[hughesey]

This was announced originally early last year. It removes the requirement for TLD and nTLD (not ccTLD) operators to have a WHOIS service available, but doesn't mandate they must shut them down.

So far the sunsetting has had little effect with most TLDs still having their WHOIS services online. In reality, I think we'll see a period of time where many TLDs and nTLDs have both WHOIS and RDAP available.

Additionally, since ccTLD's aren't governed by ICANN, many don't even have an RDAP service available. As such, there's going to be a mix of RDAP and WHOIS in use across the entire internet for some time to come.

Disclosure: I run https://viewdns.info/ and have spent many an hour dealing with both WHOIS and RDAP parsing to make sure that our service returns consistent data (via our web interface and API) regardless of the protocol in use.

[tephra]

I think RDAP is going to be adopted by more and more ccTLDs as well. WHOIS is not a particularly well liked protocol (I was at an IETF meeting where ICANN did a presentation on the timeline and people were literally cheering for the demise of WHOIS).

Disclosure: Work in the ccTLD space.

[hughesey]

100% agree that there will be more ccTLD operators that will implement RDAP. The sooner we're on a consistent protocol the better!

[dubbel]

Self-plug: I run a little mastodon/activity pub bot that monitors DNS RDAP adoption according to the official bootstrap file: https://social.haukeluebbers.de/@stateofrdap

Last post from yesterday:

> As of today 82.25% (1187) of all 1443 Top Level Domains have an authoritative RDAP service declared.

> These TLDs were added:

> .ye

[sidd_sarkar]

okay

[jbverschoor]

It's funny to see that a lot of services are finally moving from a human-readable / plain text format towards structured protocols right at the point where we can finally have LLMS parse the unstructured protocols :-)

[ajnin]

Well you can't really trust an LLM to give you reproducible output every time, you can't even trust it to be faithful to the input data, so that's nice to have a standard format now. And for like a millionth of the computing resources to parse it. Also Whois was barely human-readable, with the fields all over the place, missing or different from one registry to the other. A welcome change that should have come really sooner.

[vrighter]

we can't ever have LLMs reliably parse any form of data. You know what can parse it perfectly though? A parser. Which works perfectly, and consistently.

[derefr]

Except that the whole problem of WHOIS, that RDAP is solving, is that a WHOIS response an unstructured plaintext response, that's entirely arbitrarily formatted according to the whims of the TLD manager.

Ever tried to parse WHOIS data? You literally have to write a parser per TLD.

And things get even more stupid when you start talking about WHOIS records for IP ranges. Then you have to write a parser per IP-range delegation — starting at IANA, and working recursively, all the way down to the individual ASN. Where you have no idea how many delegating parties are going to be involved — and so get their own step in the chain, formatted however they wish — for any given IP address. (Ask me how I know.)

[robotresearcher]

> Which works perfectly

... on conformant inputs, when it has no bugs.

[kibwen]

On non-conformant inputs, a parser will barf and yell at you, which is exactly what you want.

On non-conformant inputs, there's absolutely no telling what an LLM will do, which is precisely the problem. It might barf, or it might blissfully continue, and even if the input was right you couldn't remotely trust it to regurgitate the input verbatim.

As for bugs, it is at least theoretically possible to write a parser with no bugs, whereas an LLM is fundamentally probabilistic.

[TeMPOraL]

Of course we can. Reliability is a spectrum, not a binary state. You can push it up however high you like, and stop somewhere between "we don't care about error rate this low" and "error rate is so low it's unlikely to show in practice".

It's not like this is a new concept. There are plenty of algorithms we've been using for decades that are only statistically correct. A perfect example of this is efficient primality testing, which is probabilistic in nature[0], but you can easily make the probability of error as small as "unlikely to happen before heat death of the universe".

--

[0] - https://en.wikipedia.org/wiki/Primality_test#Probabilistic_t...

