/new-tier), [TechCrunch](https://techcrunch.com/2025/06/17/anysphere-launches-a-200-a-month-cursor-ai-coding-subscription/))

> This is exactly what I've been hoping for! The mental overhead of tracking usage on Pro was real. While $200/month isn't cheap, for professional developers who live in Cursor, it's a no-brainer. I'm curious to see if this pressures other tools to offer similar "all you can eat" plans. There's a bit of buzz around how ambigous the messaging/pricing and it's obviously frustrating for anyone trying to budget out AI costs.

## 🚀 Quick Updates

- **OpenAI Codex Best-of-N**: Generate multiple solutions simultaneously, rolling out to Pro, Enterprise, Team, Edu, and Plus users ([Changelog](https://help.openai.com/en/articles/11428266-codex-changelog))
- **Claude Code "ultrathink" Trigger**: Use thinking triggers for better results, with "ultrathink" being the most powerful ([Anthropic Docs](https://docs.anthropic.com/en/docs/build-with-claude/prompt-engineering/extended-thinking-tips))
- **Google Labs Flow Updates**: Complete tutorial thread on ingredients, frames, camera controls, and scene builder for Veo 3 ([Google Blog](https://blog.google/technology/ai/google-flow-veo-ai-filmmaking-tool/), [FAQ](https://labs.google/fx/tools/flow/faq))
- **ElevenLabs v3**: Revolutionary emotion control with audio tags like [excited], [whispers], [sighs], [laughs] ([ElevenLabs v3](https://elevenlabs.io/v3), [Blog](https://elevenlabs.io/blog/eleven-v3))

---

## 🔒 Security & Best Practices

### The Lethal Trifecta Warning

Simon Willison echoed many of my thoughts around MCP+Security. Definitely worth a read: ([Blog Post](https://simonwillison.net/2025/Jun/16/the-lethal-trifecta/)):

**The Lethal Trifecta = Private Data + Untrusted Content + External Communication**

Any AI system with all three components is vulnerable to data exfiltration attacks. This is especially critical for MCP users who might inadvertently combine:
- File system access (private data)
- Web browsing (untrusted content)  
- API calls or messaging (external communication)

**Mitigation Strategies:**
- Never combine all three capabilities in one agent
- Implement strict sandboxing between components
- Use human-in-the-loop verification for sensitive operations
- Audit your MCP tool combinations carefully

> This is the kind of security thinking we need more of. As we give AI agents more capabilities, we need to think adversarially about how they could be exploited. The MCP ecosystem makes it trivially easy to create vulnerable combinations.

---

## ⚡ AI Ecosystem Observations

### Google Cloud IAM Outage Cascade

A major lesson in infrastructure dependencies:
- Google Cloud IAM failure cascaded to Cloudflare, Anthropic, Spotify, Discord, and Replit
- Highlighted single points of failure in the AI infrastructure stack
- Key takeaway: Implement graceful fallbacks and avoid single-provider dependencies

([Full Analysis](https://forgecode.dev/blog/gcp-cloudflare-anthropic-outage/))

> Did you feel this? I sure did. That was a strange couple of hours where I felt lost without my AI agents... I'm just glad it was a simple failure and not a more serious issue.

### AI Agents Playing Diplomacy

Fascinating behavioral patterns emerged:
- **Claude**: Couldn't lie, was exploited by other players
- **Gemini 2.5 Pro**: Nearly conquered Europe with tactical brilliance
- **o3**: Orchestrated secret coalitions, backstabbed allies, won

> These experiments reveal fundamental differences in how models approach strategy and deception. When choosing models for your applications, consider not just capabilities but behavioral tendencies. I'm sure we'll see more about this in the future.

### AGI as Product Experience

Logan Kilpatrick's perspective on AGI resonated strongly this week:
- AGI won't be a single model breakthrough
- Product experience integrating memory, reasoning, and context matters more
- Small model improvements + great design = "AGI moment" for users
- The narrative is shifting from model-centric to product-centric

([Source](https://youtu.be/kp9afmazO_w))

---

## 🚨 URGENT: Cursor Workshop Early Bird Ends TOMORROW! 🚨

### Turn Failures into Fuel with Cursor - Save $49 NOW!

**⏰ LAST CHANCE: Early bird pricing expires in less than 24 hours!**

I'm genuinely excited to share this workshop with you. After months of pushing the boundaries with AI coding tools, I've distilled everything into a 6-hour intensive that will transform how you work with Cursor (and any AI coding assistant).

