Go on those solo jaunts, record it, and share it with others

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Hi All, I was part of SUS W20, and 8 weeks back had an itinerary optimization product that evolved into this travel social app where users hit a record button in the app, drop the phone in their pocket and go on their trip while the app detects your visits to attractions, adds it to the itinerary. Once done you can add those unique insider tidbits to your unique itinerary and share it as a guide to others to recreate your experience. Ghumo: https://ghumo.app If you're an iOS user then we have a TestFlight Beta version here --> https://testflight.apple.com/join/nqJcQ8hm When I used to be a consultant, I traveled to different cities on a multi-week stay and loved taking jaunts during the weekends. Before I did that, I used to ask my clients(locals) about the places to see(other than my own Google research) and go experience that. I started sending out emails to my colleagues about the places to see when they used to visit those cities. While Trip Advisor and Google search results gave me the info needed but I had to put together those into an itinerary in order to consume it. I always felt the need for a single source of info not just before the trip but also during where I could look up my itinerary. While other travel social do have something that is close to this, the user experience of the creator seemed a little old school adding stops manually. Why can't my app detect that I have entered the Natural History Museum and then invite me in, send me relevant info and then maybe even act as a guide answering my questions? These questions led me to develop Ghumo, which at this moment can be used to record your jaunts, add notes to your stops and share. I'm working on the conversational agent part where relevant info and answers can be sent to the user with minimal user input. Also, in the pipeline is an Android version. If you're an Android user then please do sign up --> https://ghumo.app Thanks and Cheers!

Visit product View on Hacker NewsMar 15, 2020

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

87%87% 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, ios · Missing: supports, reddit linkedin, podcasting

79%79% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

48%48% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

29%29% 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, pipe, io · Missing: https docs, excited, just released

27%27% 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

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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