Build Anything with Claude, Here's How

Key Concepts

• Cloth (Claude): Anthropic's AI model, specifically Sonnet 4.5.
• Sonnet 4.5: A powerful and cost-effective AI model by Anthropic, surpassing Opus 4.1 in many benchmarks.
• Cloth Code 2.0: Anthropic's coding assistant, featuring checkpoints, a revamped terminal interface, and context editing.
• Vibe Coding: Rapid prototyping and UI generation using AI, exemplified by Cloth Imagine.
• Context Anxiety: A phenomenon where AI models rush to complete tasks as they approach their context window limit.
• Computer Use: AI agents controlling web browsers to perform tasks, enabled by browser extensions.
• Agent SDK: A set of tools for developers to build AI agents, mirroring Anthropic's internal infrastructure.
• Codex: OpenAI's coding model, particularly GPD5 Codex High, known for its in-depth reasoning.
• Time to First Token: The delay between sending a request to an AI model and receiving the first output token.
• Optimistic Pre-loading: A technique to reduce perceived latency by proactively sending requests to the AI model based on user input patterns.

Cloth Updates and Sonnet 4.5

• Sonnet 4.5 Superiority: Enthropic's Sonnet 4.5 is presented as a leading AI model, outperforming Opus 4.1 on benchmarks while being five times cheaper.
• SWE-Bench Performance: Sonnet 4.5 excels on the SWE-Bench verified benchmark for software engineering, both with and without reasoning enabled.
• Cloth Code 2.0 Enhancements: The update includes checkpoints for reverting to previous states, a redesigned terminal interface, context editing, and ID extension.
• Example Application: A JavaScript app with exploding particles reacting to mouse movement is created in 20 seconds using Sonnet 4.5, showcasing its programming prowess.
• Integrated Functionality: Cloth now supports creating and editing sheets, documents, slides, and PDFs directly within the platform.
• Connecting to Google Services: Users can connect Cloth to Google Drive, Gmail, and Calendar for enhanced functionality.
• Example Prompt: "Which video ideas is my team working on? List out the five most recent Google Docs." demonstrates Cloth's ability to retrieve information from connected services.

Vibe Coding 2.0 with Cloth Imagine

• Procedural UI Generation: Cloth Imagine generates custom UIs in real-time using pre-built components, offering a new approach to software development.
• Interactive Exploration: Users can explore and interact with the app as it's being built, guiding the AI's creative process.
• Example: Generating a map with 10 points of hidden treasures demonstrates Cloth Imagine's ability to create custom UIs based on user descriptions.
• Limitations: The generated software is not reproducible, and the system relies on generating tokens for every element, even simple ones.

Context Anxiety and Context Editing

• Context Awareness: Sonnet 4.5 is the first AI model aware of its context window, influencing its behavior.
• Context Anxiety Definition: As the model approaches its context limit, it tends to rush tasks, potentially sacrificing quality for speed.
• Context Editing Explanation: The system compacts older, less relevant parts of the conversation to free up space in the context window.
• Visualization: The context window is visualized with older messages compressed and newer messages retaining more detail.

Computer Use and Browser Extension

• Browser Control: Enthropic released a browser extension allowing Cloth to control the user's web browser.
• Privacy Concerns: The extension is presented as a privacy-focused alternative to AI-powered browsers like Comet, which collect extensive user data.
• Pilot Program: Access to the browser extension is initially limited to 10,000 users with the Max plan.
• Demo Example: The demo showcases Cloth's ability to read page content, identify elements, and perform actions like archiving emails.
• Custom Prompt Example: Archiving declined Google Meet call emails demonstrates the extension's ability to perform specific tasks based on user instructions.
• Automation via Shortcuts: Users can create shortcuts with custom prompts and schedules to automate tasks within the browser.
• Example Shortcut: A shortcut to archive useless emails daily at 9:00 a.m. is provided.

Agent SDK Overview

• Developer Tools: The Agent SDK provides developers with tools to build AI agents, mirroring Anthropic's internal infrastructure.
• Feedback Loop: Cloth Code operates in a feedback loop, gathering context, taking action, verifying the work, and repeating.
• Gathering Context: The SDK organizes information using folders and files, allowing agents to efficiently access relevant data.
• Selective Loading: The system only loads relevant parts of large files to avoid context overload.
• Summarization: Past conversations are automatically summarized to conserve context window space.
• Taking Action: AI agents use tools (functions or series of functions) to perform specific tasks.
• Tool Focus: Tools should be tailored to the agent's specific purpose to avoid distractions.
• Verifying the Work: The SDK allows developers to add checks to ensure actions are performed correctly.
• Verification Examples: Running tests for code, taking screenshots for visual tasks, and querying databases to confirm changes.

Cloth Code 2.0 vs. Codex (GPT-5)

• IDE Agnostic: Cloth Code can be used in any code editor, including Cursor, VS Code, and Windsurf.
• Terminal and Extension Access: Cloth Code is accessible both through the terminal and as a VS Code extension.
• Combined Approach: The speaker advocates using both Cloth Code and Codex, leveraging their respective strengths.
• Cloth Code's Strengths: User-friendly, quick, and great for explaining concepts.
• Codex's Strengths: Genius-level programming, in-depth reasoning, and fewer errors.
• Analogy: Cloth Code is likened to a friendly, competent coworker, while Codex is portrayed as an antisocial but brilliant programmer.
• Codex's Drawbacks: Can be slow, overthinks simple tasks, and uses complex language.
• Model Selection: GPD5 Codex High is recommended as the primary model for Codex.
• Vibe Coder vs. Real Coder: The speaker distinguishes between vibe coders (rapid prototypers) and real coders (those with fundamental programming knowledge).
• Importance of Fundamentals: Even with AI assistance, understanding software architecture and design is crucial for building robust applications.

Building a Time-to-First-Token Optimization Algorithm

• Project Goal: To create a chat application with faster time to first token using Anthropic's API and Sonnet 4.5.
• Algorithm Concept: Pre-loading the AI model by silently sending the message to the API when the user pauses typing for more than 700 milliseconds.
• Abort Mechanism: If the user continues typing, the previous API call is aborted.
• Technical Stack: Python backend and JavaScript frontend for simplicity.
• Vision Documentation: The speaker emphasizes the importance of organizing thoughts and planning before writing code.
• Enthropic Documentation Research: Using Perplexity to research the Anthropic documentation for implementation details.
• ENV File Handling: Instructions on how to access the Anthropic API key stored in the ENV file.

Debugging and UI Improvements

• Error Handling: The speaker demonstrates debugging a server startup error using Cloth Code and Codex.
• Websocket Issue: The initial error was related to the websocket connection.
• Git Integration: The speaker recommends learning Git for version control and collaboration.
• UI Enhancement: Codex is used to improve the UI, adding a clear conversation button and a more minimalistic design.
• Optimistic Pre-loading Implementation: The algorithm is implemented to silently send the message to the API while the user is typing.
• UI Update: The UI is updated to only display the AI's response after the user presses enter.

Conclusion

• Workflow Summary: The speaker summarizes their workflow, using Codex for risky tasks and Cloth Code for fast iterations.
• Upskilling with AI: The best use of AI is to upskill oneself and learn new concepts.
• Learning Resources: The speaker recommends using AI to learn Git, terminal commands, and software engineering principles.
• Call to Action: The speaker encourages viewers to use AI to build meaningful projects and contribute to a better future.
• Critique of AI Slop: The speaker criticizes the use of AI for creating short-form videos and AI girlfriends, calling it a societal decline.
• Final Encouragement: The speaker urges viewers to take action, execute their ideas, and build the future they want to see.