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CredAnt: Minimalist Decentralized Remote Networking – Zero Config, P2P

Hacker News

CredAnt: Minimalist Decentralized Remote Networking – Zero Config, P2P

Hey HN, We've been working on CredAnt, a new approach to remote networking that prioritizes simplicity and decentralization. Tired of complex VPN configurations and concerns about centralized servers? We were too. CredAnt is designed to be extremely easy to set up and use. Our goal is zero configuration: install and you're ready to connect to your remote devices as if they were on the same local network.

Visit productView on Hacker NewsMar 28, 2025
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Actual performance

2points
3comments
Did not reach leaderboard

Launch Intel predictions

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Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
63%63% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Product HuntUnlikely to reach the leaderboard · Strong signals: new · Missing: mac, agents, macos
50%50% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
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.
Indie HackersIH features products with proven revenue · Strong signals: vpn · Missing: supports, reddit linkedin, podcasting
27%27% 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
16%16% 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
2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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