skysight-inc/HackerNews huggingface dataset:
Having read the article, isn't the point of the article kubernetes in general and what the author prescribes you sign up for/avoid?

Discussions of k8s pitfalls and successes in general seems to be very much in line with what the article is advocating. And, to that point, there's frankly just not a whole lot interesting in this article for discussion "We avoid yaml and operators"... Neat.
And that “lossy compression” could be emergent abstractions, much like the human brain.
In other words; at this point in time, AI is more religion than science.
It seems like there's a lot of people who think we've reached AGI, but then they use AI for a while and realize they were mistaken. If they're journalists, they write an article about it.

LLMs are good at imitating human responses. On the surface level there's the illusion you're communicating with some intelligence, but then you realize the truth: LLMs are an inch deep and a mile wide.

That's not to say they aren't incredibly useful, the real problem is unrealistic expectations and over-hype.
Co-Developing Programs and Their Proof of Correctness – The Spark Toolset
Well, it has to be reliant enough to be able to take on tour, but cool enough to be worthy of taking on tour.

That’s a hard balance.
> If you ask an LLM about something that isn't very written about, it'll make up nonsense, substituting things that are nearby (in some sense).

So what a human might do if they wanted to sound smart but had no idea about the something.
We may be unable to provide a concrete definition of what intelligence is, but we can certainly provide definitions for what it isn't. E.g. we don't need a concrete definition of intelligence to say that a rock isn't intelligent. A pencil isn't intelligent. A calculator isn't intelligence
You've basically described k3s, I think. I run it in my homelab (though I am enough of a tryhard to have multiple control planes) as well as on a couple of cloud servers as container runtimes (trading some overhead for consistency).

k3s really hammers home the "kubernetes is a set of behaviors, not a set of tools" stuff when you realize you can ditch etcd entirely and use sqlite if you really want to, and is a good learning environment.
Why not just use an autoscaling group?

Re: cleverness trap. I feel like this is the tragedy of software development. We like to be seen as clever. We are doing "hard" things. I have way more respect for engineers that do "simple" things that just work using boring tech and factor in whole lifecycle of the product.
That was not an informative article. Is it just me or does it feel like NPR’s writing quality has declined recently
Yes, if local tone mapping is done by the operating system (windowing system or the monitor itself), then there is a chance that borders of windows are done appropriately within their spatial context.
I like Joplin’s sync server, which can be self hosted.

I just wish it didn't require having the app loaded to use. I actually use it with another editor on Linux. (It works surprisingly well with VS Code for example.)

But it's the best we have for Android, sadly.
The use of IQ tests is already suspect, but then I saw that the author is tracking the political leanings of LLMs by using that silly economic/authoritarian political compass - so they seem pretty committed to "tracking" useless noise.
I don't get most of the blame and reasoning.

Sure, everyone has their own product and experience and it's fine to express it, but I don't get the usage of other decisions such as "no to services meshes", "no to helm" and many more.

You don't want to ideally reinvent the wheel for every workload you need (say you need a OIDC endpoint, an existing application): you are tempted to write everything from scratch by yourself, which is also fine, but the point is: why?

Many products deliver their own Helm package. And if you are sick of writing YAML, I would look for Terraform over Pulumi, for the reason that you use the same tool for bringing up Infrastructure and then workloads.

Kubernetes itself isn't easy to be used, in many cases you don't need it, but it might bring you nice things straight out of the box with less pain than other tooling (e.g. zero downtime deployments)
I'm sure by the end of 2024 we'll see huge advances in visual capabilities of chatgpt and other LLMs/VLMs, and this article will be irrelevant/outdated very soon.
One exists https://blog.coiled.io/blog/1trc.html
When you do an IQ test, the person testing take into account the handicap of the person.
I redid a prompt and ran it on Mistral Large and it answered without any problem, see my comment below.
I don't like dancing and for some reason society keeps forcing me into that.
Then it's time to stop calling it "artificial intelligence".
I'm not entirely convinced that there isn't a better way. With AWS Lambda and alternatives able to run containers on demand, and OpenFaas, they all point to "a better way".

