Social search is a prospecting surface

Founders often treat X as a posting calendar. For the first 100 customers, search is usually more valuable than broadcasting. An agent can monitor phrases that imply pain, launch momentum, tool switching, hiring, or budget. It can then prepare reply drafts and profile notes for the founder to review.

X's recent search API documentation shows how query-based search can narrow posts by topic and recency. Turn that idea into saved agent tasks: find posts from founders mentioning a problem, find recent launches in a category, find people asking for alternatives, and find conversations where a helpful answer would be natural.

The three useful queues

Build three queues. The reply queue contains conversations where you can add immediate value. The prospect queue contains people or companies that match the signal map. The content queue contains questions that deserve a public answer, article, or demo.

A launch day should not only be a scheduled post. YC's Startup School library treats launch and first customers as core founder questions. Your agent can watch launch comments, collect objections, draft thank-you replies, and identify people who asked for a feature or use case. That turns a launch into a customer discovery sprint.

Make the founder sound like a founder

The reply draft should be short, specific, and grounded in the post. Ban generic openers. Ban fake enthusiasm. Ban links unless the person asked for one. Ask the agent for three variants: helpful answer, clarifying question, and direct invitation. The founder picks or edits the one that fits.

Use the same content downstream. A reply that gets engagement can become a section in search content. A repeated objection should become homepage copy. A direct message that converts should become a template in the sales CRM.

Measure the right thing

For the first 100 customers, measure useful conversations, qualified replies, demo requests, and lessons learned. Follower growth is a side effect. The agent should report which searches produced real people with real timing, then prune the rest.

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