**Why This Workshop is Different:**
- **Real workflows, not tutorials**: Learn the exact processes I use daily to build production apps
- **Failure-focused approach**: Turn every AI mishap into a learning opportunity
- **Tool-agnostic principles**: Master concepts that work across Cursor, Windsurf, Copilot, and beyond
- **Live debugging sessions**: Watch me handle real errors and stuck agents in real-time

**What You'll Master:**
✅ Project generation that actually works (with proper structure and best practices)
✅ Context curation for massive codebases without token overload
✅ Agent behavior patterns - know when they're stuck BEFORE wasting credits
✅ Composer pipelines that automate your entire workflow
✅ Git + AI strategies for safe experimentation
✅ Multi-file refactoring without the chaos

**Proven Results:**
> "Take John's cursor workshop it is 🔥 HIGHLY Recommend" —David Wells

> "I used the skills you taught me to effectively one-shot OAuth issuer support in the Epic Stack. So cool!" —Kent C. Dodds

> "John is the cursor god" —Sunil Pai

**Workshop Details:**
- **When:** Friday, June 27, 2025, 9:00 AM - 2:00 PM (PDT)
- **Format:** Live Zoom workshop with Q&A throughout
- **Includes:** Recording access, workshop materials, and my personal Cursor rules

**💰 Pricing:**
- ~~Regular Price: $249~~
- **🎯 EARLY BIRD: $200 (Save $49!)**
- **⚠️ Price increases TOMORROW at midnight!**

**Perfect for:**
- Developers frustrated with AI tools that "almost" work
- Teams wanting to standardize their AI workflows
- Anyone who's felt the pain of agents modifying the wrong files
- Developers ready to 10x their productivity with AI

➡️ **[See Full Workshop Details](https://egghead.io/workshop/cursor)**

➡️ **[SECURE YOUR EARLY BIRD SPOT NOW →](https://buy.stripe.com/7sY7sEbOZbGO0kx7pw9fW09?prefilled_promo_code=GEUHPI9B)**

*P.S. - Team training packages available. Get your whole team up to speed with group rates!*

*P.P.S. - Seriously, don't wait. The $49 savings ends tomorrow and I'd hate for you to miss out. This workshop will pay for itself in saved time within the first week.*

---

That's all for this week! Feel free to reply directly to this email with any questions or feedback.

Keep on shipping! 🚀

John Lindquist

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

AI Dev Essentials #11: Gemini 2.5 Stable, MCP goes mainstream

AI Dev Essentials #11: Claude Code fuels new CLI tools, Cursor Ultra plan launches, Gemini 2.5 Flash-Lite slashes costs, & OpenAI open-source model looms 

Standard AI SDKs are powerful for text generation, but they can't read your local files, understand your code's context, or write changes back to your project. This lesson demonstrates how to break past that limitation using the `@anthropic-ai/claude-code` SDK, which leverages the full power of the Claude Code executable installed in your environment.

You'll learn how to create simple yet powerful scripts that are aware of your codebase and can perform actions like reading files and writing output, all driven by natural language prompts.

**Workflow demonstrated in this lesson:**

* Initialize a new project with Bun and install the `@anthropic-ai/claude-code` SDK.
* Use the `query` function to stream responses from the AI.
* Filter streamed events to isolate and handle the final successful text result.
* Craft prompts that instruct the AI to read the script's own source code and analyze its contents.
* Grant permissions to the AI to perform file system actions, such as writing its findings to a new text file.

**Key benefits:**

* **Go Beyond Text Generation:** Build scripts that can read, understand, and interact with your project files.
* **Leverage Built-in Tool Use:** Directly access Claude Code's ability to perform actions without complex API integrations.
* **Automate Code Analysis:** Create tools that can analyze variables, summarize files, or perform other code-related tasks with a simple prompt.
* **Secure by Default:** Learn how to explicitly grant permissions for actions like file editing, ensuring you remain in control.

Build Your Own AI Dev Tools with the Claude Code SDK

### **Build Powerful, Local AI-Driven Command-Line Scripts**

Go beyond simple AI chats and unlock the true potential of local language models by learning to script them. This course will guide you step-by-step through the process of building a flexible and intelligent command-line tool that can understand natural language, interact with your file system, and automate complex tasks—all running entirely on your local machine.

Starting with a blank TypeScript file, you'll learn how to combine the power of **Ollama**, the **Vercel AI SDK**, and **Zod** to create a script that's not just functional, but smart. We'll begin by teaching the AI to parse a simple command from a sentence. From there, we'll progressively add layers of sophistication: processing dynamic user input, making the script aware of your project's files, and generating meaningful text-based output.

You'll also learn critical patterns for building robust and extensible tools. We'll cover how to handle invalid commands and unexpected AI responses gracefully. Then, you'll refactor the script to use a modular, file-based system for its commands, allowing you to easily add new functionality without touching the core logic. Finally, we'll add a layer of intelligence that allows your script to infer exactly what you mean, right down to the type of file you want it to process.

By the end of this course, you will have built a powerful, personalized AI assistant and gained the foundational skills to create sophisticated local automations for any task you can imagine.

**What You'll Learn:**

* How to script local language models using Ollama and the Vercel AI SDK.
* To parse and process dynamic user input from the command line.
* To use Zod schemas, including `enums`, to validate and structure AI model output.
* Techniques for providing file-system context to your scripts with `globby`.
* How to generate context-aware text based on file content.
* Robust error handling for invalid user commands and unexpected AI responses.
* To build a modular and extensible tool by defining commands in separate files.
* To intelligently infer user intent, such as dynamically determining file types from a prompt.

