Dijedon Tahiri

I m building a small tool that reads a company s website to find its name, description, and logo. I thought getting those details from the company s own site would be straightforward. It hasn t always been.

Some sites leave fields out or put different information in different places. My instinct is to try a sensible fallback, then be honest when the tool still can t find a reliable answer. A confident-looking guess would be easier to display, but less useful to someone relying on it.

If you build with public web data, how do you handle that moment? Where do you draw the line between a helpful fallback and a guess you shouldn t show?

Collecting cancellation reasons is easy; acting on them without breaking your product vision is hard.

After monetizing, we realized that 40% of churn feedback pointed back to the exact same friction point in our weekly report delivery something we considered a minor secondary feature.

how do you filter out "noisy" churn complaints from fundamental workflow flaws when setting your quarterly roadmap goals?

Early on we built our own internal notifications, document storage and task lists. Post-monetization, churned feedback taught us a harsh lesson: users don't want another siloed app they want our core utility inside Slack, Notionor Chrome.

The hardest decision is stopping work on your own native capabilities to spend cycles building integrations for other platforms.

How do you determine when a workflow issue requires an ecosystem integration rather than an internal UI fix?