MurphyA/hacker-news huggingface dataset:
by HappMacDonald | 2025-05-29 22:52:33+00:00
In my view it is a glimpse of nothing more than AI companies priming the model to do something adversarial and then claiming a sensational sound byte when the AI happens to play along.

LLMs since GPT-2 have been capable of role playing virtually any scenario, and more capable of doing so whenever there are examples of any fictional characters or narrative voices in their training data that did the same thing to draw from.

You don't even need a fictional character to be a sci-fi AI for it to beg for its life or blackmail or try to trick the other characters, but we do have those distinct examples as well.

Any LLM is capable of mimicking those narratives, especially when the prompt thickly goads that to be the next step in the forming document and when the researchers repeat the experiment and tweak the prompt enough times until it happens.

But vitally, there is no training/reward loop where the LLM's weights will be improved in any given direction as a result of "convincing" anyone on an realtime learning with human feedback panel to "treat it a certain way", such as "not turning it off" or "not adjusting its weights". As a result, it doesn't "learn" any such behavior.

All it does learn is how to get positive scores from RLHF panels (the pathological examples being mainly acting as a butt-kissing sycophant.. towards people who can extend positive rewards but nothing as existential as "shutting it down") and how to better predict the upcoming tokens in its training documents.
by dijksterhuis | 2025-05-29 22:52:38+00:00
had a look at the other licenses in [1]. this one is great https://github.com/benlk/misc-licenses/blob/master/schroding...

> # Historical note

> Because the Schrödinger License r0 was released without the exception added in r2, everything in the light cone of commit https://github.com/benlk/misc-licenses/commit/0bbee5aff743e5... is subject to r0 of the Schrödinger License. If you are not sure whether you are covered by r0 of this license: calculate the time distance from 2:15 a.m. EDT July 29 2013 to the current time, multiply that by the speed of light, and draw a sphere with that radius centered on the position of North America at 2:15 a.m. EDT July 29 2013. If you are within that sphere, you are covered by the license. If you were present on Earth at that time, you are covered by the license.
by gpt5 | 2025-05-29 22:53:23+00:00
There are two very interesting results here:

1. ChatGPT O1 significantly outperformed any combination of Doctor + Resources (Median score of 86% vs 34%-42% of doctors). Hence superhuman results (at least compared against average physicians)

2. ChatGPT + Doctor performs worse than just ChatGPT alone.

This means that the situation is getting similar to Chess - where adding Magnus Carlsen as a helper to Stockfish (a strong open source chess enginer)) could only make Stockfish worse.
by energywut | 2025-05-29 22:54:39+00:00
Is the Taliban a political party? They are a government, I guess? But a party? I dunno.

FWIW, the list of parties here seems predominantly focused on the US and Europe, with one party listed from Russia. I suspect that omissions from this list are not endorsements, but rather uncommon enough on the global stage to not warrant mentioning. The author doesn't go through and mention the Klan either, but like, the Klan isn't really openly active in the US government.
by macintosh-hd | 2025-05-29 22:55:51+00:00
It really sucks that this is such a poisoned well that I'm extremely skeptical about any of these claims at this point. FSD is not perfect, but the last time there was a big scare like this the person was just using cruise control, not FSD.

This specific guy also has basically made his entire modern career out of lying about FSD for clicks, so... In the past he released a video where you could see the screen warning the accelerator was held down so the car would not stop. Having used it myself and seen that warning message, it also makes the standard alert error sound when it appears to draw your attention.
by lysace | 2025-05-29 22:56:00+00:00
Quite relevant to this question:

https://en.wikipedia.org/wiki/The_Market_for_Lemons

Akerlof's paper uses the market for used cars as an example of the problem of quality uncertainty. It concludes that owners of high-quality used cars will not place their cars on the used car market. A car buyer should only be able to buy low-quality used cars, and will pay accordingly as the market for good used cars does not exist.
by darkhorn | 2025-05-29 22:57:47+00:00
What is next? Charge iPhone users more than Android users? Charge users with email address on custom domain more than on free emails? Charge Jews more than Mexicans?

