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Accelerate ME: Hackathon

lumaHosted on Luma

Fetched 11 days ago

Saturday, November 29, 2025

to Sunday, November 30, 2025

•

1 day long

Artificial Intelligence

Event Type

in person

Manchester's first AI-first, 6-hour student hackathon. Ship a working project in a single evening. Presented by Accelerate ME in partnership with RoboSociety & UniCS - sponsored by Lovable, Elevenlabs and Quanser! Three Tracks 🤖 Hardware Challenge (RoboSociety x Quanser) Robotics, AR, IoT—for makers and electronics enthusiasts. 💻 Software Challenge (UniCS x ElevenLabs) Web, mobile, AI tools—for developers and engineers. 🎨 Non-Technical Challenge (AccelerateME x Lovable) No-code MVPs, UX, strategy—for creatives and first-time founders. What You Get 6 hours of focused buildingFree food, drinks & snacksMentor support & workshopsPremium tools accessNetworking with Manchester's builder communityWinners get a prize pool worth up to (stay tuned!) Agenda (subject to change) 14:00 - 14:30 → Check-in & Welcome14:30 - 15:00 → Challenge Reveal & Team Formation15:00 - 18:00 → Hacking Session #118:00 - 18:30 → Dinner & Networking18:30 - 21:00 → Hacking Session #221:00 - 21:30 → Demos & Judging21:30 - 22:00 → Awards & Closing No experience required—just bring curiosity. Let's build.

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