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Run your own private DNS with Pi-hole, dnsdist and Caddy

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Run your own private DNS with Pi-hole, dnsdist and Caddy

I built a self-hosted private DNS resolver that supports standard DNS, DNS-over-TLS (DoT), and DNS-over-HTTPS (DoH). Stack: - Pi-hole for DNS filtering and blocklists. - dnsdist for load balancing and DoT/DoH. - Caddy for automatic TLS certificates. Features: - Works with DNS (53), DoT (853), and DoH (/dns-query on 443). - Single docker-compose setup with persistent storage. - Certificates shared between Caddy and dnsdist for DoT. Docs and setup guide: https://github.com/allenhack638/self-hostable-private-dns

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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 · Strong signals: dock, single · Missing: mac, agents, macos
71%71% 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 NewsStrong engagement from HN community · Strong signals: ide · Missing: https docs, excited, just released
58%58% 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 · Missing: mobile apps, ios, personal
39%39% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Strong signals: supports · Missing: reddit linkedin, podcasting, created
37%37% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: host · Missing: plus, platform, intuitive
37%37% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
17%17% 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
12%12% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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