Build in Public: My Lovable SaaS Journey to $1k MRR

Everyone loves a 'build in public' story until it is time to share the depressing parts. The week you get zero signups. The feature you spent 40 hours building that no one uses. The silence after a launch. This is that story. My goal was simple: build a SaaS product using Lovable AI and get it to $1,000 MRR. I decided to document it week-by-week on X, warts and all. Here is the condensed version of that three-month journey.

The Setup: An Idea and a Tool

I started with a broad, uninspired idea common among first-time founders: a generic AI content repurposing tool. The pitch was foolishly vague. 'Turn any content into any other content.' It could take a YouTube video and write a blog post, or a blog post and write tweets. The problem was, I was building a solution without a deep understanding of any specific problem. I was building a 'what if' product. What if people wanted this?

My choice of tool was more deliberate. I chose Lovable AI. The year is 2026, and the AI app builder landscape is noisy. Lovable stood out for its visual logic builder and its focus on creating robust backends, not just shiny frontends. I figured I could build and iterate on complex AI workflows quickly without getting bogged down in boilerplate code and server management. The plan was to validate the idea fast. The irony is I ended up validating that my idea was terrible, which was a success in its own right.

Weeks 1-to-4: Shipping a Solution Without a Problem

The first month was a blur of building, a classic MVP trap. I spent weeks architecting this beautiful, flexible system. It had inputs for audio files, YouTube URLs, and text blocks. The AI logic chains were complex, offering different tones and output formats. I built a slick UI with a drag-and-drop interface. At the end of week four, I launched 'RepurposeAI' to my small audience on X and an email list of about 200 people.

The result was crushing silence. I got a handful of signups, mostly from supportive friends. People logged in, clicked around, and left. Zero credit cards were entered. I received a few polite DMs saying 'Cool project!' which is code for 'I will never use this'. The product was too generic. It did everything, which meant it did nothing particularly well for anyone. It was a Swiss Army knife where every tool was slightly bent. I had built a powerful engine and attached it to nothing.

Weeks 5-to-8: The Pivot That Saved Everything

Humiliated by the failed launch, I did what I should have done in week one: I talked to a user. Well, a 'user'. I reached out to one of the three people who actually tried to upload a file. He was a podcast host. I asked for 15 minutes of his time. He said, 'Honestly, I do not need a tool that does ten things. I need a tool that does one thing perfectly. I just want to turn my latest podcast episode into a decent Twitter thread to promote it. That's it. It takes me an hour every week and I hate it.'

That was the pivot. The conversation changed everything. I decided to kill RepurposeAI and build for this one person. The new app, which I called PodThreads, would do one thing only: ingest a podcast RSS feed, grab the latest episode, transcribe it, and generate a high-quality, engaging Twitter thread based on the key moments. It was a painkiller, not a vitamin.

Lovable's architecture made this pivot surprisingly fast. I did not have to start from scratch. I just ripped out all the superfluous inputs, focused the UI on a single 'Enter RSS Feed' field, and spent my time refining the AI logic. I chained multiple AI steps: first, a transcript cleaner. Then, an AI model to identify the most compelling hooks and takeaways. Finally, another model to write the thread itself in a conversational style. In two weeks, I had a new, hyper-focused product.

Weeks 9-to-12: The Unscalable Grind to First Revenue

This new product still needed customers, and this time, I was not just going to tweet into the void. This is where the unscalable grind began. The target was narrow-podcast hosts in the tech and startup space. So I went looking for them. My entire strategy for the next month can be boiled down to a few manual, repetitive tasks:

• Prospecting: I spent days searching Spotify and Apple Podcasts for shows C-suite my ideal customer profile. I built a list of 100 podcasts.
• Manual 'Demos': I picked 20 hosts from the list. I took their latest episode, ran it through my own tool, and polished the output. This took about 5-10 minutes per host.
• Cold Outreach: I sent a direct message to each of the 20 hosts on X. No spam. It was personal. 'Hey [Name], I loved the episode with [Guest] about [Topic]. The part about X was great. I took the liberty of creating a Twitter thread with the highlights for you. Hope it is useful!' Then I pasted the thread.
• Conversion: The response was night and day compared to my first launch. About half of them replied. Several posted the thread I made. And five of them asked, 'This is great, how did you do this?' I sent them a link to PodThreads. In the first week of doing this, two of them converted to my $29/mo plan. I had made my first $58 in MRR. The feeling of that first Stripe notification was better than any vanity metric.

I repeated this process every single day. The feedback from these early customers was pure gold. They requested LinkedIn post formats. They asked for different tone options. They found bugs. Because I was using Lovable, I could ship these changes in hours, not weeks. Every feature I built was a direct response to a paying customer's request.

Crossing $1k MRR (and the Path to $5k)

Getting to $1k MRR was a slow, steady climb, not a sudden explosion. It was the result of adding two or three customers a week from direct outreach and word-of-mouth. The tipping point arrived around week 11. One of my early users, a host with a modest but highly engaged audience, gave the tool an unsolicited shout-out at the end of his show. That single mention drove more signups in one day than all my previous efforts combined. These were high-intent users who already understood the problem. Many converted within hours.

Here is what the growth looked like:

| Week | MRR | Key Event | | :--- | :------ | :-------------------------------------- | | 9 | $58 | First 2 paying customers from cold DMs | | 10 | $261 | Refined outreach, 7 new customers | | 11 | $609 | Added LinkedIn format, better retention | | 12 | $1,102 | Shout-out from a user drove a wave |

Crossing that $1k MRR line felt less like a finish line and more like a starting line. It proved I had found a real problem and built a viable solution. The next goal is $5k MRR. The path there involves less unscalable grinding and more scalable systems. I need to build a real onboarding flow, explore integrations with podcast hosting platforms, and turn happy customers into a repeatable marketing channel. The foundation is set. Now it is time to build on it.

Was Lovable the right tool for this project?

Yes, Lovable was the right choice because speed of iteration was the most important factor. When my initial idea failed, I was able to pivot the entire product in under two weeks without rewriting everything. The visual workflow for chaining AI prompts and handling logic allowed me to respond to early customer feedback almost instantly. The main downside is that you are reliant on the platform's component library. If you need a hyper-custom frontend element or a niche API that is not supported, you might have to find a workaround. For an MVP and early growth, its speed is unmatched.

How much did it cost to reach $1k MRR?

My costs over the first three months were surprisingly low. Here is the breakdown:

• Lovable Subscription: ~$99/month ($297 total)
• OpenAI API Costs: ~$40/month (scaled with use, $120 total)
• Domain Name: $15/year

Total cash burned to get to $1k MRR was roughly $432. The main investment was my own time. By avoiding custom code, servers, and complex infrastructure, I kept expenses minimal. This is a huge advantage of using AI-native builders-they abstract away the most expensive parts of starting a software company.

What was the biggest mistake you made?

My biggest mistake was falling in love with my initial solution before confirming the problem. I spent a month building a 'flexible' tool that nobody asked for. It was a classic case of premature engineering. I should have spent that first month in podcasting subreddits and on Twitter, talking to potential users and learning about their workflows. I could have validated the idea for PodThreads with zero lines of code. Instead, I built a product, watched it fail, and then started talking to users. It was an expensive but valuable lesson in humility.