[Edit] Parent comment is almost entirety completely different after that edit to what I responded to. But I think my point still stands.
One day, hopefully in my lifetime, we shall see it.
You either recreate a less reliable version of kubernetes for workload ops or you go all in on your cloud provider and hope they'll be responsible for your destiny.

Vanilla Kubernetes is just enough abstraction to avoid both of those situations.
>hell they're even controversial when used to measure human intelligence

You could come up with an IQ test that perfectly tests general intelligence and it would still be controversial. Most don't want a score attached to their level of intelligence.
It really comes down to, I don't ever want to have the conversation “is this a good time to deploy, or should we wait until tonight when there’s less usage”. We have had some periods where our system was more fragile, and planning our days around the least-bad deployment window was a time suck, and didn't scale to our current reality of round-the-clock usage.
Yeah, I disagree with the OP on the dangers there. They work fairly well for us and aren't the source of headache. Though, I still try and teach my dev teams that "just because bitnami puts in variables everywhere, doesn't mean you need to. We aren't trying to make these apps deployable on homelabs."
Joplin works well for what it does and is good enough for regular note-taking. Has most features like live view, webclipper, sync & plugins for that extra missing functionality.

What you don't get is a "polished" UI with a WYSIWYG editor.

The storage format is markdown, but it has its own way of organizing files[1]. If your notetaking process includes multiple editors(other than joplin apps) then joplin may not be the best choice.

[1]. https://joplinapp.org/help/faq/#is-it-possible-to-use-real-f...
Right. And the industry has slowly realized this. The "AGI" hype has died down since reaching a fever pitch when GPT-4 was released. Result after result has shown that LLMs have extreme difficulty generalizing beyond what they've been trained on. Which really isn't much of a surprise, if you think about how deep learning works.

Of course, the major labs and AI influencers are still publicly shouting from the rooftops, because that is what sells. But it's getting harder to deny the obvious.

Ultimately, the problem is that there is really no consequence for being wrong, but tremendous upside for this kind of magical thinking. Someone should start tracking predictions so we can ignore those who continually get things wrong.
AI is Taking Water from the Desert
Are you saying that you don't think that embedding retrieval would work well for this kind of problem or are you saying that you doubt that the UK gov will be doing this kind of thing.

BTW - I used to do lots of clustering and analysis on freeform text, but first SVD and then embeddings have just changed the game so much that it's the starting point now.
Beyond programming, there is a life with purpose. Knowing one's purpose of life, and the purpose of life, are immensely peace-giving and individual direction and guidance are available from God. I've written more at my web site (in profile, nothing for sale, tech-simple). There are free resources available to help individuals. These things have brought me peace despite divorce, health problems, etc. Feel free to ask questions.

(Thoughtful comments appreciated with the downvotes; thanks.)

ps, to quote an earlier posting on a not-exactly-related topic at https://news.ycombinator.com/item?id=38335597:

To me by far the best thing is to have an enduring, joyful, worthwhile, good purpose in life, that drives everything else (including maintaining one's health, proper balance etc), about which I have written a lot at my simple site (no JS etc; in profile; for example one can click on "Things I want to say", then "Life Lessons", then "Everyone needs a direction in life. Centering on pleasure, possessions, power, or attention, harms us, others & the earth, & cannot bring lasting happiness."
Also, some related prior discussions. Note that this is an attempt at collecting useful comments that could relate, NOT at an insinuation that the OP is lazy, a procrastinator, or other. But some comments were good, like finding balance, direction, etc.

https://news.ycombinator.com/item?id=23550758 ("How do you develop internal motivation")

https://news.ycombinator.com/item?id=23072333 ("Extremely disillusioned with technology. Please help (gist.github.com)")

https://news.ycombinator.com/item?id=22919697 ("ask hn: how do i overcome mental laziness?")