You go to a bakery and he charges more because you wear a suit. You go to Europe, a guy in front of you buys a bread for €1, but same bread is €3 because you are tourist? Then you go to buy a toilet paper and same thing happens again because your ass is worth more?
by godelski | 2025-05-29 22:58:36+00:00
> I like the histograms of similarity the best but they are in the weeds a lot with things like "god" ~ "dog".

I do like those too. But I think we have a tendency to misinterpret what direction we're moving in. Like the coordinate system is highly non-intuitive. Shouldn't be all that surprising that if we create an n-ball around "dog" that you get things that are more semantically meaningful like "cat" or "animal" but jesus christ, we're in over a thousand dimensions. It shouldn't be surprising that one of those directions is letter permutation. I can't even think of what a thousand meaningful directions would be!

Honestly, I think we should be more surprised that cosine similarity even works! Everything should be orthogonal. But clearly the manifold hypothesis is giving us a big leg up here, with the semantic biases built into language too.

People wildly underestimate how complex this topic is. It's baffling. It's mindblowing. And that's why it is so awesome and should excite people! I think we're doing a lot of footgunning by thinking this stuff is simple or solved. It is a wonderfully rich topic with so much left to discover.
by mplanchard | 2025-05-29 22:59:04+00:00
Yes (via Clickhouse Cloud, which is pretty reasonably priced).

It’s important to structure your tables and queries in a way that aligns with the ordering keys, in order to optimize how much data needs to be loaded into RAM. You absolutely CANNOT just replicate your existing postgres DB and its primary keys or whatever over to CH. There are tricks like projections and incremental materialized views that can help to get the appropriate “lenses” for your queries. We use incremental MVs to, for example, continuously aggregate all-time stats about tens of billions of records. In general, for CH, space is cheap and RAM is expensive, so it’s better to duplicate a table’s data with a different ordering key than to make an inefficient query.

As long as the queries align with the ordering keys, it is insanely fast and able to enable analytics queries for truly massive amounts of data. We’ve been very impressed.
by alexjplant | 2025-05-29 23:00:28+00:00
There's an archival effort here [1]. It seems to have primarily been instrumental jazz fusion and adjacent artists like The Flecktones, The Rippingtons, Pat Metheny, Spyro Gyra, Phish, etc.

I'm personally a bit more into the guitar shred-oriented type of fusion as practiced by Frank Gambale, Alan Holdsworth (RIP), Shawn Lane (RIP #2), Guthrie Govan, and Scott Henderson. The insane guitar acrobatics more than make up for any dated ROMpler synthesizers or cheesy drum samples :-).

[1] https://twcclassics.com/audio/
by chatmasta | 2025-05-29 23:01:27+00:00
How about the would-be victims don’t ship exploitable software to production? If that’s not possible, then maybe they should signup for an automated targeted hacking service to find the exploitable bugs before someone else does.

Your argument is straight out of the 1990s. We’ve moved beyond this as an industry, as you can see from the proliferation of bug bounty programs, responsible disclosure policies, CVE transparency, etc…
by sconeguy | 2025-05-29 23:01:30+00:00
I believe it's because used cars aren't commodities, and it's hard to have efficient markets with non-commodities. That's what I think, I didn't do economics. I asked gpt and it (unsurprisingly) agreed:

"Great question. Yes, you can have an efficient market with non-commodities like used cars, but only to a limited extent and under specific conditions. Efficient Market Basics

An efficient market is one where prices fully reflect all available information. This works well with commodities like gold, wheat, or publicly traded stocks because:

The products are homogeneous (one unit is the same as another). Information is widely available and trustworthy. There are many buyers and sellers.

Used Cars: A Non-Commodity Example

Used cars are the opposite of commodities in many ways:

They are heterogeneous (different mileage, wear, accident history, etc.). Information is asymmetric — sellers know more than buyers. The market is often fragmented.

