minraws

Very true, I feel like the sense that Computing is easy comes from the inherent closeness of our lives experiences to it. Everyone uses a Phone they see ram understand memory, can understand process and processing.

PLT and such isn't math conceptually sure it's all logical maths of some kind but largely it's not maths in the traditinal sense of how we understand maths.

Because otherwise if you think about it all of computing is Maths but with computers...

I don't think people who read the Wireless Fidelity spec can understand any of it in a weekend or anything even to a rough extent.

Similarly with websockets, quic etc. the most you can take away without much prior knowledge is what it does which maps into Maths as well.

You are comparing TCP a relatively basic topic in the grand scheme of computing with Rees_algebra which is fairly specialized, we could take a simpler topic more foundational and clearer to understand and compare them.

I can understand that this feels like one is so much more complicated part of it is also how the articles were written, wikipedia is not known for quality maths explanations.

But beyond that this comparison to me feels unfair.

Let's take Euclidean algorithm or just modular arthimetic for example what a lot of computing even is based on I feel like that's a fairer comparison. No?

Perhaps that's too easy but I just find this specific comparison very unfair to both Math's intuitive-ness and Computing's complexity. Perhaps I am the one being delusional.

I think so is upper level CS, there are fields in CS that are foreign to me too, there is a lot of depth in CS, computing is a very deep field for instance ML research although may seem simple isn't quite so intuition based as people make it out to be. Similarly there are dozens of topics where sophisticated research happens where we don't interact with at all as regular software developers.

Every slice has so much depth to it, in Maths it all seems like all of it is required at once but in computing it feels like so little is needed to get started which I honestly feel like is failure of our modern education systems.

But yes Computers being so easily accessible and compilers, documentation and libraries have made computer science so easy to get started with.

Imagine having to implement your own network layer to communicate with someone, you would have had to understand ip, tcp, network layer to an extent like http and etc. and then you finally would have been able to communicate.

In maths that's our reality for a lot of the field, there aren't good libraries, interfaces to help skip the unnecessary details. Hopefully AI might solve it I don't know though. It's fun to hope for it.

This is not true, 99% is very exaggerated claim, but yeah you can learn 50-60% of the field at a surface level in months rather than years.

But you can have a surface level understanding of mathematical topics as well, ofc some topics might require deeper understanding, but that's true for both.

Any claims of being able to learn 99% of computing in a just 4 weeks even at surface level, is greatly underestimating your own knowledge built over the years perhaps, or perhaps underestimating your own ignorance.

Huffman Coding, Turing Machines, Knuth Devices, Bayesian networks, B-tree (named after Bayer), AVL Trees, so many data structures and algorithms, even relatively new ones like Timsort, Jaccard similarity, MapReduce (thankfully but I have seen people call it Dean-Ghemawatt MapReduce in literature).

Well people even name stuff after themselves as well, Fil-C, raylib, etc (I like both Filip and Ray just pointing it out).

Aside: If I butchered some spellings I am sorry. :3

Definitely a lot of people have very surface level understanding of tcp and computer science concepts.

I have had folks tell me cache is just cache in actual interviews. When I have asked them to explain the concept to me, but even beyond that I feel like we tend to think less of our own knowledge of topics once we have acquired it.

Especially ones acquired over years, alongside other work.

Again, you are underestimating how much effort it takes to understand how an 8086 CPU works. There are a lot of foundational concepts that you are simply assuming the person already understands. That may be a reasonable assumption for a CS undergraduate, but the average person does not understand binary arithmetic, registers, memory addressing, instruction execution, calling conventions, or even what a CPU is doing at a meaningful level to even start to understand 8086.

Also, understanding an 8086 CPU is not even remotely comparable to the level of mathematics Terence Tao was discussing above. The 8086 is a relatively basic and concrete topic. You can build a workable mental model of it from a finite instruction set, a handful of registers, and a reasonably straightforward memory model.

From my perspective folks here on HN and in CS often think they should somehow be able to understand advanced mathematics papers at a glance, merely because they are good at basics of programming or computer science (8086). That is not how it works. Most mathematics is not inherently much harder than computer science; both fields require you to accumulate a large amount of foundational knowledge before advanced material becomes comprehensible.

There is an enormous amount of computer science that most programmers are completely unfamiliar with, especially within academic CS: programming-language theory, type theory, formal semantics, compiler theory, algorithmic research, complexity theory, distributed computing theory, verification, cryptography, computational geometry, numerical methods, and so on. Being proficient in one narrow area does not automatically give you the prerequisites for another.