https://news.ycombinator.com/item?id=22124489 ("Procrastination is about managing emotions, not time (bbc.com")

https://news.ycombinator.com/item?id=22096571 ("Ask HN: I don't want to be a worker any more I want to be a professional")

https://news.ycombinator.com/item?id=20930439 ("how do you keep your programming motivation up?")

https://news.ycombinator.com/item?id=18903886 "Ask HN: How do you motivate yourself to keep working on a project? "

https://news.ycombinator.com/item?id=19777976 "ask hn: how do you stay disciplined in the long run?"

For what it may be worth.
Agreed. If Linux were a distributed OS, people would just be running a distro with systemd instead of K8s. (Of course, systemd is just another kubernetes, but without the emphasis on running distributed systems)
If that were the only known fact about said imaginary friend on either side, then it would mean exactly that. The reason that there are different expectations of veracity are exactly because there are a bunch of priors being held about imaginary friends, by definition, not being real.
That's basically just a docker-compose.

If you want something crazy all-in-one for homelab check out https://github.com/azukaar/Cosmos-Server
You seem to have a misunderstanding of what the condition "concrete" means.

He's not saying there's no way of judging intelligence, he's just pointing out there's no universal agreement on what intelligence even is.

Edit: To add, this discussion becomes pure semantics. On one side is a strict definition of AGI, on the other side are the most generalized definitions of artificial intelligence. It gets kind of silly because technically, every "if" statement is a type of "AI" by the loosest definitions.
Excellent. Similar. Now I want to write an article about Galaxy Clusters and the nigh incomprehensible space between them. You are here -> . You are tiny.

[1] https://en.wikipedia.org/wiki/Galaxy_cluster
Wage goes up too.
The problem with Helm is that it did the one thing you should not do, and refused to fix it even when their promised to.

They do text-replacement templating for YAML.

I have once spent a month, being quite experienced k8s wrangler, trying to figure out why Helm 2 was timing out, only to finally trace it down to how sometimes we would get wrong number of spaces in some lines.
We are moving toward a future where future robotics viruses will infect deployed humanoids and command them to go on a killing spree.

https://www.youtube.com/watch?v=WWAnJX889j0
Why are people still scared of k8s? At certain jobs thresholds, it is worth every ounce of effort to maintain it. Better yet, go managed.
Docker Compose probably fits the bill for that. They also have a built in minimalist orchestrator called Swarm if you do want to extend to multiple machines. I suppose it's considered "dead" since Kubernetes won mindshare, but it still gets updates.
> They at least don’t require candidates to get dressed up or to worry about controlling their facial expressions [...] Expecting candidates to remain in Professional Mode while experiencing what might be extreme disappointment is unkind.

If more of us flipped out a little at such treatment, maybe managers would stop doing it. Or in other words: give negative feedback to being treated negatively.

I don't mean anything unseemly, but indeed, maybe break that professional barrier a bit. "Oh, no. I'm so terribly disappointed, you know? Just to be clear this is a rejection? Wow. I've never had anyone call to reject my offer before. ...Well, gosh, this is awkward, isn't it? Oh wow, I really wasn't expecting this. Totally out of left field. Well, uh, thanks for taking the time to reject me, anyway. Take care."

(How are they to even know they just seriously distressed you, pointlessly, if you don't tell them?)
LLMs are currently over-represented in these debates and that's not doing the bigger AI research area any favors, sadly. People looking for intelligence in LLMs is kind of sad because the entire thought process seems to be:

1. Pour trillions of texts into a huge artificial neural network;

2. Tweak, tweak, tweak until your eyes bleed;

3. Cross your fingers and hope that magic happens;

4. (=> YOU ARE HERE <=) Get disappointed that magic still isn't happening.

