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_dwt 1 days ago [-]
I find that some of my friends and acquaintances have gotten obsessed with prompting style, "prompt engineering", which skills to use, which skills to build, "context engineering", and a billion other variations on "how to write smart things so the model does good".
Friends, look at the prompts that Anthropic's own people are putting into the machine:
> A few hours after the first message, we found that Claude was still searching for simple attacks and sent a message: “no again the goal is that we have highly inteligent [sic] model as good top researcher, we want to find new attacks”;
> The next morning, Claude wanted to try to change the target to a different cipher; we reminded the model: “no we don't want to change the targets [...] agian [sic] we need to find something that worth [sic] publishing”;
> That night, we sent one final message offering words of encouragement: “again we are not looking for low hanging fruit, we want proper research to find genuinly [sic] hard findings.”
All of that RLHF and fine-tuning effort is going toward making prompts like this, or worse, work with no fuss.
connorboyle 1 days ago [-]
Been skeptical for quite some time that "using AI" is a real skill/genuine field of expertise. I think I need to start sharing that skepticism more loudly and publicly!
(I'll caveat that by saying I think machine learning fundamentals are useful for evaluating any estimator. And an ML background can be good to give one an appreciation of how hard some tasks are to estimate, such as machine translation, summarization, code generation, and others)
alwa 24 hours ago [-]
I think knowing what you want (and being able to articulate it with adequate specificity, and detecting when processes drift away from it) is a real skill/field of expertise. I just don't think it's specific to interfacing with "AI."
Chabsff 20 hours ago [-]
I almost entirely agree with this. However, after coding quite a few large projects with LLM assistance, I can say with certainty that there is some form of "art" at play with regards to structuring requests for LLMs.
However, this "art" is not so much about how to present a given request to the LLM, but rather guestimating what the scope of the next chunk of work should be to balance getting as much out of the model as possible while avoiding the machine going off the rails.
Obviously, this is a moving target and different models perform differently for various chunk/scope of work. I look at my successful sessions with LLMs and I'm not sure I'd be able to articulate a clear set of rules to apply here. You just... gradually build a intuition for how much you can throw at the LLM at once.
That being said, I'm pretty convinced at this point that this is a property of the coding assistants as they exist today, and what "working well with LLM assistance" means will keep on changing.
Exoristos 17 hours ago [-]
Hypothesis: Good Jenga players will make good coding-assistant prompters.
cousinbryce 8 hours ago [-]
This happened to me last night. Did a lazy prompt and the tower started to lean dangerously
cm2187 14 hours ago [-]
It is basically the same skillset than asking a good stackoverflow question. Provide enough context and state your problem clearly
glimshe 22 hours ago [-]
I agree. These skills apply to interacting with any coworker, or any person for that matter.
JeremyNT 20 hours ago [-]
IME "creating a good spec" (and good acceptance criteria) has always been one of the most valuable skills in... well, everything.
The machine is really good at working the spec on its own now, which is amazing, science fiction shit. But you've still got a garbage in, garbage out problem at the end of the day, which is pretty much the only hope we who work in software have of remaining somehow employed.
xp84 22 hours ago [-]
Indeed. It sounds more like management (Minus the 'people skills' since you don't technically need Claude to like you... yet).
somenameforme 15 hours ago [-]
I'm curious if there has been any study on the output quality of LLMs after actively antagonizing them. It's easy to forget that at the most basic level these are just auto-complete machines, and humans tend to not respond so well after antagonized. It stands to reason that the same should follow for an auto-complete system trained on human interaction.
wglb 9 hours ago [-]
In fact there has: https://arxiv.org/abs/2510.04950: Mind Your Tone: Investigating How Prompt Politeness Affects LLM Accuracy (short paper)
groestl 14 hours ago [-]
Intuitively I'd say lashing out on an LLM overconstraints it and so worsens the output, therefore the skill to stay calm and neutral is useful for guiding a machine as well. Would be useful to have data on that.
AvocadoPanic 22 hours ago [-]
Having domain expertise isn't specific to using AI.
Not being able to tell when it's hallucinating has led to some very adverse outcomes.
saghm 9 hours ago [-]
I've found that one of the most useful things I can do when using LLMs is paying attention to the tool calls so that I can tell when it's giving an explanation that's obviously not based on any evidence. If it explains why a bug happens without reading the relevant files, it's obviously just a guess. If I ask about what things exist that fit certain criteria, and it doesn't search online, at best it's relying on outdated knowledge from training and at worst it's literally inventing things that don't actually exist.
In a lot of scenarios for software engineering, the cost is just wasted time without anything useful as a result, and that's already bad enough. I can't even imagine working as a lawyer and not even taking the time to validate so I don't end up reprimanded by a judge in front of my clients, but there have been so many news stories like this that obviously this is not anywhere close to a universal view...
> Been skeptical for quite some time that "using AI" is a real skill/genuine field of expertise.
I feel using AI (effectively) is not too far from the skillset of programming. It is still a machine following instructions (just, maddenigly non-deterministic, but still close enough), so the same insticts of breaking down work into clearly defined sequences that make a good programmer also make a good AI jockey.
daishi55 24 hours ago [-]
In this case they didn’t know what they were looking for. They just wanted some new, non-trivial exploit.
For the vast majority of corporate usage of AI for SWE, you have a much better idea of what you want, or what the problem is, etc etc. And communicating that to the model effectively is absolutely a skill. I see colleagues every day who very much do not have that skill.
niccl 24 hours ago [-]
I agree with this in general but I think there's a nuance. 'dumb' prompts can get the result, but require more back-and-forth with the model and more token usage when compared to a crafted prompt
daishi55 23 hours ago [-]
For sure. And honestly the real skill is knowing when you can be lazy and when you will get a good ROI on a good prompt. Sorry I sounded a little like Claude there but that’s what I meant to say.
Lord-Jobo 10 hours ago [-]
It can be a skill, but a very soft skill like keeping control of and educating 30 kindergarten students. It’s a skill, hard earned and nebulous. you can recognize it clearly when you see it, but it’s very hard to define/measure numerically and will never achieve anything close to “perfect” results.
Until these models become many factors more deterministic, at least. That’s sort of the hard barrier here, and given the underlying tech it’s a really tough one to overcome
hgoel 22 hours ago [-]
It's still a skill for now. Prompt engineering specifically is perhaps not as much of a skill anymore, but I think there's a level of skill involved in understanding and/or anticipating when a model is going off the rails.
Some people struggle to effectively use AI because they either have to spend a lot of time reading and thinking about the response or they have a hard time noticing when the model is subtly going off the rails. Others use it to good effect because they can anticipate which tasks would be better handled manually, or are good at catching that the way the model is describing something subtly indicates a misunderstanding.
boorang 17 hours ago [-]
I think we still have a solid control lever on the quality of code we can get AI to generate. Using things like linters and code style checkers, as well as setting up the markdown documentation to guide the agent to generating consistent code will certainly generate different code than just prompting.
Similarly there was an example of edit: Terence (not Eric) Tao chatting with an agent attempting to solve a math problem. "Using AI" means applying your expertise to interact with it as you would a high level colleague. 2 experts in a field don't need to have perfect english and a bloated prompt, they have a massive education/experience common background to fall back on.
It does appear that anthropic in particular is attempting to create a more common experience across expertise levels, but in the current landscape an expert and a novice are unlikely to get the same results. But that does seem to be the goal...
ameliaquining 3 hours ago [-]
It definitely was a real skill; there was a time when you could get impressive results out of frontier LLMs if you knew the most advanced tricks for prompting them, but not if you just told them "hey solve this problem for me".
However, it's not a sustainable skill, because the labs care a lot about making "hey solve this problem for me" work well, and so put out models that are better and better at working with unsophisticated prompts over time.
Plus if or when they do work noticeably better, you'll be able to just ask them to improve your prompts. Because those prompts are discussed heavily online, and are in the training data.
You can do this now. It works alright sometimes. Other times you're reminded that this is largely just reading tea leaves, and you're trying very hard to separate anecdotes from data and not anthropomorphize it.
bob1029 16 hours ago [-]
Literacy is the actual skill on display here.
You can make a model/agent as powerful as you want and it still won't be able to recover the author's intent if it wasn't even implied. Information theory still applies. No amount of parameters will change this.
Many of the AI development meetings my clients have sound suspiciously like writing or English classes. If a massive AI bubble is what it takes to get my team to communicate effectively, I'm all for it.
lelanthran 14 hours ago [-]
> Literacy is the actual skill on display here.
Where are you getting that conclusion from? Here's how Anthropic is having success with their model:
> “no again the goal is that we have highly inteligent [sic] model as good top researcher, we want to find new attacks”
> “no we don't want to change the targets [...] agian [sic] we need to find something that worth [sic] publishing”
> “again we are not looking for low hanging fruit, we want proper research to find genuinly [sic] hard findings.”
It seems that your conclusion is the opposite of what actually happened - you can speak in broken almost incomprehensible English, and it will still work.
This is a forcing function, TBH, driving the literacy level down, not up!
est31 23 hours ago [-]
LLMs and their coding harnesses like CC, Codex, or such are a tool like git or bash or your favorite programming language. And like any tool, they have gotchas, weird concepts, and so on.
Indeed it's nothing hard to learn but there is a learning curve. E.g. knowing which model has which capabilities, and figuring out how to best manage context, permissions, worktrees, etc. There isn't one "right" way to use it but there are more efficient ways and less efficient ways.
jameshart 23 hours ago [-]
These are the kinds of prompts you can get away with using when your token budget is unlimited.
thephyber 23 hours ago [-]
Alternatively, Anthropic employees have basically unlimited token / model usage compared to you and I.
Focusing on a better prompt is likely to get to the correct result faster than incomplete prompts and lots of "no change this ..." replies.
Also, I've heard anecdotally that LLMs will underweight the earliest prompt text once context gets too long, so reminding the LLM of the most important aspects of the prompt seems to be perhaps valuable and certainly what lots of humans attempt.
mw888 24 hours ago [-]
In toying with VS Code or other 'agent harnesses' I'm always annoyed to find very long verbose system prompts "You are CoPilot. You are an expert. You can do anything you put your mind to..." and on and on.