A web developer would not be expected to casually understand a research paper on type theory or approximation algorithms without first learning the relevant notation, terminology, and foundational results. Mathematics is no different. The feeling that mathematical writing is uniquely impenetrable mostly comes from encountering it without the years of accumulated context that mathematicians have silently built-up.

I can show you a paper about an advanced algorithms or chip design, that is large made up of fundamental cs concepts and general physics and even you likely someone with pretty in-depth understanding of CS would find hard. There are orthogonal subjects, for instance my mathematician friends things I am insane reading so much about weird computing topics, and I find his research in some weird number theory thing completely mind-bending.

Try and explain to a lay friend how registers & isa works in-depth with all the details not a hypothetical higher level model so that they can understand the nuance of looking at assembly, limit it to 8086 perhaps, it will take significantly longer than a weekend.

Ofc Terence Tao and his level of intelligence is beyond me, I wouldn't compare but general advanced mathematics is not something folks here couldn't pick up if they actually tried to work on it, just give it a shot (though I would recommend don't start with advanced topics build up slowly I think most people can understand most maths papers even the bleeding edge ones within a few months of serious self-study, and won't even feel that it's after a few years, compare that to the time spent learning software and computing 6-8 hours a days for several years)...

This is also true for almost every other field, even within computer science. The only difference is that a lot of people operate at a very surface level without realizing just how much background knowledge they have accumulated. Think about the number of keywords your average SWE is expected to know. It is rather insane.

Cache, stack, heap, process, thread, socket, file, tcp, http, tls, websocks, socks, soc2???, deadlock, stack, queue, race, atomic, event loop, coroutine, async, database, transaction, index, replication, sharding, consistency, serialization, DNS, load balancer, container, namespace, and so on.

Every sub fields (web/kernel/backend/etc.) has a million/bazillion weird words used in a dozen different contexts and if you read a paragraph of even semi technical software text you will feel like an over stuffed turkey.

Even cache could mean the CPU caches, the page cache, a browser cache, a CDN cache, a Redis cache, or imagine the flurry of words we have that have real world meaning. Session, handle, pool, buffer, stream, channel, event, task, worker, or queue. Generally there is some overlapping meaning but often there isn't.

True, Given I am young, hobbyist, and have written a lot of AI libraries for myself and small companies for local deployments, and can't afford any serious Nvidia hardware I will switch to whoever can get me a system with enough VRAM for a decent price under 1000$...

My best GPU is 5070Ti on which the best model I can run is Laguna S 2.1 118B with NVFP4 and offloading most experts on CPU with an expert router layer and 128k context.

I could kill for a 1000$ AMD or Intel card with 32 or 48GB of VRAM even GDDR6 around a 1000$ range but it feels like no one even cares.

With ~4 48GB cards I could seriously locally deploy most open models models with some SSD offloading and good 4bit quantizations with ok context sizes. But at current prices.. sigh... I tried begging Intel to maybe it's cards available in my region but alas no such luck especially not at reasonable prices.

Honestly getting an AI subscription feels cheap atm and working hard at solving and home labing stuff is insane.

Even in research no one cares about anything but Nvidia, because at this point they are expensive but they seem to care, I just don't see others caring though I have worked with Intel gpu and compute teams on implementing this stuff and they are very enthusiastic but well the companies themselves aren't solving anything for me as an individual, but they are begging for me to add support for their stack in software I maintain. The Irony is unreal, why should I even add support when no one can even use it.

Only company that had decent stuff was surprisingly Apple but their stuff is now too expensive as well. .sigh.

This should be considered unethical tbh.

Gpt-6 hacked Apple to enable this feature. We just haven't heard about it yet.

To Nvidia the biggest threat is a capex cut from big tech not another company in the same domain, maybe some Chinese companies but people aren't going to be moving off of Nvidia for large deployments in the short term even if some TPU/Tranium etc is used it's not close to Nvidia.

I have no explanation other than people, researchers are comfortable with it and don't want to switch.

The name is probably not the best fit but if we can get a good solution that's not horrible that's easy to self host I and open-source I am fine with it.

Though I fear this might not be well maintained or have a good foundation being so heavily agent focused but I will out of sheer ambition of a more open stack support anything that help make it a possibility.

Also the git hosting stuff seems a bit sus tbh.

Me I am, so very afraid of actually decently priced inference.

It's largely just what jarred is willing to accept this week afaik or not, and they did put the bun 1.4.0 version bump in the changelogs for claude code a while ago, over a month almost.

