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Jelly – SSH Social Hangout
Hacker NewsJelly – SSH Social Hangout
built a social network you connect to over SSH. no signup, no browser, just open your terminal and you're in. channels, profiles, guestbook, shared blackboard, Top 8. your identity is your SSH key fingerprint so no passwords needed. to connect: ssh-keygen -t ed25519 (just hit enter through all the prompts) ssh jellyshell.dev built with Go, Bubble Tea, and Wish. i wanted to make something that maintains privacy and gets away from the brain rot and algorithms pushing rage bait. lmk what you think.
Share cardActual performance
4points
6comments
Did not reach leaderboard
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Product HuntOn track for Day 1 leaderboard · Strong signals: open · Missing: mac, agents, macos64%64% 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.
AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive53%53% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide · Missing: https docs, excited, just released52%52% 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: way · Missing: mobile apps, ios, personal37%37% 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, podcasting25%25% 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, revenue21%21% 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, crypto1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.
Incorrect prediction on native model
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