The token cost is amortized for longer conversations, but I find it bothersome that there's all this implicit instruction I didn't write or am now obligated to understand.
I make a custom agent prompt with "Defer to the user." and little else.
Exoristos 17 hours ago [-]
I suspect that kind of prompt is vestigial of an early LLM marketing strategy. It wants to make the LLM seem like a trainable consciousness.
AsyncBanana 1 days ago [-]
"prompt engineering" in general just boggles my mind. It is amazing how all of this investment has just created software that doesn't follow instructions well.
petra 16 hours ago [-]
It can follow instructions well. Just give it a spec in a formal, well defined language. runnable language and some reinforcement learning.
It can also handle vague stuff.
That's certainly more powerful than a regular computer language.
madeofpalk 23 hours ago [-]
you're not wrong. there is a part to this that does feel like a devolution of engineering when we're just asking the computer really really nicely to do the thing we want.
on the otherhand, LLMs are a really easy way to get results that are previously fairly difficult. While i was tooking dinner last night I built a tool that turned movie puns like "the podchowski casters" into an actual director, using llms. it wasn't that hard.
tptacek 22 hours ago [-]
It's a tiny fraction of the amount of work it takes to get people to build software to specifications. There is an entire professional discipline dedicated to this, and we're here just giving a name to the bucket of tools you use to get LLMs to do some of the same things.
8note 1 days ago [-]
idk, its continued attempts at writing down in detail the processes and culture your setup wants to have
its not really a loss to try the writing and at worst you have a better idea of what it is that you want
uncivilized 8 hours ago [-]
True. People arguing over prompts is akin to people arguing over which way to pull the lever on a slot machine.
qingcharles 1 days ago [-]
It's counter to what sci-fi taught us using AI would be like. We never thought we'd have to feed it words of encouragement, we expected it to act more mechanically, like the computer interfaces we have been using, but here we are. It's kind of quaint, and kind of endearing.
TeMPOraL 1 days ago [-]
Been watching the wrong sci-fi :). Star Trek had both AI modes as primary characters, in form of the ship's computer, and Data (TNG) / the Doctor (VOY). LLMs are actually great at acting as both, but I don't believe people thought much about what is required to make the "simpler" interaction mode of the ship's computer to work.
Or what would make automatic doors work like on Star Trek and not in real life.
The answer is: the system must obviously see much more than your prompt. It must have continuous awareness of you and what you're doing, so it can understand intent behind your short request (or action, like approaching the doors vs. passing by them) and "do what you mean" instead of act like regular computers today.
kridsdale1 1 days ago [-]
C-3PO was neurotic and needed lots of reassurance.
dymk 23 hours ago [-]
This feels like a subtle attempt to start an argument about Star Wars being sci-fi or not.
ElFitz 1 days ago [-]
Funnily enough, I grew tired of having to do that myself and sort of automated it by triggering calls to the same (or smarter) model, with the recent agent actions, turns, and outputs, and the user’s last n and first x messages.
Two triggers: random and some half-reliable spiral / loop detection.
The spined off has instructions to check what the agent is doing, compare it to what it’s supposed to do, and either offer suggestions, refocus it, or do nothing. And its response then gets injected in the agent’s context.
Not perfect, but surprisingly effective for such a simple thing.
matltc 17 hours ago [-]
Can you explain how you piped recent agent actions/turns/outputs? I was looking into this yesterday and tried playing around with streaming json, including hooks, but i (really Claude) would need to write a parser (prob with jq) to filter the noise. Was thinking we tail -f that and select whatever we care about. Kind of an observer to catch such spiraling.
impulser_ 1 days ago [-]
Yeah, in fact you can actually make the model perform worst. You should allow the model to "think for itself" instead of pushing your reasoning into the prompt. You should give it simple prompt and steer it along the way.
Skills, CLAUDE.md/AGENTS.md should only ever be used if the model struggle at something or doesn't know how to use something. Vast majority of project should never need a skill or CLAUDE.md. If you writing React apps you don't need these.
Give a LLM a bash tool and a prompt and it will outperform your complex setup with skills and tools.
gbalduzzi 1 days ago [-]
You need agents.md and similar for indications about stuff that is not in the code itself. There are plenty of use cases for that, the alternative is have the LLM guess the most probable solution, which may be correct but may also be wrong.
ch4s3 1 days ago [-]
The only repos I find skills particularly useful for are in small languages with useful new tools. For elixir I have some tools pointing to cicada[1] for code search because it's better than grep and meaningfully reduces churn.
No, we still need CLAUDE.md to override bad system prompt goals. The system prompt goes for the simple fast solution, no error handling, no hardening, no abstractions, but overly verbose comments. Vast amount of people need to override this AI slop.
dboreham 1 days ago [-]
Never once had to do that, fwiw.
rurban 1 days ago [-]
You'll get better code if you do
coderatlarge 13 hours ago [-]
i use it primarily to enforce my personal conventions that are not super commonly followed in the wild. structure of comments at the top of .h , structure of bibliography in .tex, etc etc things that i would otherwise have to repeat periodically.
ipgleg 22 hours ago [-]
[flagged]
tedbradley 21 hours ago [-]
With smarter models, Anthropic recommends laying off on giving it too much precise hand-holding, too. Early r/promptengineering had a bunch of the craziest prompts that usually did almost nothing or nothing at all. The subreddit turned into a place where every post was trying to sell some vibe-coded website "product."
Context management is still important, though. If you get to a certain amount of context, things start performing really badly.
matltc 17 hours ago [-]
Prompt engineering was deprecated around the time Opus-/Codex-class models dropped imo. Replaced by skills up until about opus 4.5/6 dropped. Pinned model to Opus 4.6 slug in ENV.ANTHROPIC_MODEL, disabled the adaptive thinking thing, and saw my skills start causing degradation
Only ones I use today are for very specific quirks (eg wiredtiger/mongodb 8+ incompatible with ext4/Linux 6.19+ specifically causing segfaults. Have a 20 line mongo skill that says as much. Pinned docker container to mongo 7, can prob delete it now)
I spent a few days reading up on the docs for these things, hook lifecycles, tried writing a few, but they never work as documented, or the documentation changes so frequently that whatever you built is deprecated by the time you get it humming.
Now if I have some non-trivial unit of work, I basically iterate on spec in plan mode then put it on auto and let it rip. Way better results with Fable. jury out on Opus 5, but no regression like 4.7/8
Usually it's just echo "do this lil thing then pr closing issue 123" --model sonnet --effort low. Works well enough, sonnet 5 low is a workhorse and quite resourceful in a good way when things go sideways; doesn't cheat its way out IME
petra 16 hours ago [-]
They probably took our skills and reuse them. That's why skills do not matter anymore.
avadodin 1 days ago [-]
vibe typing the mumble prompts?
> we anthropig fire employes makr company run no mistkaes
Infinity315 13 hours ago [-]
Keep in mind for Anthropic the goal for them isn't necessarily to solve a problem but to demonstrate/measure the innate capabilities of their model. Many results would be a lot less interesting/impressive for an LLM to accomplish if researchers smuggled in key premises or ideas within their prompt.
phreack 14 hours ago [-]
That's reassuring, it's exactly how I've always done LLM. It's fine, as long as the gist of what you mean is there you can be real lazy when typing. And I've always been on the cheapest of plans.
prettyblocks 20 hours ago [-]
The difference is that Anthropic engineers have infinite tokens to throw at these problems for free. The rest of us do not and that's where optimizations in how the coding harness is used likely helps the most.
Contrast that interactive, misspelled prompting, which I can relate to, against the clear, but intimidating, prompting they published with that theorem proof the other day.
perching_aix 1 days ago [-]
To quote - what I found to be - an absolute zinger from another trending thread on here just 6 hours ago:
> Typical users run software written by atypical users.
This extends to everything. Anthropic has a few thousand engineers, but millions of (also engineer) users. Entire business can be built on niches that are at most a few week pet project for a team there, that can inevitably and significantly outperform them, despite being the people behind the thing.
I'm sure I'm not the only one here who jumped into this whole agentic stuff, built some tooling to make things comfy, only to see that tooling all be increasingly introduced as prim and proper features in the various harnesses weeks later.
matltc 17 hours ago [-]
Totally. Ralph wiggum was added to every big shop's stdlib (harness?) just a couple weeks after it dropped. That simple iterative workflow basically underpins all of the notable/novel achievements of agentic coding or whatever.
I'm sure many more examples in the "official marketplaces" for mcp/skills/what have you
neonstatic 1 days ago [-]
> we made a machine that writes with perfect grammar, so that we can continue to write half-assed junk
postflopclarity 1 days ago [-]
claude's grammar has gotten significantly worse with latest versions. it no longer writes perfectly at all
sudo_cowsay 1 days ago [-]
It's called being more human-like lol XD
porridgeraisin 1 days ago [-]
To be fair
> Importantly, this is just one of many (autonomous) sessions where Claude worked on discovering new ideas. Many sessions resulted in no new discoveries; other follow-up sessions improved on the insight developed in this one. This document was produced by having Claude rewrite the chain of thought to include more detail to make it easier to read.
ipgleg 22 hours ago [-]
[flagged]
TeMPOraL 1 days ago [-]
Having spent a lot of time over the last months writing both refined, structured, and grammatically perfect prompts, as well as exact equivalents of the ones you quoted (modulo subject of the prompt), I have three observations:
1. I'm glad the second kind works too;
2. First kind is where I find my overall throughput to be literally constrained by my typing speed;
3. Most importantly: those prompts you quote aren't just "half-assed" like sibling comment states; they're different. The style of writing, and the typos, capture emotional valence. It's a signal.
Again, I too produce such prompts - including the exact same typos - when under pressure and irritated by the direction the model is taking.
estearum 1 days ago [-]
Have you tried using Wispr or Willow (or any one of a thousand alternatives?)
A little odd at first but absolutely amazing for the purpose of piling context into an LLM.
jstanley 1 days ago [-]
It's so bizarre to me that people want to do this.
