AdventureList – Social Travel Diary

Hacker News

Hey HN! I've been building a tool to help me keep track of all the places I want to go and have already been. You can create lists for different travel goals, add places to those lists, and add a personal rating/note when you visit those places along with the date and time of your visit. All the places you add are visualized on a map and each list has a timeline view that shows your visits in order like a travel diary. I recently went on a trip to London where I used AdventureList to: 1. Gather and organize all the places I wanted to visit beforehand 2. Log all the places I visited along the way Now I can look back on my trip and remember how it went: https://adventurelist.app/users/akash/lists/london-trip-2024... Another use-case that I have been building for is for more general, unstructured exploration, e.g. wanting to try out more restaurants in your home city. I like going to new coffee shops in my city (Chicago) and this is how I keep track of cool ones I want to visit: https://adventurelist.app/users/akash/lists/chicago-coffee-s... Initially this started off as an alternative to Google Maps Lists where you could check off places after you've visited them (I had many, many places pinned in Google Maps and got frustrated with that experience), but it has slowly grown to include more features like following other users to see their recent travel activity. Things are very basic right now but any thoughts and feedback would be much appreciated!

Visit product View on Hacker NewsJul 31, 2024

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Product HuntOn track for Day 1 leaderboard · Strong signals: google, user, new · Missing: mac, agents, macos

82%82% 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.

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

64%64% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

49%49% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: personal, google, visualize · Missing: mobile apps, ios, entrepreneurs

47%47% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released

46%46% 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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