The decision to build Browserless was validated when founder Joel Griffith found a GitHub issue about running a specific browser technology in production. The high volume of comments and activity was a clear signal that he had stumbled upon a widespread, painful problem worth solving.

Cursor's initial failed attempt at a 3D CAD tool highlights the "blind man and the elephant" problem. Despite interviewing engineers, the founders lacked an intuitive, first-hand feel for the user's daily workflow. This failure underscores that deep, personal domain experience is critical for founder-market fit, and cannot be replaced by secondhand research.

Many failed ventures come from founders who either understand an industry but can't build, or can build but don't understand its nuances. True disruption happens at the intersection of these two archetypes, as embodied by the founding team.

Founder Joel Griffith found his initial users by participating in GitHub issue threads, providing genuine help to developers struggling with the exact problem his product solved. He would only pitch his solution after first offering a direct, free answer.

When a startup finally uncovers true customer demand, their existing product, built on assumptions, is often the wrong shape. The most common pattern is for these startups to burn down their initial codebase and rebuild from scratch to perfectly fit the newly discovered demand.

Technical founders often create a perfect solution to a real problem but still fail. That's because problem-solution fit is useless without product-market fit. An elegant solution that isn't plugged into the market—with the right GTM, pricing, and messaging—solves nothing in practice. It's unheard and unseen.

Instead of searching for a market to serve, founders should solve a problem they personally experience. This "bottom-up" approach guarantees product-market fit for at least one person—the founder—providing a solid foundation to build upon and avoiding the common failure of abstract, top-down market analysis.

Initially building a tool for ML teams, they discovered the true pain point was creating AI-powered workflows for business users. This insight came from observing how first customers struggled with the infrastructure *around* their tool, not the tool itself.

TeamBridge initially built a scheduling tool, but customers revealed the real problem was workflows and automations stuck in spreadsheets *surrounding* the schedule. Pivoting to solve this deeper, systemic pain led to making more money in one month than the previous two years combined.