[kbolino]

There are two problems with this comparison. First, probabilistic prime generation has a mathematically proven lower bound that improves with iteration. There is no comparably robust tuning parameter with an LLM. You can use a different model, you can use a bigger variant of the same model, etc., but these all have empirically determined and contextually sensitive reliability levels that are not otherwise tunable. Second, the prime generation function will always give you an integer, and never an apple, or a bicycle, or a phantasm. LLMs regurgitate and hallucinate, which means that a simple error rate is not the only metric that matters. One must also consider how egregiously wrong and even nonsensical the errors can be.

[waterhouse]

I think the better statement is that, if, say, you're running the Miller-Rabin test 10 times, you can be confident that an error in one test is uncorrelated with an error in the next test, so it's easy to dial up the accuracy as close to 1 as desired. Whereas with an LLM, correlated errors seem much more likely; if it failed three times parsing the same piece of data, I would have no confidence that the 4th-10th times would have the same accuracy rate as on a fresh piece of data. LLMs seem much more like the Fermat primality test, except that their "Carmichael numbers" are a lot more common.

[billbrown]

I compare LLMs to a door with a slot where you put a piece of paper with a request on it and you get something back related to that request. If it's the same every time, great. But it might be different or completely wrong. You don't know what goes on behind the door and measuring the error rate tells you little predictive.

[dcow]

The general point is not that the feature currently exists to dial down the LLM parse error rate, it’s that the abstract argument “we can’t use LLMs because they aren't perfect” isn’t a realistic argument in the first place. You’re probably reading this on hardware that _probably_ shows you the correct text most all of the time but isn’t guaranteed to.

[wizzwizz4]

There's no such thing as a perfectly-watertight roof, therefore there's no qualitative difference between fixing the roof and buying a bigger bucket.

[TeMPOraL]

Precisely this. People dismiss utility of LLMs because they don't give 100% reliability, without considering the basic facts that:

- LLMs != ChatGPT interface, they don't need to be run in isolation, nor do they need to do everything end-to-end.

- There are no 100% reliable systems - neither technological nor social. Voltages fluctuate, radiation flips bit, humans confabulate just as much if not worse than LLMs, etc.

- We create reliability from unreliable systems.

LLMs aren't some magic unreliability pixie dust that makes everything they touch beyond repair. They're just another system with bounded reliability, and can be worked into larger systems just like anything else, and total reliability can be improved through this.

EDIT: In fact, my example with probabilistic primality tests is bad because those tests are too nice - they let us compute tight bounds on the error rate in advance. LLMs are not like that. But then, a lot of systems we rely in our daily lives also have this property - their reliability is established empirically, i.e. we improve them until they work reliably enough, and then we hope they'll keep on working, and deal with random failures when they occur. So that's nothing new, either.

[_ache_]

If you job is to be a referent, to have authority. You absolutely don't want to make any error. Pretty safe isn't enough, you need to be absolutely sure that you control the output.

You only have one job, don't delegate authority.

[francislavoie]

But isn't using LLM for that really expensive? Seems wasteful.

[adrianmonk]

I wouldn't use LLMs, but if I did, I would try to get the LLM to write parser code instead.

If it can convert from one format to another, then it can generate test cases for the parser. Then hopefully it can use those to iterate on parser code until it passes the tests.

In a sense, asking it to automate the work isn't as straightforward as asking it to do the work. But if the approach does pan out, it might be easier overall since it's probably easier to deploy generated code to production (than deploying LLMs).

[genewitch]

My desktop GPU can run small models at 185 tokens a second. Larger models with speculative decoding: 50t/s. With a small, finetuned model as the draft model, no, this won't take much power at all to run inference.

Training, sure, but that's buy once cry once.

Whether this means it's a good idea, I don't think so, but the energy usage for parsing isn't why.

[smokel]

A simple text parser would probably be 10,000,000 times as fast. So the statement that this won't take much power at all, is a bit of an overstatement.

[boxed]

50 tokens per second. Compared to a quick and dirty parser written in python or even a regex? That's going to be many many orders of magnitude slower+costlier.

[anthk]

awk would run millions times faster, not to mention mawk and awka.

[berkes]

In order to make the point that

> energy usage for parsing isn't why

You'll need to provide actual figures and benchmark these against an actual parser.

