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Ten_cubed – Artificially restricted social graph

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Ten_cubed – Artificially restricted social graph

Just wanted to share an idea I've had for a while for creating social networks that are useful but don't lead to eventual enshittification. The idea is simple: - Each user gets 10 connections - User networks go out to MAX 3rd degree connections - Users can set their max network preference (1st, 2nd or 3rd degree) This leads to networks that have a maximum 1,110 theoretical connections. It leads to all sorts of fun side-effects, like coveting 1st degree connections and volatile networks that swell and contract as connections change. Let me know your thoughts!

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63%63% 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.
TrustMRRLess likely to generate early MRR · Strong signals: users · Missing: mobile apps, ios, personal
50%50% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: user · Missing: mac, agents, macos
49%49% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: users · Missing: plus, platform, intuitive
47%47% 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, io · Missing: https docs, excited, just released
43%43% 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
14%14% 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
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

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