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A dashboard can show activity without showing how many people outside your team have actually tried your product. In one solo founder’s account, PostHog showed about 40 visitors and 15 first conversations; a database view that excluded the founder’s own accounts showed two external people. The difference changed the question from “How is my funnel performing?” to “Have enough people tried this yet?”
Why did the dashboard and database disagree?
The account comes from DEV Community author innerlove_ai, who described building an AI companion app solo for seven months with Next.js, Supabase, and Claude. The author reported that PostHog showed about 40 visitors, 15 first conversations, and no returns. But after identifying and excluding accounts belonging to the founder, a database view showed two external people.
The counts described different populations and behaviors. A visitor or conversation in an analytics dashboard does not necessarily mean a distinct outside person has tried the product. In the author’s account, the dashboard also showed 32 conversations and five accounts, most of which the author said came from their own product testing. These are figures reported for one product, not independently verified data or a general benchmark.
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The author said they added a Boolean is_founder column to the profiles table, marked accounts they had confirmed were their own, and created a SQL view that filtered those accounts out. The view counted external people and conversations, people with a second conversation, returns within 48 hours, memory rows, and the latest conversation timestamp.
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That approach makes the included population explicit: accounts identified as the founder’s are excluded, and the remaining accounts are treated as external. It depends on correctly identifying the founder’s accounts; any unmarked test account would remain in the external count. The account describes this as the author’s implementation, not code that has been independently reviewed or tested.
The author also said the view should be kept private by revoking access for the anon and authenticated roles. A database view containing internal product metrics should not be exposed through application-facing roles unless that access is intentional and secured.
What did the filtered counts reveal?
According to the author, two external people had tried the app, but their behavior differed sharply:
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- One person had six conversations and 390 messages in a single day, then returned within 48 hours.
- The other had one conversation and did not return.
These counts are the author’s report; the underlying database was not available for independent verification. Two people are also far too few to establish whether the product is broadly useful, whether users generally reject it, or whether acquisition is the only issue.
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What should a founder count?
Before interpreting a funnel, define the population, event, and time window behind each metric. These distinctions help explain a mismatch without treating every dashboard number as a count of unique external users.
- Population: all accounts, or only people who are not you, your team, or other test accounts?
- Event: visits, accounts created, first conversations, or a later action that signals continued use?
- Return window: what counts as a return, and how soon must it happen? The author’s view included a 48-hour window.
Keep the measures distinct rather than treating them as interchangeable. A visit is not necessarily a person, an account is not necessarily an outside user, and a conversation count is not a return rate. For a very early product, a small, manually checked external-user count may be more useful for deciding what to do next than a funnel with many events but little outside usage.
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What does a sample of two tell you?
It tells you that, in this account, at least two external people tried the product and that their observed behavior was different. It does not settle product-market fit or explain why the second person left. Nor does one return prove that the product will retain a wider audience.
The author’s interpretation was that they had been optimizing a funnel before showing the product to enough people. They distinguished a product nobody has tried from one that people try and then reject. That is a useful way to frame the next decision, as long as a two-person sample is treated as an early signal rather than a verdict. The author said the next priority was to show the product to more people.
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- 【Boost Followers & Reviews Effortlessly】Perfect for: Small business owners Event promoters Influencers Restaurant staff Retail stores Trade shows & pop-up events Turn real-world interactions into digital growth.
- 【Built-In Analytics Dashboard】Track how many taps and scans your wristband receives. Monitor engagement and measure your marketing performance in real time.
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As innerlove_ai put it: “Analytics count browsers and sessions. In a product with almost no users, the founder is most of the data.”
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