> because every DDNS service I tried was designed around 2010-era networks
I am not an expert in the domain of DDNS. Wanted to bring your attention to desec.io, in case you didn't knew about them. They offer a similar feature set like you mentioned (IPv6, DNSSEC, BYOD, ...). It is an open source project and they offer a very reliable free hosted service. As you said, they originated from the 2010-era (2014). I've used them for several years now and they bring everything to the table that I need.
For inspiration:
They even have a feature that I use which I haven't spotted in your documentation (but maybe I just didn't looked close enough): Support for IPv6 prefix delegation. Routers that get assigned an IPv6 prefix from the ISP, can update the IPv6 prefix of arbitrary domains. In Europe this prefix is not static and rotated each time a new connection to the ISP is established. This feature allows the router to automatically update the IPv6 _prefix_ of selected domains. The host part of the IP is left untouched, but the network part is updated.
I don't think I've tried Google Cloud Vision on that particular image, no. In my experience, based on some tests from a year ago or so, Azure Document Intelligence impressed me the most in terms of OCR - out of the big three players: GCP, AWS and Azure.
I should retry the experiment because there has been a lot of progress since then and I could imagine that GCP improved there vision models since then.
I totally understand, and I can't blame you for that. I wouldn't think otherwise. I am a long-time follower of YC but never posted any comments. I wanted to share that experience which is the reason I created the account. I don't know how I can proof to you that I am a legitimate person who has _no_ affiliation whatsoever with Interfaze. I can only ask to try it out for yourself. I was genuinely impressed by the results.
I just tried the OCR capabilities with a photo of a DIN A4 page which was written with a typewriter. The image isn't the easiest to interpret. The text perspective is distorted because the page is part of a book and the page margin toward the spine of the book is very small. There are also many inline corrections due to typing errors while the page was written (backspace couldn't erase characters back then, and arrow keys couldn't be used to add text in between existing words). Over the past months I've tried to use several LLMs on this very same image already (1 out of 200 pages that seek digitization). The result is by far the most accurate so far. Only some very minor errors (which are also non-trivial for human translators) were made.
This page induced costs of about 25 cent. I assume I could tweak the input image a little more to consume less input tokens. OCR-ing all 200 pages would otherwise cost a juicy 50$ - although there is a generous 20$ of free credits.
// Edit: I just re-tried the same task utilizing a capability of the API to only run a specific part of the model (e.g. _only_ OCR). This cuts cost by 3x (to ~8c/page) but significantly worsens the result. The result is missing entire lines of the original document. There are also many error in the text that was recognized.
dig @9.9.9.9 rsgm.dev NS