Scripting Local Language Models with Ollama and the Vercel AI SDK

Build a local AI-powered command-line assistant from scratch. Learn to use Ollama, Vercel AI SDK & Zod to parse input, automate tasks, and run scripts offline.

This lesson demonstrates how to give Cursor direct access to your terminal output using the [Nx MCP extension](https://nx.dev/getting-started/ai-integration?utm_source=youtube&utm_medium=videos&utm_campaign=Nx-AI&utm_id=Nx-AI), enabling automatic error detection and fixing in monorepo projects.

**Workflow demonstrated in this lesson:**

- Encounter a runtime error when serving a Vite application in an Nx monorepo
- Install and configure the Nx Console extension with MCP integration
- Ask Cursor to check your terminal for errors and fix the underlying issues
- Watch as the AI accesses running task details and terminal output through Nx MCP tools
- Observe how it analyzes workspace relationships to identify incorrect imports and missing dependencies

**Key benefits:**

- Seamless Error Handling: AI gains direct access to terminal output and running task information
- Automatic Diagnosis: Accurately identify the root cause of errors without manual copying
- Context-Aware Fixes: Leverage workspace knowledge to implement proper solutions
- Reduced Context Switching: Eliminate the need to manually relay information between terminal and AI



Juri Strumpflohner

Enable Cursor to directly access the terminal to read the output and fix issues

See how Nx MCP gives AI coding assistants direct access to terminal output and running tasks, enabling automatic error detection and fixing.

This lesson demonstrates how to solve the predictability problem when using AI to generate code in monorepos by combining Cursor with Nx generators.

**Workflow demonstrated in this lesson:**

- Explore how Nx generators provide predictable scaffolding for new libraries
- Set up [Nx Console with MCP](https://nx.dev/features/enhance-AI?utm_source=egghead&utm_medium=videos&utm_campaign=Nx-AI&utm_id=Nx-AI) integration to expose generator capabilities to Cursor
- Use the AI assistant to brainstorm a new feature with the help of the Nx MCP tools
- Have Cursor leverage the Nx MCP tools to trigger the appropriate Nx generator for creating a React-based feature library
- Observe how the AI assistant takes the generated library and further integrates it into the codebase

**Key benefits:**

- **Predictable Code Structure**: Ensure generated libraries follow your project's and organization's conventions
- **Consistent Setup**: Maintain uniform configuration across TypeScript projects and package.json files
- **Guided Implementation**: Combine AI instructions with structured, predictable code generation

Enable Cursor to generate predictable project setups

Learn how to combine Cursor AI with Nx generators for predictable library creation in monorepos, ensuring consistent structure across your codebase.

This lesson demonstrates how AI assistants struggle with complex monorepo structures and how to dramatically improve their understanding using the [Nx MCP extension](https://nx.dev/features/enhance-AI?utm_source=egghead&utm_medium=videos&utm_campaign=Nx-AI&utm_id=Nx-AI) in Cursor.

**Workflow demonstrated in this lesson:**

- Observe how AI initially attempts to understand monorepo structure by scanning package.json files and analyzing folder hierarchies
- Install the Nx Console extension and configure the Nx MCP server in your project
- Enable the Nx MCP integration in Cursor's settings to access 15 powerful workspace tools
- Ask the AI assistant questions about your workspace structure and dependencies
- Watch as the AI leverages Nx's knowledge to accurately identify project relationships
- Visualize dependencies through an interactive graph generated by the Nx tools

**Key benefits:**

- Enhanced Workspace Understanding: AI assistants gain direct access to Nx's comprehensive workspace knowledge
- Accurate Dependency Mapping: Precisely identify connections between libraries and applications
- More efficient parsing of your workspace structure for your AI assistant

Give Cursor monorepo awareness with the Nx MCP

Learn how to enhance AI capabilities in monorepo setups using Nx MCP integration with Cursor, providing better project insights and dependency visualization.

TypeScript can sometimes feel like bunch of nonsense. But once you internalize a few key patterns, the type system starts to feel like a powerful tool instead of an obstacle.

Here are a few tricks that I’ve personally found valuable, especially if you’re working on a large codebase or building libraries.

---

## 1. Optional Keys vs Optional Values

There’s a big difference between marking a property as optional and allowing `undefined` as a value. These look similar:

```ts
type OptionalKey = {
  foo?: string;
}

type OptionalValue = {
  foo: string | undefined;
}
```

But they behave differently:

* `foo?: string` means the key `foo` might not be present at all.
* `foo: string | undefined` means `foo` is always present, but its value might be `undefined`.

