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Dribdat – a honeycomb challenge board for sweeter hackathons

Hacker News

Dribdat – a honeycomb challenge board for sweeter hackathons

This is a demo website of Dribdat: a self-hosted open alternative to Devpost or HackerEarth for tinkering on prototypes with friends and hacktivists. It started out as a humble cookiecutter template for Flask in Python, an attempt to streamline the set-up for open data & open hardware hackathons. Before we knew it, the years went by, it's been hacked on by hundreds and used by thousands of people - and became a sustained initiative. Dribdat's design is inspired by hard-working honeybees, and the hexagonal stickers that are a staple of tech communities. It features a wiki-like content area with revision history, journals for teams, synchronization with popular code & data repositories, a PWA for silky smooth preloaded presentations, tools for distributing certificates, Hack Code of Conduct & Creative Commons by default. In the summer, our tools have become the subject of national R&D project, and there are bigger plans up ahead. I've polished off the documentation and Open Collective, updated the demo site with the latest release. Please let me know if you think it can help you run an event, or just to 'own your data' and mirror the results on another event platform. Some alternative products are listed in our market study ("Awesome Hackathon"), and I'm always happy to hear about your experiences. All this has been much inspired by the YC community, and I am looking for a bizdev partner in Startup School, so if this is up your alley: get in touch! Give it a spin at https://demo.dribdat.cc and see our code at https://github.com/dribdat Oleg ^seism

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Indie HackersFits the IH revenue-focused audience · Strong signals: started · Missing: supports, reddit linkedin, podcasting
81%81% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: new, presentations, code · Missing: mac, agents, macos
78%78% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
54%54% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
39%39% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform, host · Missing: plus, intuitive, reviews
27%27% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
13%13% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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