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Transiter, backend web service for transit data

Hacker News

Transiter, backend web service for transit data

About 6 years ago, when I was first looking for a software job out of grad school, one of my portfolio projects was a train time app for the New York City subway (realtimerail.nyc). Eventually I realized most of my backend code was agnostic to the specifics of the NYC subway. Over time, I evolved it into a general purpose backend that (in theory!) works with any transit system that distributes realtime data in the GTFS format. Nowadays you can run Transiter, install a transit system you're interested in, and then use Transiter's REST API to query data like "when is the next train". There's a demo site at demo.transiter.dev.

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Product HuntOn track for Day 1 leaderboard · Strong signals: new, code · Missing: mac, agents, macos
70%70% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
64%64% predicted probability of success on Indie Hackers, 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
48%48% 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
31%31% 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
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
18%18% 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
2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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