Latest / The Indie Hacker Podcast with Fexingo: Solo Developers, SaaS Side Projects, and Independent Tech / How a Solo Dev Hit 10K MRR With a Micro-SaaS That Scrapes Twitter
Transcript
- Lucas: So we have talked a lot on this show about finding a narrow wedge — a single, specific pain point that you can solve better than anyone else. But today's story might be the most focused example I've come across. Luna: I am ready. What is it — a tool that does one thing and one thing only? Lucas: Exactly. It's called TweetGenie, built by a solo dev named Taylor. And the product does exactly one thing: it tells you the best time to tweet. That's it. No scheduling, no analytics dashboard, no content generation. Just a simple API call that analyzes your follower activity patterns and gives you a recommended time window. Luna: And that got to ten thousand a month in recurring revenue? Lucas: Yep. Taylor hit 10K MRR in about 11 months. He charges $9 a month, so that's roughly 1,100 paying customers. And he did it with zero funding, no paid ads, and a product that's essentially a single API endpoint wrapped in a clean web interface. Luna: Okay, so walk me through how it actually works. Because I have seen a dozen tools that claim to optimize your posting time — most of them are pretty heavy. Lucas: Right, and that's the point. Most of those tools are full-fledged social media suites. Hootsuite, Buffer, Sprout Social — they do scheduling, analytics, team collaboration. TweetGenie is the opposite. You connect your Twitter account via OAuth, the tool pulls your followers' activity data from the Twitter API, and it runs a simple algorithm that identifies when they are most active. Then it tells you: 'Post between 8 AM and 10 AM Pacific on weekdays.' That's it. Luna: And people pay $9 a month for that? I mean, I get the value, but what stops someone from just eyeballing it? Lucas: Taylor would tell you that the value is in the data. He uses the Twitter API v2, specifically the 'followers' endpoint to get a sample of follower profiles, and then he analyzes their tweet timing over a rolling 14-day window. The algorithm weights recency and engagement rates. So it's not just 'post when everyone else is posting' — it's 'post when your specific followers are most likely to engage.' That's the secret sauce. Luna: I can see why a creator with a decent following would pay for that. But how did Taylor get those first thousand customers? No ads, no funding — so word of mouth? Lucas: Mostly one tweet. Taylor launched on Product Hunt and got a modest boost — maybe 200 sign-ups. But the real explosion came when a creator with about 150,000 followers tweeted: 'I just found a tool that tells me the best time to tweet, and it's creepily accurate.' That tweet got a few thousand likes and drove about 80 percent of Taylor's initial user base. Luna: So the product itself became the marketing. That's the ideal indie hacker loop. Lucas: Exactly. Taylor didn't have a blog, didn't run ads, didn't do cold outreach. He just built something useful, and the people who needed it found it through the very platform the tool optimizes. It's a perfect distribution fit. Luna: But there is a risk there, right? Being entirely dependent on Twitter's API. If they change their terms or deprecate the endpoint, the whole business goes poof. Lucas: That is the single biggest risk, and Taylor is aware of it. In fact, earlier this year Twitter tightened access to the v2 API, reducing the free tier from 500,000 tweets per month to 100,000. That forced Taylor to adjust his algorithm to be more efficient — he now caches follower data for 24 hours instead of fetching it fresh every time. But it's a constant game of compliance. Luna: And that is a good moment to mention something — if you find stories like this valuable, if you are an indie hacker yourself or just someone who likes hearing how small tools win, listener support is what keeps this show going and ad-free. You can find us at buy me a coffee dot com slash fexingo. Lucas: Appreciate that, Luna. And yeah, every bit helps us keep digging into these specific case studies. Anyway — back to Taylor. The API dependency is real, but he also made a deliberate choice to keep the product narrow so he could be the best in that niche. Luna: So narrow that he doesn't even offer a free tier? I noticed on his site it's a 7-day free trial, then $9 a month. No freemium. Lucas: Right. Taylor told me he ran a free tier for the first three months and saw conversion rates below two percent. When he switched to a time-limited trial, conversion jumped to about eight percent. He also raised the price from $5 to $9 after month six and saw no drop in retention. That tells me he was underpricing initially. Luna: Classic indie hacker mistake — charging too little because you don't believe in the value yourself. But it sounds like he corrected it. Lucas: He did. And his churn is around three percent monthly, which is decent for a micro-SaaS. The average customer stays about 11 months, so lifetime value is roughly $99. That's not huge, but with low overhead — just server costs and the Twitter API — his margins are excellent. Luna: What's the tech stack? Because I imagine he's not running this on a massive cloud setup. Lucas: It's lean. Node.js on the backend, a simple React frontend, PostgreSQL for storing user preferences and cached data. The whole thing runs on a single $40-a-month VPS. He uses Redis for caching follower activity to avoid hammering the API. Total monthly infrastructure cost is under $100. Luna: So at 10K MRR, his profit margin is north of 90 percent. That is the dream. Lucas: Absolutely. And Taylor is doing this part-time — he works a day job as a backend engineer at a mid-size fintech company. He spends maybe 10 hours a week on TweetGenie now, mostly on customer support and keeping up with API changes. Luna: That's sustainable. But what about growth? He's at 10K MRR — can he get to 20K, 50K with a product that only does one thing? Lucas: That's the big question. Taylor has started experimenting with a 'pro' tier at $19 a month that adds a few extra features — like competitor analysis, where you can see when your competitors' followers are most active. Early data suggests about 15 percent of new users opt for the pro plan. That could push MRR higher without needing more customers. Luna: So he's expanding the price ladder rather than the feature set. That's smart — keep the core simple, let power users pay more. Lucas: Exactly. And he's also considering an api only plan for developers who want to integrate the timing data into their own tools. That could open a whole new channel. Luna: I like that — staying in the same niche, just serving it in different ways. So what's the one lesson you think other indie hackers should take from Taylor's story? Lucas: I think it's this: you don't need to build a platform. You need to build a utility that people can't easily replicate. Taylor's algorithm might not be patented, but by focusing on one metric — optimal posting time — and making it dead simple, he created a product that has a clear 'before and after' for the user. You use it, you see better engagement, you stay. That kind of focused value is hard to beat. Luna: And you don't need a huge marketing budget if your product naturally lives on the platform you're optimizing for. Lucas: Right. So the question for anyone listening is: what's the one metric your target audience obsesses over that you could turn into a simple, $9-a-month tool? Because that wedge might be wider than you think.