Three weeks ago, I published a retrospective about shelving ZENLYM after eight months of building. The post ended with rules for my next project: ship within four weeks, daily work, conversations before code. What I didn't mention was that ZENLYM wasn't dead. I just needed to stop building and start selling.

The MVP is now live at zenlym.com. And this week, I faced something I've avoided my entire developer career: marketing.

The Problem With Being a Developer Who Hates Marketing

I've always believed that good products sell themselves. Build something useful, and people will find it. That's a comforting lie developers tell ourselves so we can keep coding instead of promoting. The truth from my retrospective was brutal: I built for eight months and got zero signups because I never talked to anyone.

So I forced myself to do the thing I hate. Reddit seemed like the right place to start because that's where my potential users hang out. Subreddits like r/productivity, r/Entrepreneur, and r/smallbusiness are full of people complaining about email overload. My plan was simple: find posts where people describe problems ZENLYM could solve, then leave genuinely helpful comments mentioning my product. Not spammy, just authentic participation in conversations that were already happening.

The problem? There are thousands of posts across dozens of subreddits. Reading through them manually, deciding which ones are relevant, crafting thoughtful responses for each: this would take hours every day. I'm a developer. I automate things. Surely I could build a tool to help.

The "Proper" Way: Full Stack App

My first instinct was to build a proper application. FastAPI backend, PostgreSQL database, Streamlit frontend. The system would crawl Reddit posts from my target subreddits, use AI to classify which ones discussed email problems, generate recommended comments, and display everything in a nice UI where I could review and act on leads.

I spent an entire day on this approach. Set up Docker containers, wrote database migrations, implemented the Reddit crawler, integrated OpenAI for classification. The backend worked fine. Then I started on the frontend and everything fell apart. Streamlit components wouldn't render correctly, state management was a mess, and I burned hours debugging CSS that refused to cooperate. By midnight, I had a buggy interface that sort of displayed posts but crashed whenever I tried to filter them.

The next morning, I opened my laptop to continue debugging and realized something embarrassing. I was doing exactly what killed my last project. Building infrastructure when I should be validating. Polishing features when I should be shipping. I was one day into a marketing tool and already drowning in technical complexity.

The Pivot: Just Use Google Sheets

I stepped back and asked a question I should have asked from the start: who is going to use this tool? The answer was obvious and humbling. Just me. This isn't a SaaS product. It's a personal automation. I don't need authentication, user management, or a polished UI. I need relevant Reddit posts in a place where I can quickly review and act on them.

Google Sheets does everything I actually need. It displays data in columns I can customize. I can sort, filter, and search instantly. I can add a "Status" column to track which posts I've commented on. I can access it from any device without setting up anything. And critically, I already know how to use it.

The pivot took two hours with Claude Code. I stripped out the entire frontend and database layer. The new system is dead simple: a Python script that crawls Reddit, sends each post through GPT-4o-mini for classification, and appends relevant ones directly to a Google Sheet. No PostgreSQL, no migrations, no UI debugging. Just a cron job that runs daily and populates a spreadsheet.

Two hours. That's all it took to replace a day of frustrated full-stack development. The tool now runs on my server, and every morning I wake up to a Google Sheet with fresh Reddit posts about email problems, complete with AI-generated reasoning and suggested comments. I scan through them over coffee, copy the ones I like, and go engage.

The Lesson I Validated

In my retrospective, I wrote: "One core feature only. No 'and it also does this.' One thing, done exceptionally well." I believed this intellectually but didn't feel it until this week. The full-stack approach wasn't wrong because of the technology. It was wrong because it solved problems I didn't have. I don't need a database because I'm not tracking historical data. I don't need a frontend because Google Sheets is better than anything I'd build. I don't need authentication because there's no one else to authenticate.

The simplest solution that works is always the right solution. Not because simple is virtuous, but because every piece of complexity is a thing that can break, a thing that needs maintenance, a thing that steals time from what actually matters. For a personal tool I'll use daily, "works reliably" beats "architecturally elegant" every single time.

What This Means for ZENLYM

The Reddit crawler taught me something important about the marketing I've been avoiding. I don't hate marketing. I hate doing marketing badly. Spamming promotional posts to subreddits, begging friends to share links, writing fake-enthusiastic tweets: that's the marketing I was dreading. But finding people who already have the problem my product solves and genuinely helping them? That feels like building. That feels like what I actually enjoy.

The AI does the tedious part: reading thousands of posts and identifying which ones are relevant. I do the human part: deciding which conversations to join and crafting responses that add real value. It's a collaboration between automation and authenticity that makes marketing feel less like selling and more like connecting.

I'm one week into this approach and I've already had more conversations with potential users than in eight months of building ZENLYM. Some of them visited the landing page. A few signed up for the waitlist. More importantly, I'm learning what language people use to describe their email problems, what solutions they've already tried, and what would make them switch. This is the validation I skipped last time.

The Tool Stack (For Fellow Builders)

For anyone curious, here's what the final system looks like. A single Python script using PRAW for Reddit API access and LangChain with GPT-4o-mini for classification. The script runs daily via cron, crawls configured subreddits for new posts since the last run, evaluates each one against my target problem criteria, and appends relevant posts to a Google Sheet via gspread. Total infrastructure: one small server I already had running. Total ongoing cost: about $0.50/day in OpenAI API calls.

The Google Sheet has columns for date, subreddit, title, body preview, author, URL, relevance score, AI reasoning, and recommended comment. I added a "Status" column that I update manually when I engage with a post. Nothing fancy. It just works.

Ship the Smallest Thing That Works

If you're building a side project, especially a tool for yourself, resist the urge to over-engineer. Ask who will actually use this and what they actually need. If the answer is "just me" and "see some data," maybe you don't need a full-stack application. Maybe you need a script and a spreadsheet. The goal isn't to build impressive software. The goal is to solve the problem and move on to the next thing.

I spent eight months building ZENLYM before anyone used it. I spent two hours building a Reddit tool that I've used every day this week. The difference isn't skill or effort. It's asking the right question upfront: what's the smallest thing I can build that actually helps?

Now if you'll excuse me, I have some Reddit posts to reply to.