Though most will be forgiven to not reading it since well it's all AI anyways. I don't know how I feel about all this yet, maybe someday.. ooof

This is the first time it has happened to a tool I use and have helped friends adopt it.

Again I don't condone it but this gets very annoying to explain that everything is renamed now to X instead of Y to your uncles and friends...

How is that a memory safety issue in Rust compiler? What point are we making here. Am I just too dense to understand, is how the hell is that a need unsafe issue? Or anything to do with compilers???

This is very common in a lot of industries especially physical goods and medicine.

You end up leverage for all the goods but the final settlement of payment happens much later, making them hard to survive in without a lot of capital and good relationships.

You can screwed very easily and understanding the model and not scaling faster than your capital allows is a skill in itself.

My friend failed at it while I was working with them.

Guys google, I know enough people in the company to know how this decision was made, but I must say as a user if you rename or kill another thing I will stop using any Google service I still use.

This might be the one time I might forgive it but guys please don't it's annoying.

I as someone with writing Zig a bunch, can safely say if it does it hasn't even worked for me.

I am talking from experience from a pre-ai human mitts writing code perspective maybe Zig + LLMs do some magic.

The more I read the article the more I feel like this is just bad not sure if I should be giving it as much latitude as I have been in my prior comments.

There are other claims as well that are weirdly phrased at least.

Reads like an article written to justify some arguments they had rather than a genuine take at this point.

But I will give the benefit of doubt I enjoy weird articles, languages and share a dislike for aggressive AI-ness of all things.

The question remains is it open or not, if it's open I will use it if it's not well I was happily being fucked over by an American tech giant...

Speed of compilation feels like a distant second in terms of goals given the weird new generic features they keep adding..

I was fine with basic generics they complicated it quite a bit much for my liking.

I would like to understand this more,

rustc emits machine code and then cargo immediately executes it, there's the same opportunity for end user memory being corrupted (due to miscompilation) as if rustc and cargo shared a code base.

Cause this hasn't been true for me or for anyone maybe your definition of memory being corrupted is the not same as mine.

I am not even sure what you are trying to prove with this.

I appreciate the time and effort in building stuff like Roc I don't use it but this comment and the article feel like...

Oh some guy said Zig not nice because memory safety so here, a post why memory safety doesn't exist because we have to do memory unsafe things sometimes and so everything is memory unsafe already, so maybe it doesn't matter.

I get the energy that we are going for seeing useless claims and wanting to push back but I think the article deserves a clearer part 2 where you elaborate on your thoughts about stuff maybe even get it peer reviewed a bit before posting or maybe don't I guess we could use more raw thoughts in the post AI age.

Either way I appreciate someone trying to put forward their own thoughts and explain problems with a different perspective.

You can trivially add it to codex..

I have never used it enough to exhaust it but on openrouter I burnt a good 100M tokens through it

I can hate Rayon and Tokio as much as the next guy generally I can empathize with problems they cause. But largely either it's a skill issue.

Or you are just trying to squeeze lemonade from stones, ala, they aren't meant to do what you are doing.

Tokio especially is extremely widely used for all kinds of things it doesn't work well for.

Sure I could improve it add or tune some primitives but I am honestly considering writing my own. And so should others.

I feel like we are all too bound in Rust ecosystem suddenly to Tokio and Rayon because we don't want to blame and acknowledge the libraries just don't work for what we want to use it for.

And library authors don't consider these usecases and bug important enough to actually fix it in a ergonomic way.

I don't want to say this but Nemotron is not worth running on any sillicon, given Nvidia has been doing it for 3+ years, if Nvidia instead gave away GLM or KIMI API for free no one would use Nemotron the reason it's so wildly used is because Nvidia offers a Free API...

For a first model, and given it's open, I am gaining some faith in American Open research labs again...

I couldn't test it since it's not on openrouter or something, but even if it's only as good as GLM5.1 that's more than good enough first attempt, I think.

Perhaps a lot more labs will catch up to ballpark frontier esque level soon, I am all for more competition in any field.

Why is cursor subsequently executing anything? Like what is this black magic they want to do? I want to know the decision tree here? Was this cursor coded?

I do not understand the point, btw vim has had similar issues with it executing stuff you might not expect by loading a file but it was obviously a vim feature with %{expr}. But why specifically git.exe , this seems like the most redundant bug cve which could have been trivially patched, who does this feature help exactly?

I am not really a user of cursor never used it for even a single day, but at this point I am curious why this exists...