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DDNS-go – Over-engineered DDNS service with native OS integrations

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DDNS-go – Over-engineered DDNS service with native OS integrations

I have this tendency to over-engineer things, and sometimes it can be a good learning opportunity. ddns-go is what happens when you take DDNS seriously, and try to do everything in the most efficient way possible. On Linux it opens a netlink socket and listens for network interface changes. If you use WireGuard for all outgoing traffic, and still want to be able to handle incoming connections from the physical interface, ddns-go can even help you maintain the policy routing rules (see ip-rule(8)). On the BSDs it opens a route(4) socket for the same thing, except it is a bit more complicated here because for historical reasons the messages do not contain IPv6 address flags. You have to call ioctl(2) to retrieve the flags, if you want to filter out deprecated and temporary IPv6 addresses. On Windows ddns-go uses the IP Helper API to get notified for interface address changes. Of course the API does not behave quite like how the documentation describes, so there needs to be workarounds. ddns-go also speaks asuswrt's web API and can get the WAN IPv4 address directly from the router. As a result of working on this project, I sent 3 separate CLs to the x/sys module. [1] https://go-review.googlesource.com/c/sys/+/597915 [2] https://go-review.googlesource.com/c/sys/+/598895 [3] https://go-review.googlesource.com/c/sys/+/603755

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Product HuntOn track for Day 1 leaderboard · Strong signals: google, physical, open · Missing: mac, agents, macos
66%66% 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.
Indie HackersFits the IH revenue-focused audience · Strong signals: para · Missing: supports, reddit linkedin, podcasting
58%58% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
57%57% 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: google, way, para · Missing: mobile apps, ios, personal
45%45% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: interface, efficient · Missing: plus, platform, intuitive
43%43% 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
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
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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