MurphyA/hacker-news huggingface dataset:
by chisleu | 2025-06-28 19:47:26+00:00
Right on. I also have a laundry list of ML things I want to do starting with fine tuning models.

I don't mind paying for models to do things like code. I like to move really fast when I'm coding. But for other things, I just didn't want to spend a week or two coming up on the hardware needed to build a GPU system. You can just order a big GPU box, but it's going to cost you astronomically right now. Building a system with 4-5 PCIE 5.0 x16 slots, enough power, enough pcie lanes... It's a lot to learn. You can't go on PC part picker and just hunt a motherboard with 6 double slots.

This is a machine to let me do some things with local models. My first goal is to run some quantized version of the new V3 model and try to use it for coding tasks.

I expect it will be slow for sure, but I just want to know what it's capable of.
by karmakaze | 2025-06-28 19:47:40+00:00
Either I'm an outlier or this is a bad article/study.

All the reasons given are fears:

Output Quality - Fears that... Ethical - Fears about.. Risk - Fears that... Human Connection - Fears that... Impairment - Fears that... Creativity - Fears that...

My disuse is all about flow and value, not fear. The ways I use it is in refining ideas at a higher level, not outputting code/content/etc (except for rote work).
by il-b | 2025-06-28 19:47:44+00:00
I still use nvAlt (formerly Notational Velocity) for note taking. It is synced to a Dropbox folder. I can use the native Dropbox app to search, view, and edit the files. What I really like is the speed of note taking and searching in the nvAlt app. All my notes have a title in a loose format such as "project_name keyword ... keyword". It takes a second to find the note I need. Therefore, nvAlt serves as a bookmark manager as well. Obsidian feels clunky and slow, and I couldn't get it to switch to the .txt file extension (which is possible to edit via Dropbox on an iPhone).
by Bender | 2025-06-28 19:48:01+00:00
And also disable MDN's [1] in stand-alone email clients and discard them in MTA's if your user-base is cool with it.

# grep MDN /etc/postfix/header_checks change WARN* to discard to drop them. /^Subject: MDN: / WARN MDN_Seen_1000 /^Subject: Read-Receipt-To: / WARN MDN_Seen_1001 /^Subject: Disposition-Notification-To: / WARN MDN_Seen_1002 /^Message-ID: \<receipt/ WARN MDN_Seen_1003 /^Subject: Read: / WARN MDN_Seen_1004

using WARN as testing example, change to DISCARD to drop them

[1] - https://en.wikipedia.org/wiki/Return_receipt
by ben_w | 2025-06-28 19:48:22+00:00
IMO:

People trying to plan accurately, end up with a list of things to think about containing more than seven items, and human psychology is such that this makes it *feel* infinite despite us being able to see that it isn't.

People who don't worry to much and vibe it, get pregnant/cause pregnancy by accident, often but not always as teens. Despite not planning carefully, and even in pre-industrial societies where a lot more problems were rapidly fatal and the best you could hope for in such cases regarding childcare allowance was a shotgun marriage, vibing it generally works.

With more certainty:

I'm not sure what the distribution is of women hearing about painful births and saying "nope!", but I do know it's more than none.

I know a few deliberately childfree couples who like the higher income and lack of responsibility, and have zero interest in proposals such as this.
by xtiansimon | 2025-06-28 19:48:45+00:00
Maybe Ivy League schools develop students for the job? My state school was known to develop students to “work” in Silicon Valley.

Those individuals with the highest grades are judged, by their professor’s grades, to be the most successful at that goal?

Plus, if you’re going to an Ivy League school, maybe your support network (parents, extended family and friends, mentors) is also operating at the same level. By growing up in that environment you have instilled in you the values or standards required for entrepreneurship. And don’t forget, if you’re going to find yourself in a jam, does that network have the knowledge and experience to help? Do they have disposable cash to make a mistake go away faster (years rather than decades). If life was unlimited and wellness undiminished by age, then I’m sure we’d have so many more 80+ year-old success stories.

It’s just life’s selection process, and each of these benefits make the possible outcomes more probable.

