# Reddit Roasted My Landing Page - So I Used E2B Agent Sandboxes to Fix It Source: https://www.youtube.com/watch?v=1ECn5zrVUB4 Recap page: https://rapidrecap.app/video/1ECn5zrVUB4 Generated: 2025-11-17T14:37:31.636+00:00 --- ## Quick Overview The video demonstrates using E2B Agent Sandboxes to iteratively improve a landing page by running multiple, isolated agent experiments in parallel, successfully addressing negative feedback from a Reddit thread and validating engineering practices like context, tools, model, and prompt utilization. **Key Points:** - The creator used E2B Agent Sandboxes to implement four iterative versions of a landing page fix based on Reddit feedback regarding poor design and unclear pricing. - The process involved running parallel agent experiments (four forks with three agents each) to test changes like scroll-snapping navigation and transparent pricing. - Agent Sandboxes provide isolation, scale (running 13 versions in parallel), and agency, allowing agents to operate autonomously with specific tools (CLI, E2B Tooling, Skills, etc.). - The system successfully implemented required changes, including social proof badges and updated pricing sections, demonstrating exponential engineering gains. - The demonstration highlights the importance of engineering practices (Context, Tools, Model, Prompt) over mere AI coding in achieving production-ready results. - The core value proposition of Agent Sandboxes is enabling isolated, scalable, and autonomous agent execution for complex engineering tasks. ![Screenshot at 2:06: The demonstration initiates parallel agent experiments using the run cloude command, showing three initial forks running concurrently to implement smooth-scrolling navigation fixes based on user feedback.](https://ss.rapidrecap.app/screens/1ECn5zrVUB4/00-02-06.png) **Context:** The video showcases a workflow where an engineer uses AI agent sandboxes, specifically E2B Agent Sandboxes, to rapidly iterate on and fix issues found in a landing page's design and information presentation, which had been criticized on Reddit. The demonstration focuses on how this parallel, isolated environment allows for robust, scalable testing of agent-driven code changes. ## Detailed Analysis The video details a workflow for rapid, parallelized AI agent experimentation using E2B Agent Sandboxes to address specific feedback received on a Reddit thread concerning a landing page. The initial Reddit feedback criticized elements like the landing page's design, lack of transparent pricing, and overall perceived 'grift' quality. The creator uses the `run cloude` command to spin up multiple agent sandboxes (starting with three forks, then scaling to four, and finally running 13 versions) concurrently to test proposed solutions. Key concepts highlighted are Agent Isolation (each agent runs in its own isolated environment), Scale (the ability to run many experiments simultaneously), and Agency (the agent's autonomy in executing tasks using specific tools like CLI, MCP Server, Skills, and E2B Tooling). The agent's system prompt is shown to be highly detailed, defining environments, variables (like repo URL, branch, fork number), and allowed tools, which guides the agent to perform complex tasks like updating Git branches, installing dependencies, starting local servers, and opening live previews. The successful completion of multiple experiments (e.g., smooth-scrolling navigation, pricing transparency, social proof badges) is shown in the dashboard, validating the effectiveness of this parallel testing approach for quickly iterating on improvements and addressing user concerns before merging changes. ### Reddit Feedback & Initial Problem - User asks for feedback on a course landing page; initial comments suggest poor design, hidden pricing, and overall distrust ("it looks like a scam"). - The creator acknowledges the need for improvement, especially around transparent pricing and navigation. ### Agent Sandbox Execution - The process begins by running `run cloude` with specific parameters, launching parallel agent forks (3, then 4, then 13 versions) to test fixes in isolated environments. - Key variables like repo URL, branch, and fork number are dynamically injected into the agent system prompt. ### Agent Tooling & Control - Agents utilize various tools (CLI, MCP Server, Skills, E2B Tooling) and operate within a custom agent environment defined in `agents.py` and configured via `sandbox_link_agent_system_prompt.md`. - The system prompt dictates tool usage, file access (allowed directories), and agent permissions, ensuring controlled experimentation. ### Workflow Results & Iteration - After execution, the results show 4 successful runs out of 4 total (in the first test), with generated public URLs for review. - The agent successfully implemented changes like updated navigation and pricing across multiple forks, demonstrating parallel development capability. ### The Agentic Path & Value - The video concludes by framing this process as the future of engineering—moving from manual coding to templating and building systems that build systems, emphasizing Isolation, Scale, and Agency as core benefits. ![Screenshot at 0:00: Overhead shot of hands typing on a laptop with glowing green light effects suggesting AI/digital processing.](https://ss.rapidrecap.app/screens/1ECn5zrVUB4/00-00-00.png) ![Screenshot at 0:01: A Reddit thread titled "Anyone purchase ImtyDan course?" is displayed, showing initial user skepticism about the course.](https://ss.rapidrecap.app/screens/1ECn5zrVUB4/00-00-01.png) ![Screenshot at 0:04: An overlay highlights a Reddit comment expressing distrust: "But I somehow don't trust any product with this kind of landing page..."](https://ss.rapidrecap.app/screens/1ECn5zrVUB4/00-00-04.png) ![Screenshot at 0:51: The video transitions to the E2B platform homepage, introducing "AI Sandboxes for Reinforcement Learning."](https://ss.rapidrecap.app/screens/1ECn5zrVUB4/00-00-51.png) ![Screenshot at 1:12: The creation of a workflow using the Cursor editor, setting up the command to clone a GitHub repo and run the agent.](https://ss.rapidrecap.app/screens/1ECn5zrVUB4/00-01-12.png) ![Screenshot at 1:20: A diagram illustrating the Agentic workflow involving an Engineer, Orchestrator, and multiple specialized Agents \(Agent 1-4\).](https://ss.rapidrecap.app/screens/1ECn5zrVUB4/00-01-20.png) ![Screenshot at 2:25: The code in constants.py showing the ALLOWED\_TOOLS list, defining which sandbox operations agents can perform.](https://ss.rapidrecap.app/screens/1ECn5zrVUB4/00-02-25.png) ![Screenshot at 3:31: A visual representation of the Agentic Path showing progression from Base Agent to Better, More, Custom agents, managed by an Orchestrator.](https://ss.rapidrecap.app/screens/1ECn5zrVUB4/00-03-31.png) ![Screenshot at 4:01: The terminal output showing the agent running multiple parallel forks \(--forks 4 command used previously\) against the repository.](https://ss.rapidrecap.app/screens/1ECn5zrVUB4/00-04-01.png) ![Screenshot at 4:44: The E2B Sandbox dashboard showing 9 running agents, each with its own isolated environment and associated metrics like usage and status.](https://ss.rapidrecap.app/screens/1ECn5zrVUB4/00-04-44.png)