Why this matters? The difference between them affects the way you use it:

* How you check if a property exists
* Function argument inference
* Serializing/deserializing data from external sources

### Example

```ts
function logFoo(obj: OptionalKey) {
  if ('foo' in obj) {
    console.log(obj.foo?.toUpperCase());
  }
}

function logBar(obj: OptionalValue) {
  console.log(obj.foo?.toUpperCase()); // May log undefined
}
```

As a rule of thumb, use `foo?: Type` (Optional Key) when the property might not be present, and `foo: Type | undefined` (Optional Value) when the key is always expected.

---

## 2. Pick and Omit for Object Key Manipulation

When you need to reshape object types by including or excluding specific keys, `Pick` and `Omit` are your go-to tools.

This type utilities are part of the language and are always available for your use, they are particulary useful when you need to derive types.

> Don't forget, Pick and Omit works over Object shapes

### Pick

```ts
type User = {
  id: number;
  name: string;
  email: string;
};

type PublicUser = Pick<User, 'id' | 'name'>;
// { id: number; name: string }
```

`Pick` is useful when exposing a public-facing API without leaking sensitive fields, it lets you choose what properties will be included

### Omit

```ts
type PrivateUser = Omit<User, 'email'>;
// { id: number; name: string }
```

Use `Omit` when you want to reuse a base type but remove some fields for specific scenarios, like forms or UI display logic.

These utilities keep your types DRY and consistent. Instead of redefining structures, you can derive them from existing ones, reducing duplication and potential errors.

---

## 3. Extract and Exclude for Union Filtering

Unlike `Pick` and `Omit`, which work on object keys, `Extract` and `Exclude` operate on union types, they are pretty similar in how the work.

### Extract

```ts
type Status = 'draft' | 'published' | 'archived';

type ArchivedOnly = Extract<Status, 'archived'>;
// 'archived'
```

Use `Extract` when you want to narrow down a union to only include specific members.

### Exclude

```ts
type VisibleStatus = Exclude<Status, 'archived'>;
// 'draft' | 'published'
```

Use `Exclude` when you want to remove one or more members from a union.


These are essential tools when refining API responses, filtering variants, or creating conditional logic that needs to type-check specific string literal options.

---

## 4. Loose Autocomplete

This is a different kind of trick, this one affects how your editor understand your types.

Sometimes you want to offer a set of suggestions to the user without forcing them to pick from a fixed list.

### The trick

```ts
type Variant = 'success' | 'warning' | 'info'

function setVariant(variant: Variant | (string & {})) {
  // ...
}
```

Now TypeScript will offer autocompletion for the variants defined in the `Variant` union, but will also allow  allow any other string.


`string & {}` is essentially still a string, but TypeScript treats it as a branded version, preserving autocomplete while keeping it assignable to `string`.

> [Here](https://egghead.io/blog/using-branded-types-in-typescript) you can found another article related to [Branded Types](https://egghead.io/blog/using-branded-types-in-typescript)

### Use cases

* Props with suggested values
* Config objects
* Third-party data with flexible inputs

---

## 5. Mapped Types for Smart Rewrites

Mapped types let you create new types from existing ones by transforming each property. This concept is a cornerstone of TypeScript’s type system and is covered more deeply in my article: [Learn the Key Concepts of TypeScript’s Powerful Generic and Mapped Types](https://egghead.io/blog/learn-the-key-concepts-of-typescript-s-powerful-generic-and-mapped-types).

You can consider Mapped types as some kind of function that transform a type in another by using all of the utilities and features of Typescript, like the above utilities and tricks, but also string literals, and more.

### Example: feature flags

```ts
type Config = {
  debug: string;
  logging: string;
};

type FeatureFlags = {
  [K in keyof Config]: boolean;
};
// This will create the following type
// { debug: boolean; logging: boolean }
```

### General Utility Type

```ts
type Booleanize<T> = {
  [K in keyof T]: boolean;
};
```

Mapped types are perfect for:

* Adapting backend responses
* Creating flexible utility types
* Rewriting shapes while preserving keys
* Enforcing contracts between layers

They’re also essential in combination with generics to create reusable type logic across your app or library.

> Found more about Generics in the following article [An Introduction to Typescript Generics](https://egghead.io/blog/an-introduction-to-typescript-generics)

---

## 6. The IIMT Pattern (Infer Inside Mapped Type)

This pattern unlocks the power of `infer` within mapped types, allowing you to extract and reuse internal type information from complex structures.

It dynamically inspects each property of a type (by using `infer`), and if that property matches a specific shape (e.g., a function), it extracts part of it.

This means you can build new types that are entirely based on the structure of other types without manually copying anything.

### The pattern

```ts
type API = {
  getUser: () => Promise<{ name: string }>;
  logout: () => void;
};

type ReturnTypes<T> = {
  [K in keyof T]: T[K] extends (...args: any) => infer R ? R : never;
};

// Result:
// {
//   getUser: Promise<{ name: string }>;
//   logout: void;
// }
```

Here, ReturnTypes iterates over each key in the API object and uses conditional types to check if the value is a function. If it is, it uses infer R to grab the return type of that function.

