How I built an AI wrapper SaaS in 1 day

From idea to working code with the help of cursor and OpenAI's O1 🤖

Introduction

Thanks to state-of-the-art AI models, coding has transformed into a conversational process. By simply defining your project's constraints, a back-and-forth dialogue with the AI can take you from idea to product in just one day—without AI, this could take one to two weeks of trial and error.

Planing

Before starting the project, I sought to identify the problem I wanted to solve. With the Babel project, I aimed to use AI to translate localization strings for iPhone apps. I needed to localize my app soon, and the only alternative was to pay $150+ per month for services like Localise.com. How hard could it be? Famous last words 😅 (But not this time.😏)

Identifying the Problem

• High Cost: Existing solutions like Localise.com are priced at $150+ per month.

• Dependency on Third Parties: Relying on external providers can be risky—they may increase prices, shut down, or change their services unexpectedly.

• Lack of Free Online Hosting: Other solutions exist, but they don't offer free cloud-based translation hosting.

• Quality Concerns: Language translation is complex, and human translators still outperform even the best AI models.

Identifying the Solution

• OpenAI's GPT-4 is affordable, costing as little as $0.01 to $1.00 for multiple language translations.

• We can trigger OpenAI to update translations whenever needed by creating an automated workflow with GitHub Actions.

• Hosting the project in a GitHub repository allows users to fork it and integrate it into their own app projects using their own OpenAI API key.

• The latest OpenAI models offer near-human translation quality, verified in various translation benchmarks.

Defining the Agentic Flow

To build the agentic flow, I first researched the necessary components using pplx.ai here are the steps I needed to accomplish my AI wrapper app:

1. Read the English version into a list.

2. Send this list to OpenAI and request translations into the required languages.

3. Store the returned data in language files.

4. Perform basic testing with a unit test to ensure the translations have the correct format.

5. As a bonus, notify a Slack channel when the translations are finished, including a receipt from OpenAI detailing the cost of the batch job.

The Coding Part

• I asked pplx.ai with the prompt: "In GitHub Actions, how do I perform steps 1, 2, 3, 4, and 5 one after the other?"

• Then I stored the returned answers as issues in my Babel project.

• Next, I started to copy and paste the code into Cursor.com.

• Once all five parts were copied over, I began asking Cursor to improve the code. I didn't even upload to GitHub; I wanted to get my bearings first and understand how everything worked.

• I'm not a native Python coder, nor am I familiar with GitHub Action YAML format or JavaScript. However, I knew how to articulate what I wanted to achieve in the five steps for my agentic AI flow.

• I then started to ask Cursor and O1 to comment on each part of the code and kept asking the AI if there was anything I could add or improve upon.

• The comments helped the AI understand my intent.

• If I didn't understand something, I went back to pplx.ai and asked about concepts I didn't fully grasp. After a few hours, I had the five steps coded up in five different GitHub Action workflows.

• Then I took a long break before uploading the project with GitHub Desktop and began debugging errors.

• GitHub Actions immediately reported errors in the code, starting from the first step.

• So I copied the error and pasted it into Cursor and O1, asking them to solve the issue.

• O1 would then provide new code that I copied over and uploaded to GitHub.

• This process continued for a few hours, clearing one step after another, and soon enough, all five steps were complete.

Shipping

With the code working as intended—you upload English text and receive translations in multiple languages—it was time to wrap up the project. I created a README.md document outlining how it could be used. The final step was to announce the project to the world by making a social media post on LinkedIn.

The launch of my first AI wrapper went exceptionally well. I posted it late on a Sunday, and by Wednesday, more than 14,000 people had viewed the LinkedIn post. 🚀

During this project, using O1 cost me approximately $50–$100 for 10–12 hours of non-stop coding. However, with DeepSeek R1, which rivals O1, the cost should now be reduced to $5–$10. Utilizing deluxe models is essential for this kind of high-level coding.