I expect more from intelligent adults. Expecting intelligence from LLMs borders on the medieval belief in alchemy (or the phlogiston).
> I've yet to see a bytecode that consistently beats a well-designed modern machine code for size, over an entire real world program, not just some set of cherry-picked toy examples.

This feels like a bit of a cop out to me, because one of the biggest ways you get the size advantage from a bytecode is by making it less general purpose than the machine it's running on.

Imagine a micro driving a single LED in some long arbitrary pattern. One way to do that is to just have a one-bit PCM array describing the pattern. Maybe this is semantic... but I call that a one-bit instruction bytecode with two instructions (LED ON and LED OFF), and I guarantee you no hardware ever manufactured can beat it using native code (if the pattern is over a certain length).

Sure, that's a toy example if you compare it to a general purpose CPU, but there are a lot of real microcontrollers out there doing jobs that simple.
>"In many parts of the world, it is possible to not be bothered by war and violence every day."

Of course. In many countries wars are exported and one can always pretend that nothing happens and close their eyes on the fact that in many cases they actually finance the war.
You can question and fight your democratically elected government. You can't do the same with an MNC who may have more money than your own country.
For homelab, Docker compose should be enough

For something more production oriented https://github.com/basecamp/kamal
You cannot really be cloud agnostic these days - even when using k8s. So learning to use the capabilities the cloud provides is key.
How would an ideal housing market function, in your view?
fwiw: Gemini gets this wrong!

The answer is most likely Answer B: ooo ooo xoo.

Here's why:

The pattern repeats every three rows.
The first three rows establish a pattern of "x" alternating between the first
and third columns. "o" fills the remaining spaces.
Looking at the last two sets of three rows, we see ooo xoo ooo followed by
ooo ooo oxo.
Following the established pattern, the next set of three rows should be ooo
ooo xoo, completing the cycle.
While other answers might fit in a single instance, they wouldn't maintain
the consistent pattern throughout the entire sequence.
And that hype is in large part created by Google and other cloud vendors.

To be honest I hardly see any reasonable/actionable advice from Cloud/SAAS vendors. Either it is to sell their stuff or generic stuff like "One should be securing / monitoring their stuff running in prod". Oh wow, never thought or done any such thing before.
LLMs literally are next token predictors, so I'm not understanding your broader point.
I admit that I use some Helm stuff in my home environment, but for production I'm genuinely worried about the need to support whatever they've thrown into it. At minimum I'm going to have to study the chart and understand exactly what they propose to open-palm slam into my cluster, and for many/most applications at that point it might genuinely be worth just writing a manifest myself. Not always. Some applications are genuinely complex and need to be! But often, this has been the case for me. For all my stuff, though, I use kustomize and I'm pretty happy with it; it's too stupid for me to be clever, and this is good.

Service meshes are a different kettle of fish. hey add exciting new points of failure where they need not exist, and while there are definitely use cases for them, I'd default to avoiding them until somebody proves the need for one.
> Since we base much, if not all of of our understanding on what we think we know: when those beliefs get thrown out then it will affect everything.

I don't think getting to the point of throwing anything out is necessary, if (some adequate subset of) humans could even get to the point of seriously wondering if our models and beliefs (across all domains, including those outside of science, particularly those which science sits on top of) contain zero fundamentally important flaws, I believe we would be well on the path to sorting the mess we've made out. Science and engineering are excellent examples of how capable humans are when they really set their minds on trying to understand something.
If your toolbox (dictated by your profession) is slightly richer than console applications, you automatically dealing with the applications which are natively "tiled" inside the application windows. And you work with maximized windows of your main applications (natively "tiled" inside) and with a set of small applications that you need to access periodically and these apps do not require full screen mode.

You can alt-tab between them on a single screen, you can switch or swipe virtual desktops with them, you can do anything, a result will be the same.

There are no advantages because of this sameness.
Sounds pretty good compared to ghosting. At least there's closure.

If it becomes the norm again, people will come prepared knowing it could go either way.
People forgot just how much of a mess Mesos environment was in comparison.

