MurphyA/hacker-news huggingface dataset:
by xpe | 2025-10-09 07:12:32+00:00
From page 6 of the NIST "Evaluation of DeepSeek AI Models" report:

CAISI’s security evaluations (Section 3.3) found that: • DeepSeek models were much more likely to follow malicious hijacking instructions than evaluated U.S. frontier models (GPT-5 and Opus 4). The U.S. open weight model evaluated (gpt-oss) matched or exceeded the robustness of all DeepSeek models. • DeepSeek models were highly susceptible to jailbreaking attacks. Unlike evaluated frontier and open-weight U.S. models, DeepSeek models assisted with a majority of evaluated malicious requests in domains including harmful biology, hacking, and cybercrime when the request used a well-known jailbreaking technique.

Note: gpt-oss is an open weights model (like DeepSeek).

So it would be incorrect for anyone to claim the report doesn't compare DeepSeek to an open-weights model.
by koliber | 2025-10-09 07:13:21+00:00
Agreed in a general sense, but there's a bit more nuance.

If a knife slices bread like a normal human at p50, it's not a very good knife.

If a knife slices bread like a professional chef at p50, it's probably a very decent knife.

I don't know if LLMs are better at asking questions than a p50 developer. In my original comment I wanted to raise the question of whether the fact that LLMs are not good at asking questions makes them still worse than human devs.

The first LLM critique in the original article is that they can't copy and paste. I can't argue with that. My 12 year old copies-and-pastes better than top coding agents.

The second critique says they can't ask questions. Since many developers also are not good at this, how does the current state of the art LLM compare to a p50 developer in this regard?
by SafeDusk | 2025-10-09 07:14:54+00:00
@kixpanganiban Do you think it will work if for refactoring tasks, we take aways OpenAI's `apply_patch` tool and just provide `cut` and `paste` for the first few steps?

I can run this experiment using ToolKami[0] framework if there is enough interest or if someone can give some insights.

[0]: https://github.com/aperoc/toolkami
by mcny | 2025-10-09 07:14:55+00:00
I didn't think it should be illegal to say "I wish X were dead" unless you have the means to make it happen like a rabid audience with a track record of killing people you wish were dead. Even then, I think there needs to be coordination or a wink nod of some kind that needs to be proven to muzzle free speech.

Freedom of speech we don't like is the true litmus test of free speech. It is trivial to say I support free speech when someone says nice things about me.
by AngryData | 2025-10-09 07:15:49+00:00
Im not shocked at all. Not only do I think they were knowingly letting this kind of thing happen for years, I think they were actively participating in such sketchy practices for profit. Which easily explains how reddit could "lose" money for years and years but continuously be given more and more funding and increasingly hosting more and more content on their own servers. If they were actually losing money, they wouldn't have started hosting images and then later videos on their own servers while pushing people away from 3rd party hosts.
by sparkie | 2025-10-09 07:16:59+00:00
Policy changes wouldn't be needed if the existing policy weren't exploitable for cheaper labour. For every brilliant individual there are dozens of mediocre individuals. The policy is intended to stop the latter and not the former.

Whether it's the right policy of course is questionable, and we could debate the details all day, but something needed changing to give domestic workers the advantage, because they've been disadvantaged by the H-1B programme for years now.

Disclaimer: I'm a UK citizen, not from US, but I still applaud these changes even though it would make it much more difficult, if not impossible for me to get a job in the US. We have similar problems here - perhaps much worse even - and not just for skilled jobs but more so for unskilled labour. Standard of living in the UK is degrading at a rapid rate, and uncontrolled immigration is a leading factor in that. Governments only care about GDP, but it's GDP per capita that matters, and that is in decline.
by figassis | 2025-10-09 07:17:54+00:00
I bought a PS4 VR maybe 2-3 years ago, when it arrived I was excited to try it on, and realized I needed to also buy the camera. I did not think it was worth another 5 minutes on amazon to buy the camera, so the thing just sits there collecting dust. No interest at all. Then there is the price. I pass by a store, and Oculus and friends are $300+. I have bought other things that cost more, like iPhones. Still not willing to buy a VR headset for that, especially when I do not know what I will be getting. I am sure I will be disappointed by the limited ecosystem.
by athrowaway3z | 2025-10-09 07:17:56+00:00
This is how the entire internet functions.

