From product launchesto new possibilities.

When the Product Hunt connection is configured, the app requests up to 50 posts from its API, ordered by votes. It reads each product’s name, tagline, Product Hunt link, vote count, creation date, and topics.

The request does not set a launch-date filter, so these are not necessarily today’s launches. It reads the listing fields rather than the full discussion, reviews, or the product’s website.

Each product gets a simple score: its total votes divided by its age in days, with a minimum age of one day. The resulting score helps select which saved items become context for the generator.

Names, taglines, and topics suggest an audience or a task worth exploring. Votes show attention on Product Hunt; they do not establish revenue or demand for your own version.

The generator selects the 8 highest-scoring saved Product Hunt listings, with names, taglines, vote counts, and topics. Each source is ranked on its own, so a handful of launches always makes it into the context even though GitHub star counts run far higher than Product Hunt votes.

That context sits alongside your categories, difficulty, and available time, or your written prompt. The generator uses it as inspiration while producing four project ideas around your requirements.

The generator reads a saved snapshot rather than requesting Product Hunt live with every generation. Freshness depends on the last successful refresh, and Product Hunt collection requires a working connection.

If the connection is unavailable, a refresh can still collect GitHub data. Previously saved Product Hunt entries may remain until they are cleared by a later refresh. If no source data is available at all, idea generation falls back to general model knowledge.