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Scapehouse – A new take on group conversations

Hacker News

Scapehouse – A new take on group conversations

You're probably already a member of several online group chats. It usually goes: somebody creates a group for some purpose, like a project group, an event group, or a family group, and then adds people to it. You might not know everyone in the group, or even care about the group's purpose, and leaving might make you look bad. I spent my free time for the past few months building a service that doesn't allow you to create groups, but rather automates the process while making it less awkward. Whenever two people connnect, it automatically detects mutual friends between the two people who all know each other, and then creates a private conversation space for each group. Groups dynamically grow as mutual friends connect with one another. Rather than making it chat-style, conversations within each group are threaded to keep things organised. All you have to do is add (or remove) people you know, and Scapehouse takes care of the rest for everybody involved. There's also a public conversation space (a "living room") for each country that people access the site from. You can only make new threads in your own country's living room, but you can reply to threads in other countries. We're starting out with just a few countries but more will be added soon. I've released a public beta at https://scapehouse.com . The site is quite barebones at the moment, but it's fast and I made sure it works nicely on mobile as well as the desktop. To join, the only things required in the registration form are a username and password; everything else is optional and browsing living rooms doesn't require an account. Feedback is welcome!

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
80%80% 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, new · Missing: mac, agents, macos
78%78% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Strong signals: soon · Missing: plus, platform, intuitive
56%56% predicted probability of success on AppSumo, 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
48%48% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: month · Missing: mobile apps, ios, personal
43%43% 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
16%16% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency
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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