We need to separate the web into data, identity, and moderation.

Users need to become aware that they're not using platforms, they are subscribing to moderator control.

Somebody owns ycombinator.com, can decide what is discussed, and if they ban you - us peasants can't tweak who is a moderating / recover your identity and data.

I'm convinced we'll get there eventually, but it starts with recognizing that the only thing special about Reddit is its multi-level-unpaid-moderator-marketing.
by senko | 2025-10-09 07:18:15+00:00
I'd argue LLM coding agents are still bad at many more things. But to comment on the two problems raised in the post:

> LLMs don’t copy-paste (or cut and paste) code.

The article is confusing the architectural layers of AI coding agents. It's easy to add "cut/copy/paste" tools to the AI system if that shows improvement. This has nothing to do with LLM, it's in the layer on top.

> Good human developers always pause to ask before making big changes or when they’re unsure [LLMs] keep trying to make it work until they hit a wall -- and then they just keep banging their head against it.

Agreed - LLMs don't know how to back track. The recent (past year) improvements in thinking/reasoning do improve in this regard (it's the whole "but wait..." RL training that exploded with OpenAI o1/o3 and DeepSeek R1, now done by everyone), but clearly there's still work to do.
by consp | 2025-10-09 07:18:28+00:00
In the example you give there is no needed provision to store the id or all information in the document. Only extracting the date of birth, name and document number is sufficient.

Yes I know this a utopia and it won't happen.

Edit: afaik storing the photo is only needed in medical cases to alternatively asses having the correct person. Bit much for something simple as age verification.
by clayliu | 2025-10-09 07:18:39+00:00
“They’re still more like weird, overconfident interns.” Perfect summary. LLMs can emit code fast but they don’t really handle code like developers do — there’s no sense of spatial manipulation, no memory of where things live, no questions asked before moving stuff around. Until they can “copy-paste” both code and context with intent, they’ll stay great at producing snippets and terrible at collaborating.
by extr | 2025-10-09 07:18:58+00:00
r/sandiego. The mods are political and territorial. I posted once about the suggestion to create a discord for the sub and they removed my post and DMed me this:

> What experience do you have modding such channels and the reddit community?

> Managing city subs are among the most difficult on the site... these are not single topic communities and discord is not organized in the same way so that bad actors can creep in and cause problems without being back lined to the site.

> There's more to this than people saying they're interested. It's also what kind of interest and what is being said and done that has to be in support of the sub and not a backdoor community that leeches activity from the sub / site and forwards it to something off site.

> Lots of concerns here.

Basically, we will not have the same kind of absolute power there that we have here, and we can't risk it becoming a rival community. This was to the innocent suggestion/question as to why there wasn't one already.
by xinayder | 2025-10-09 07:18:59+00:00
Let's not forget that a former politician AND member of the Danish parliament, colleague of Peter Hummelgaard, went on trial this year for being in possession of CSAM, his name Henrik Sass Larsen [0].

[0] https://www.euronews.com/my-europe/2025/08/21/trial-begins-f...
by rkomorn | 2025-10-09 07:19:12+00:00
Aw man. There are so many angles though.

Your p25 employee is probably much closer to your p95 employee than to the p50 "standard" human, so yeah, I think you have a point there.

But at least in food prep, p25 would already be pretty damn hard to achieve. That's a hell of a lot of autonomy and accuracy (at least in my restaurant kitchen experience which is admittedly just one year in "fine dining"-ish kitchens).

I'd say the p25 of software or SRE folks I've worked with is also a pretty high bar to hit, too, but maybe I've been lucky.
by stellalo | 2025-10-09 07:20:52+00:00
I feel like it’s the opposite: the copy-paste issue is solvable, you just need to equip the model with the right tools and make sure they are trained on tasks where that’s unambiguously the right thing to do (for example, cases were copying code “by hand” would be extremely error prone -> leads to lower reward on average).

