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Waywise – an iOS travel guide for any* city

Hacker News

Waywise – an iOS travel guide for any* city

Hi everyone About a year ago I was travelling to Mexico for a trip and forgot to cancel my Duolingo trial (because of course I did), so accidentally committed myself to learning Spanish badly. My Spanish-speaking partner noticed that I'd been learning a slightly more latin version so it was a good thing I was going to Mexico, but it got me thinking: most translation apps/phrasebooks are one language-to-another language without much consideration for local dialects. I'd been wanting to mess about and build a little side project so made an early prototype of a city-specific translation app using the GPT-4 API. After poking around the App Store I realised that the travel category was so saturated with AI-generated itinerary planners or tour guide resellers. So one thing led to another and I decided to start from scratch and build an AI-augmented city guide app: Waywise. The idea being that I don't really want to "recommend" anything as such; I just want to surface useful information for travellers that will allow them to explore a city in their own way. Some of the features: Transport information - Detailed guides on airports and how to get into the city centre. Train and bus station and local public transit information. Local language: Translations, colloquialisms, and a comprehensive phrasebook for all major spoken languages in the city. Currency tips: Information on cash, cashless payments, stored value cards, tipping etiquette and a currency conversion tool. Neighbourhood insights: Find the best places to stay, shop, party and explore the sights. *Currently there are a handful of cities that I've verified the information for which are purchasable and downloadable for a one-off fee (all future updates included too), and the remainder are AI-generated. If there isn't a city guide available, subscribers can generate one for almost any city with a population of over 100,000. There's scope to do lots more and I've been squeezing this in between client work so it's taken me the best part of a year to finally get out the door but any feedback would be super useful! Cheers :)

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Indie HackersFits the IH revenue-focused audience · Strong signals: ios · Missing: supports, reddit linkedin, podcasting
87%87% 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: apps, using · Missing: mac, agents, macos
52%52% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: ios, apps, way · Missing: mobile apps, personal, entrepreneurs
39%39% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, 000, io · Missing: https docs, excited, just released
34%34% 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 · Missing: plus, platform, intuitive
34%34% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: subscribers · Missing: arr, mrr, revenue
21%21% 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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