Supabaseant_wilsonAnnouncement
Launch Week 6 Hackathon
Summary
Supabase announces its official Launch Week 6 Hackathon, which starts at 8:00am Pacific Time on Friday 9th December 2022 and ends at 00:01am PT on Monday 19th December. The event gives individuals or teams of up to five people ten days to create a new Open Source project using Supabase. Entries may be projects, apps, tools, or libraries in any language or framework, but all design elements and code must be created during the event and each team may submit once. Five categories offer winner and runner-up prizes, including $1500 in GitHub sponsorships paid as three monthly $500 sponsorships for the best overall project, plus Supabase keyboards for its team members. Supabase judges the main categories, while Deno judges Best Edge Functions Project; judging considers creativity, smooth functionality, visual appeal, technical impressiveness, and use of Supabase features.
Context
The hackathon runs in parallel with Supabase Launch Week 6 and gives participants a ten-day period to create and submit a new Open Source project using Supabase.
Approach / What changed
Participants build individually or in teams of up to five, using any language or framework. Projects must use Supabase, be Open Source, include a source-code link in the submission, and have all design elements and code created during the event. Supabase judges most categories, while Deno judges Best Edge Functions Project.
Takeaways
The hackathon runs from 8:00am PT on 9th December 2022 to 00:01am PT on 19th December 2022, with no late entries accepted.
The best overall project receives $1500 in GitHub sponsorships paid as three monthly $500 sponsorships, plus a Supabase Keyboard for each team member.
Entries are judged on creativity, smooth functionality, visual appeal, technical impressiveness, and use of Supabase features; deep use of one feature or broad use of several are both acceptable.
Source and freshness
- Source
- supabase.com
- Published
- Added to Yomu
- Last updated
Summaries are generated from the original by a language model. Check the original before relying on it.
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