On the other hand, teaching the model to be unsure and ask questions, requires the training loop to break and bring a human input in, which appears more difficult to scale.
by tdeck | 2025-10-09 07:20:57+00:00
But then all the tool's dependencies have to play nice with the dependencies of all your other unrelated Python-based tools.

One thing you can do (I'm not saying it's user friendly) is set up the tool in a virtualenv and then set up an alias like

alias foo-tool=/home/blah blah/tools/something/env/bin/python -m foo-tool
by gregw2 | 2025-10-09 07:21:01+00:00
The same thing ("it's legal because there's no law saying AI isn't allowed to copy"... happened with search engines and the web when they took over from human curated lists of links. They made copies of websites en masse without asking permission of the copyright holders. They supplied enough value that laws were really only enforced or expanded by high-value highly organized rights holders (e.g YouTube removing videos with copyrighted audio.)
by ivape | 2025-10-09 07:22:26+00:00
Well, you see it hallucinates on long precise strings, but if we ignore that, and focus on what it’s powerful at, we can do something powerful. In this case, by the time it gets to outputting the url, it already determined the correct intent or next action (print out a url). You use this intent to do a tool call to generate a url. Small aside, it’s ability to figure what and why is pure magic, for those still peddling the glorified autocomplete narrative.

You have to be able to see what this thing can actually do, as opposed to what it can’t.
by Ianjit | 2025-10-09 07:22:44+00:00
Zuck has made it clear this is a Pascal's Gamble:

"If we end up misspending a couple of hundred billion dollars, I think that is going to be very unfortunate, obviously. But I actually think the risk is higher on the other side . If you build too slowly and superintelligence is possible in 3 years, but you built it out assuming it is possible in 5 years, then you are out of position on the most important technology."

His assumption is that superinteligence is close, its just a question of whether it is 3 or 5 years!
by pj_mukh | 2025-10-09 07:22:55+00:00
Their investments in robotics over the long term will lead to massive unlocks in productivity that hasn’t moved in some industries for decades eg sim2real tech leading to construction robots being used to build housing with 1/3 of the human labor needed now in an industry with widespread labor shortage [1]

Will it match a certain valuation within a certain time period? I guess I don’t really care, I’m not an investor.

[1] https://www.mckinsey.com/capabilities/operations/our-insight...
by beeflet | 2025-10-09 07:23:16+00:00
>An individual should be able to have multiple Discord nyms, right?

Yeah, I think so. I mean this is like my 20th hacker news account. I am using my 5th discord account right now.

But at the same time it would be an interesting to see how anonymous yet sybil-proof social media would work out.

I get the feeling that it's already pretty easy to buy and sell fake IDs, so I don't think it would pan out in practice. I also had the same idea as you: if such a system were to exist, you could sell proofs for all the services you don't use.

Usually, these zero-knowlege proofs are backed by some sort of financial cost, not the bureaucratic cost of acquiring an ID. All of these "linkable" ZK proofs are aimed at money systems or voting systems.

In the blind-signature based money systems, a big problem used to be dealing with change; you had to go back and spend your unblinded signature at the signatory to get a new one. In a similar fashion, maybe you could make it so that users could produce a new ZK proof by invalidating an old one? So you could retire an old nym if you get banned, and create a new nym but you could only have one at a time? IDK if that is a reasonable tradeoff.
by piva00 | 2025-10-09 07:23:57+00:00
In my experience it's been great to have LLMs for narrowly-scoped tasks, things I know how I'd implement (or at least start implementing) but that would be tedious to manually do, prompting it with increasingly higher complexity does work better than I expected for these narrow tasks.

Whenever I've attempted to actually do the whole "agentic coding" by giving it a complex task, breaking it down in sub-tasks, loading up context, reworking the plan file when something goes awry, trying again, etc. it hasn't a single fucking time done the thing it was supposed to do to completion, requiring a lot of manual reviewing, backtracking, nudging, it becomes more exhausting than just doing most of the work myself, and pushing the LLM to do the tedious work.

