Netsplits, missed messages and bot wars over channel and nick ownership were an integral part of IRC UX, and they were direct consequences the IRC protocol. If Discord was run on top of IRC protocol, it would have the same. Discord would probably be its own network and the people who prefer IRCnet, EFnet or QuakeNet would never touch it.
It's not inherent to the protocol. https://ergo.chat/ does not have netsplits (from having a single server) and https://github.com/Libera-Chat/sable replaces the server-to-server protocol to eliminate netsplits as well.
And even when not eliminated entirely, they are infrequent and barely visible on well-managed networks like Libera.Chat. Many chat platforms have more (and longer) outages than Libera has netsplits.
Solved decades ago thanks to NickServ and ChanServ (though I'll admit they are ad-hoc additions on top of the protocol). And ~15 years ago we got native support for authentication (https://ircv3.net/specs/extensions/sasl-3.1)
So... Usually it's claimed that one of the advantages of IRC is that it doesn't depend on a single server, so using a single server feels a bit like cheating. Replacing the server-to-server protocol sounds a lot like it's not really IRC protocol any more. The chathistory link says "This specification may change at any time and we do not recommend implementing it in a production environment." right on top. And yes, NickServ and ChanServ exist on some networks and IIRC they were one of the major points in debates over which network is the best and which ones to not touch with a ten feet pole. A hypothetical IRC-based Discord-like service could have it.
I mean the word "Relay" in Internet Relay Chat was meant to refer to relaying between servers. Larger networks even had some hub servers that didn't allow users to connect at all, and existed to be server interchanges.
IRCv3 missed the boat by years. By 2016, when the working group was formed, IRC was already well past its glory years. Even then, it took til the 2020s before any major network fully adopted it. Because - and I say this as a nerd who held an O line on two of those major networks at one point in my life - a bunch of nerds got hung up on arguing about implementation specs rather than looking at features and functionality organically. Ironically, in the quest to avoid becoming a closed Discord/Slack/what-have-you ecosystem product, they needed a product manager to remind them that what they needed to build in that working group was an evolution to IRCv2, not endless arguments over the format of configuration files for server daemons, for but one example.
IRCv3 was already late to the party and when I saw that the Working Group's mailing list was composed of lots of debates on formats for server daemon configuration files, it was clear many couldn't see the forest for the trees.
>Like “the UX of HTTP is horrible”? Still doesn’t make any sense
Sure it does, when all browsers have more or less the same, and the context makes clear we're not talking about the mere programmatic consumption of HTTP (like through some REST api).
"But it's a protocol and not a client" is pedantically irrelevant, given that the clients for that protocol all follow the same conventions. The parent already said they meant the UX of it "which is arguably similar between implementations".
Besides, protocols impose some concepts and models of interaction and consumption, which informs any UX created on top of them. So it's not like that client sameness is merely accidental and unrelated to the protocol either.
At the universities I’ve been to (as a student and now faculty), «applied mathematics» and «statistics» have been the two largest divisions. But perhaps that’s a bias from engineering-heavy universities?
"Applied Math" and "Statistics" are distinct fields from "Mathematics," not subfields of it. People in those two departments are often closer to Computer Science or the statistics subfield in a domain science field (e.g. biostatistics, econometrics) than to Mathematics in terms of what they actually teach and research.
That is perhaps fair, is that distinction common internationally?
Again, in the universities I’ve been to, «applied math» and «statistics» have generally been placed under the department of mathematics. I myself am a physicist, and consider applied physics, biophysics, etc. to be subfields of physics and not distinct fields of study, but I don’t know what outer physicists think.
> not needing the pinky for keys like backspace/enter/esc.
Probably not so efficient in terms of WPM, but after my previous issues with RSI, I somehow ended up pressing those keys with my middle fingers instead of my pinkie. Usually multiple fingers hit the key simultaneously, either long finger + ring finger or long finger + index finger.
Requires more hand movement but certainly more comfortable.
Okay. Do you then consider an equilibrium to be inherently unsustainable?