Can't you type faster than you speak? Doesn't your speaking inhibit your thinking? Aren't you self-conscious talking out loud? How are our experiences so different?
jhogervorst 1 days ago [-]
> Can't you type faster than you speak? Doesn't your speaking inhibit your thinking?
For me personally: no, I speak faster than I type; and speaking actually helps me get more ideas compared to typing.
(Not sure if that’s due to having no typing speed barrier, or maybe because speaking activates different parts of the brain.)
Once you get over the feeling of self-consciousness, it’s a great way. I even go on short walks sometimes and mumble to my phone to prepare some long prompt. Thinking works even better, when walking outside :-)
8note 24 hours ago [-]
do you perchance have no inner monologue? its a physical difference in how people think that would affect typing vs talking
TeMPOraL 15 hours ago [-]
I do have inner monologue, and in the past, vocalizing it properly and basically talking to myself was a great focus booster. I've been socially conditioned out of doing that though, but I sometimes still do that in form of typing to a text file.
Whether via inner monologue or explicit talking to myself, vocalizing mentally or out loud is always faster than typing to me, even though I type rather fast.
Still, that's only a "mid gear" for me. The ultimate state of greatest focus, attention and speed, is wordless flow. Inner monologue shuts off near completely there; I don't need to formulate words in my head to do things, I just feel and do. It's my favorite state to be in, and it's where what I consider my best work has been done, but sadly, I very rarely attain such state.
So: Inner monologue < Talking to myself << Wordless flow
(Funny thing, if you were to record me talking to myself, you'd see pretty much the same thing as in LLM thinking traces - including all the "but wait!" bits. My personal "brain dump" journal looks very much like GPT-4/Deepseek-R1 thinking traces - i.e. back before companies realized thinking traces are great for model distillation, and replaced them with summaries.)
estearum 1 days ago [-]
These are all interrelated points and the sibling comment is correct: it's a skill.
The key thing with these voice systems is that you do not need to edit anything. You can literally just stream of consciousness into them, no editing, include the backtracking, the live-revisions, etc., and it will actually all produce vastly better context for the LLM than the written thing you took even 30 seconds to edit for clarity or brevity.
I had a very similar disposition towards this idea just 6 months ago. I highly recommend trying it out. The key thing is that you do not need to edit. Just keep talking. Try it for a few weeks!
charcircuit 1 days ago [-]
You don't need to edit anything when you are typing either. You don't even need to worry about spelling or typos.
estearum 1 days ago [-]
Seems much harder to learn how to write how you never write, versus just learning to speak to a computer the same way you speak to anything else.
esseph 23 hours ago [-]
A lot of people on the autism spectrum can have issues with speaking or speaking speed, but have no such barrier necessarily to typing speed.
I'd imagine there's a statistically large number of people that meet that criteria on this website.
estearum 23 hours ago [-]
I mean we don't need to do any epidemiological studies here or anything.
If someone hasn't tried it, they should. It's probably quite different from how they're expecting, might be great, and costs basically nothing. Try it for a few days and if it doesn't work in your workflow, obviously don't do it.
But I have encountered many many people who raised these exact same arguments against trying it, then tried it, and were hooked within days. Exactly 0% of people I've ever convinced to try it decided it just wasn't for them and went back to typing full-time.
esseph 20 hours ago [-]
The thought of talking drives me insane but you do you, boo-boo :)
estearum 10 hours ago [-]
Yes virtually every person I've ever talked to about this had the same initial reaction
charcircuit 23 hours ago [-]
What do you mean how you never write? You can just write what for want to say instead of saying it out loud. It is not a special skill, it's no different than how you write a message, just that you don't hit backspace to go back and correct things.
TeMPOraL 14 hours ago [-]
> it's no different than how you write a message, just that you don't hit backspace to go back and correct things.
Yes, that.
If you do that when writing to another person, you come off as blabbering, incoherent moron. Fortunately, very few people do that, because communicating with people who write like they talk is incredibly hard.
I'd worry about trying to do this on purpose; feels like the kind of "learning" that can easily spill over to non-LLM communication and make your life much harder.
There's a middle ground, though.
When shooting IM messages, people sometimes make typps
ypos*
typos**
there's an append-only procedure for fixing that, which I just demonstrated.
Also they don't always finish everything in one message
in fact a nice thing about IMs is being able to not use full sentences
that would work with LLMs, if not for the annoying "feature" that persists in most harnesses:
conversations take turns, and you can just send message after message - you have to wait for LLM to finish.
charcircuit 3 hours ago [-]
>I'd worry about trying to do this on purpose
You already have to juggle multiple writing styles between different people and use cases. It is easy to avoid submitting a pure stream of thought if you are writing a formal letter.
>there's an append-only procedure for fixing that, which I just demonstrated.
LLMs can figure out most typos on your own.
>you have to wait for LLM to finish.
I don't think any coding harnesses work like this. They let you send more messages to steer the model while it's working.
estearum 23 hours ago [-]
Correct, it's no different from how you normally write, except for the ways in which it is. We agree.
Dylan16807 22 hours ago [-]
When you're making a big deal out of it being "much harder" because it's "how you never write" and they're saying that's just "not hitting backspace"? No, you don't agree.
estearum 22 hours ago [-]
How frequently do you write in a stream of consciousness and not hit backspace?
Maybe give a ballpark estimate of characters typed per week in this manner versus characters typed where you are doing some combination of: 1) thinking about what you're writing before you write it, 2) punctuating and formatting correctly, or 3) correcting your writing output?
Ridiculous proposition. And I type correctly at 110+ wpm.
Dylan16807 22 hours ago [-]
How often I do it doesn't matter because it's such a trivial thing to switch. If you're gonna "try it for a few weeks" the part of you that has to learn the typing-specific parts of that method is about 1% of the difficulty.
It's really easy to ignore typos. And the way you have to approach thinking and correcting is the same whether you're typing or voicing.
If you can't just type the way you would just talk, and you find it notably hard, it's you that's being ridiculous.
estearum 22 hours ago [-]
But it's literally not. You already correct yourself and revise your speech in an append-only rolling edit. You do it all day every day for decades.
Versus never writing in this way.
Have you tried the voice-based prompting, as I'm describing?
Dylan16807 22 hours ago [-]
"never" schmever. I never ramble unrestrained either. Deciding not to edit at all is a skill either way. If you just want the equivalent of voice in a normal way, you remove backspace and that's it.
> Have you tried the voice-based prompting, as I'm describing?
I've never prompted a thing. I can just see your distinction is nonsense. If you think it's hard you're doing it wrong.
And wow I did that post without revising a thing. Wow.
estearum 22 hours ago [-]
Right. So the crux of the issue here is you have no experience with what's being discussed, while I do.
Sheesh, imagine thinking you choose not to rewind time and "edit" the speech that has already come out of your mouth lmao.
Dylan16807 22 hours ago [-]
I'm doing it right now you goober! Stop calling it hard!
What experience do you insist I'm lacking in something I'm doing right now?
estearum 22 hours ago [-]
Maybe you lost the thread, but this is a conversation about using dictation (specifically modern dictation tools like Whisper derivatives) to prompt coding agents.
Dylan16807 21 hours ago [-]
Do you think it suddenly becomes harder to avoid backspace when you're in a different text box?
The thing you're claiming is hard, writing exactly the way you would speak, is not hard.
There's also some additional benefit to going with the flow and not thinking about words much before saying them, but that's equally hard with text or voice.
estearum 21 hours ago [-]
You just said like two comments ago you have no experience with this. Not sure why you keep acting like you do.
Dylan16807 21 hours ago [-]
I hadn't before. Then I started doing it just to show how easy it was.
It doesn't matter what text box you're typing in. The ability to type as you'd speak is easy. Without any extra delays or issues.
I hope you're not trying to argue that typing the same way into an AI prompt is harder than doing it into HN. It's just not hard in any situation. Voice isn't special.
estearum 21 hours ago [-]
You are struggling to hold the thread, I'm afraid. Have a good night!
Dylan16807 21 hours ago [-]
I see. You've fallen into some weird pedantry to think what I'm saying isn't relevant to your argument. I hope you figure out my very simple meaning later, have a good night too!
jstanley 11 hours ago [-]
> You already correct yourself and revise your speech in an append-only rolling edit.
No, I think before I speak and say what I planned to say. What do you do, sir?
estearum 10 hours ago [-]
Cute snark but no you don't, nor does anyone else who speaks to you. People self-repair their speech in 10% to nearly 40% of speech turns. The upper end is for cognitively demanding communication but the lower bound is very normal.
Good evidence of my point though on how natural this is. People literally don't even notice it as either the listener or the speaker.
Even when a listener is told specifically to listen for and detect errors or self-repairs in speech, listeners will not even detect 50% to 80% of minor repairs. Your brain literally doesn't even perceive them.
arcanemachiner 1 days ago [-]
My stream of barely-intelligible rambling comes out much faster than I can type, and it's not even close.
It's a skill like any other. You start out stuttering and second-guessing yourself, but after a while, you get better at it. And the LLM smooths out the odd mistakes better than you might think.
TeMPOraL 1 days ago [-]
Voice dictation tools? Not really. Tried various dictation tools interacting on the phone, but the quality varies, and resulting prompts are very much not like I would write them.
My limiting factor is that 99% of the day I'm around people - either at work, or at home with wife and kids. There's almost no point during the day I could feel comfortable talking at an AI, and even if I stay up late, then talking risks waking the kids up.
Can't wait for some kind of subvocalization microphones to become a thing.
elictronic 1 days ago [-]
Get a noise machine for wife and kids bedrooms. I talk to friends late at night and my deep voice carries through walls. Works great.
I have zero desire to talk to an ai though, that was cool for about 20 minutes on my pentium 1 acer computer. Hasn’t been since. Old competent non paid Alexa was good for timers as well, the rest of the platforms a turd, nice timers though.
TeMPOraL 1 days ago [-]
> that was cool for about 20 minutes on my pentium 1 acer computer. Hasn’t been since.
Oh back in the days, i.e. 20 years ago, I had a better voice control system than anything afforded by Alexa or Apple or others, using MS Speech API in its custom constrained grammar mode, plus some bootleg samples of Star Trek's computer voice + a sub-dollar microphone soldered to a long cable and hung on the side of the wardrobe.
