A Platform to Explore Hackathons Around the World

Hacker News

I built a website which lists all hackathon posting from various other sources (currently Devpost and Unstop), on click it redirects to the original source. Surprisingly, the APIs these websites used were open, which they used to populate their page with hackathons data. So by spending maybe around 3 hours? This is the end result. It is built using Nextjs 15. I tried to make it as best as I can, but hey, would love to hear any comments on improvements. There maybe some mistakes but yeah, I am here to learn. Also, not the greatest of the UIs, but I wanted to get out the MVP, while most of my time was spent in normalizing the JSON from different hackathon website APIs. I am looking to add more sources, eg. devpost, hackerearth, hack2skill, etc. I might need to scrape their data as their HTML is prepopulated when it is fetched, or have to find some other turnarounds.

Visit product View on Hacker NewsDec 23, 2024

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Indie HackersFits the IH revenue-focused audience · Strong signals: mistakes · Missing: supports, reddit linkedin, podcasting

85%85% 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: using, apis, open · Missing: mac, agents, macos

82%82% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released

47%47% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoMay struggle as an AppSumo deal · Strong signals: platform · Missing: plus, intuitive, reviews

46%46% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

40%40% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

12%12% 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.

Correct prediction on native model

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