If you take «growth» to be defined as d(something)/dt>0, I’d posit that any equilibrium by definition has zero net growth, whether it’s a static or dynamic equilibrium.
Maybe not for a chemist, but as a physicist it’s certainly useful. Liquid He cooling, Bose-Einstein condensation, superfluidity, p-wave triplet pairing in He-3, etc. while being basically chemically inert!
In general, gaffer's tape is the superior product, but for this use case, I'm thinking that duct tape with its solid backed film and thicker adhesive might be more airtight.
To be fair, that one came from an editor not a physicist; the physicist wanted to call their book «the goddamn particle», and it got censored/editorialized to «the god particle».
I've heard that story and it doesn't ring true to me. It's not that aggravating to find. Try measuring a neutrino mass, which is still an open problem and looks as if it will remain so for a very long time.
Interesting. Someone should (or maybe have?) run a cluster analysis on the symptoms to define more specific subgroups. But I suppose getting access to the required health data at that scale is nontrivial?
It’s not that hard to get a long list of symptoms for long covid. Just watch this thread as it grows, and you’ll easily find dozens. Things like this end up being a lint trap for people who just feel bad for whatever reason (which is all of us, at various points in our lives!) Nobody likes to be told that their symptoms are idiopathic.
Massaging this kind of data (clustering, etc.) is much lower value than finding fixed criteria that define a consistent group of patients who have objectively defined symptoms that cannot be more readily explained by another diagnosis. This is a pre-requisite for any further study. It can be done, but it’s hard, and it tends to lead to criticisms because you end up excluding a large number of people who fervently believe they have the illness, but don’t fit the objective standards.
Just for example: it’s not enough to claim that you have “brain fog”. A more valid endpoint might instead attempt to classify people based on standardized tests of thinking. Even that has problems, of course, but if you can just claim that you are fatigued and unable to think clearly, there’s a huge problem of confounding (i.e. maybe your symptoms are caused by something else), let alone the unverified nature of the original claim.
Leading research into Long Covid is already doing this. You’re seeing neural and auto immune clusters gathering around certain immune dysfunction and previously rare diagnosis like Small Fiber Neuropathy. Autonomic dysfunction is being measured in young and healthy people also, and that has its own set of objective testing.
Everything you are saying is happening. But because the suspicion seems more and more that it’s an auto immune condition of some sort, and that we are only catching the downstream effects as some of the immune dysfunction isn’t mapped yet, we are seeing the clusters that you say emerge - overwhelming numbers of symptoms, relatively incoherent connection.
But autonomic dysfunction, small fiber neuropathic and detectable auto immune dysfunction are all known and increasingly mapped positive markers for the condition. Have you read the latest studies ?
> You’re seeing neural and auto immune clusters gathering around certain immune dysfunction and previously rare diagnosis like Small Fiber Neuropathy.
Everything I've personally seen in this space is exactly what I described: they start with a set of people who claim to have the illness, then go on a statistical fishing expedition to look for "signs of immune disfunction" (or whatever, but you're right that these researchers tend to focus on immune-related metrics), then use whatever signals they happen to find to create a class. This is not the same thing as what I'm talking about, and it isn't valid.
I'm not going to claim comprehensive knowledge of the space, but the papers I've read that make it into the high-profile journals are of this sort.
The papers cited by this Lowe article are better than most at least in the sense that they have control groups and are doing experiments. But let's be clear -- the first one is claiming to see "long covid" pain symptoms in mice who are injected with whole human IgG (a notoriously messy and subjective approach) [1], and the other is exactly the kind of fishing expedition I'm describing, where they indiscriminately look for "targets" of said antibodies [2]. The former is at least doing an experiment that I suppose could lead to some kind of claim of cause, but the latter (despite the exaggerated title) provides no evidence that the correlations they're seeing are meaningful in any disease process.
I guarantee that using the high-dimensional screening that the latter paper in particular is doing, I can take 1000 random people, split them into two arbitrary classes ("fooists" and "non-fooists"), and find some "statistically significant" difference in immune marker profile between them. That is the fundamental problem with the approach.