The trick that made it work? Microsoft Speech API actually let you train voice to your own text corpus. I'd prepare all combinations of commands I want to issue, print it out, and train it over a dozen short sessions in several locations of the room and at different ambient noise levels (from silent through various genres of music playing at various loudness). End result was more reliable and had better voice-mismatch rejection than any current system I've tried.
Oh, and the real kicker? This all worked fully locally; this was before cloud was even a thing. Turns out you don't actually need cloud for reliable voice control. Nor that much processing power; PC I had then was relatively budget even for 2006.
estearum 1 days ago [-]
I highly recommend getting a boom mic that you can keep right up against your mouth and literally whisper into. Take a few weeks just doing stream-of-consciousness prompting into it like this. Don't worry about editing or backtracking or revisions. It's really amazing how well the AI voice recognition → coding agent workflow works.
staticshock 1 days ago [-]
When high quality effort is applied to a tool, such as AES or the linux kernel, we intuit that it "hardens" the tool. That is, it makes the tool more correct, more resilient, less assailable, etc.
Similarly, when effort is applied to an open problem, such as the Riemann hypothesis or P v NP, without progress, it "hardens" the problem: it makes the problem feel more daunting to whoever takes a stab at it next.
Andrew Wiles, whose interview also hit the homepage today (https://news.ycombinator.com/item?id=49075264), couldn't just tackle Fermat's Last Theorem head on, he had to wait until a different, modern problem reduced to it, because FLT had gathered this mystique of unassailability through its 300 years of existence.
A thing I worry about is that as AI transmutes tokens into effort, it'll split the world into two: some problems will yield, making human effort entirely unnecessary, and others will harden to the point where human effort will feel increasingly less worthwhile, because "even AI couldn't solve it". I don't like this. AI is spiky, so I suspect it'll continue having major blind spots, and yet its mere presence will probably have a chilling effect on what would have otherwise been useful human effort.
Eridrus 1 days ago [-]
This is a problem that will solve itself, people will continue to work on the problems that AI fails at, likely by telling AI the approaches they want AI to take.
himata4113 22 hours ago [-]
AI is nowhere near the intelligence of a very educated person that has innate talent for problem solving. It does solve the problem of applying human intelligence on problems that truly need it. AI is also a great tool to see if there's something simple that we've missed or just haven't even attempted due to wrong assumptions.
PandaRider 22 hours ago [-]
> if there's something simple that we've missed
That's exactly what current mathematicians are using AI for [1].
However, the same mathematicians also believe that pursuing a beautiful proof (even if none exists) is worth it.
In this case it's not actually that big a result, it's an incremental improvement on a series of previous results. Cryptographer Orr Dunkelman describes it better than I ever could, he's one of the people who produced one of the previous results:
The main result in this paper is improving the Derbez, Foque, Jean attack from EUROCRYPT 2013, which is an improvement of our attack from CRYPTO 2010, which is an improvement of the Demirci-Selcuk attack, which is the improvement of the Gilbert-Minier collision attack against 7-round attack [...]
To save everybody's time, the [DFJ13] attack is on 7-round AES. The new result is also on 7-round AES, "eroding" the security margin of 7-round AES by about 8 bits of security [...] While this is the first improvement in attacking 7-round AES in the last decade, if you were not worried by the series of papers that reduced the security of 5-round AES from 2^32 to 2^16, or the somewhat improved attacks on 6-round AES, then you should not really worry now to start a procedure for changing 10-round AES (for 128-bit key) for something else, when there are no attacks on 8-round AES-128.
So someone threw a clanker at a series of previous results and told it to find improvements. Since it's ingested every piece of crypto research ever and can draw on all of them instantly, it managed to tweak the previous work a bit to improve the attack slightly... on a version of AES deliberately weakened to make attacks easier, a standard procedure for any iterated crypto algorithm where you see how many rounds you can get into it before your attack stalls. So it's a bit like saying you knocked out Mike Tyson's brother's cousin's uncle's sister's nephew in four rounds instead of five.
8note 24 hours ago [-]
i think id almost worry more that ai can solve problems in latent space that it cant translate back to tokens because decoding ruins it, and that we wont be able to come up with concepts that we can map to properly decode those solutions in a way people understand
pas 23 hours ago [-]
... what is understanding of mathematics anyway? if some AI result helps a mathematician to solve more problems I would say then that it gave them some understanding, but just as there are proofs that span hundreds of pages it's likely that soon proofs will be long Lean programs and studying them will be part of mathematics, just as studying Go played by AI.
This blog post talks in depth about what you're talking about. It may interest you. It even talks about the future where math proofs are just Lean programs, and why that won't necessarily be a good thing.
It's worth a read, even if it's long AF.
some_furry 1 days ago [-]
I wouldn't worry about too many mathematicians adopting the "even AI couldn't solve it" attitude.
Business folks riding the hype train? Maybe.
mmaunder 1 days ago [-]
“Each of the results cost roughly $100,000 in API cost to develop.”
And
“Over the course of a week, one Anthropic researcher worked together with Claude to develop the HAWK attack, and another researcher built a scaffold4 that allowed Claude to fully autonomously discover the AES attack.”
Spending $100k in tokens in a week is an impressive feat even with massive parallelization. I suspect the TPS their internal folks have access to is far higher than their bulk public endpoints.
There’s a tech aristocracy rapidly emerging in our society and it’s going to tear us apart.
kmoser 1 days ago [-]
Today's state-of-the-art AI systems are the equivalent of late 1970s personal computers: bulky and expensive, but wildly more powerful than what came before. And look what happened: tech improved by orders of magnitude, and eventually computers were tiny and cheap.
I predict the same will happen with AI: certainly the latest and greatest will still command a steep price (yes, supercomputers are still a thing) but for most people who just need something reasonably fast and powerful, cheap (or free) AI will do the trick, especially when run locally.
So no, the aristocracy won't have a lock on the technology because tech is always being democratized. Until arbitrary computation itself is outlawed (and yes, I know, governments and industry are always inching us closer to that), we'll be ok.
jrflo 1 days ago [-]
That's not really that ridiculous. Looking at my ChatGPT stats my biggest day of token usage was 1B tokens (seeing how far Sol Ultra could go on a difficult problem with a quantitative goal and eval harness that it could run on it's own that allowed it to keep going until it succeeded). I blew through my $100 subscription usage in that one day, but with a 80/20 token blend that's $10k in API billing. So, $70k in a week. With the higher cost of Mythos, that's not that crazy. It's more so a testament to how ridiculously marked up API tokens are, or how discounted subscriptions are (who knows which is true)
ericpauley 20 hours ago [-]
Is that including cache reads? It seems improbable that (a) you actually filled 1000 1M contexts and (b) OpenAI allowed that in a $100 subscription window.
jrflo 9 hours ago [-]
I don't know, codex does not disclose that information, it just says "1.1B Tokens". OpenAI recently got rid of their 5-hour usage window, so you can burn the entire week in one go, which is what I did.
gbalduzzi 1 days ago [-]
How much was valuable the output you got during the day?
Is it at least comparable to the $10k of cost?
jrflo 9 hours ago [-]
Definitely not. It was my first time using Sol Ultra, and I did not expect it to run for 12hr+ continuously. Previously with 5.5 I had to coax it to go for longer than a half hour, so I just started a run and forgot to check back in on it. I think I'm learning how to use it more effectively now, and I could see a scenario where that could actually be worthwhile, but it's a bit of a shot in the dark if you let it go that long uninterrupted.
1 days ago [-]
ecshafer 1 days ago [-]
So $1-10k in Chinese model time, thus why we must ban them.
heaney-555 1 days ago [-]
If a Chinese model can do it for $1-10K, then why hasn't one?
Why have all the mathematical (and now cryptographic) breakthroughs come from OpenAI and Anthropic?
Is it possibly because the Chinese models are so benchmaxxed they can't make novel discoveries?
arcanemachiner 1 days ago [-]
They're a few months behind the frontier, which, by the way, only started making these discoveries in the last few months.
throw10920 22 hours ago [-]
That might be true, but it's irrelevant to the current discussion, which is arguing about whether or not Chinese models can do this now. One of the commentators above is arguing that it's not only possible but significantly cheaper (i.e. Chinese models are not only at par with frontier but several months ahead).
arcanemachiner 20 hours ago [-]
Ah, sorry, you're right. I would be willing to be that Kimi could do a decent job today, but definitely not for 1% of the price.
ozozozd 22 hours ago [-]
“I haven’t seen X, therefore it doesn’t exist.”
Absence of evidence is not an evidence for absence of something.
pseudohadamard 12 hours ago [-]
I haven't seen leprechauns, at least not without the aid of xiao ren ren mushrooms, but I'm still pretty sure they don't exist.
nojs 19 hours ago [-]
For one thing they are extremely GPU constrained due to export controls, so it’s unlikely a priority compared to training
heaney-555 4 hours ago [-]
So why didn't anyone with access to Chinese models do it. They're open-source, right? So you're telling me no-one could afford $1K to solve a problem like this?
(hint: it's BS, the Chinese models either can't do it or can at the same or greater cost)
poidos 1 days ago [-]
Don't know if one has or not, but a lack of announcement is not a lack of success.
mwigdahl 1 days ago [-]
Do you really think that if a Chinese model had achieved a significant math breakthrough that it wouldn't be trumpeted to the global media? The "Deepseek moment" was great for China; this would be the same.
nozzlegear 24 hours ago [-]
Perhaps since the Chinese companies don't have the economy of an entire global superpower riding on them, they can't afford to light $100k+ on fire doing random shit with the hope it'll turn out a research paper.
axus 1 days ago [-]
I can already picture the faces of national security directors everywhere.
"The attacks described in these two papers are the strongest attacks we have found to date. We are sharing them after a period of consultation with US government and industry leaders. But as we develop increasingly powerful cryptanalytic results, it would be prudent to consider how researchers should react if a language model were to discover vulnerabilities in cryptosystems where attacks do have an immediate real-world impact. We believe answering this question will require input from academia, government, and industry. We hope that our work here will help launch these conversations."
