Tripnotes.ai: Intelligent Travel Planner

Hacker News

Hey HN, We're excited to announce the public beta preview of Tripnotes, an intelligent travel planner that mixes a custom recommendation engine, our own data, and a bit of GPT to help you find the right places to go while traveling. Our team has been working hard on this project for 6 months, and we're excited to finally share it with the HN community. With Tripnotes, you can keep track of all your notes about travel when planning to visit a city. Paste in article URLs, videos, emails, text messages, any other text, and if you need some help with research, ask our Concierge which will provide useful recommendations and itineraries for the city in question - with a fallback to GPT for things we didn't quite understand. If you're not ready to dive right in, here are a few short highlight videos: https://twitter.com/matthewer/status/1619047755271536640 https://twitter.com/matthewer/status/1621172173598756864 https://twitter.com/matthewer/status/1623703548545445890 https://twitter.com/matthewer/status/1638010661694234625 All the places by Tripnotes are from our own database, but we rely upon GPT to understand the intent of the incoming prompts, as well as to help with the prose in our response to ensure the place descriptions fit what's being asked for. Since we tweeted about our preview release on Monday, we've had over 15,000 people come and try at least one prompt, with over 40,000 unique notes generated from prompts about cities all over the world. We'd love for you to give Tripnotes a try and let us know what you think. The "unauthed" version is open to everyone. If you're interested in using the full version, please join our waitlist. The full version maintains a history of your notes and allows you to edit the text and auto-highlights any text you write/paste in on the fly, which can be useful for pasting recommendations from friends (say via SMS or Email) and having them auto-highlighted and mapped. You can also paste in links from articles, websites, blogs, even Tiktok or Youtube. We scrape those urls, gather the list of places that are mentioned, and then show them on the map along side the rest of your note. We'd love to hear your thoughts and feedback, so please feel free to leave a comment or reach out to us directly. Thanks for your support!

Visit product View on Hacker NewsMar 23, 2023

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

83%83% 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: email, using, notes · Missing: mac, agents, macos

75%75% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: excited, ide, 000 · Missing: https docs, just released, exist

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.

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

37%37% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

29%29% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

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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