A web to see nearby TFL trains

Hacker News

This was mostly vibe coded over the bank holiday but the funny thing is that it was derived from an existing stale iOS swift project I started and never finished nor shipped that was built by hand, over the length of multiple months. It's just a ui that asks for your location and shows platform arrivals on your closer tube and overground stations using tfl api. Learned a lot about the setting up of stuff and all my api modelling and schemas hopefully went into shaping this. But it's quite impressive that now any feature can be built and deployed within seconds by claude remotely.

Visit product View on Hacker NewsMay 25, 2026

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2points

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

83%83% 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.

TrustMRRFits verified-revenue profile · Strong signals: ios, month · Missing: mobile apps, personal, entrepreneurs

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

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

59%59% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

57%57% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

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

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

23%23% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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