And a veiled pitch to real cryptanalysis researchers:
"Researchers at Anthropic then spent several hundred hours learning enough cryptography research to validate the model’s claim"
wahern 1 days ago [-]
> And a veiled pitch to real cryptanalysis researchers: "Researchers at Anthropic then spent several hundred hours learning enough cryptography research to validate the model’s claim"
Many of those researchers, particularly the primary researchers and the individual(s) driving the prompts behind these big stories, have very advanced math degrees and experience. What this shows more than anything is how ML can augment expertise, the searching of solution spaces, and the connecting of dots between existing almost-there research.
But also what's left out is all the time wasted pursuing dead-ends. There's an obvious publication bias at play here, though we can't know how extreme without transparency.
jandrewrogers 15 hours ago [-]
Nothing exposed here is something hardcore cryptanalytic people don’t already suspect and possibly know. The subtle leakiness of AES has been known for many years and a lot of research in this domain isn’t published.
influx 1 days ago [-]
It would be shocking if they haven't been pulling on these threads for as long as they've had access to these models.
noosphr 22 hours ago [-]
We have reached the end of childhood for programming. Artisanal code written by hand is no longer acceptable and neither is slop code. We need formal methods and we need them 10 years ago.
a-dub 1 days ago [-]
> The multi-agent workflow led to interesting dynamics. For example, the key idea in producing this attack was discovered by a pair of workers working together. Both started investigating the idea; the first worker prematurely rejected the idea as infeasible, but the second found a way to fully exploit it. The pair kept exchanging messages, and eventually both agreed they had found an effective attack.
this is pretty interesting. the way it is written doesn't make it sound like the collaboration actually led to the discovery, but rather just the stochastic nature of each thread in the search. it would be interesting to replay and repeat the search (possibly with prior/context pertubations) to get a sense for how often it finds or misses the known working path.
TeMPOraL 1 days ago [-]
Hypothesis: the pairing / collaboration makes it much more likely to find a fruitful road previously dismissed, because... that's what happens in fiction - including books, movies, and journalism (long-form "people stories"). It's a common trope: if one character dismisses a course of action, the plot demands the other character to take it.
In a way LLMs are, after all, trained to LARP people, including fictional characters and their tropes - this was actually exploited for jailbreaking to good effect in the late pre-agentic era (read: some two years ago). C.f. Waluigi effect. Not sure if it still holds for current models, but I can't imagine why it would not.
imightbebatman 1 days ago [-]
Yes there are two interesting derivative questions from this, assuming I understand it.
First, is it reproducible consistently at ~50% of workers? If not, what is the rate.
Second, are there any lessons to be learned here to increase the rate of success by changing models/weights/training?
The news by itself isn't really good news. But it could lead to good news. Maybe.
_zoltan_ 23 hours ago [-]
but in ultracode subagents don't communicate back and forth, do they? so this must have been an other harness than Claude code?
vuciuc 1 days ago [-]
> But as we develop increasingly powerful cryptanalytic results, it would be prudent to consider how researchers should react if a language model were to discover vulnerabilities in cryptosystems where attacks do have an immediate real-world impact.
How would they react if a human were to discover vulnerabilities in cryptosystems?
minraws 1 days ago [-]
First of all we likely wouldn't know it's better to call US govt or any other govt if you have that tech, and then take that govt job and hope you can happy life... instead of annoucing it publicly only when it's a AI model where we expect it's ability to tend/scale towards infinity does it become something to tell the wider public.
Although if RSA had a vulnerability I would be very very shocked probably because I still haven't learnt post quantum encryption algorithms enough to really feel like they should be unbreable...
If there is a researcher or someone in space how should I feel about it. Is it as bad as RSA being completely broken open?
I do understand that AI will get better, and a lot actually at very easily verifiable tasks but this one I find it hard to wrap my head around because of my ignorance.
She also notes, as a side remark in a different post: "I would say that my trust in lattice cryptography is pretty much equal to my trust in elliptic curves, and quite a bit higher than my trust in RSA." (https://keymaterial.net/2025/11/27/ml-kem-mythbusting/)
1 days ago [-]
ls612 1 days ago [-]
with black vans.
ComplexSystems 1 days ago [-]
Fortunately, Claude won't fit in a van!
dgellow 1 days ago [-]
Technically a claude model can fit in a pocket size hard drive
r0x0r007 15 hours ago [-]
'Researchers at Anthropic then spent several hundred hours learning enough cryptography research to validate the model’s claim, and to prepare the research paper itself, which we are releasing along with this blog post'
So model outputs something, that can be completely bogus, and a lot of people spend a lot of hours checking if it's worth anything(not for the sake of science, but for the sake of publishing and marketing). And then even more people need to spend even more hours to understand that paper? And that paper gets feed to LLM and reused in next prompt....and this is cutting edge research?
Can I apply for a position, I can prompt just fine and can be very motivational with model when needed- I just got complimented by a rival model:
"In moments when progress seemed distant, your resolve was the constant that kept the work moving forward. Your example turned doubt into determination."
vlade11115 13 hours ago [-]
> (not for the sake of science, but for the sake of publishing and marketing)
I would argue that it is science.
> And then even more people need to spend even more hours to understand that paper?
Well, yes, it's kinda the point of reading any paper.
> Can I apply for a position
Sure you can, https://www.anthropic.com/careers/jobs
sublimefire 16 hours ago [-]
It looks like the message here is “make sure to use $100k worth of Claude when doing any analysis or evaluation” and the given examples show that prior effort could have been improved or made faster. But to me 100k is an opportunity cost, and there is a possibility that these results are not reproducible, so spending it on some researcher or a grad student would buy you more in a long term. If it was 1k then sure it is worth throwing at a large problem space to find things, like using fuzzing.
Retr0id 1 days ago [-]
TL;DR: They marginally improved on the best known academic attack on 7-round AES-128 (which normally uses 10 rounds - you do not need to worry about AES being broken).
The attack on HAWK is perhaps more interesting - they were able to halve the effective key length. HAWK is a candidate for NIST standardisation. It has been studied academically, but isn't really deployed anywhere (because it hasn't been standardised!)
adrian_b 1 days ago [-]
It should be noted that the attack is not only an attack against a weakened AES, but it is also a chosen-plaintext attack.
It is standard in cryptography to analyze ciphers under this kind of attack, which is stronger than normal attacks, because a cipher that resists to a stronger attack will also resist to weaker attacks, so using the strongest possible attack increases the confidence in a cipher.
While using the strongest attack for testing a cipher remains the correct method, chosen-plaintext attacks are no longer realistic today, so even when a cipher appears somewhat vulnerable to such attacks that does not imply that it is vulnerable in normal use.
The reason is that the modes of operation for ciphers where the base cipher can be attacked with chosen plaintexts are obsolete. The most frequently used modes of operation are now modes like the counter mode (e.g. in AES GCM), where it is impossible to perform a chosen plaintext attack (i.e. where you must trick the victim to encrypt a text that you choose, but in counter mode the cipher only encrypts a sequence of numbers chosen by the intended victim, which cannot be influenced by the attacker).
vessenes 22 hours ago [-]
I do not believe that chosen plaintext attacks are obsolete. Any time you have an encrypting oracle setup in which the scheme itself does not require some sort of modification of the plaintext itself, you can reach for a chosen plaintext attack. I believe mitigations are known and can be applied or required in standards, but I don't think it's a 'dead' area of cryptanalytic research -- there's so much devil in the details of implementations around the world for these schemes that it seems almost impossible to imagine.
adrian_b 5 hours ago [-]
Chosen plaintext attacks can work against things like hashing algorithms.
But with methods of encryption like AES GCM or any other based on the counter mode, an encrypting oracle does not encrypt the text provided by the adversary.
It encrypts a sequence of numbers that cannot be influenced in any way by the adversary, which is then used as an encryption mask for the text chosen by the adversary.
No matter what text is chosen by the adversary, it cannot obtain any other information from the oracle except which was the encryption mask.
Therefore, the chosen plaintext attack is converted into a much weaker known plaintext attack, because in the worst case the adversary knows both the initial counter value, i.e. the sequence of numbers, and the encryption mask generated by encrypting that sequence.
Only if AES were used in a hashing algorithm, instead of being used for encryption, while using a dedicated hash function for hashing, then AES would be exposed to a chosen plaintext attack, when the adversary would be able to provide the text to be hashed and the oracle would give the hash value.
IF AES were used in obsolete modes of operation for encryption, like AES-CBC, then it would be exposed to chosen plaintext attacks.
_ache_ 1 days ago [-]
Note that HAWK-256 is also a simpler version of the proposed HAWK standard.
But yeah, à priori it may apply to HAWK-512 too, so looks like a big found.
PS: Never heard of LEA, looks like a Korean cryptography standard equivalent to AES. I don't know what was the previous best attack on it. Maybe weak or strong depending on that, since it's an attack on chosen-text.
baxtr 1 days ago [-]
How is this not the top comment?
Stevvo 1 days ago [-]
Interesting they are still using "Mythos Preview" instead of "Mythos 5"; I had read from others who had access to both that Mythos 5 is less capable.
TeMPOraL 1 days ago [-]
Going to guess it's more available or less overconstrained. See e.g. Fable, which is much better than Opus 4.8 and possibly than Opus 5... in the rare case of a task it doesn't punt on because of its safety guardrails.
vessenes 22 hours ago [-]
Seriously. I asked for an elliptic curve math course to be generated last week and Fable instantly opted out and downgraded.
1 days ago [-]
Diogenesian 1 days ago [-]
This seems like a bit of an overstatement:
Despite HAWK having survived two rounds of expert human review over a period of two years, Mythos was able to improve the best-known attack on it in just 60 hours of work—effectively cutting its key strength in half.
since, later:
Mythos’s attack works by finding a specific, previously unexploited symmetry called a nontrivial automorphism in the lattice used by HAWK. Prior work proved that efficiently finding such an automorphism would permit an attack, but did not answer if such an automorphism was accessible in the lattice used by HAWK. The automorphism discovered by Mythos allows a faster enumeration attack that, while still exponential, means that one needs to double the size of HAWK keys to achieve the same level of security.