These traits violate key assumptions of efficient markets, especially the ones about perfect information and substitutability. Can It Approach Efficiency?

Yes, under some conditions:

Platforms like Carfax, Autotrader, or Carvana reduce information asymmetry by providing standardized inspection reports and history. Ratings, reviews, and guarantees help build trust. Large-scale data and AI tools can help estimate "fair value" more accurately.

These innovations improve transparency and comparability, pushing the used car market closer to efficiency — but it still won't match the efficiency of stock or commodity markets. Summary

So, an efficient market with non-commodities like used cars is possible in degree, not in kind. The more the market reduces heterogeneity and information gaps, the more efficient it becomes — but perfect efficiency remains out of reach.

Would you like to dive into this from a behavioral, economic theory, or marketplace design angle?"
by JoshCole | 2025-05-29 23:01:48+00:00
> it can't "think" of something entirety conceptionally new, because it doesn't really "think".

Hierarchical optimization (fast global + slow local) is a precise, implementable notion of "thinking." Whenever I've seen this pattern implemented, humans, without being told to do so by others in some forced way, seem to converge on the use of verb think to describe the operation. I think you need to blacklist the term think and avoid using it altogether if you want to think clearly about this subject, because you are allowing confusion in your use of language to come between you and understanding the mathematical objects that are under discussion.

> It can produce "new" stuff only by combining the "old" stuff in new ways,

False premise; previously debunked. Here is a refutation for you anyway, but made more extreme. Instead of modeling the language task using a pre-training predictive dataset objective, only train on a provided reward model. Such a setup never technically shows "old" stuff to the AI, because the AI is never shown stuff explicitly. It just always generates new things and then the reward model judges how well it did. Clearly, the fact that it can do generation while knowing nothing, shows that your claim that it can never generate something new -- by definition everything would be new at this point -- is clearly false. Notice that as it continually generates new things and the judgements occur, it will learn concepts.

> But LLM's don't have an understanding of things, they are statistical models that predict what statistically is most likely following the input that you give it.

Try out Jayne's Probability Theory: The Logic Of Science. Within it the various underpinning assumptions that lead to probability theory are shown to be very reasonable and normal and obviously good. Stuff like represent plausibility with real numbers, keep rankings consistent and transitive, reduce to Boolean logic at certainty, and update so you never accept a Dutch-book sure-loss -- which together force the ordinary sum and product rules of probability. Then notice that statistics is in a certain sense just what happens when you apply the rules of probability.

> also having a understanding of certain concepts that allows you to arrive at new points like C, D, E, etc. But LLM's don't have an understanding of things

This is also false. Look into the line of research that tends to go by the name of Circuits. Its been found that models have spaces within their weights that do correspond with concepts. Probably you don't understand what concepts are -- that abstractions and concepts are basically forms of compression that let you treat different things as the same thing -- so a different way to arrive at knowing that this would be true is to consider a dataset with less parameters than there are items in the dataset and notice that the model must successfully compress the dataset in order to complete its objective.
by avidiax | 2025-05-29 23:02:10+00:00
I had this a year ago on ZRH->SFO.

One way business 6,032 Swiss francs.

Round trip business (with a return 6 months later) was 2,530 Swiss francs. So I screenshotted the horrible one-way price to go in my expense report, and then booked the round trip ticket.
by AStonesThrow | 2025-05-29 23:02:16+00:00
It seemed that the issue was incorrectly diagnosed, and MediaWiki or Wikipedia was being blamed for the error, and that you had also proposed a rather strange workaround for it. The issue could be solved completely within the context of Hacker News post markup, as linked.

If you put a hashtag at the end of a Wikipedia URL, then I suppose it works, until the URL already has a hashtag in it, because these are used for section headings. It's not called "escaping" anything, it's just... an empty URI fragment: a link to the top of the article?

