Experimental) livestream anything, tree+mesh network

Hacker News

This is an experiment. It does include blocking and rate limiting, so it should meet the minimum requirements. As you can see in the write-up, it is a tree+mesh architecture. It might eventually be able to act as a replacement for YouTube Live or TikTok. For now, there isn't much there. You can start a livestream or join the livestream of anyone else. You can see view counts. Chat isn't saved. Have fun exploring it and let me know any features you'd like added.

Visit product View on Hacker NewsDec 23, 2025

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Actual performance

2points

Did not reach leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Missing: mac, agents, macos

54%54% 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.

Hacker NewsMay not resonate with HN audience · Missing: https docs, excited, just released

44%44% 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 · Missing: plus, platform, intuitive

39%39% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

35%35% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

20%20% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

15%15% 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

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

Correct prediction on native model

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