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How the dashboard and database told different stories
In a DEV Community account, the author, writing as innerlove_ai, described building an AI companion app alone for seven months with Next.js, Supabase, and Claude. Their dashboard showed about 40 visitors, 15 first conversations, and no returns. Those numbers looked like enough activity to start diagnosing a retention problem.
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But the dashboard was counting activity without clearly separating the founder’s own product testing from use by other people. The author said the dashboard also showed 32 conversations and five accounts, most of which reflected their own testing. When they examined the accounts in the database and excluded those confirmed as theirs, the count of external people was two.
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How the author separated their accounts
The author added a boolean is_founder column to the profiles table and marked accounts they had confirmed were theirs. They then described creating a SQL view that excluded those accounts and counted activity among the remaining people.
The view was intended to show more than a single total. It included the number of external people, conversations, people with a second conversation, returns within 48 hours, memory rows, and the latest conversation timestamp. That distinction matters: a visit, an account, and a conversation are different events, and each answers a different question about product use.
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The author also said to keep the view private by revoking access for the anon and authenticated roles. This is a description of the author’s example, not independently reviewed or tested SQL. Treat it as a design idea to adapt and validate for your own schema and access-control needs, rather than copying blindly.
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According to the author, one of the two external people had six conversations and 390 messages in a single day, then returned within 48 hours. The other had one conversation and left. In that small set, the headline total of 15 first conversations obscured two very different experiences: one person engaged repeatedly, while the other did not continue.
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That pattern is useful for deciding what to investigate next, but two people cannot establish whether the product has product-market fit, whether most users would return, or whether acquisition is the only problem. The account supports a narrower conclusion: the founder’s own testing had made the apparent audience much larger than the number of external people the author identified.
What to count when checking your own product
Before interpreting a funnel or retention chart, define the population, event, and time window behind each number. A dashboard total can be accurate for its chosen definition and still answer the wrong question.
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- Population: Decide whether the count includes every account or only people outside your team. Excluding founder accounts requires a reliable way to identify them; do not assume an account is yours based only on a name or email pattern.
- Event: Keep visits, accounts, first conversations, and repeat conversations distinct. A visitor count does not mean that many people created accounts or used the product.
- Time window: State what “returned” means, including the interval being measured. The author’s example looked at a return within 48 hours; that is a case-specific measure, not a universal retention standard.
Once those definitions are clear, compare the dashboard’s intended population and events with the database query that produces your count. If the totals differ, inspect the underlying accounts and event records before deciding whether the discrepancy reflects founder testing, different definitions, or another tracking issue.
Why the author changed priorities
The author’s interpretation was that they had spent time optimizing a funnel before enough people had seen the product. They framed the next step as showing it to more people, rather than treating the limited data as proof that the product itself had failed.
That is a sensible distinction for this reported case: a product few external people have tried is not the same as a product that many people try and reject. But the small sample cannot tell a founder which explanation applies broadly. It can show that the current evidence is too thin to support confident conclusions about retention or product quality.
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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