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How I validated a niche garden app SaaS before writing a line of code (I will not promote)
Product background, not a developer (although I work with software engineers everyday). Spent probably too long thinking about how to avoid building something nobody wants, which is the thing a lot of founder's say and then does anyway. Here's what I actually tried. I was looking at the plant care space. There's lots of subreddits where people post photos of dying plants asking what's wrong. I kept noticing the same thing over and over. Someone posts a photo, gets a bunch of replies, and rarely were the obvious questions asked. Watering frequency. Drainage. Soil type. When the symptoms started. Without those you're just guessing. So before building anything I ran an experiment. Three weeks answering plant diagnostic questions using a structured intake, same 5 questions every time. No product mentions, no links, just actual answers. Results: 70\_% reply rate. Average of 12+ follow-up questions per thread. That felt like real signal that the structured approach was actually working, not just that people were being polite. What it didn't tell me was whether anyone would pay. That's a totally different test and I knew going into build that I hadn't answered it yet. Next 8 weeks building with Lovable, Supabase, OpenAI. PM background helped with the discovery and product decisions, the tools handled the actual code. Soft launch now, working on distribution, business model is still the thing I'm trying to figure out. The one thing I'd pass on is that running it manually first was worth the time. I had actual signal going into build instead of just a hunch. Also follow-up questions turned out to be a better signal than upvotes. Upvotes just mean something got attention. Follow-up questions mean something was useful. Curious what others are doing for willingness-to-pay validation before launch. Surveys? Landing page signups? Something else?
The founder demonstrates high product discipline by validating a specific user pain point through manual testing before building, which reduces early execution risk. However, the venture currently lacks a proven monetization strategy and faces significant competition from established AI-driven plant care apps, making its long-term defensibility and scalability uncertain.