When I say that you have to start from an objective measurement of symptoms, it means literally that -- not starting from an assay result that is unlinked to any symptom.
Then you should fund it. The entire field is to my understanding absolutely starved of science funding.
There are two fairly strong clusters of findings that are objective, repeatable, and consistent. And that is the autonomic testing in long COVID patients is coherent in its dysfunction, and so is the Small Fiber Neuropathy testing that is now consistently showing abnormalities.
Lets go step by step.
Small Fiber Neuropathy. Nerve fiber density is a count with age/sex-normed reference ranges. In previously healthy post-COVID patients with no diabetes and no risk factor, then the test shows whether the nerves are there or they aren't.
If your argument is that people are showing up with abnormalities, then diagnosed with Long Covid, then spurious biomarkers are associated to it - you are just wrong. Wrong multiple times. Demonstrably so.
What we are seeing is more likely to be exactly what it looks like - an novel condition being captured by downstream effects of previously unknown or understudied mechanisms.
All of those are examples of exactly what I told you about: they take a group of people claiming to be sick, and go hunting for signals to claim as “significant”.
The MRI studies are particularly egregious examples of this. Just because you see a difference on an MRI does not mean that the difference is due to the thing you’re blaming. In fact, it almost never is.
> If your argument is that people are showing up with abnormalities, then diagnosed with Long Covid, then spurious biomarkers are associated to it - you are just wrong. Wrong multiple times. Demonstrably so.
I am? I have now followed every link. Literally every paper you posted is following this exact pattern. I don't know how you could possibly conclude otherwise, unless you just didn't read past the titles.
They each take a (typically small) cohort of people who self-identify as "long covid sufferers", they subject them to random combinations of tests, and report only what they find to be significant. It's literally the XKCD comic about jelly beans.
You are just ignoring the evidence, being unscientific, and unless you work for a top medical lab somewhere, plain arrogant.
The UK Biobank study scanned participants before and after infection with matched controls. The difference is measured against their own pre-infection brain. That is the opposite of what you're describing.
> You are just ignoring the evidence, being unscientific, and unless you work for a top medical lab somewhere, plain arrogant.
If you don't know how to interpret evidence, then I suppose it would sound like I am being overly critical. I didn't bother to pick on just one, but since you chose it [1]...
> The UK Biobank study scanned participants before and after infection with matched controls. The difference is measured against their own pre-infection brain. That is the opposite of what you're describing.
It is not. The longitudinal nature of the study is a distraction from the fundamental issues with the approach.
They did a longitudinal case-control study, one group of which had positive covid tests in the past, and the other one did not at the time of the second scan (2021). That's the entire evidence base that this study is built upon -- it has nothing to do with "long Covid", and it's only barely plausible that the control group is actually a control for the factors of interest.
Next, they took two scans for all participants - one from before the pandemic, and one made after (again, in 2021). They made over 6000 different images, and then cherry-picked the ones with differences for further analysis (~70). Ultimately only 6 of these fishing expeditions survived family-wise error correction:
> The main case-versus-control analysis between the 401 SARS-CoV-2 cases and 384 controls (Model 1) on 297 olfactory-related cerebral IDPs yielded 68 significant results after FDR correction for multiple comparisons, including 6 that survived FWE correction
So first off, no statistical correction can compensate for this fundamental bias. You cannot start with thousands of different samples - even if they're taken from the same people at different time points - and winnow that down to a handful by filtering on the outcome of interest, Applying a multiple-sample correction will not fix it. It's not even clear that there is such a correction that is valid for the underlying distribution of the data involved.
But setting that aside, the differences observed, even between longitudinal samples, do not have to be due to Covid! Even if they're not random (which we cannot grant; see previous paragraph) they could be due to everyone being locked inside during 2020. They could be due to factors completely unexamined by the study, like, say, increases in drinking or drug use, or lack of exercise. Or any of a million other things. We don't know. The authors don't know. They're just not intellectually honest enough to admit that they don't know.