Stay in school. Don’t do drugs.
by timr | 2025-06-28 19:49:06+00:00
I started learning Japanese after age 30 (currently around CEFR B1; JLPT N2), but I did it by moving to Japan. I don't know if the "language study", per se, provided the benefit, but the act of moving there so radically transformed my daily life that it was like being 20 years younger.

David Sedaris did a long interview on learning French (he also became proficient late in life) where he said something like: when you first start learning a language, everything is new and interesting. Eventually you become fluent, you get into a pattern, and 'living in a foreign country' is just 'living'. (heavily paraphrased -- I'll try to find the original).

Anyway, my point is that I think "learning a language" is probably as good as anything else when it comes to "brain stimulation", but in my opinion, the real value comes from being completely immersed in a new culture and kicked forcefully out of any sense of routine.

Edit: interview is here - https://www.thisamericanlife.org/165/transcript

Relevant bit:

---

Someday, David says, he'll be more comfortable in French. His accent will improve and that daily anxiety will be removed from his life.

David Sedaris: But when it is removed from me, then I probably won't be interested in living here anymore. I'll probably leave.

Ira Glass: Because it'll be just like living back home.

David Sedaris: Plus the more you learn, the more disappointed you wind up being. It's easy to like somebody when you don't know what they're saying.
by lvl155 | 2025-06-28 19:49:11+00:00
Thank you to everyone behind this effort. At some point, decades ago in the past, Oracle added value to the tech ecosystem. Now, they’re a giant rent-extracting behemoth. I hate the fact that we can’t have nice things in 2025 simply because Oracle owns the IP. Oracle is what happens when corporations become lazy and hand over the keys to some brand names just because “no one ever got fired for buying/hiring _____.” I hope those days are past us.
by leakycap | 2025-06-28 19:49:42+00:00
Modern email clients are getting too clever for their own good, and I have no choice on what client others use.

For decades, inline replies worked perfectly—you'd quote the relevant part and respond right underneath it. But now email apps are "helping" by trimming messages into compact views, cutting off replies right at the first quoted section unless someone taps "show more."

I've basically abandoned inline replied and have gone back to dumping everything at the top like it's 1995.

The irony is these apps think they're making email better by hiding "clutter," but they're actually making conversations harder to follow.
by amy214 | 2025-06-28 19:50:22+00:00
California is like this, for different reasons. Mostly the leaders think nature > humanity so the more they cap the knees of civilization, more for nature, and that's a good and meaningful legacy in their minds. Of course this is a politically dangerous thing to speak up about publicly, so it's more along the lines of "uhhh we need to make sure the house you build is safe, so you need 50,000 pages describing how safe it is, must be evaluated by an army of Phds, and rejections take 5 years"
by thaumasiotes | 2025-06-28 19:50:31+00:00
> Incidentally, Gödel's theorem eventually comes down to a halting-like argument as well (well, a diagonal argument).

> There is a presentation of it that is in like less than one page in terms of the halting problem

Those are two very different ideas. Your second sentence says that Gödel's theorem is easy to prove if you have results about the halting problem. Your first one says that in order to prove Gödel's theorem, you need to establish results about the halting problem.
by fpoling | 2025-06-28 19:51:15+00:00
When ChatGPT appeared I have been working at a small startup for couple of months that was planning to hire another programmer.

CTO became extremely enthusiastic about ChatGPT and said that the programming would be a dying job and tried to show during presentation how good ChatGPT was and asked it to write a basic code related to our tasks. It produced total garbage that could not be used even as a starting point. CTO tried to prompt it to the needed directions, but it made things worse.

After the presentation I tried to search for the task from the presentation. It turned out there were very few StackOverflow or GitHub entries about it as the topic was rather specialized and ChatGPT tried to average those into the task.

In a month I and another recent hire departed from the company. And a year later the company was hiring programmers again.

Out of curiosity I repeated the task few times with different models all the time resulting in the same garbage.

