June 29 – July 2, 2026 · San Francisco, CA · imported from ai.engineer's public schedule feed

AI Engineer World's Fair 2026 — unofficial import demo

Unofficial demo. This programme was imported from the AI Engineer World's Fair's own public schedule feed to show vibeboard at real conference scale. Not affiliated with, or endorsed by, the organisers.

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AI in GTMSession

How We Got LLMs to Recommend Our Open Source Library (Without Paying or Plug-ins)

Christopher Burns

When
Thursday, July 21:55 PM – 2:15 PM · 20 min
Where
Track 6San Francisco, CA · imported from ai.engineer's public schedule feed
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About this session

Over the past year, we’ve seen a new distribution channel emerge: AI assistants. Instead of SEO, ads, or integrations, developers are discovering tools through models like Claude. In this talk, I’ll break down how we got our open source library recommended organically by LLMs in under a year, without plugins, paid placements, or partnerships. We’ll cover what actually influences model outputs today, how developer-first products behave differently in this channel, and the practical steps we took to make our project show up when it matters. This is not theory. It’s a real case study of how distribution is changing, and how you can design your product and content to be picked up by AI systems directly.

Speaker

Christopher Burns
Christopher Burns

Founder, Inth

Christopher Burns is the founder of Inth, building developer-first privacy compliance infrastructure for modern software teams. Inth started with c15t, an open-source consent SDK with 2.6M npm downloads, used by teams including Vercel, Cal.com, Zed, Infisical, Sanity, and others. Inth helps companies move privacy compliance closer to the product itself, giving developers infrastructure for consent, data rights, policy enforcement, and evidence instead of slow dashboard-first tooling. Christopher is a second-time founder and first-time YC founder. His previous company, Everfund, built enterprise nonprofit donation infrastructure and exposed the compliance problems that led to Inth: the deepest privacy failures are usually not in policy docs, but in the product itself.

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