Tahir

We have been digging into how AI search behaves across different types of content, and one interesting pattern we observed and wanted to share with y'all.

AI search performs well when the guidance is interpretive. Advice that a human can adapt, contextualize, and apply flexibly tends to survive summarization and generation. Minor inaccuracies do not invalidate the outcome.

Developer workflows are different. Most developer queries require instructions that must execute correctly in a specific environment. Versions, configs, tooling choices, and project conventions matter. When AI search retrieves common patterns and smooths over missing context, the answer often looks correct but fails when applied.

This explains why AI search feels reliable for explanations and fragile for real-world implementation. The system optimizes for what is most repeated, not what is most precise.

That's what @fmerian, one of the most active and successful hunters on Product Hunt, shared with us while discussing how developer tool launches work today.

Product Hunt works as a repeatable surface when teams launch early and continue returning with progress. An early launch creates visibility, feedback, and a baseline presence on the platform. Each subsequent launch builds on that foundation.

Early adopters anchor this process. An initial launch brings the first group of users into the product. As the product evolves, those users provide context during future launches by sharing how they use the tool and what has changed since the last release.

@Supabase followed this approach. Their first Product Hunt launch happened when the product was still in alpha. They kept shipping, gathering feedback, and launching again with meaningful updates. Over time, this built familiarity and momentum, leading to stronger outcomes in later launches.