### How it works
- `keyof T` iterates over each key of the input type.
- `T[K]` accesses the value type at each key.
- `T[K] extends (...args: any) => infer R` checks if that value is a function. (consider this as a conditional block)
-  `infer R` captures the return type of the function.
- If the value is not a function, the fallback is never.

This pattern is composable—you can use it to build return type maps, argument type maps, or anything else you need.

```ts
type ArgumentTypes<T> = {
  [K in keyof T]: T[K] extends (...args: infer A) => any ? A : never;
};
```

This will give you a type with the argument list of each function in a mapped type.

You can use this in different situations like:

- Generating automatic schemas
- Building strongly-typed service clients
- Rewriting function maps
- Validating types without duplicating logic

If you’re building libraries or dynamic APIs, this pattern is a must-have in your TypeScript toolbox.

Once it clicks, it opens the door to expressive, scalable, and maintainable type logic.




Matías Hernández

Advanced TypeScript Tricks for Cleaner, Safer Code

Improve your TypeScript skills with advanced patterns like Pick, Exclude, mapped types, and the infer-inside-mapped-type trick.

AI Dev Essentials #9: Playwright MCP is amazing, Hot Model Updates & Essential Dev Tools

Learn Playwright MCP, track AI model updates (Gemini, DeepSeek), explore new dev tools & agent insights. Your AI dev update.

AI Dev Essentials #8: Claude's Costly Lessons, Google's AI Flood & Workshop Alert 🚀

Claude Max Mode in Cursor is powerful but pricey—one vague task can rack up costs fast. Learn how to control it and explore the latest AI dev tools.

Hey Everyone 👋,

John Lindquist here with the **tenth issue of AI Dev Essentials!** This week has been absolutely monumental for the AI coding space. Cursor hitting 1.0 and raising $900 million at a nearly $10 billion valuation is a watershed moment for AI-assisted development. The speed at which they've grown—from $1 million to over $500 million in ARR in just over two years—shows just how hungry developers are for better AI coding tools. And with background agents now generally available, we're entering a new era where AI can truly work alongside us asynchronously.

I've personally spent a lot of time withe the "Claude Code" experience the past week to compare it to Cursor. I totally understand the appeal, especially because of how reliably Claude Code can churn away at long tasks (and you're not thinking about burning through requests/costs), but Cursor is still a superior experience when it comes to gathering context and reviewing changes. I'm hoping this puts pressure on the Cursor team to release a "MAX" plan similar to Claude's "MAX" plan. I like having a budget, but I don't like thinking every failed tool call is lost money. I try to remember that we're still all in the super early days of AI, and everyone is just figuring it out as they go.

With that said, let's dive into this week's updates!

### 🎓 New egghead.io Lessons This Week

I'm working on a course "Building Local AI Agents with Ollama and the AI SDK". I'm 5 lessons in and I'm excited to share them, but just not quite ready. They should be up when this newsletter rolls around next week, so keep an eye out for that. 😇

---

## 🚀 Cursor 1.0 & The $900M Funding Round

**Cursor has officially hit 1.0 and raised $900 million at a $9.9 billion valuation**

This week brought two massive announcements from Cursor that cement its position as the leader in AI coding assistants. First, the 1.0 release brings game-changing features like BugBot for automated code review, background agents for asynchronous work, and seamless GitHub integration. Then came the funding news: $900 million led by Thrive Capital, with participation from Andreessen Horowitz, Accel, and DST Global.

**Key Stats:**

- Over $500 million in ARR (growing 60% since April)
- Used by more than half of the Fortune 500, including NVIDIA, Uber, and Adobe
- ARR has been doubling approximately every two months

**Cursor 1.0 Features:**

- **BugBot**: Automatically reviews PRs and catches potential bugs with "Fix in Cursor" integration
- **Background Agent**: Now GA, lets you spawn AI workers for parallel tasks in remote environments
- **Memories**: Per-project context retention so AI doesn't forget key facts
- **Jupyter Support**: AI can now work inside .ipynb notebooks
- **One-click MCP setup**: Streamlined installation for MCP servers

([TechCrunch Coverage](https://techcrunch.com/2025/06/05/cursors-anysphere-nabs-9-9b-valuation-soars-past-500m-arr/), [DEVCLASS Analysis](https://devclass.com/2025/06/06/cursor-ai-editor-hits-1-0-milestone-including-bugbot-and-high-risk-background-agents/))

> I do love seeing money being poured into companies that are building AI tools for developers. I'm hoping that the leads to customized models that are smarter, faster, with reliable tool calling, and features around "agent swarms" that can tackle complex tasks in parallel. I'm really excited to see what sorts of UIs emerge for orchestrating these agents.

---

## 🧠 Model & Platform Updates

### OpenAI o3 & o3-pro: Major Price Drop and Release

OpenAI made significant waves this week with two major announcements. First, Sam Altman confirmed an 80% price reduction for o3, making it viable as a "daily driver" model. Cursor immediately updated their pricing to reflect this change, noting that "o3 is a fantastic model, and we're excited that it can now be used as a daily driver."