I've written parsers for larger-scale server stuff. And while I too don't have these benchmarks available, I'll dare to wager quite a lot that a dedicated parser for almost anything will outperform an LLM magnitudes. I won't be suprised if a parser written in rust uses upwards of 10k times less energy than the most efficient LLM setup today. Hell, even a sed/awk/bash monstrosity probably outperforms such an LLM hundreds of times, energy wise.

[chgs]

How many times would you need to parse to get an energy saving on using an lm to parse vs using an llm to write a parser, then using the parser to parse.

[RUnconcerned]

It sounds like you need to learn how to program without using a LLM, but even if you used one to write a parser, and it took you 100 requests to do so, you would very quickly get the desired energy savings.

This is the kind of thinking that leads to modern software being slower than software from 30 years ago, even though it is running on hardware that's hundreds of times faster.

[GTP]

> using an llm to write a parser

You're assuming OP needs an LLM to write a parser, since they mentions writing many during their career they probably don't need it ;)

[Hercuros]

It’s not just about the energy usage, but also purchase cost of the GPUs and opportunity cost of not using those GPUs for something more valuable (after you have bought them). Especially if you’re doing this at large scale and not just on a single desktop machine.

Of course you were already saying it’s not a good idea, but I think the above definitely plays a role at scale as well.

[SlightlyLeftPad]

You’re right, I could be trying to get Crysis to run at 120 fps.

[tmtvl]

If you have spare GPU time you could donate it to projects like Folding@Home.

[anthk]

My Atom n270 netbook with mawk and a few lines parsing the files with a simple regex will crush down your GPU+LLM's on both time and power usage.

[genmon]

My assumption is that models are getting cheaper, fast. So you can build now with OpenAI/Anthropic/etc and swap it out for a local or hosted model in a year.

This doesn't work for all use cases but data extraction is pretty safe. Treat it like a database query -- a slow but high availability and relatively cheap call.

[Cthulhu_]

While it will become cheaper, it will never be as fast / efficient as 'just' parsing the data the old-fashioned way.

It feels like using AI to do computing things instead of writing code is just like when we moved to relatively inefficient web technology for front-ends, where we needed beefier systems to get the same performance as we used to have, or when cloud computing became a thing and efficiency / speed became a factor of credit card limit instead of code efficiency.

Call me a luddite but I think as software developers we should do better, reduce waste, embrace mechanical sympathy, etc. Using AI to generate some code is fine - it's just the next step in code generators that I've been using throughout all my career IMO. But using AI to do tasks that can also be done 1000x more efficiently, like parsing / processing data, is going in the wrong direction.

[relistan]

I know this particular problem space well. AI is a reasonable solution. WHOIS records are intentionally made to be human readable and not be machine parseable without huge effort because so many people were scraping them. So the same registrar may return records in a huge range of text formats. You can write code to handle them all if you really want to, but if you are not doing it en masse, AI is going to probably be a cheaper solution.

Example: https://github.com/weppos/whois is a very solid library for whois parsing but cannot handle all servers, as they say themselves. That has fifteen + years of work on it.

[LadyCailin]

But.. that’s exactly what this thread is about. RDAP is the future, not WHOIS.

[ohmygoodniche]

Requesting that people think before transferring mission critical code into the hands of LLMs is not being a Luddite lol.

Can you imagine how many ridiculous errors we would have if LLMs structured data into protobufs. Or if they compiled software.

It's more than 1000x more wasteful resources wise too. The llm swiss army knife is the Balenciaga all leather garbage bag option for a vast majority of use cases

[GTP]

Still, I wouldn't use an LLM for what's essentially a database query: by their very nature, LLMs will give you the right answer most of the times, but will sometimes return you wrong information. Better stay on a deterministic DB query in this case.

[lucianbr]

As usual, arguments for LLMs are based on rosy assumptions about future trajectory. How about we talk about data extraction at that point in the future when models are already cheap enough. And in the meantime just assume the future is uncertain, as it obviously is.

[permo-w]

deepseek API costs are quite literally pennies per million tokens

[klysm]

Which world would you rather live in: * structured protocols that can be parsed by machines * unstructured protocols that are unreliably parsed by LLMs that require significant power and latency

[mmooss]

In addition to ~determative machines and LLMs, what about humans reading the data?

