Built an app to replace our failed group podcast experiment

Hacker News

After college, my friends scattered across time zones. We tried starting a podcast to stay in touch, everyone would record updates, uploads them to Google Drive, and we planned to eventually publish episodes. We never published one. Turns out we didn't want a public podcast, but "the podcast" gave us a reason to keep in touch and share stories/thoughts. Google Drive was terrible for our needs. Record in one app, then upload. No notifications, bad organization. The worst part was when responding to something specific, I'd have to manually timestamp it and repeat their point before commenting. Completely broke the flow. So I built Roads Audio. Private audio messaging where you can reply to specific moments with timestamped comments. Conversations branch naturally instead of stacking linearly. The hardest technical problem was handling infinite nested timestamped threads and querying position in the conversation tree, then displaying that in a user-friendly UI. I built with Flutter/Django and have been working on it for a few years (here's the original micro-podcasting launch: https://news.ycombinator.com/item?id=37758099 ). Initially positioned as a podcast alternative, but I've realized it's really about staying connected through longer-form audio without immediate response pressure. The app has evolved a lot over the years and I'm always looking for more feedback from people who are interested in async audio. I still use it regularly with the same friend group; if it seems interesting let me know what you think!

Visit product View on Hacker NewsJan 28, 2026

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Product HuntOn track for Day 1 leaderboard · Strong signals: google, user, new · 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: podcasting · Missing: supports, reddit linkedin, created

78%78% predicted probability of success on Indie Hackers, 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

41%41% 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 · Strong signals: friendly · 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: google, way · 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 · Missing: arr, mrr, revenue

17%17% 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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