So my rule of the thumb is that if a task generates a lot of search hit, then perhaps a LLM can average the knowledge into something reasonable. If not, averaging is not possible.
by skydhash | 2025-06-28 19:51:27+00:00
Because the semantic for each term in a programming language is pretty much a 1:1 relation to a sequential and logic-based ordering of terms in bytecode (which are still code).

> Also you wouldn’t want that, humans can’t review bytecode

The one great thing about automation (and formalism) is that you don't have to continuously review it. You vet it once, then you add another mechanism that monitors for wrong output/behavior. And now, the human is free for something else.
by leakycap | 2025-06-28 19:51:38+00:00
I have a very small company and reached out to Gruber about advertising with him once when he had a weird small opening. He was incredibly honest and said he didn't think his audience was a good fit... and I noticed he never sold that spot for that week when I went on his site.

I have the budget now, but my product is still probably not a good fit for his site and I will always remember the honesty.
by drivingmenuts | 2025-06-28 19:51:44+00:00
Of the four top concerns (Output Quality, Ethical Implications, Risk and Human Connection), I agree with the first three and am ambivalent about the fourth. I think the first three are also inseparably interlinked. The Human Connection issue is a bit different - that's more about the individual than the technology, to my mind. As long as no one is forced to use an AI and as long as final decisions are made by responsible humans, we might be OK.
by deathanatos | 2025-06-28 19:51:48+00:00
The last time I sat in a plane "with power and WiFi", the seat outlet crashed¹ when I plugged into it.

The time prior to that, the outlet had been so abused that the plug just fell out of the socket.

The time prior to that, the plane did not have the advertised features.

Perhaps the signal strength is great, but the actual problems I as a passenger experience seem to be a bit divorced from the potatoes here.

¹"You can crash an outlet?" I'm an SRE. I can do anything. Plug plug into outlet, green light turns off. Wait like 5s, light turns back on. 1s later, green light turns off. Loop. It's a crashloop, essentially. No power provided.
by alganet | 2025-06-28 19:52:25+00:00
> Yes, shell is definitely too weak to parse JSON!

Parsing is a trivial, rejecting invalid input is trivial, the problem is representing the parsed content in a meaningful way.

> bash completion scripts try to parse bash in bash

You're talking about ble.sh, right? I investigated it as well.

I think they made some choices that eventually led to the parser being too complex, largely due to the problem of representing what was parsed.

> Also, OSH is now FASTER than bash, in both computation and I/O.

According to my tests, this is true. Congratulations!
by standardUser | 2025-06-28 19:52:52+00:00
At least with Stack Overflow there's upvotes and comments to give me some confidence (sometimes too much confidence). With LLMs I start hyper-skeptical and remain hyper-skeptical - there's really no way to develop confidence in it because the mistakes can be so random and dissimilar to the errors we're used to parsing in human-generated content.

Having said that, LLMs have saved me a ton of time, caught my dumb errors and typos, helped me improve code performance (especially database queries) and even clued me into to some better code-writing conventions/updated syntax that I hadn't been using.
by alwa | 2025-06-28 19:52:56+00:00
With technological products in particular—where an idiosyncratic nerd with an old computer under the desk can run a vibrant forum, a couple of plucky young “cofounders” can conjure a company from nothing, and HN (at least at one point [0]) can sway the entire tech culture from a single process on a single server—isn’t it an option to… not grow?

I guess the LLM era makes credible products more capital-intensive than they used to be, but even so, the vendors are pricing their stuff aggressively, and even when they try to squeeze the prices later, half these foundation models that are better-than-last-year’s-SOTA are open-source!

If you want to play with lots of money and seek out lots of money, there’s lots of money swirling around seeking to involve you in that game. But if you just want to make something nice and human-scale and small, what better time than now?

The path to billions of bucks may require mercenary bucks-extracting behavior, but that’s not the same as a growth imperative being an inevitable force of nature.

I can’t help but feel like the Small Web folks are on to something.