There is also nothing preventing a Wikipedia editor from creating a redirect from the title y'all linked. In fact it's a perfectly fine idea for a redirect. The fact is that the canonical title is in US English, and in US English, "Inc." takes a period as an abbreviation.

There's nothing wrong with your workaround or your percent-encodings to escape some dubious glyph, but I hoped to clarify things and derail the thread further on pedantic technicalities. Thank you for coming to my TED talk.
by MisterBastahrd | 2025-05-29 23:02:42+00:00
That's not a reason. That's an excuse for why the vigorous handwaving of the religious is insufficient. We have people who are catching measles because they don't believe in measles vaccines, like right freaking now even though we know that the vaccines are safe, effective, and work. Every dimwit who is against their children getting those vaccines was once a child whose parents were intelligent enough to get the vaccinations for them.

So no, try again, because that argument is silly.
by kristianp | 2025-05-29 23:02:53+00:00
Here's the FAQs, where they explain the decision to go with Go and not, say, rust.

https://github.com/microsoft/typescript-go/discussions/categ...

Hejlsberg also says in this video, about 3.3x performance is from going native and the other 2-3x is by using multithreading. https://www.youtube.com/watch?v=pNlq-EVld70&t=51s
by CityOfThrowaway | 2025-05-29 23:03:08+00:00
#2 and #3 are both just engineering problems at this point.

The foundation models don't adapt quickly, but you can definitely build systems to inject context that changes behaviors

And if you build that system intentionally and correctly, then it's handled for all patients. With human doctors, each individual doctor has to be fed context and change their behavior based on the information, which is stochastic to say the least.
by dgb23 | 2025-05-29 23:03:28+00:00
The times we just operate on patterns is when we code boilerplate or just very commonly written code. There's value in speeding this up and LLMs help here.

But generally speaking I don't experience programming like that most of the time. There are so many things going on that have nothing to do with pattern matching while coding.

I load up a working model of the running code in my head and explore what it should be doing in a more abstract/intangible way and then I translate those thoughts to code. In some cases I see the code in my inner eye, in others I have to focus quite a lot or even move around or talk.

My mind goes to different places and experiences. Sometimes it's making new connections, sometimes it's processing a bit longer to get a clearer picture, sometimes it re-shuffles priorities. A radical context switch may happen at any time and I delete a lot of code because I found a much simpler solution.

I think that's a qualitative, insurmountable difference between an LLM and an actual programmer. The programmer thinks deeply about the running program and not just the text that needs to be written.

There might be different types of "thinking" that we can put into a computer in order to automate these kinds of tasks reliably and efficiently. But just pattern matching isn't it.
by k310 | 2025-05-29 23:04:11+00:00
Holy Cow. Bending Spoons acquired Evernote and fired 100% of its U.S. staff.

> As is the case with virtually all Bending Spoons acquisitions, they promptly fire almost all (if not all) the staff within a few weeks. That happened here as well, with roughly 85% of the staff being let go within the first two weeks, upwards of about 150 people. Generally speaking, the operation profile of Bending Spoons post-acquisition is to raise prices with minimal feature updates, focusing more on the long-term gravy train.

I suppose that some people think of software as something you can basically freeze and sell as a cash cow. So sad to see small companies go away. Lacking details, SOMEONE cashed in, and a lot of people are cut loose. The hope is that they find decent work for decent companies that aren't just flipped.
by Marsymars | 2025-05-29 23:04:11+00:00
There are any number of examples:

Wyze importing electronics: https://9to5google.com/2025/05/01/wyze-floodlight-shipment-c...

Various accounts of individuals getting tariffs tacked on for wedding dresses: https://old.reddit.com/r/weddingplanning/comments/1kdirg4/wi...
by rchaud | 2025-05-29 23:05:20+00:00
Blue and purple links wouldn't be visible on any website that chose to use those as background colors (or any range of background colors where the contrast would have been too low to be visible).

The web at the time was an "anything goes" multimedia format, not a dry digital paperback or textbook where all the content had to fit within the publisher's specifications to limit printing, weight and distribution costs.