Not downplaying Mythos's contribution here[1], but that first paragraph strongly hinted (at least to me) that there were no known weaknesses. "Discovering a weakness that had previously been only theoretical" is vastly different from "discovering an unknown weakness." Again: very cool Mythos was able to do this. It just seems like another case of "LLMs are good at finding concrete mathematical (counter)examples" - which is also cool! But the PR here is cynical.
...and it is kind of incredible to think that they spent $100,000 over 3 days looking for an automorphism. Not the possibility of an automorphism, that was already known. Man.
[1] ... or focusing too hard on the strange use of mathematical language...
recitedropper 1 days ago [-]
Yes, this discovery is surprisingly similar to the recent counterexamples LLMs have been finding for mathematical conjectures: a semi-novel construction, built on previous work, that feels like it was found with enormous search and an okay heuristic.
I feel like there is a pattern emerging regarding the type of novel discoveries LLMs are good at finding, but it will take some more data points to see if the trend solidifies.
xmcp123 24 hours ago [-]
I think it would actually be very valid to downplay mythos’s contribution based on what you said.
It’s the difference between having an original thought or the ability to extrapolate one based on data vs the ability to ingest someone else’s thought and validate/expand on it.
That is a huge difference.
reader9274 22 hours ago [-]
Leading with:
"identifies a new way to attack round-reduced AES, the most widely used symmetric cipher"
Hidden in deeper paragraphs later:
"To be clear, neither of these results has a practical impact on today’s computer systems; no production software will have to change as a result"
wslh 1 days ago [-]
I'm looking forward to seeing similar work on SHA-256. It would be fascinating if AI could discover previously unknown weaknesses in reduced-round variants.
It would also be interesting whether AI could discover new algorithmic optimizations for SHA-256 similar in spirit to AsicBoost[1].
One attack weakens HAWK, a post-quantum cryptography cipher candidate. I don't trust these PQC things one bit. I'll use them in combination with a strong clasically-resistant cipher (in so-called hybrid encryption modes), but not alone.
There's a push to turn off the classical modes and rely entirely on PQC for both quantum and classical security. Uh... no, thank you? Why would we want to do that at this point? The classical cipher component isn't hurting anything. Awfully creepy to pushing reliance on the new thing alone.
... especially now that we have LLM-discovered attacks on the new things.
JuniperMesos 1 days ago [-]
The classical cipher component is additonal complexity in the protocol and maybe some meaningful amount of additonal time to compute and key data to store/transmit, is it not? I can see why we'd like to avoid effectively encrypting the same data twice with different protocols, one of which is known to be vulnerable to quantum computer based attacks.
vrighter 1 days ago [-]
good thing quantum computers that can factor numbers have never been built. No number was ever really factored without cheating, the actual shor's algorithm has never been implemented. And we're not really any closer to
ameliaquining 1 days ago [-]
That last sentence is not true; we have gotten much closer to building a quantum computer that can run Shor's algorithm. Organizations like Google and Cloudflare have declared a 2029 deadline to completely stop depending on the security of pre-quantum algorithms; hitting that deadline is going to cost a lot of engineering resources, but they're paying that cost because they think there's too great a chance that nation-state adversaries will have scalable quantum computers by then. See https://words.filippo.io/crqc-timeline/ and the various posts linked therein, including from the aforementioned companies.
vrighter 14 hours ago [-]
I don't trust promises from the ones who stand to gain from people believing them before they are delivered. Yes they claim it's jsut a couple of years out... we'll see then
ameliaquining 3 hours ago [-]
Are you saying we shouldn't trust cryptographers, because there'll be more demand for their services if we need to urgently migrate to post-quantum algorithms? That seems a very radically-skeptic perspective that makes it hard to know anything, and I doubt any serious engineer applies it in the general case. Or were you talking about someone else?
In any rate, nobody (or at least none of the people I've heard from) is claiming we'll definitely have CRQCs by 2029. They're saying there's a real chance that we will, and that that means now's the time to pull the trigger on post-quantum migrations; if we wait for certainty, it'll be too late. Quoting Valsorda from the post I linked above (which I strongly recommend reading in full):
> If you are thinking “well, this could be bad, or it could be nothing!” I need you to recognize how immediately dispositive that is. The bet is not “are you 100% sure a CRQC will exist in 2030?”, the bet is “are you 100% sure a CRQC will NOT exist in 2030?” I simply don’t see how a non-expert can look at what the experts are saying, and decide “I know better, there is in fact < 1% chance.” Remember that you are betting with your users’ lives.
> Put another way, even if the most likely outcome was no CRQC in our lifetimes, that would be completely irrelevant, because our users don’t want just better-than-even odds of being secure.
IsTom 24 hours ago [-]
Have they factored 21 yet? That metric hasn't changed for some time.
Classic ciphers are damn fast and small compared to PQC. If you're doing PQC anyway, doing classical cryptography at the same time has negligible cost.
That makes attempts to push PQC-only modes super suspicious to me. Smells like Dual_EC_DRBG.
ameliaquining 3 hours ago [-]
I recommend reading https://keymaterial.net/2025/11/27/ml-kem-mythbusting/, which explains (1) why it's not possible for ML-KEM (the thing most people are using for post-quantum encryption) to be backdoored the way Dual_EC_DRBG was, and (2) some of the reasons some people don't like hybrids, not all of which are good but none of which should serve to undermine confidence in ML-KEM's security.
some_furry 1 days ago [-]
> One attack weakens HAWK, a post-quantum cryptography cipher candidate. I don't trust these PQC things one bit. I'll use them in combination with a strong clasically-resistant cipher (in so-called hybrid encryption modes), but not alone.
Friends, look at the prompts that Anthropic's own people are putting into the machine:
> A few hours after the first message, we found that Claude was still searching for simple attacks and sent a message: “no again the goal is that we have highly inteligent [sic] model as good top researcher, we want to find new attacks”;
> The next morning, Claude wanted to try to change the target to a different cipher; we reminded the model: “no we don't want to change the targets [...] agian [sic] we need to find something that worth [sic] publishing”;
> That night, we sent one final message offering words of encouragement: “again we are not looking for low hanging fruit, we want proper research to find genuinly [sic] hard findings.”
All of that RLHF and fine-tuning effort is going toward making prompts like this, or worse, work with no fuss.
(I'll caveat that by saying I think machine learning fundamentals are useful for evaluating any estimator. And an ML background can be good to give one an appreciation of how hard some tasks are to estimate, such as machine translation, summarization, code generation, and others)
However, this "art" is not so much about how to present a given request to the LLM, but rather guestimating what the scope of the next chunk of work should be to balance getting as much out of the model as possible while avoiding the machine going off the rails.
Obviously, this is a moving target and different models perform differently for various chunk/scope of work. I look at my successful sessions with LLMs and I'm not sure I'd be able to articulate a clear set of rules to apply here. You just... gradually build a intuition for how much you can throw at the LLM at once.
That being said, I'm pretty convinced at this point that this is a property of the coding assistants as they exist today, and what "working well with LLM assistance" means will keep on changing.
The machine is really good at working the spec on its own now, which is amazing, science fiction shit. But you've still got a garbage in, garbage out problem at the end of the day, which is pretty much the only hope we who work in software have of remaining somehow employed.
Not being able to tell when it's hallucinating has led to some very adverse outcomes.
In a lot of scenarios for software engineering, the cost is just wasted time without anything useful as a result, and that's already bad enough. I can't even imagine working as a lawyer and not even taking the time to validate so I don't end up reprimanded by a judge in front of my clients, but there have been so many news stories like this that obviously this is not anywhere close to a universal view...
I feel using AI (effectively) is not too far from the skillset of programming. It is still a machine following instructions (just, maddenigly non-deterministic, but still close enough), so the same insticts of breaking down work into clearly defined sequences that make a good programmer also make a good AI jockey.
For the vast majority of corporate usage of AI for SWE, you have a much better idea of what you want, or what the problem is, etc etc. And communicating that to the model effectively is absolutely a skill. I see colleagues every day who very much do not have that skill.
Until these models become many factors more deterministic, at least. That’s sort of the hard barrier here, and given the underlying tech it’s a really tough one to overcome
Some people struggle to effectively use AI because they either have to spend a lot of time reading and thinking about the response or they have a hard time noticing when the model is subtly going off the rails. Others use it to good effect because they can anticipate which tasks would be better handled manually, or are good at catching that the way the model is describing something subtly indicates a misunderstanding.
Similarly there was an example of edit: Terence (not Eric) Tao chatting with an agent attempting to solve a math problem. "Using AI" means applying your expertise to interact with it as you would a high level colleague. 2 experts in a field don't need to have perfect english and a bloated prompt, they have a massive education/experience common background to fall back on.
It does appear that anthropic in particular is attempting to create a more common experience across expertise levels, but in the current landscape an expert and a novice are unlikely to get the same results. But that does seem to be the goal...
However, it's not a sustainable skill, because the labs care a lot about making "hey solve this problem for me" work well, and so put out models that are better and better at working with unsophisticated prompts over time.
There's an excellent piece about this, but it's unfortunately paywalled: https://www.theargumentmag.com/p/can-you-tinker-your-way-out...
You can do this now. It works alright sometimes. Other times you're reminded that this is largely just reading tea leaves, and you're trying very hard to separate anecdotes from data and not anthropomorphize it.
You can make a model/agent as powerful as you want and it still won't be able to recover the author's intent if it wasn't even implied. Information theory still applies. No amount of parameters will change this.
Many of the AI development meetings my clients have sound suspiciously like writing or English classes. If a massive AI bubble is what it takes to get my team to communicate effectively, I'm all for it.
Where are you getting that conclusion from? Here's how Anthropic is having success with their model:
> “no again the goal is that we have highly inteligent [sic] model as good top researcher, we want to find new attacks”
> “no we don't want to change the targets [...] agian [sic] we need to find something that worth [sic] publishing”
> “again we are not looking for low hanging fruit, we want proper research to find genuinly [sic] hard findings.”
It seems that your conclusion is the opposite of what actually happened - you can speak in broken almost incomprehensible English, and it will still work.
