LL

LLMs to discover how much your loyalty points are worth

Hacker News

LLMs to discover how much your loyalty points are worth

Hey HN - co-founder of Gondola AI here. We’ve built a travel site that uses language models to pull together your entire travel ecosystem: - Sync your email to see how many loyalty points (i.e. United, Marriott, Delta) you have and how much they are worth - Generate an interactive map of every place you’ve flown and hotel you’ve stayed at - View all your upcoming trips in one place - Make optimized decisions about when it's best to use cash or points for hotel deals - Use agentic AI to perform bookings directly with major hotel brands (earning maximum loyalty points) The HN referral link will bypass the waitlist. We’re super excited to put this out there and would love to hear what you think!

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
78%78% 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: agent, model, agentic · Missing: mac, agents, macos
73%73% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
43%43% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: excited, io · Missing: https docs, just released, exist
38%38% 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 · Missing: mobile apps, ios, personal
32%32% 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, active · Missing: mrr, revenue, profit
14%14% 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
4%4% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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