RegionCast – A crowdsourced audio guide for the world

Hacker News

Hi HN! I’ve been making an audio guide app for travelers of all kinds. Primarily it is intended for use on road trips, while biking, and on trains. It’s an audio guide app where users can submit text stories and we (I) will approve and narrate the story. Eventually the review process could be crowdsourced, but for now it is set up where each story is reviewed by me, the backend is set up to make the review and narration process as quick as possible. I want to collect all kinds of interesting stories and make them available in the app. There are layers of hidden information and history all around us and I want to collect and bring them to life. Types of posts I want to highlight are: interesting family histories, landmarks, lesser-known natural formations, ghost stories, funny stories, urban legends, lesser-known nature experiences, interesting infrastructure, homes where famous people have lived etc. Honestly anything interesting that might make a good audio story! I am using AI to try and bootstrap some initial content, and most of it is in Austin, Texas and surrounding cities right now because that’s where I am based. Here’s an example of a history-themed post: https://www.regioncast.com/cast/b7c26eb7-fb96-4cdb-b485-f7d5... I would love some feedback, I have never launched anything before on my own and am kind of scared but I’ve been sitting on this for too long and need to just show someone so I can move onto another project if there is no interest. Please let me know what you think and I am happy to answer questions!

Visit product View on Hacker NewsAug 29, 2024

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

89%89% 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: user, using · Missing: mac, agents, macos

66%66% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

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

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

37%37% predicted probability of success on TrustMRR, 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

35%35% 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 · Strong signals: arr · Missing: mrr, revenue, profit

10%10% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: audio · 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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