SFTransit - easy San Francisco transit times

Hacker News

Hello HN, I built www.sftransit.us to make it easier to get live transit arrival/departure times on your cellphone. Right now, it does Muni and BART. You don't have to download anything, and it takes one click to find times for nearby stops. I don't live in San Francisco right now (but am moving out there in a few months). I'm really keen to know if this is a useful service, and to know if there's anything that would make it more useful to you. I'm aware there are a few existing tools for this (like Routesy, and the official site [of course]), but I don't think any are as hassle free... http://www.sftransit.us

Visit product View on Hacker NewsAug 25, 2012

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Hacker NewsStrong engagement from HN community · Strong signals: exist, existing · Missing: https docs, excited, just released

63%63% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive

60%60% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

54%54% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Missing: mac, agents, macos

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

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

42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

16%16% 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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