Radiopaper – Troll-resistant public conversations

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Hi HN! We're a bootstrapped team of 4 and have been building Radiopaper for around 16 months alongside other full-time, part-time, and consulting jobs. I wanted to highlight a couple of the unique characteristics of Radiopaper that may not be immediately apparent when browsing https://radiopaper.com/explore * It's possible to interact with Radiopaper entirely by email, and never log-in interactively. The notification emails contain context that explains that if you reply to the email, your message will be published on https://radiopaper.com * The key mechanism that makes Radiopaper different from other social networks, and more resistant to trolling and abuse, is that messages are not published until the counterparty replies or accepts your comment. You can read more about this in our manifesto at https://radiopaper.com/about The technical stack is a Vue/TypeScript app talking to an API backend written in Go, running on Cloud Run, and using Firestore for persistence, Firebase Auth for authentication. Email processing is handled through the Gmail API hooked up to a Cloud Pubsub notification which triggers another Cloud Run service. Outbound emails go through SendGrid. The whole stack "scales-to-zero", and on days that we have a few hundred active users, we're still under the free limits of Firebase Hosting, Cloud Run & Firestore, so this has allowed us to operate for a long time without funding or revenue. Our overall burn rate is around $40/month, mostly from the smattering of other SaaS offerings we use: Sentry, Mixpanel, Github & SendGrid. Dave & I discuss our tech stack in a little more detail in this conversation: https://radiopaper.com/conversation/4PsvfxLX2Q5NHLBs8nuN The team (myself, daave, davidschaengold, youngnh) will be around to answer any questions!

Visit product View on Hacker NewsApr 29, 2022

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

90%90% 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, email, context · Missing: mac, agents, macos

68%68% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

61%61% 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: host, users · Missing: plus, platform, intuitive

42%42% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

40%40% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: revenue, saas, bootstrapped · Missing: arr, mrr, profit

24%24% 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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