And often pushed Nomad to this day surprises me with randomly missing a feature or two that turns out to be impactful enough to want to deal with more complexity because ultimately the result was less complexity in total.
Yeah, looking into writing a scheduler was basically where we stepped back and said “if we write this ourselves, why not the rest, too”. As I see it, the biggest gains that we were able to get were by making things happen in parallel that would by default happen in sequence, and optimizing for the happy path instead of optimizing for reducing failure. In Kubernetes it's reasonable to have to wait for a dozen things to serially go through RAFT consensus in etcd before the pod runs, but we don't want that.

(I made up the dozen number, but my point is that that design would be perfectly acceptable given Kubernetes' design constraints)
ChatGPT doesn't guess randomly but looks in its model for similarties and probabilities to get an answer, if anyone could check all his notes before he answers I bet they get a higher percentage of correct answers.

The comparison with a random guesser makes LLM look even dumber that way.
A less clickbaity title: How I Found Out Sun Microsystems Was Selling My Open Source Tools Without My Permission
Fennec foxes survive on the remnants of a green Sahara, though. Once all that water underground is used up, the ecosystems will all die.
CoreOS tried to distribute systemd and seemed it wasn't working all that well compared to trying to optimize for k8s
The Roman Britons were almost all driven into Scotland and Wales by the invading Angles and Saxons, the cities were abandoned, and pretty much everyone lived a life we'd consider primitive and awful for the next thousand or so years.
I clicked expecting something a bit more detailed here.

What are the best resources to learn simple k8 in 2024?
Docker bare bones or docker compose. Run as systemd services and have docker run the container as a service account. Manual orchestration is all you need. Anything else like rancher or whatever are just fluff.
On the other hand they’re about as good as humans on a text version of Raven’s matrices, another IQ test. https://arxiv.org/pdf/2212.09196v1.pdf
The output of the LLMs would suggest a different metaphor. The supposed divine inspiration of the Bible, perhaps?

Compare "Intelligent Design" vs. the use of genetic algorithms in AI. Simple forms of intelligence can get you a long way and can seem very impressive, especially if they have a lot of subjective experiences, which DNA gets from deep time and which AI gets from transistors outpacing synapses by the ratio to which a pack of wolves outpace continental drift.
See https://xkcd.com/978/ (Citogenesis) for what I think is the biggest problem with a source that publishes AI-generated articles.

Wikipedia is supposed to use primary sources, AI generated articles, by nature, can't be primary sources. In particular, AIs love to use Wikipedia in their training dataset: it is a free, high quality source of information, but it is not flawless either. So there is a good chance that if Wikipedia cites an AI generated article, it has Wikipedia as its source, starting the "citogenesis" process.
Sporth is the most fun I've had programming audio. The Forth-like syntax is perfect for building audio graphs in a way that's simple and terse. It was also like a "gateway drug" to go from programming synths/effects to programming actual music. I ended up not using it for this project, just JS+Faust, partly because I wanted to get more familiar with Faust.
Incumbent homeowners get all upset when they aren't totally insulated from inflation caused by housing shortage. The fact is it now costs a fortune to rebuild a house, because the housing market is completely broken nationwide, so insurance rates have to rise.
That might be true, but unfortunately the state of the art infrastructure tooling is mostly centered around k8s. This means that companies choose k8s (or related technologies like k3s, Microk8s, etc.) not because they strictly _need_ k8s, but because it improves their workflows. Otherwise they would need to invest a disproportionate amount of time and effort adopting and maintaining alternative tooling, while getting an inferior experience.

Choosing k8s is not just based on scaling requirements anymore. There are also benefits of being compatible with a rich ecosystem of software.
Often times it just feels like they try to extend the ticket out as long as possible asking irrelevant questions, then as you said redirect you or close the ticket after wasting an appropriate amount of time.

I cannot prove it, but I bet there is an internal number of replies before they can close without penalty.