I could go on, and point out more flaws (e.g. the "significant" results mostly disappear when you exclude hospitalized patients, yet oddly, the difference between "hostipitalized" and "control" cohorts is not itself significant, indicating inadequate statistics), but this post is already too long.
I'm sorry that you think this is arrogant, but this is how we actually read papers.
This seems to me like a performance at this point and not serious analysis.
It’s true I conflated this with long covid. It’s not a long covid study.
I am tired and done with this. You made several errors in this comment.
Your biggest error is the lockdown one.
This makes no sense whatsoever - the controls also lived through lockdown. If this is the rigorous analysis you’re bringing to the studies you read, I’m not surprised none of them pass the muster.
“No correction can fix it” is wrong because the olfactory IDPs were pre-specified. “Could be lockdown” is wrong because controls lived through the same lockdown. “Results disappear excluding hospitalized” is wrong because the paper says they persisted.
The statistical weaknesses you describe are in the papers own limitations section. You just read them back as limitations that can’t be surpassed while evidence based researchers in the field disclose them as meaningful but not exclusionary.
Unless you want to continue with debunking every other strong paper I’ve posted with similar limited and likely to be demonstrably wrong takedowns, then I can’t help you. You have unfalsifiable priors, are constantly ignoring evidence and seem to believe you know better than the top researchers in the field - people who are saving lives - because you catch some statistical limitations and imply that they debunk the entire thing, instead of accepting them as limits of incomplete research into a real condition that’s crippling millions of people.
> the controls also lived through lockdown. If this is the rigorous analysis you’re bringing to the studies you read, I’m not surprised none of them pass the muster.
You've missed the point. I'm not suggesting that the other factor or factors has to be "lockdown". I'm just giving examples that illustrate the idea: even if you assume that the differences between the control and the experimental group are non-random and significant, you still cannot attribute the longitudinal difference to the one factor alone. If you don't like my theory, it's easy to find another, if you're even a little bit imaginative.
> “Results disappear excluding hospitalized” is wrong because the paper says they persisted.
No. They lose all but one. The final "significant" result is teetering on the edge of insignificance. See table 4 [1]. Models 2-4.
> the statistical weaknesses you describe are in the papers own limitations section.
Yes, because they're real. It's great that they wrote them in the paper, but they're fatal flaws.
"We openly disclosed the reason our study is nonsense!" is not the damning indictment you're suggesting that it is.
It’s lockdown and now no lockdown. Could be anything.
All observational studies are wrong. The stated limitations are fatal flaws. You heard it here first in HN. All medical research is fatally flawed, says user “timr”.
> All observational studies are wrong...You heard it here first in HN.
No, but most of them are wrong, and all of them need to be treated with an incredibly high degree of skepticism. This is critical review 101. When you push on this paper, even lightly, it falls over.
Not all papers are bad, but this one is bad, and while there are a great many well-done studies in the world, the subject of "long covid", to date, has essentially ~none of them.
I knew this would be the conclusion. Again - good luck. You are always right.
If you’re right and everyone else is wrong about hundreds if not thousands of studies, then you should be writing a book, not comments in HN.
We started at “some studies have errors” and we ended in “an entire field of research is wrong”.
You have already decided the field has no valid studies. Even when given dozens of examples you picked one and made up a series of points about one study. You made mistakes, never admitted it, and now are calling into question an entire field of medical research.
I’m not even sure you understand how evidence based medicine works.
Afaik evidence based medicine ranks mechanistic analysis near the bottom of the hierarchy — below controlled trials and systematic observation. I believe that ordering was a deliberate choice.
You seem obsessed with something that modern medical research often doesn’t focus on - by design. We still don’t know how lithium works 50 years past its introduction. We don’t know how the conditions that it treats - psychosis or bipolar - work either. Yet lithium is used all over the world- because the effects data and reports show that it works. Your mechanistic obsession isn’t just wrong - it’s directionally incorrect as far as a lot of medical research goes.
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