[0] https://news.ycombinator.com/item?id=5229522
by diggan | 2025-06-28 19:52:58+00:00
Re the ethics part, something I haven't quite understand myself yet:

On one hand, training it isn't "copying" per se, but "learning", so maybe it isn't straight up copyright infringement, unless it can reproduce large parts identically. It could also allow small team/individuals to have much large impact in the world and could lower the barrier to entry for research and experimentation, maybe even other endeavors. It certainly could help with knowledge sharing and accessibility, where downstream creativity and usefulness can outweigh diffuse individual harm. Maybe it expands the creative field rather than shrinks it, that'd be a good thing.

But then on the other hand, many models (datasets) are built with copyrighted works without permission or royalties, with the effect that LLM availability could reduce demand for human livelihoods, leading to eroding fields instead of expanding them. Most releases today are kind of opaque with their training datasets, most are undisclosed and it's hard if not impossible for authors to have agency over if their work is included or not. Maybe if LLMs remain it'll be hard to sustain cultural production instead, that'd be good for no one.

So then what is the best approach for someone who doesn't want the forfeit the usefulness they themselves experience, but also not go directly against what the ethical considerations bring up? In the end I don't know if there is an easy or right side to take, I guess usually the optimum sits somewhere around the middle, not at the extremes at least.
by tucnak | 2025-06-28 19:53:33+00:00
"Blending," or translating arbitrary substrings to prefixes, is a real curious one, & likely become a prerequisite for running dataset-scale LLM inferences at scale.

See https://arxiv.org/abs/2405.16444v3

> To speed up the prefill of the long LLM inputs, one can pre-compute the KV cache of a text and re-use the KV cache when the context is reused as the prefix of another LLM input. However, the reused text chunks are not always the input prefix, which makes precomputed KV caches not directly usable since they ignore the text’s cross-attention with the preceding texts. Thus, the benefits of reusing KV caches remain largely unrealized.

> This paper tackles just one challenge: when an LLM input contains multiple text chunks, how to quickly combine their precomputed KV caches in order to achieve the same generation quality as the expensive full prefill (i.e., without reusing KV cache)? [..] We present a scheme that reuses the pre-computed KV caches, regardless prefix or not, and selectively recomputes the KV values of a small subset of tokens to partially update each reused KV cache.

I had recently touched on benefits of compute-in-network for KV cache management https://news.ycombinator.com/item?id=44371227 largely making arguments contra Bluefield. The CacheBlend authors note that the delay from recomputing some tokens can be hidden by pipelining it with KV loads. Note that the various systolic array/NoC architectures are well-suited for accelerating string matching tasks. A compute-in-network FPGA could therefore manage the entire process: identify viable chunks by indexing and matching of the hot substrings, prefetch the corresponding KV caches from network storage, and stitch up a new prefix before passing it to the primary inference hardware. It may as well be one of those weird cases where hard-coding the algorithm is possible in theory, but intractable in practice—because the optimal paths would be highly-dependent on topology.

Nobody wants one-trick hardware.

In view of Xilinx acquisition, AMD's death in the AI space appears to be greatly exaggerated!
by Jach | 2025-06-28 19:53:54+00:00
Talking about averages is really misleading. Talk about capabilities instead, framed in tool language if you must.

Quoting https://buttondown.com/hillelwayne/archive/ai-is-a-gamechang... about https://zfhuang99.github.io/github%20copilot/formal%20verifi... "In the post, Cheng Huang claims that Azure successfully used LLMs to examine an existing codebase, derive a TLA+ spec, and find a production bug in that spec." This is not the behavior of the "average" anything.
by jerf | 2025-06-28 19:54:00+00:00
I'm fairly certain that's wrong, and I see a couple of other people may be making that mistake elsewhere in this conversation too. A Turing Machine is a Turing Machine. The execution trace of a Turing Machine is fully determined by its ruleset and its initial input. It doesn't matter which axioms you "take", nor does it matter what the intent of the initial construction of the 748-state machine was, or indeed even if that proof is somehow flawed. The definition of a Turing Machine is effectively the axiom set for this particular case. There is a finite set of 748-state Turing machines, and it is absolutely the case that there is a set of them that loop infinitely, the complementary set that do not, and that there is a maximum length amoung the set that do not. There is no situation where "the next step" of the Turing Machine "depends on your axioms" and could thereby be affected by such a decision.

