---
title: "How We Got LLMs to Recommend Our Open Source Library (Without Paying or Plug-ins)"
category: "talks"
date: "2026-07-01"
time: "1:55pm-2:15pm"
track: "AI in GTM"
room: "Track 6"
speakers: ["Christopher Burns"]
sourceLabels: ["Official conference schedule", "Public YouTube metadata"]
scheduleTrack: "AI in GTM"
scheduleRoom: "Track 6"
scheduleLabels: ["AI in GTM", "Track 6", "session", "confirmed"]
---
# How We Got LLMs to Recommend Our Open Source Library (Without Paying or Plug-ins)

## Conference Context
- Date/time: 2026-07-01 · 1:55pm-2:15pm
- Track/room: AI in GTM · Track 6
- Speaker(s): Christopher Burns
- Session type/status: session · confirmed

- Track: AI in GTM
- Room: Track 6
- Session type: session
- Status: confirmed

## Session Description
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.

## Media Evidence
No related AI Engineer channel video found yet.

## Evidence Graph
This evidence graph is generated from currently linked source material: official schedule text, related video pages, cached transcripts, visible slide text, dense/reconstructed slide pages, and AI slide-classification audits.

### Media Signals
No linked video, transcript, or slide source has been attached yet.

### Agent Reading Notes
Use these signals to refine the synopsis, topic links, people/company context, and method notes. If a source is a related external video rather than an exact official recording, keep it framed as supporting evidence.

## Transcript Status
No official session recording transcript was found by exact title match on the AI Engineer YouTube channel during this run.

## People
- [[christopher-burns]]

## Notes
- Pending transcript synthesis when an official recording or confirmed matching video is available.

## Synthesis
### Synthesized Breakdown
# How We Got LLMs to Recommend Our Open Source Library (Without Paying or Plug-ins) ## Conference Context - Date/time: 2026-07-01 · 1:55pm-2:15pm - Track/room: AI in GTM · Track 6 - Speaker(s): Christopher Burns - Session type/status: session · confirmed - Track: AI in GTM - Room: Track 6 - Session type: session - Status: confirmed ## Session Description 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.

### Speaker And Company Context
- [[christopher-burns|Christopher Burns]] — Founder at [[inth|Inth]].

### Topics Covered
- [[agentic-search]]
- [[coding-agents]]

### Derived Links And Source Material

### Novel Concepts / Clever Methods
- No highlighted novel concept has been detected yet.

### Evidence Boundary
This synthesis is based on the official schedule and linked source pages. It should be revisited when exact session recordings or transcript-backed secondary sources are available.