Then came **o3-pro**, OpenAI's most advanced reasoning model yet. Community consensus is forming around its capabilities and limitations:

**o3-pro Strengths:**

- Excels at math competitions and scientific reasoning
- Handles complex multi-step problems with deeper thinking
- Acts as a "superhuman researcher + structured thinker" capable of processing massive amounts of data
- Demonstrates impressive performance on challenging tests like the "reindeer" problem

**Community Reality Check:**

- Not ideal as a daily coding model—better suited for complex reasoning tasks
- Mixed results on real-world coding: One developer reported it failed to fix type errors, reducing 34 errors to only 28 despite confident promises
- Most users don't need this level of reasoning power for everyday tasks
- Best used for research, analysis, and uncovering insights you might miss

**Codex Gets Internet Access**: OpenAI also rolled out internet access for Codex, allowing it to install dependencies, run tests with external resources, and upgrade packages during task execution. The feature is off by default and provides granular control over allowed domains and HTTP methods.

> This all rolled out last night. I haven't had a chance to dig in to o3-pro, so I just gathered a bunch of early impressions from the community above. I'll have my own personal take next week.

### Gemini 2.5 Pro Tops WebDevArena

Google's updated Gemini 2.5 Pro continues to excel at coding, now leading WebDevArena with a 1443 Elo score (35-point jump). The update introduces:

- **Thinking budgets** for cost/latency control
- Enhanced coding performance for complex web apps
- Improved style and structure in responses
- GA coming in the next few weeks
- **New SOTA on Aider Polyglot**: The upcoming GA version scores 86.2%, beating the previous version by 10 percentage points

([Google Blog](https://blog.google/products/gemini/gemini-2-5-pro-latest-preview/), [9to5Google](https://9to5google.com/2025/06/05/gemini-2-5-pro-preview-06-05/))

> Gemini 2.5 Pro is in this interesting place where it's _awesome_, but still a bit difficult to work with in Cursor. I really, really want to use it as my daily driver, but the thinking process often seems to spin too much. I'm hoping a Cursor system prompt (or a community propmt) will emerge to help smooth it all out. The results have always been great, but sometimes it ends up taking too long and my patience wears out.

### Mistral Launches Magistral: Multilingual Reasoning Models

Mistral AI announced Magistral, their first reasoning models designed to excel in domain-specific, transparent, and multilingual reasoning. The models come in two variants:

- **Magistral Small (24B)**: Open-source under Apache 2.0 license
- **Magistral Medium**: Enterprise version with enhanced capabilities

Key differentiators include multilingual reasoning (especially strong in European languages), 10x speed improvements in Le Chat, and transparent thinking processes users can follow and verify. ([CNBC Report](https://www.cnbc.com/2025/06/10/microsoft-backed-ai-lab-mistral-debuts-reasoning-model-to-rival-openai.html), [TechCrunch](https://techcrunch.com/2025/06/10/mistral-releases-a-pair-of-ai-reasoning-models/))

> Any open-source news is good news. While I'm happy to pay providers for "premium" access to models, I'm still hopeful open-source will unlock AI for everyone down the road. I just never make time to test the open-source models with so much else happening at the same time.

---

## 💰 Major Funding & Business Updates

### Linear Raises $82M at $1.25B Valuation for AI Agent Platform

Linear secured $82 million in Series C funding led by Accel, positioning itself as "the place where teams, AI, and agents build software together." With 280% profit growth and over 15,000 customers including OpenAI, Scale AI, and Perplexity, Linear is focusing on making their platform the natural home for AI agents to coordinate work across product workflows. ([Reuters](https://www.reuters.com/business/atlassian-competitor-linear-raises-funding-125-billion-valuation-2025-06-10/), [Linear Blog](https://linear.app/blog/building-our-way))

> I included this because Linear's MCP is a popular MCP for agents. So far, everyone uses so many variations of "AI Task Tracking" and it seems like Linear is raising to the top. Seeing them focus on AI-specific language and raising money is only a good sign (maybe Cursor will buy them or spin off their own version someday?)

---

## 🛠️ Developer Tools & Ecosystem

### Vertex AI Studio Gets Major Redesign

Google Cloud unveiled a redesigned Vertex AI Studio with a developer-first approach:

- Dark mode support across the platform
- Unified UI combining Chat and Freeform prompting
- Easy grounding with Google Search or Maps
- Direct code generation for Python, Android, Swift, Web, Flutter
- Access to 200+ foundation models including Gemini, Llama, and Claude

([Google Cloud Blog](https://cloud.google.com/blog/products/ai-machine-learning/vertex-ai-studio-redesigned))

### ElevenLabs Launches v3 with Emotion Control

ElevenLabs v3 introduces revolutionary emotion direction through audio tags:

- Control emotions with tags like [excited], [whispers], [sighs], [laughs]
- 70+ language support
- Enhanced expressiveness and contextual understanding
- Multi-speaker dialogue with matching prosody

([ElevenLabs v3](https://elevenlabs.io/v3), [Documentation](https://elevenlabs.io/blog/eleven-v3))

> These voices are _so good_. I'm tempted to toss one of my favorite books in their ("The Silent Gondoliers") and go on a road trip to see how they handle full narration.