[axegon_]

https://deviq.com/antipatterns/shiny-toy

[RealStickman_]

Off topic thank you for runnig viewdns.info. I don't use it regularly, mainly for the occasional WHOIS information lookup and it has always worked perfectly.

[hughesey]

Thanks for the kind words and glad it's been useful :).

[tecleandor]

It's kind of funny some operators have never had it in practice. For example, .es never had a public whois, and need to register with a national ID (and I think with a fixed IP address) to get access to it.

[berkes]

That need for a national ID hasn't been in place for a long time, AFAIK.

I have a .es (my nickname berkes, domain berk.es) for almost 16 years now, and live in the EU, but not in Spain. In the beginning I used a small company that offered services for non-spanish companies to register .es through them (I believe they technically owned the domains?). But today it's just in my local domain registrar without need for an ID.

That .es has no whois has struck me as somewhat of a benefit actually. Back in the days, it kept away a lot of spam from spammers that'd just lift email-addresses off the whois. My .com, .nl and other domains recieve(d) significant more such spam. Let alone phone-number and other personal details delivered over an efficient, decentralized network. Though recent privacy addons(?) have mitigated that a little.

[tecleandor]

I meant for accessing the whois, not for registering. If you try any type of WHOIS request you'll be replied with a message sending you to nic.es site, where you'll be presented with a captcha if you try to get information about a registered domain.

It's not very well documented, but you can register at a government site using a national ID and they'll open WHOIS access for a fixed IP address, for a maximum of 10 queries a minute. [0]

Context for any of you not used to the .es ccTLDs: Until some years ago, and simplifying a bit, if you wanted to register a .es TLD you had to be an Spanish national or company, and be the legal holder of the domain name you wanted to register (or your name and surnames).

--

  0: https://sede.red.gob.es/es/procedimientos/solicitud-de-acceso-servicio-de-whois-por-el-puerto-43

[belorn]

Usually, the need to use an ID is only for private persons (and usually only if they are nationals). Anyone else should not need that. The general theory is that a nation can only verify data that they themselves have.

Some ccTLD's have rules against registrations by people not located within the country that owns the ccTLD, in which case a valid national id or organization number would be required. From what I can see, .es does not have that requirement.

[tecleandor]

Se my other comment [0] but I meant for accessing the WHOIS service, not for registering. If you try any type of WHOIS request you'll be replied with a message sending you to nic.es site, where you'll be presented with a captcha if you try to get information about a registered domain.

--

  0: https://news.ycombinator.com/item?id=43392356

[teddyh]

Requiring a captcha is not even close to requiring a national ID.

[tecleandor]

If you read my linked comment, I'm talking about using the WHOIS service [0] ICANN is sunsetting that's been talked about in the post, not about getting domain information in the web.

The only way (that I've found) to use the WHOIS service with the .es ccTLD is whitelisting a fixed IP address with your national ID at a government site [1]. And even then, you're rate limited to 10 queries per minute.

--

  0: https://en.wikipedia.org/wiki/WHOIS
  1: https://sede.red.gob.es/es/procedimientos/solicitud-de-acceso-servicio-de-whois-por-el-puerto-43

[reaperducer]

For example, .es never had a public whois, and need to register with a national ID (and I think with a fixed IP address) to get access to it.

Is this new? I had an .es domain around 2011, and am not Spanish, or even European.

[tecleandor]

Se my other comment [0] but I meant for accessing the WHOIS service, not for registering. If you try any type of WHOIS request you'll be replied with a message sending you to nic.es site, where you'll be presented with a captcha if you try to get information about a registered domain.

--

  0: https://news.ycombinator.com/item?id=43392356

[account42]

You don't need WHOIS to register a domain.

[spurgu]

Hey, I've been looking for a tool that can do reverse NS lookup for a nameserver pairs (ie. which domains have nameservers ns1.example.com and ns2.example.com) but all the services out there that I've found can only do one. Is this something you would consider implementing?

[notRobot]

Thank you so much for running your service. I've used it for years, and LOVE how functional and useful it is!