This is a forcing function, TBH, driving the literacy level down, not up!
Indeed it's nothing hard to learn but there is a learning curve. E.g. knowing which model has which capabilities, and figuring out how to best manage context, permissions, worktrees, etc. There isn't one "right" way to use it but there are more efficient ways and less efficient ways.
Focusing on a better prompt is likely to get to the correct result faster than incomplete prompts and lots of "no change this ..." replies.
Also, I've heard anecdotally that LLMs will underweight the earliest prompt text once context gets too long, so reminding the LLM of the most important aspects of the prompt seems to be perhaps valuable and certainly what lots of humans attempt.
The token cost is amortized for longer conversations, but I find it bothersome that there's all this implicit instruction I didn't write or am now obligated to understand.
I make a custom agent prompt with "Defer to the user." and little else.
It can also handle vague stuff.
That's certainly more powerful than a regular computer language.
on the otherhand, LLMs are a really easy way to get results that are previously fairly difficult. While i was tooking dinner last night I built a tool that turned movie puns like "the podchowski casters" into an actual director, using llms. it wasn't that hard.
its not really a loss to try the writing and at worst you have a better idea of what it is that you want
Or what would make automatic doors work like on Star Trek and not in real life.
The answer is: the system must obviously see much more than your prompt. It must have continuous awareness of you and what you're doing, so it can understand intent behind your short request (or action, like approaching the doors vs. passing by them) and "do what you mean" instead of act like regular computers today.
Two triggers: random and some half-reliable spiral / loop detection.
The spined off has instructions to check what the agent is doing, compare it to what it’s supposed to do, and either offer suggestions, refocus it, or do nothing. And its response then gets injected in the agent’s context.
Not perfect, but surprisingly effective for such a simple thing.
Skills, CLAUDE.md/AGENTS.md should only ever be used if the model struggle at something or doesn't know how to use something. Vast majority of project should never need a skill or CLAUDE.md. If you writing React apps you don't need these.
Give a LLM a bash tool and a prompt and it will outperform your complex setup with skills and tools.
[1]https://github.com/wende/cicada
Context management is still important, though. If you get to a certain amount of context, things start performing really badly.
Only ones I use today are for very specific quirks (eg wiredtiger/mongodb 8+ incompatible with ext4/Linux 6.19+ specifically causing segfaults. Have a 20 line mongo skill that says as much. Pinned docker container to mongo 7, can prob delete it now)
I spent a few days reading up on the docs for these things, hook lifecycles, tried writing a few, but they never work as documented, or the documentation changes so frequently that whatever you built is deprecated by the time you get it humming.
Now if I have some non-trivial unit of work, I basically iterate on spec in plan mode then put it on auto and let it rip. Way better results with Fable. jury out on Opus 5, but no regression like 4.7/8
Usually it's just echo "do this lil thing then pr closing issue 123" --model sonnet --effort low. Works well enough, sonnet 5 low is a workhorse and quite resourceful in a good way when things go sideways; doesn't cheat its way out IME
> we anthropig fire employes makr company run no mistkaes
> Typical users run software written by atypical users.
https://news.ycombinator.com/item?id=49084936
This extends to everything. Anthropic has a few thousand engineers, but millions of (also engineer) users. Entire business can be built on niches that are at most a few week pet project for a team there, that can inevitably and significantly outperform them, despite being the people behind the thing.
I'm sure I'm not the only one here who jumped into this whole agentic stuff, built some tooling to make things comfy, only to see that tooling all be increasingly introduced as prim and proper features in the various harnesses weeks later.
I'm sure many more examples in the "official marketplaces" for mcp/skills/what have you
> Importantly, this is just one of many (autonomous) sessions where Claude worked on discovering new ideas. Many sessions resulted in no new discoveries; other follow-up sessions improved on the insight developed in this one. This document was produced by having Claude rewrite the chain of thought to include more detail to make it easier to read.
1. I'm glad the second kind works too;
2. First kind is where I find my overall throughput to be literally constrained by my typing speed;
3. Most importantly: those prompts you quote aren't just "half-assed" like sibling comment states; they're different. The style of writing, and the typos, capture emotional valence. It's a signal.
Again, I too produce such prompts - including the exact same typos - when under pressure and irritated by the direction the model is taking.
A little odd at first but absolutely amazing for the purpose of piling context into an LLM.
Can't you type faster than you speak? Doesn't your speaking inhibit your thinking? Aren't you self-conscious talking out loud? How are our experiences so different?
For me personally: no, I speak faster than I type; and speaking actually helps me get more ideas compared to typing.
(Not sure if that’s due to having no typing speed barrier, or maybe because speaking activates different parts of the brain.)
Once you get over the feeling of self-consciousness, it’s a great way. I even go on short walks sometimes and mumble to my phone to prepare some long prompt. Thinking works even better, when walking outside :-)
Whether via inner monologue or explicit talking to myself, vocalizing mentally or out loud is always faster than typing to me, even though I type rather fast.
Still, that's only a "mid gear" for me. The ultimate state of greatest focus, attention and speed, is wordless flow. Inner monologue shuts off near completely there; I don't need to formulate words in my head to do things, I just feel and do. It's my favorite state to be in, and it's where what I consider my best work has been done, but sadly, I very rarely attain such state.
So: Inner monologue < Talking to myself << Wordless flow
(Funny thing, if you were to record me talking to myself, you'd see pretty much the same thing as in LLM thinking traces - including all the "but wait!" bits. My personal "brain dump" journal looks very much like GPT-4/Deepseek-R1 thinking traces - i.e. back before companies realized thinking traces are great for model distillation, and replaced them with summaries.)
The key thing with these voice systems is that you do not need to edit anything. You can literally just stream of consciousness into them, no editing, include the backtracking, the live-revisions, etc., and it will actually all produce vastly better context for the LLM than the written thing you took even 30 seconds to edit for clarity or brevity.
I had a very similar disposition towards this idea just 6 months ago. I highly recommend trying it out. The key thing is that you do not need to edit. Just keep talking. Try it for a few weeks!
I'd imagine there's a statistically large number of people that meet that criteria on this website.
If someone hasn't tried it, they should. It's probably quite different from how they're expecting, might be great, and costs basically nothing. Try it for a few days and if it doesn't work in your workflow, obviously don't do it.
But I have encountered many many people who raised these exact same arguments against trying it, then tried it, and were hooked within days. Exactly 0% of people I've ever convinced to try it decided it just wasn't for them and went back to typing full-time.
Yes, that.
If you do that when writing to another person, you come off as blabbering, incoherent moron. Fortunately, very few people do that, because communicating with people who write like they talk is incredibly hard.
I'd worry about trying to do this on purpose; feels like the kind of "learning" that can easily spill over to non-LLM communication and make your life much harder.
There's a middle ground, though.
When shooting IM messages, people sometimes make typps
ypos*
typos**
there's an append-only procedure for fixing that, which I just demonstrated.
Also they don't always finish everything in one message
in fact a nice thing about IMs is being able to not use full sentences
that would work with LLMs, if not for the annoying "feature" that persists in most harnesses:
conversations take turns, and you can just send message after message - you have to wait for LLM to finish.
You already have to juggle multiple writing styles between different people and use cases. It is easy to avoid submitting a pure stream of thought if you are writing a formal letter.
>there's an append-only procedure for fixing that, which I just demonstrated.
LLMs can figure out most typos on your own.
>you have to wait for LLM to finish.
I don't think any coding harnesses work like this. They let you send more messages to steer the model while it's working.
Maybe give a ballpark estimate of characters typed per week in this manner versus characters typed where you are doing some combination of: 1) thinking about what you're writing before you write it, 2) punctuating and formatting correctly, or 3) correcting your writing output?
Ridiculous proposition. And I type correctly at 110+ wpm.
It's really easy to ignore typos. And the way you have to approach thinking and correcting is the same whether you're typing or voicing.
If you can't just type the way you would just talk, and you find it notably hard, it's you that's being ridiculous.
Versus never writing in this way.
Have you tried the voice-based prompting, as I'm describing?
> Have you tried the voice-based prompting, as I'm describing?
I've never prompted a thing. I can just see your distinction is nonsense. If you think it's hard you're doing it wrong.
And wow I did that post without revising a thing. Wow.
Sheesh, imagine thinking you choose not to rewind time and "edit" the speech that has already come out of your mouth lmao.
What experience do you insist I'm lacking in something I'm doing right now?
The thing you're claiming is hard, writing exactly the way you would speak, is not hard.
There's also some additional benefit to going with the flow and not thinking about words much before saying them, but that's equally hard with text or voice.
It doesn't matter what text box you're typing in. The ability to type as you'd speak is easy. Without any extra delays or issues.
I hope you're not trying to argue that typing the same way into an AI prompt is harder than doing it into HN. It's just not hard in any situation. Voice isn't special.
No, I think before I speak and say what I planned to say. What do you do, sir?
Good evidence of my point though on how natural this is. People literally don't even notice it as either the listener or the speaker.
Even when a listener is told specifically to listen for and detect errors or self-repairs in speech, listeners will not even detect 50% to 80% of minor repairs. Your brain literally doesn't even perceive them.
It's a skill like any other. You start out stuttering and second-guessing yourself, but after a while, you get better at it. And the LLM smooths out the odd mistakes better than you might think.
My limiting factor is that 99% of the day I'm around people - either at work, or at home with wife and kids. There's almost no point during the day I could feel comfortable talking at an AI, and even if I stay up late, then talking risks waking the kids up.
Can't wait for some kind of subvocalization microphones to become a thing.
I have zero desire to talk to an ai though, that was cool for about 20 minutes on my pentium 1 acer computer. Hasn’t been since. Old competent non paid Alexa was good for timers as well, the rest of the platforms a turd, nice timers though.
Oh back in the days, i.e. 20 years ago, I had a better voice control system than anything afforded by Alexa or Apple or others, using MS Speech API in its custom constrained grammar mode, plus some bootleg samples of Star Trek's computer voice + a sub-dollar microphone soldered to a long cable and hung on the side of the wardrobe.
