Cu

Cultural event scraper and listing site (in Haskell)

Hacker News

Cultural event scraper and listing site (in Haskell)

I used to maintain a list of cultural events (art, dance, theatre, music etc.) in London manually for friends and family, but it got quite tedious to maintain, and I kept forgetting sites. I built this application to automatically scrape events into a queue which I can then curate, and people can see (only the upcoming ones, more filtering to come). Built in Haskell with almost no JS (the admin pages have a single TS function to make the forms nicer to work with). This was really fun to build (and is my first Haskell sideproject to make it "into production"). At the moment there's only one source (The Barbican) but I'm planning to add more now that the core application is there. I'd also like to give it a design makeover before it gets seriously consumed as it could probably be a bit nicer to use, but for now I'm using bootstrap just to get it out the door -- design ideas welcome). Source: git.sr.ht/~gtf/culture-vulture Issues: todo.sr.ht/~gtf/culture-vulture

Visit product View on Hacker NewsJul 5, 2023

Actual performance

2points

Did not reach leaderboard

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Product HuntOn track for Day 1 leaderboard · Strong signals: single, using · Missing: mac, agents, macos

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best fitHighest predicted score across all platforms for this description.

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

81%81% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Missing: mobile apps, ios, personal

52%52% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

48%48% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

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

nativeThis product was originally launched on this platform.

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

14%14% 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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