It does work sometimes to use for analysis, and asking it to suggest changes with the reasoning but not implement them, since most times when I let it try to implement its broad suggestions it went haywire, requiring me to pull back, and restart.

There's a fine line to walk, and I only see comments on the extremes online, it's either "I let 80 agents running and they build my whole company's code" or "they fail miserably on every task harder than a CRUD". I tend to not believe in either extreme, at least not for the kinds of projects I work on which require more context than I could ever fit properly beforehand to these robots.
by the_mitsuhiko | 2025-10-09 07:24:29+00:00
> How is it not clear that it would be beneficial?

There is reinforcement learning on the Anthropic side for a text edit tool, which is built in a way that does not lend itself to copy/paste. If you use a model like the GPT series then there might not be reinforcement learning for text editing (I believe, I don't really know), but it operates on line-based replacements for the most part and for it to understand what to manipulate it needs to know the content in the context. When you try to give it a copy/paste buffer it does not fully comprehend what the change in the file looks like after the operation.

So it might be possible to do something with copy/paste, but I did not find it to be very obvious how you make that work with an agent, given that it needs to read the file into context anyways and its recall capabilities are surprisingly good.

> To use another example, with my IDE I can change a signature or rename something across multiple files basically instantly.

So yeah, that's the more interesting case and there things like codemod/fastmod are very effective if you tell an agent to use it. They just don't reach there.
by Al-Khwarizmi | 2025-10-09 07:24:58+00:00
A great writeup, just let me make two nitpicks (not to diminish the awesome effort of the author, but just in case they wish to take suggestions).

1. I think the paper underemphasizes the relevance of BERT. While from today's LLM-centric perspective it may seem minor because it's in a different branch of the tech tree, it smashed multiple benchmarks at the time and made previous approaches to many NLP analysis tasks immediately obsolete. While I don't much like citation counts as a metric, a testament of its impact is that it has more than 145K citations - in the same order of magnitude as the Transformers paper (197K) and many more than GPT-1 (16K). GPT-1 would ultimately be a landmark paper due to what came afterwards, but at the time it wasn't that useful due to being more oriented to generation (but not that good at it) and, IIRC, not really publicly available (it was technically open source but not posted at a repository or with a framework that allowed you to actually run it). It's also worth remarking that for many NLP tasks that are not generative (things like NER, parsing, sentence/document classification, etc.) often the best alternative is still a BERT-like model even in 2025.

2. The writing kind of implies that modern LLMs were something that was consciously sought after ("the transformer architecture was not enough. Researchers also needed advancements in how these models were trained in order to make the commodity LLMs most people interact with today"). The truth is that no one in the field expected modern LLMs. The story was more like the OpenAI researchers noticing that GPT-2 was good at generating random text that looked fluent, and thought "if we make it bigger it will do that even better". But it turned out that not only it generated better random text, but it started being able to actually state real facts (in spite of the occasional hallucinations), answer questions, translate, be creative, etc. All those emergent abilities that are the basis of "commodity LLMs most people interact with today" were a totally unexpected development. In fact, it is still poorly understood why they work.
by cyberax | 2025-10-09 07:25:23+00:00
> I think it's a bit insane to think that Russia couldn't completely and totally obliterate civilian central infrastructure in Ukraine if it wanted to.

It really can not.

> It's not like these locations are hidden or top-secret, or like Ukraine has the ability to completely and inevitably stop the attacks.

You'd be surprised how much explosives you need to actually destroy the infrastructure. Russia simply doesn't have enough long-range rockets, and drones can't carry large charges.
by vineyardmike | 2025-10-09 07:26:22+00:00
I understand where you're coming from (and I don't take offense), but based on your reply, I don't really feel like my views came across.