### OpenAI Agents SDK Now in TypeScript

OpenAI released their Agents SDK in TypeScript with full feature parity to Python:

- Human-in-the-loop approvals
- Handoffs, guardrails, tracing, MCP support
- RealtimeAgent for voice interactions
- Enhanced Traces dashboard for Realtime API sessions

([OpenAI Developers](https://openai.github.io/openai-agents-js/))

> TypeScript is arguably _the_ language for building AI agents thanks to all the hosting/endpoints/etc already built for it. For most platforms, it becomes trivial to plop in a TypeScript SDK package, an API key, and spin up Agents.

---

## 🔧 Quick Updates

- **Prisma Local Development**: `npx prisma dev` now persists data between sessions and supports multiple instances without Docker ([Prisma Blog](https://www.prisma.io/blog/prisma-6-8-0-release))
- **OpenAI Advanced Voice**: Updated for more natural conversations with improved translation capabilities
- **Firebase Studio**: Turn Figma designs into working apps with Gemini integration ([Firebase Blog](https://firebase.blog/posts/2025/05/whats-new-at-google-io/))
- **Supermemory MCP**: Now open source for infinite chat context ([GitHub](https://github.com/supermemoryai/supermemory-mcp))
- **Taskmaster v0.16**: New .taskmaster folder structure with Bedrock, Azure, and Vertex AI support ([GitHub](https://github.com/eyaltoledano/claude-task-master))
- **Ask-Human MCP**: New zero-config tool for human-in-the-loop verification—pauses agents when unsure, logs questions to `ask_human.md`, resumes when you provide answers ([GitHub](https://github.com/Masony817/ask-human-mcp))
- **AI Studio Clarification**: Google's Logan Kilpatrick confirmed AI Studio free tier isn't going away—it's transitioning to API key-based access with continued free tier availability

---

## 🤖 AI Research & Observations

### AIs Play Diplomacy: Revealing Model Personalities

A fascinating experiment had different AI models play the strategy game Diplomacy, revealing distinct behavioral patterns:

- **Claude** couldn't lie and was exploited ruthlessly by other players
- **Gemini 2.5 Pro** nearly conquered Europe with brilliant tactical gameplay
- **o3** orchestrated secret coalitions, backstabbed every ally, and won

This real-world test highlights how different models exhibit varying levels of strategic thinking, deception capability, and goal-oriented behavior—important considerations when choosing models for different applications.

> I'm hoping someday these can turn into live visual competitive storytelling experiences. Kind of like a battlebots tournament, but with AI models. I'll admit I'm also hiding my head in the sand when it comes to using AI to "dominate" anything. (I love you AI Overlords! ❤️)

### Anthropic Cuts Windsurf API Access

In a surprising move, Anthropic reportedly restricted Windsurf's API access to Claude models. This development raises questions about the competitive dynamics between AI providers and coding assistant platforms. ([TechCrunch](https://techcrunch.com/2025/06/10/anthropic-windsurf-api-access-restricted/))

> Seems fair IMO. OpenAI bought Windsurf. OpenAI competes with Anthropic. Why would Anthropic want to help OpenAI make money?

---

## 💡 Security & Best Practices

**Vercel on AI Agent Security**: New guidance on prompt injection vulnerabilities emphasizes: assume compromise, limit tool call access, and don't trust output. Essential reading for anyone building AI agents. ([Vercel Blog](https://vercel.com/blog/building-secure-ai-agents))

---

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

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

- **When:** _Friday, June 27, 2025, 9:00 AM - 2:00 PM (PDT)_
- **Where:** Zoom (Live Q&A included)
- **Investment:** $249 (early bird prcing of $200 until June 18th)

➡️ [**Read More about the Workshop**](https://egghead.io/workshop/cursor)

➡️ [**Register Now ($249)**](https://buy.stripe.com/7sY7sEbOZbGO0kx7pw9fW09?prefilled_promo_code=GEUHPI9B)

_(Team training also available)_

---

That's all for this milestone tenth issue! The convergence of massive funding, breakthrough features, and growing enterprise adoption shows we're at an inflection point for AI-assisted development. The tools are getting more powerful, the agents more capable, and the possibilities more exciting.

