100 upvotes, 18k views, 900 users… all from one Reddit post.

the part youre underselling is that 70 feature requests and people tearing your assumptions apart is worth way more than the 900 visitors. traffic is a one-time spike, but that feedback just handed you a prioritized roadmap from your exact target user for free. the trap now is treating this as a launch win and moving on. two things id do fast while the iron is hot. first, go back INTO that thread and reply to the people who ripped it apart, thats where your first real power users are hiding, the harshest critics are usually the ones who care enough to want it fixed. second, close the loop publicly when you ship one of their suggestions, nothing earns a local community like proof you actually listened. one honest question: of those 900 visitors, how many came back a second day? that number tells you whether you built something people want or just something people looked at once.

The 'AI slop' comment that turned into 70 feature requests is the part most people miss. Negative feedback is almost always more accurate because it's specific. I had a moment like this when a user called one of my features 'basically useless' then wrote 4 paragraphs describing exactly what they needed. Those 4 paragraphs became 3 sprints of work. 900 users from one Reddit post with zero ad spend is a distribution story most founders would kill for. Which subreddit got the most traction?

Reframing harsh comments into a free roadmap is the healthiest way to read criticism like that. Validating the price data against an official source first would probably earn the most trust back fastest.

The subreddit choice is the quiet lesson here: Pune-specific subs beat any generic startup sub because everyone reading has the exact problem. Local/niche communities are high-intent audiences wearing a forum costume.

On the "AI slop" criticism - we build a location-data product too, and the thing that converted our skeptics wasn't a better model, it was verification + provenance. Every data point gets checked against an authoritative structured source before it's shown (for you that'd be the official/government datasets commenters mentioned), and the UI shows where each number came from. A price estimate with a visible source and a confidence range reads as research; the same number naked reads as slop. Same data, opposite trust.

Curious about the week-after numbers: of the ~900 visitors, how many came back? Spike traffic from Reddit is famously leaky, but for a where-should-I-buy tool the return visit IS the product working - people deciding on a home come back many times, so even a small returning cohort would validate this harder than the 18k views do.

The 70 feature requests are the part I'd be most careful with. I collected a similar pile on one of my products and later wired up first-touch attribution from traffic event to payment — the features people asked for loudest and the pages that actually produced revenue barely overlapped. Requests measure how easy something is to articulate, not how badly someone wants it.

A cheap filter before you build any of them: go back through the thread and separate the requests where someone described their own situation in detail from the one-line suggestions. "I'm buying in Kothrud next year and can't tell whether the metro extension is priced in yet" is a person with a real transaction ahead of them. "You should add heatmaps" is someone being helpful. Only the first kind tells you what to build.

The "AI slop" comments are worth reading literally too — in a data product the durable answer to that criticism is showing provenance in the UI, so every estimate carries the source it came from. That single change tends to turn the harshest critics into your most useful testers.

The number that will tell you if this worked is next week's: how many of the 900 come back once the criticism cycle ends. Local subreddit traffic is unusually high-intent for a launch (actual Pune homebuyers rather than fellow builders), so returning users are a real demand signal, not vanity. And on the "AI slop" data complaints, the durable fix in a data product is provenance in the UI: every estimate linking its official source. People forgive wrong numbers with visible sources far more readily than right numbers without them.

900 users and 70+ requests gives you a lot of evidence very quickly, but also a lot of ways to interpret it.

You concluded that people were rejecting the data quality rather than the underlying idea. What did you see in their actual behavior or feedback that gave you confidence in that distinction?