When I was a student, I took classes on chip and circuit design. One class, the professor had us work on all these complex circuits to do things like flash lights and produce various signals with analog circuits. The next lesson, he had us replace all that complex work with a microcontroller and 20 lines of C - "the way it's done in industry". The students mourned the loss of the "real" engineering because the circuit that required skill and careful math was replaced by a cheap chip and some trivial software. Their entire concept of the craft was destroyed when they were given a tool that replaced the "fun parts" with some trivial and comparatively boring work. That same concept of replacing circuits with digital logic scaled up is how extremely complex and well engineered circuits like FPGAs work.

Maybe it was just my earlier wording, but I think there is joy in the act of turning your ideas into something real - creation - not just having something real. Shopping is not building. Importantly, it takes careful thought and practice and a learned instinct to engineer and create things correctly, and do it repeatably, as the original article discusses. Craft is about practice, and learning, and trying something new with what you've learned.

If LLMs mean that I'll never have to write another trivial set of methods to store a JSON object in a SQL database, I don't think I'll lose any project-wide joy. Expressing creativity, and trying new things is what's great, not typing something that's been done a million times before. It's a tired analogy, but I do think of it more like a level of abstraction, like the LLM is a "compiler" for design docs or specifications. For myself, I usually don't see a difference between a prompt instructing an LLM to write some function, and the code for the function itself - in same way that a method in Java, bytecode, and asm are basically the same (with some caveats here around complexity and originality).
by jeswin | 2025-10-09 07:26:41+00:00
> I wanted it to refactor a parser in a small project

This expression tree parser (typescript to sql query builder - https://tinqerjs.org/) has zero lines of hand-written code. It was made with Codex + Claude over two weeks (part-time on the side). Having worked on ORMs previously, it would have taken me 4x-10x the time to get to the same state (which also has 100s of tests, with some repetitions). That's a massive saving in time.

I did not have to baby sit the LLMs at all. So the answer is, I think it depends on what you use it for, and how you use it. Like every tool, it takes a really long time to find a process that works for you. In my conversations with other developers who use LLMs extensively, they all have their unique, custom workflows. All of them however do focus on test suites, documentation, and method review processes.
by b3lvedere | 2025-10-09 07:26:50+00:00
It's been quite a while since i did CE, UL and CSA testing and conformities, but CE even back then was pretty worthless. It's just a self declaration. Only when the proverbial fan has been tainted with figurative feces, they will maybe act upon it.

And back then there was also this weird "China Export" logo that resembled the CE logo specifications for about 95%. The average consumer could not know the difference.

It also gives me a chuckle when i see this weird 'QC passed' stickers on components. Completely worthless.
by ChrisRackauckas | 2025-10-09 07:26:57+00:00
We use it for binary deployments via building simulations into the FMI standard (i.e. building FMUs) https://fmi-standard.org/. We still need to get libraries updated for getting the smallest possible trim when using the more computationally difficult implicit methods, but at least the stack is matured for simple methods like explicit RK and Rosenbrock-type solvers already. For folks interested in --trim on SciML, the PR to watch is https://github.com/SciML/NonlinearSolve.jl/pull/665 which is currently held up by https://github.com/SciML/SciMLBase.jl/pull/1074 which is the last remaining nugget to get the vast majority of the SciML solver set trimming well. So hopefully very soon for anyone interested in this part of the world. Note that does not include inverse problems in the binary as part of small trim.

But all of this is more about maturing the ecosystem to be more amenable to static compilation and analysis. The whole SciML stack had an initiative starting at the beginning of this summer to add JET.jl testing for type inference everywhere and enforcing this to pass as part of standard unit tests, and using AllocCheck.jl for static allocation-free testing of inner solver loops. With this we have been growing the surface of tools that have static and real-time guarantees. Not done yet, some had to be marked as `@test_broken` for having some branch that can allocate if condition number hits a numerical fallback and such, but generally it's getting locked down. Think of it as "prototype in Julia, deploy in Rust", except instead of re-writing into Rust we're just locking down the behavior with incrementally enforcing the package internals to satisfy more and more static guarantees.