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Build Your Own AI Companion Online Hackathon

lumaHosted on Luma

Fetched about 5 hours ago

Tuesday, June 16, 2026

to Tuesday, July 14, 2026

•

1 month long

Artificial IntelligenceFintech

Event Type

in person

9

Participants

$500

Prize Pool

0

Est. Projects

An online hackathon to discover what AI companions people actually want to come back to. Build a Telegram bot AI companion that solves one real human need — your own. Test it with real users for 3 weeks. Win $5000 award. What you'll build A Telegram bot (text + image only) designed around one real human need that you deeply understand. Not another general-purpose assistant. Key dates Jun 15 — Applications open, Jul 13 — Applications close, Jul 14–27 — 2-week build period (DIDI provides technical support), Jul 27 — First demo submission, Aug 4–22 — 3-week stickiness testing with real users, Aug 24 — Final demo, Aug 31 — Awards ceremony. Who should apply Builders, designers, psychologists, HCI researchers, and anyone who has ever wished an AI could understand them better. Solo or teams of 2–3. How to apply Hit "Register" below to join the event and get all updates. Then complete our short Pre-work form — link sent after RSVP. Applications are reviewed on a rolling basis—final cutoff: July 13, 2025. Powered by DIDI Labs.

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