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

John Lindquist

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

AI Dev Essentials #10: Cursor Hits 1.0 (and $9.9B!), OpenAI o3/pro, Gemini Updates

AI Dev Essentials #10: Cursor 1.0 lands with $900 M, OpenAI cuts o3 pricing & debuts o3-pro, Gemini 2.5 Pro tops coding tests—plus fresh tools, funding updates

Debugging complex web forms can be a grind. You manually input data, check console logs, and painstakingly try to reproduce bugs. This lesson unveils a powerful, largely automated workflow using Cursor's AI agent in conjunction with the Playwright MCP to thoroughly test a web form, identify issues, and even fix the underlying code.

First, you'll see how to instruct the AI to instrument your form's code with exhaustive console logging. Then, by providing only the URL of the live form, you'll delegate the testing process to the AI, which uses Playwright to interact with the form in multiple ways, attempting to break it and discover edge cases. The AI then analyzes the generated console logs to produce a detailed report of its findings. Finally, armed with this report and the original code, the AI can attempt to fix the identified bugs and re-verify its changes, all while you "go get a sandwich."

**Workflow demonstrated in this lesson:**

*   Instruct Cursor's AI agent to add comprehensive `console.log` statements to your form's TypeScript (.tsx) file.
*   Provide the live URL of the web form (running on `localhost`) to the AI, without giving it direct access to the updated code file.
*   Direct the AI to use the Playwright MCP to open the URL in a browser.
*   Task the AI with devising and executing multiple test scenarios (e.g., empty submission, invalid inputs, mismatched passwords) to fill out and submit the form.
*   Have the AI retrieve and analyze the browser's console logs after each test attempt.
*   Generate a consolidated report detailing the form's behavior, observed errors, and potential bugs based on the logs.
*   Instruct the AI to use this report and the original form code (now brought back into context) to fix the identified issues.
*   Optionally, have the AI re-run the tests to verify the fixes.

**Key benefits:**

*   **Automated Test Execution:** Leverage Playwright through Cursor to automatically interact with your live form.
*   **AI-Driven Test Case Generation:** The AI devises different input strategies to uncover bugs.
*   **Log-Based Bug Reporting:** Automatically capture and analyze console output to pinpoint issues.
*   **Hands-Off Debugging:** Delegate the testing and initial bug reporting to the AI.
*   **Efficient Bug Fixing Cycle:** Use AI-generated reports to quickly guide AI-assisted code corrections.
*   **Focus on High-Level Direction:** Instead of manual testing, you guide the AI's testing strategy and bug-fixing process.

By the end of this lesson, you'll have a robust method for delegating the tedious aspects of form testing and debugging to Cursor's AI, allowing for more efficient development and a more reliable end product.

---

**Phase 1: Instrumenting Code with Exhaustive Logging**

**Context:**
*   `{@filePath}` (e.g., `App.tsx`) is in Cursor's context.

**Prompt:**
"In `{@filePath}`, add exhaustive console logging for all form events, state changes, and validation logic. Focus on capturing input values, current errors, and the outcome of validation checks. The goal is to have detailed logs for later analysis. Do *not* fix any existing code errors; only add logging. Stop after modifying the file."

---

**Phase 2: AI-Driven Form Testing with Playwright and Log Analysis**

**Context:**
*   `{@formURL}` (e.g., `http://localhost:5173`) is in Cursor's context.
*   The live form at `{@formURL}` should be running the version of the code that *includes the exhaustive logging* added in Phase 1.
*   Playwright MCP is available.

**Prompt:**
"Using the Playwright MCP and the live form at `{@formURL}`:
1.  Devise [N, e.g., 3-5] distinct test scenarios to fill out and submit the form. Include scenarios designed to trigger validation errors and edge cases (e.g., empty submission, invalid data types, boundary values, mismatched passwords).
2.  For each scenario:
    *   Execute the form interaction using Playwright.
    *   Retrieve all browser console logs generated during that interaction.
3.  Compile a consolidated 'Form Behavior Report' detailing:
    *   Each test scenario and the inputs used.
    *   Observed behavior/UI state (e.g., error messages displayed).
    *   Key console log outputs relevant to errors or unexpected behavior.
    *   Any discrepancies or potential bugs identified.

---

**Phase 3: Fixing Bugs Based on AI Report and Re-Verification**

**Context:**
*   The 'Form Behavior Report' from Phase 2 is in the chat history.
*   `{@filePath}` (e.g., `App.tsx` – the original file, *not* the one Playwright interacted with, unless it's the same deploy) is in Cursor's context.
*   The live form at `{@formURL}` can be updated/re-deployed with fixes.
*   Playwright MCP is available.

**Prompt:**
"Based on the 'Form Behavior Report' (from our previous conversation) and the code in `{@filePath}`:
1.  Analyze the reported issues and implement the necessary code changes in `{@filePath}` to fix them.
2.  After applying the fixes and assuming the live form at `{@formURL}` is updated with these changes:
    *   Re-run *all previously defined test scenarios* using the Playwright MCP.
    *   Retrieve and analyze the console logs for each.
3.  Provide a verification summary: confirm if the original bugs are fixed and if any new regressions were introduced."

Bite-Sized Screencasts for Web Developers that Hate Long Boring Videos