I Vibe Coded A $100,000 A Day App (My Step by Step Process)

The Power of Agentic Coding for SaaS Companies [00:05]

• Key Insights:

  • LLMs, particularly Opus 4.5, have unlocked the ability for non-technical founders to code and make substantial improvements to their SaaS companies.
  • This capability is akin to giving a college student a highly potent energy drink, leading to rapid and significant progress.
  • Agentic coding, a more scientific term for vibe coding, is not about replacing engineers but about augmenting development processes.
  • The primary value lies in understanding the process of planning, structuring, and developing with LLMs, not just the specific tools.
  • Complex setups with multiple agents or intricate loops are often unnecessary; a clear understanding of the process and context is paramount.

  "And just like discovering Opus 4.5, it was the point where code from LLM has hit the point where as a non-technical person, you can build substantial significant improvements to your SAS company or just vibe code a company from scratch if you know what you're doing."

Distinguishing Agentic Engineering from "Vibe Coding" [01:36]

• Key Insights:

  • "Vibe coding" is dismissed as a term implying a lack of genuine understanding, operating purely on intuition.
  • "Agentic engineering" is preferred, framing the AI-assisted coding as a more structured and deliberate engineering process.
  • This process involves the same planning, building, and integration steps as traditional software development but leverages agents to write and review code.
  • It is applicable to large-scale production applications, allowing for significant improvements and the creation of legitimate software companies from scratch.
  • Developers are not obsolete; software development still requires engineering principles, architecture understanding, and planning.

  "I don't like the term vibe coding because you really don't know what the [expletive] you're doing and you're just going off vibes. I do like the term agentic engineering, which is just basically a fancier way to say vibe coding. It makes me feel better about myself."

Strategic Application of Agentic Coding in SaaS [07:37]

• Key Insights:

  • The speaker's company, Hyros, which generates $40 million annually, utilizes agentic coding for significant development efforts, with the CEO spending half his day on it.
  • Agentic coding can be applied to build Minimum Viable Products (MVPs) rapidly, find and implement technical solutions, and optimize features.
  • One key application is creating tools that help customers set up and install complex software, which was a major blocker for Hyros.
  • Another is building infrastructure apps that streamline internal team workflows and documentation management, reducing manual effort.
  • User-facing applications can also be developed, such as a Chrome extension that monitors user screens during setup and provides step-by-step guidance.

  "So, for example, one of the things you can do is build parts of your app that you want to have, build the features out and find the optimal ways for them."

The Importance of Segregation and Risk Management [13:30]

• Key Insights:

  • A critical strategy is to "segregate" the AI-developed components from the core, complex software architecture.
  • This segregation allows for building mini-apps and features with significantly reduced risk, as they can be developed and tested outside the main production code.
  • Examples include Chrome extensions that work externally or API-called services that integrate with the main software but operate independently.
  • Even when building within the main software, focusing on less critical areas like the UI or onboarding reduces the risk of breaking core functionality.
  • This approach allows non-technical founders to make substantial progress on customer-facing features and improvements without directly touching complex backend systems.

  "This is important because you need to realize when you go in and mess with actual software, the deep internal guts of the software, you're not going to blow everything up."

The Planning and Development Process with AI [21:35]

• Key Insights:

  • The core process involves starting a conversation with Claude (or similar LLM) in a simple chat interface, not necessarily complex terminals or multi-agent setups.
  • Planning begins by describing the intent, problem, and desired functionality to the AI, encouraging it to ask clarifying questions.
  • The "artifact" feature within Claude is crucial for iterative planning, allowing the AI to build and test features within the chat environment before full deployment.
  • Before coding, a Product Requirements Document (PRD) or a detailed explanation of the product's intent, context, and features is essential for the AI's understanding.
  • Understanding the AI's context window is vital; providing comprehensive documentation ensures the AI remembers its purpose and the project's goals.

  "And so what you're going to want to do first off is you want to describe to Claude what you're looking to build. And you want to describe the intent of what you want to build and where it's going to fit into your company and what your company's trying to do with it."

Building and Debugging with Agentic AI [33:00]

• Key Insights:

  • While context window management was critical in the past, current LLMs like Claude have significantly improved their ability to retain information.
  • The process involves clearly defining rules and responsibilities for the AI (e.g., acting as a CTO) and providing detailed context through MD files.
  • The AI should be instructed to study codebases thoroughly, communicate its actions, and flag any misunderstandings or deviations from the plan.
  • Bug fixing requires more than just stating a problem; it involves deep analysis of the codebase, identifying the root cause, and considering potential side effects of fixes.
  • Detailed logging and the creation of self-testing mechanisms by the AI can drastically improve the bug-fixing process, moving beyond guesswork.

  "And the more complicated your system is, the more technical docs, you can see this doc area right here that I have in VS Code right here, the more advanced your Claude and your architecture and your your documents going to be."

Final Tips for Effective Agentic Coding [49:02]

• Key Insights:

  • Always ensure that AI-generated code is well-documented, with updated PRDs, architecture layouts, and Claude MD files.
  • Segmenting different parts of the software is crucial for managing the AI's context and preventing interference between modules, akin to having separate plumbing systems for each floor of a skyscraper.
  • This segmentation allows the AI to focus on specific tasks without needing to retain the context of the entire, potentially massive, codebase.
  • For non-technical founders, this approach enables them to build features and tools that directly benefit customers and drive results, even if they don't delve into the complex backend.
  • Ultimately, the goal is to leverage AI to maximize development time on money-making features, rather than on intricate code maintenance or low-level engineering.

  "Make sure Claude explicitly builds in ways that parts of the software are segmented from each other because remember that context window."