How To Optimize Your Apps For The "AI Era" (as a solo founder / complete plan)

If you've been wondering how to make your app more agent-friendly, but you're not sure where to start or what that even means, then you just need to ask yourself these three questions. How does an AI agent find my app? Do agents understand what my app does and how it works? And how can agents actually use my app through interfaces like MCP servers, CLIs, and APIs? In this video, I'll walk you through a practical step-by-step guide on how you can optimize your apps in these key areas so that you can be in a place where not only humans can find and use your app, but also AI agents. And not only does this mean more users and more revenue, but hopefully it will help future-proof your apps in this modern AI era. The

first thing to optimize is making your landing page discoverable by search engines and AI agents. Step one, search for similar apps in your space on Google, ChatGPT search, Perplexity. Look at what shows up and make notes of the exact language they used to describe the niche, their customers, their use cases, and the outcome of their product. Step two, apply those findings to your own landing page. Update your headers and body text so it clearly defines your niche, your ideal customer type, the tools and software you integrate with, and the results your app helps your users achieve. Because if your page says something vague as the headline, the agent has to guess what your app actually does. And if it has to guess, it's probably going to recommend a competitor that's easier and simpler to understand. Step three, do a quick technical SEO audit of your landing page. I would only focus on three things to start. First, make sure your pages can actually be crawled and indexed. Check that it returns a 200 status code when it's requested. Then, make sure your robots. txt is not blocking any major user agents. Because if you care about AI search visibility, you should make sure you are not accidentally blocking any of the major AI search bots. Then the second step, make sure your sitemap includes important public pages like the homepage, pricing page, and any documentation. One thing I've

noticed with AI coding tools is that they make it much easier to create bigger PRs, which sounds good in theory until you are the person who has to review them. Suddenly, one pull request touches the database, the API, some UI components, and some config files, and then maybe a random bug fix the agent decided to include along the way. This is where Code Rabbit comes in. Code Rabbit is an AI code review tool that reviews your pull requests and gives developers context-aware feedback, summaries, and suggestions to make PRs easier to understand and review. They have a dedicated interface called the review UI, which organizes your PRs into something that is structured and logical, and it has just received three big upgrades, all aimed to help you save time in your daily workflows and better understand your PRs. They recently released a useful feature called cohorts, which splits large PRs into related chunks of work, so you can review one piece of logic at a time instead of trying to context switch and hold the entire diff inside of your head. And with Code Peak, you can inspect functions and type definitions without opening another tab and losing your place in the review, which is useful if you spot something that looks unfamiliar and you just need a quick answer to understand what it does. And with Agentic Chat, it lets you ask follow-up questions about the PR, so you can verify the changes are what you expected or even discover blind spots you might have missed. For solo founders who are using AI to help them build and ship faster, you still need a way to verify and understand what has been changed before you merge it to production. So, if you're already using AI to help you write code, you should definitely try out Code Rabbit to assist with the review process as well. You can try Code Rabbit for free with the first link in the description. Once your app

is actually discoverable, the next step is making sure it's legible to agents. And all that means is when the agent lands on your page, it can understand what your app does, how it works, and who needs it. And the easiest place to start optimizing is your semantic HTML. Semantic HTML just means using the correct HTML tags to describe an element. So, instead of using a random div tag for everything, you should use a button tag for an action, a link tag for navigation, a form tag for user inputs, and the correct heading tags to explain the structure of your page. The clearer your HTML structure is on your pages, the less guessing AI agents have to do to understand it. After you've sorted out your HTML, the next step is adding documentation for your app. For example, you could add a short get started page that explains the main workflow of your app. Or you could acknowledge any common errors and how to fix them, because documentation is obviously useful for humans to read and follow, but it's also very useful for any agents that visit your page. Because now they have a clear explanation of what your product does, what the main workflow is, and how it's supposed to be used. Now, for the final

stage, the goal is to make your app actually usable by AI agents. There are three interfaces worth considering: APIs, MCP servers, and CLIs. Let's start with APIs, because your app probably already has a back end to handle the data and business logic for your users. Then, it's just a matter of safely exposing some of those core endpoints, such that it can be called by other systems. You can start with one to two core actions an outside system would actually need if they were to integrate with your app. Security-wise, you should make it require an API key or an access token to prevent abuse. You should also validate and sanitize the request inputs, implement rate limits, and start logging requests to track usage or any errors or signs of abuse from hackers. After the API, the next interface is a CLI. The caveat here is that it only works if your audience is comfortable in a terminal. For a consumer app, that's a big ask. But for developer tools, especially, offering a CLI can sometimes be more useful and a cleaner solution because it allows users to run commands locally to perform specific actions instead of making them read your API docs and have to wire everything up manually in their own code base. Then finally, we have MCP servers. The easiest way to think about it is this: an API is built for developers and programmers, but an MCP server is built for AI agents. They expose a structured list of tools the agent can discover and call to help them with the work. MCP servers were created because web APIs can vary widely in terms of what data it returns and what kind of inputs it accepts. So, having MCP servers means we can expose every app's APIs in a way that is consistent and usable for all AI agents. And in some ways it's even more powerful than a traditional API or CLI because it can prompt agents to run local commands, access local files, and create and run automations all by itself. And that is why you can see MCP servers are growing so quickly. All of the major software companies are now offering MCP servers for their apps to allow their users to connect their AI agents to use their apps through these interfaces. And I've actually started implementing this into my own apps as well. And from what I can see, it has actually boosted engagement and usage. And if you want some useful guides on how to create your first MCP server, I'll include some links in the description below. And if you need a private community of solo founders and resources to help you grow your apps, feel free to check out Startup Club.