For that to be the case, there would have to be some symbol under the tape and some state the machine is in for which the action the machine takes and the next state it goes to would depend on the axioms taken somehow. There is no place where the Turing Machine has somehow been running for so long and just gotten so large that its behavior becomes non-deterministic somehow.

What this means is that even if we lived in a universe where we had the unfathomable resources to actually have this number somehow meaningfully "in hand", we would be unable to prove that it was the correct one with just ZFC. Maybe one of the really quite numerous other machines still spinning away would in fact terminate in the future and be the real BB winner, because even this staggeringly monstrously large universe is still piddling nothing next to infinity and the other machines still require infinite resources to run them to discover they never terminate. But that doesn't do anything to affect whether or not there in fact is a single concrete integer that corresponds to BB(748).

Although one imagines that any universe with the resources to "have" BB(748) in it might also have some much more powerful axiom systems to play with in the process. The amount of computational power this universe apparently possesses is beyond all comprehension and who knows what they could know. But even if they used a more powerful system, it wouldn't change what BB(748) is... it just might affect whether or not they were correct about it.
by akavi | 2025-06-28 19:54:03+00:00
But for most human endeavors, "operational precision" is a useful implementation detail, not a fundamental requirement.

We want software to be operationally precise because it allows us to build up towers of abstractions without needing to worry about leaks (even the leakiest software abstraction is far more watertight than any physical "abstraction").

But, at the level of the team or organization that's _building_ the software, there's no such operational precision. Individuals communicating with each other drop down to such precision when useful, but at any endeavor larger than 2-3 people, the _vast_ majority of communication occurs in purely natural language. And yet, this still generates useful software.

The phase change of LLMs is that they're computers that finally are "smart" enough to engage at this level. This is fundamentally different from the world Dijkstra was living in.
by moritzwarhier | 2025-06-28 19:54:21+00:00
I'm not sure.

I agree that the "law of the jungle" trope seems like an oversimplification.

But the fact that cooperation was also present in pre-modern societies does not completely refute the point.

That's why they are talking about tribes, not just families or individuals.

I an not a professional historian or anthropoligist, but the scope and phenomenology of cooperation in ancient societies is not a question with a global, settled answer, as far as I'm concerned.

I'm currently reading this book:

https://www.amazon.de/R%C3%BCckblick-auf-die-Natur-Geschicht...

It deals with many of these topics, especially the transition from hunter-gatherer societies to agriculture.

It is more academically rigorous than the submission post, and it quotes other research.

Unfortunately, there are two flaws:

a) the author has transitioned to what we'd call a far-right activist prior to his suicide. He became known outside the scientific community due to his book "Finis Germania" (which I haven't read). But the book that I am reading was published much earlier, and well-received by the scientific community. The same goes for other works by him. He focused on the living conditions that allowed for a humanitarian mindset to even emerge, basically what humans do to provide the resources for living.

b) there seems to be no English translation
by edanm | 2025-06-28 19:54:24+00:00
I'll provide context too - I'm a Jewish Israeli. I'd probably be considered left (or even far-left) by Israeli standards, but I'm in the "pro-Israeli" camp as conventionally understood online.

This Haaretz article is very troubling. To the extent it's accurate, there's not much question that it reflects war crimes.

A few thoughts:

1. The article itself says there is an ongoing investigation into some of these accusations. I hope that, to whatever extent this is happening, it's not widespread, and anyone committing war crimes is very visibly and publicly tried in court.

2. There is clearly something broken with the GHF and the new aid delivery - dozens dead every day for weeks. We really need some answers on what's going on.

3. From Haaretz today:

> The U.S.-backed Gaza Humanitarian Foundation on Saturday urged Israel to investigate reports that soldiers opened fire towards unarmed Palestinians near aid distribution sites, detailed in a Haaretz expose, calling the allegations "too grave to ignore," while denying that any such incidents occurred within its facilities.

> GHF Interim Director John Acree stated, "There have been no incidents or fatalities at or in the immediate vicinity of any of our distribution sites."