MVP Creation Guide Starting with Lovable
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SRC From: [https://lovable.dev/](https://lovable.dev/)
This article is based on what I recorded in a chat with Gemini while working.
I would like to share my experience of developing an RSS-based web service using Lovable, an AI development platform. This project, which is integrated with Supabase, GitHub, and OpenAI API, is more than just a side project; it is a good example of the potential of development automation using the latest tools.
Why Lovable?
Lovable is a cutting-edge AI-based development tool that has the following advantages over existing Cursor and Windsurf:
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Direct integration with Supabase and GitHub: The setup process is automated, so developers don’t have to do complex connections themselves. API keys, table settings, and deployment processes are all automated.
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Low barrier to entry: You can easily create prototype-level web services without separate backend knowledge.
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Automatic setup based on up-to-date information: Eliminates the lack of documentation that previous tools had and provides seamless integration with platforms like Supabase.
Summary of development flow
1. Start the project: Implement a basic web service UI in 2 minutes
After running Lovable and connecting to GitHub, the basic UI structure is automatically configured as soon as the project is created. The home screen in the form of a list of posts is set up, and it only took 2 minutes to get there.
2. Supabase connection and data structure creation
The Supabase project is automatically created and Lovable automatically connects it (basic account creation is required). After that, Lovable automatically inserts sample data and a table (SQL-based) for storing RSS data, completing the database configuration.
This part was very attractive to me. Previously, when using Cursor and Windsurf, I had to set it up myself, but for me, who was not a developer, it was more difficult than I thought. However, Lovable solves the Supabase and DB settings just through chat, so it is very helpful for beginners.
DB의 CRUD가 필요할 때 자동으로 SQL을 작성해주는 모습
3. RSS feed integration
Since the first prompt request already had the basic RSS parsing logic in place, I was able to fetch the feed right away by simply entering three website URLs.
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From here, the structure for saving feeds to Supabase in real time and outputting them from a web service has been completed.
4. Filter only the latest posts
As the amount of data grew, I asked the logic to output only the last 3 feeds, and Lovable automatically updated the query with ORDER BY and LIMIT conditions.
From here, I realized the importance of logic. When adding a new source, I wanted to add only the three most recent feeds as samples, but due to “transfer errors + rough logic,” the automatic collection also ended up bringing in three each.
Exception Handling and Debugging
I had issues with 2 out of 3 websites not loading feeds properly, and this was due to differences in the RSS structure of each site. In particular, URLs ending in .html were often plain HTML pages, not RSS, and Lovable sometimes did not recognize them as RSS.
Lovable helped us debug this situation by providing error logs and fix options, and some URLs required manual adjustments. This was a good example of how even fully automated tools still require human intervention.
But it hasn't solved all the websites yet.
Automatic summary function through OpenAI API integration
As an additional feature, we have also implemented an automatic feed summary feature using the OpenAI API. When you enter the API key within Lovable, it is automatically saved to Supabase, and then the AI function that summarizes the body of the RSS feed is integrated.
During the improvement work, the API was called repeatedly or the structure was called every time it was refreshed, so I changed it to update the summary result in the DB and always get it only from the DB when outputting the web page. Also, because it was inconvenient to modify the prompt in the code, I created a table for the prompt in the DB and changed it to make it easier to modify it directly.
개선 전 후의 극적인 토큰 사용량 변화..
Interim Settlement
1. Resource usage
Deliverables to date: https://digest-chair.lovable.app/
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Day 1 Consumption: 5 hours, 87 credits (Pro base is 100 credits/month)
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Debugging using 13+10 credits (5 credits given for free every day)
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There are still many websites that can't fetch feeds.
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Hacker News is not fetching the feed properly
2. Insight
While this project gave me a real sense of how AI development tools can really improve developer productivity, it also revealed the same issues as other LLM offerings.
Some of the biggest insights I gained from Lovable were:
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The speed of idea implementation is dramatically accelerated. It is expected that MVP level construction will be possible within 1 to 8 hours. Since design and DB configuration are also directly controlled, the time can be shortened even further.
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The importance of planning and content increases more than the difficulty of development. I asked for a fairly detailed (1920 characters) work content in the first prompt. It was mostly about UI, main functions, and web page structure. Thanks to this, I was able to complete the basic service structure in 2 minutes after the first request.
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If you ask well, they will make it well. It is the same as GPT and conversation, but clear requirement delivery is essential. The logic part mentioned above is difficult for non-developers to understand. Because they don't need to know what happens when you click the button. I am a non-developer, but I am a specialist, so I know the basic structure and logic. This difference causes a difference of more than 10 credits when implementing one function.
3. After-use thoughts
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If you can make an MVP for about 30,000 to 50,000 won, I think it is a very low cost compared to the existing ones. I think this powerful tool is the starting point for the production of various applications, just like the numerous apps that were produced when the App Store was opened.
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If I were to study development a bit more, I would probably start a project with Lovable, grab the code from Github, and work with Cursor or Windsurf.
Do
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