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GTM Buildathon: AILC x LIT School

lumaHosted on Luma

Fetched about 2 hours ago

Saturday, August 1, 2026

to Saturday, August 1, 2026

Artificial Intelligence

Event Type

in person

14

Participants

1

Est. Projects

This buildathon is a GTM playground for operators, founders, and AI builders. 4-hour sprint designed for serious builders who want to ship solutions to actual marketing and GTM challenges. We provide the problem statements. You bring the stack, the strategy, and the execution. The Playbook 11:30 AM: Kickoff & Problem Statements Drop (Curated by Litschool)4-Hour Sprint: Build solo or team up. Use any stack, tool, or AI agent you want. It’s completely open-ended.Show & Tell: Present what you built directly to a room of peers and mentors. Who Should Apply? GTM Operators & Marketers looking to build automated systems or distribution engines.Founders who want to solve real growth bottlenecks in real-time.AI Builders eager to deploy LLMs and agents to tackle modern growth challenges. Strictly Limited to 20 Seats. We are filtering heavily for serious builders who can execute fast. If you just want to network, this isn't the room for you. If you want to build, grab your spot now. This event is proudly hosted by AI Learn Circle in collaboration with Litschool

Judge Accessibility

Organizer email available25/25
Student-run event15/15
Actively looking for judges25/25
Small event (120 participants)10/10
No corporate sponsors10/10
New or emerging organizer10/10
Public registration available5/5
Online format (judge from anywhere)10/10

Top signals

Organizer email available
Student-run event
Actively looking for judges

Organizers

Alex Johnson

alex@example.org

Jamie Rivera

jamie@example.org

Sam Chen

sam@example.org

Estimated Audience

Mostly Students
ExperienceStudent
OccupationStudents
Beginner Friendly
Women in Tech

Technical Focus

AI95%
Web80%
Mobile25%

Industries

Healthcare
Education
Climate

Technologies

Python
React
OpenAI

Why this estimate

  • • Hosted by a university
  • • Open to students
  • • MLH member event

Estimate inferred from event metadata, not actual attendee data.

Quality Score

Quality Score

72/100
High confidence
Organiser16/20
Event Maturity14/20
Sponsors18/25
Participants12/20
Operations12/15

Why this score

Strong organiser track record
Returning event
Well-sponsored

Missing data

Prize details
Code of conduct