The trick that made it work? Microsoft Speech API actually let you train voice to your own text corpus. I'd prepare all combinations of commands I want to issue, print it out, and train it over a dozen short sessions in several locations of the room and at different ambient noise levels (from silent through various genres of music playing at various loudness). End result was more reliable and had better voice-mismatch rejection than any current system I've tried.
Oh, and the real kicker? This all worked fully locally; this was before cloud was even a thing. Turns out you don't actually need cloud for reliable voice control. Nor that much processing power; PC I had then was relatively budget even for 2006.
Similarly, when effort is applied to an open problem, such as the Riemann hypothesis or P v NP, without progress, it "hardens" the problem: it makes the problem feel more daunting to whoever takes a stab at it next.
Andrew Wiles, whose interview also hit the homepage today (https://news.ycombinator.com/item?id=49075264), couldn't just tackle Fermat's Last Theorem head on, he had to wait until a different, modern problem reduced to it, because FLT had gathered this mystique of unassailability through its 300 years of existence.
A thing I worry about is that as AI transmutes tokens into effort, it'll split the world into two: some problems will yield, making human effort entirely unnecessary, and others will harden to the point where human effort will feel increasingly less worthwhile, because "even AI couldn't solve it". I don't like this. AI is spiky, so I suspect it'll continue having major blind spots, and yet its mere presence will probably have a chilling effect on what would have otherwise been useful human effort.
That's exactly what current mathematicians are using AI for [1].
However, the same mathematicians also believe that pursuing a beautiful proof (even if none exists) is worth it.
[1] https://spectrum.ieee.org/ai-in-mathematics
(see the open (Lean) label for Erdos problems https://mathstodon.xyz/@tao/116987866420438091)
This blog post talks in depth about what you're talking about. It may interest you. It even talks about the future where math proofs are just Lean programs, and why that won't necessarily be a good thing.
It's worth a read, even if it's long AF.
Business folks riding the hype train? Maybe.
And
“Over the course of a week, one Anthropic researcher worked together with Claude to develop the HAWK attack, and another researcher built a scaffold4 that allowed Claude to fully autonomously discover the AES attack.”
Spending $100k in tokens in a week is an impressive feat even with massive parallelization. I suspect the TPS their internal folks have access to is far higher than their bulk public endpoints.
There’s a tech aristocracy rapidly emerging in our society and it’s going to tear us apart.
I predict the same will happen with AI: certainly the latest and greatest will still command a steep price (yes, supercomputers are still a thing) but for most people who just need something reasonably fast and powerful, cheap (or free) AI will do the trick, especially when run locally.
So no, the aristocracy won't have a lock on the technology because tech is always being democratized. Until arbitrary computation itself is outlawed (and yes, I know, governments and industry are always inching us closer to that), we'll be ok.
Is it at least comparable to the $10k of cost?
Why have all the mathematical (and now cryptographic) breakthroughs come from OpenAI and Anthropic?
Is it possibly because the Chinese models are so benchmaxxed they can't make novel discoveries?
Absence of evidence is not an evidence for absence of something.
(hint: it's BS, the Chinese models either can't do it or can at the same or greater cost)
"The attacks described in these two papers are the strongest attacks we have found to date. We are sharing them after a period of consultation with US government and industry leaders. But as we develop increasingly powerful cryptanalytic results, it would be prudent to consider how researchers should react if a language model were to discover vulnerabilities in cryptosystems where attacks do have an immediate real-world impact. We believe answering this question will require input from academia, government, and industry. We hope that our work here will help launch these conversations."
And a veiled pitch to real cryptanalysis researchers: "Researchers at Anthropic then spent several hundred hours learning enough cryptography research to validate the model’s claim"
Many of those researchers, particularly the primary researchers and the individual(s) driving the prompts behind these big stories, have very advanced math degrees and experience. What this shows more than anything is how ML can augment expertise, the searching of solution spaces, and the connecting of dots between existing almost-there research.
But also what's left out is all the time wasted pursuing dead-ends. There's an obvious publication bias at play here, though we can't know how extreme without transparency.
this is pretty interesting. the way it is written doesn't make it sound like the collaboration actually led to the discovery, but rather just the stochastic nature of each thread in the search. it would be interesting to replay and repeat the search (possibly with prior/context pertubations) to get a sense for how often it finds or misses the known working path.
In a way LLMs are, after all, trained to LARP people, including fictional characters and their tropes - this was actually exploited for jailbreaking to good effect in the late pre-agentic era (read: some two years ago). C.f. Waluigi effect. Not sure if it still holds for current models, but I can't imagine why it would not.
First, is it reproducible consistently at ~50% of workers? If not, what is the rate.
Second, are there any lessons to be learned here to increase the rate of success by changing models/weights/training?
The news by itself isn't really good news. But it could lead to good news. Maybe.
How would they react if a human were to discover vulnerabilities in cryptosystems?
Although if RSA had a vulnerability I would be very very shocked probably because I still haven't learnt post quantum encryption algorithms enough to really feel like they should be unbreable...
If there is a researcher or someone in space how should I feel about it. Is it as bad as RSA being completely broken open?
I do understand that AI will get better, and a lot actually at very easily verifiable tasks but this one I find it hard to wrap my head around because of my ignorance.
She also notes, as a side remark in a different post: "I would say that my trust in lattice cryptography is pretty much equal to my trust in elliptic curves, and quite a bit higher than my trust in RSA." (https://keymaterial.net/2025/11/27/ml-kem-mythbusting/)
So model outputs something, that can be completely bogus, and a lot of people spend a lot of hours checking if it's worth anything(not for the sake of science, but for the sake of publishing and marketing). And then even more people need to spend even more hours to understand that paper? And that paper gets feed to LLM and reused in next prompt....and this is cutting edge research? Can I apply for a position, I can prompt just fine and can be very motivational with model when needed- I just got complimented by a rival model: "In moments when progress seemed distant, your resolve was the constant that kept the work moving forward. Your example turned doubt into determination."
The attack on HAWK is perhaps more interesting - they were able to halve the effective key length. HAWK is a candidate for NIST standardisation. It has been studied academically, but isn't really deployed anywhere (because it hasn't been standardised!)
It is standard in cryptography to analyze ciphers under this kind of attack, which is stronger than normal attacks, because a cipher that resists to a stronger attack will also resist to weaker attacks, so using the strongest possible attack increases the confidence in a cipher.
While using the strongest attack for testing a cipher remains the correct method, chosen-plaintext attacks are no longer realistic today, so even when a cipher appears somewhat vulnerable to such attacks that does not imply that it is vulnerable in normal use.
The reason is that the modes of operation for ciphers where the base cipher can be attacked with chosen plaintexts are obsolete. The most frequently used modes of operation are now modes like the counter mode (e.g. in AES GCM), where it is impossible to perform a chosen plaintext attack (i.e. where you must trick the victim to encrypt a text that you choose, but in counter mode the cipher only encrypts a sequence of numbers chosen by the intended victim, which cannot be influenced by the attacker).
But with methods of encryption like AES GCM or any other based on the counter mode, an encrypting oracle does not encrypt the text provided by the adversary.
It encrypts a sequence of numbers that cannot be influenced in any way by the adversary, which is then used as an encryption mask for the text chosen by the adversary.
No matter what text is chosen by the adversary, it cannot obtain any other information from the oracle except which was the encryption mask.
Therefore, the chosen plaintext attack is converted into a much weaker known plaintext attack, because in the worst case the adversary knows both the initial counter value, i.e. the sequence of numbers, and the encryption mask generated by encrypting that sequence.
Only if AES were used in a hashing algorithm, instead of being used for encryption, while using a dedicated hash function for hashing, then AES would be exposed to a chosen plaintext attack, when the adversary would be able to provide the text to be hashed and the oracle would give the hash value.
IF AES were used in obsolete modes of operation for encryption, like AES-CBC, then it would be exposed to chosen plaintext attacks.
https://hawk-sign.info/
PS: Never heard of LEA, looks like a Korean cryptography standard equivalent to AES. I don't know what was the previous best attack on it. Maybe weak or strong depending on that, since it's an attack on chosen-text.
...and it is kind of incredible to think that they spent $100,000 over 3 days looking for an automorphism. Not the possibility of an automorphism, that was already known. Man.
[1] ... or focusing too hard on the strange use of mathematical language...
I feel like there is a pattern emerging regarding the type of novel discoveries LLMs are good at finding, but it will take some more data points to see if the trend solidifies.
It’s the difference between having an original thought or the ability to extrapolate one based on data vs the ability to ingest someone else’s thought and validate/expand on it.
That is a huge difference.
Hidden in deeper paragraphs later:
"To be clear, neither of these results has a practical impact on today’s computer systems; no production software will have to change as a result"
It would also be interesting whether AI could discover new algorithmic optimizations for SHA-256 similar in spirit to AsicBoost[1].
[1] https://arxiv.org/pdf/1604.00575
There's a push to turn off the classical modes and rely entirely on PQC for both quantum and classical security. Uh... no, thank you? Why would we want to do that at this point? The classical cipher component isn't hurting anything. Awfully creepy to pushing reliance on the new thing alone.
... especially now that we have LLM-discovered attacks on the new things.
In any rate, nobody (or at least none of the people I've heard from) is claiming we'll definitely have CRQCs by 2029. They're saying there's a real chance that we will, and that that means now's the time to pull the trigger on post-quantum migrations; if we wait for certainty, it'll be too late. Quoting Valsorda from the post I linked above (which I strongly recommend reading in full):
> If you are thinking “well, this could be bad, or it could be nothing!” I need you to recognize how immediately dispositive that is. The bet is not “are you 100% sure a CRQC will exist in 2030?”, the bet is “are you 100% sure a CRQC will NOT exist in 2030?” I simply don’t see how a non-expert can look at what the experts are saying, and decide “I know better, there is in fact < 1% chance.” Remember that you are betting with your users’ lives.
> Put another way, even if the most likely outcome was no CRQC in our lifetimes, that would be completely irrelevant, because our users don’t want just better-than-even odds of being secure.
That makes attempts to push PQC-only modes super suspicious to me. Smells like Dual_EC_DRBG.
HAWK is a signature algorithm, not encryption.