An app for couples who want to break monotony of boring dates

Hacker News

A project I just started working on — an app designed to help couples break out of repetitive date routines. The Problem Many couples find themselves stuck in a cycle of the same date nights - dinner and a movie, again and again. While familiar, this routine can become monotonous. The Solution Our app offers curated date ideas that cater to different preferences and situations. Here’s what makes it unique: Personalized Recommendations – The app learns your preferences over time to suggest dates that match your tastes. Shared Calendar – To suggest and remind you to plan thoughtful dates. How It Works Sign Up & Create a Profile – Couples create a joint profile, specifying their interests, budget, and any special preferences (e.g., long-distance). Get Personalized Suggestions – The app generates tailored date ideas based on your profile. Plan & Schedule – Choose a date, set reminders, and get tips to make it extra special. Give Feedback – Rate dates, and the app will refine future recommendations accordingly. Tech Stack Frontend: React Native Backend: Node.js with Express Database: MongoDB Machine Learning: Python & TensorFlow-powered recommendation engine Future Plans Integration with Local Services – Exclusive deals and experiences from partner businesses. Join Our Beta! We’re currently in beta testing and would love your feedback! If you’re interested in trying out the app and shaping its future, sign up for our beta program by giving feedback on the vercel page and leaving your email.

Visit product View on Hacker NewsMar 21, 2025

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

73%73% 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 HuntUnlikely to reach the leaderboard · Strong signals: mac, email · Missing: agents, macos, agent

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: personal · Missing: mobile apps, ios, entrepreneurs

44%44% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

27%27% 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

17%17% 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

12%12% 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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