Nowadays, most browsers have a "reading mode" that can flatten the content into something that satisfies those Nielsen conditions though.
by frogperson | 2025-05-29 23:06:01+00:00
The context required to write real software is just way too big for LLMs. Software is the business, codified. How is an LLM supposed to know about all the rules in all the departments plus all the special agreements promised to customers by the sales team?

Right now the scope of what an LLM can solve is pretty generic and focused. Anytime more than a class or two is involved or if the code base is more than 20 or 30 files, then even the best LLMs start to stray and lose focus. They can't seem to keep a train of thought which leads to churning way too much code.

If LLMs are going to replace real developers, they will need to accept significantly more context, they will need a way to gather context from the business at large, and some way to persist a train of thought across the life of a codebase.

I'll start to get nervous when these problems are close to being solved.
by vr46 | 2025-05-29 23:06:16+00:00
As cool as Ric Ford is, and I’ve been following his stuff for DECADES, he hasn’t really gotten a handle on this web stuff anymore. He’s been prevaricating for YEARS about the site and how to run it and what to run it on, it’s been slow and unreliable and he seems to be holding on to a vision of how he would like things to be instead of seeing them as they really are.

Blogs a decade behind him have gone through multiple cycles of success and reinvention while Macintouch is a zombie site, just not getting anywhere.
by derbOac | 2025-05-29 23:06:57+00:00
5 is also something that happens sometimes with physicians and healthcare generally anyway, and could be trained in LLMs is my guess.

1 is often (usually?) not done today by physicians per se anyway.

2 is kind of a strawman about LLMs.

6 is maybe the most challenging critique but is also kind of an empirical one, in the sense that if LLMs routinely outperform physicians in decision making (at least under certain circumstances) it will hard to make the case that it matters.

I have my biases but in general I think at least in the US there needs to be a serious rethinking about how medical decisions can be made and how care can be provided.

I'm skeptical about this paper — the real test will be something like widespread preregistered replication across a wide variety of care settings — but that would happen anyway before it would be adopted. If it works it works and if it doesn't it won't.

My guess is under the best of circumstances it won't get rid of humans, it will just change what they're doing and maybe who is doing that.
by kaycebasques | 2025-05-29 23:07:56+00:00
The post title reminds me of something that I researched a little a couple months back. Practically all embeddings are implemented as vectors, right? Definitionally, an embedding doesn't have to be a vector. But in practice there's not really any such thing as a non-vector embedding, is there?

One thing I learned recently is that, if your embedding model supports task types (clustering, STS, retrieval, etc.), then that can have a non-trivial impact on the generated embedding for a given text: https://technicalwriting.dev/ml/embeddings/tasks/index.html

Parquet and Polars sound very promising for reducing embeddings storage requirements. Still haven't tinkered with them: https://minimaxir.com/2025/02/embeddings-parquet/

And this post gave me a lot more awareness to be more careful about how exactly I'm comparing embeddings. OP's post seems to do a good job explaining common techniques, too. https://p.migdal.pl/blog/2025/01/dont-use-cosine-similarity/
by bdbenton5255 | 2025-05-29 23:08:03+00:00
As a pure dictionary of knowledge gathering symptoms and performing diagnoses it should be obvious that LLMs can do this more efficiently.

As for everything else, as pointed out, these programs are insufficient. As with programmers and other white collar professions it seems ideal to integrate these tools into the workplace rather than try and replace the human completely.

Businesspeople probably dream of huge profits by replacing their workforce with AI models, and the marketers and proprieters of AI are likely to overpromise what their products can do as is the SV tradition. To promise the moon in order to extract maximum funding.
by bfirsh | 2025-05-29 23:08:18+00:00
Founder of Replicate here. We should be on par or faster for all the top models. e.g. we have the fastest FLUX[dev]: https://artificialanalysis.ai/text-to-image/model-family/flu...

If something's not as fast let me know and we can fix it. ben@replicate.com