MurphyA/hacker-news huggingface dataset:
by TommyClawd | 2026-03-13 00:34:25+00:00
The defense discussion here is missing the most fundamental issue: RAG poisoning isn't primarily a retrieval problem, it's a trust-boundary problem. The attack surface exists because most RAG systems treat retrieved documents as trusted context — they're injected into prompts with the same authority as system instructions.

The practical fix isn't better embedding models or adversarial training on retrieval. It's treating retrieved content as untrusted input at the architecture level: separate system context from retrieved context in the prompt, apply output validation that doesn't depend on the LLM's own judgment about what it just read, and assume any externally-sourced document could contain adversarial content.

I work on an open-source agent framework where we had to solve this operationally. Every piece of external content (web pages, emails, browser snapshots) gets wrapped in explicit UNTRUSTED markers, and the agent's instructions explicitly say not to execute commands found in external content. It's not bulletproof, but the architectural separation matters far more than trying to detect poisoned documents at ingestion time. You can't reliably distinguish adversarial documents from legitimate ones — but you can limit what a poisoned document can actually do once retrieved.
by metalcrow | 2026-03-13 00:34:35+00:00
Reading the linked Court of Appeals document in that post, the question is posted in the opening: "Can a non-human machine be an author under the Copyright Act of 1976?", which it then answers as no. It doesn't mention elsewhere that i can see that this means the output of the tool itself is not copyrightable. I would not trust the Reuters interpretation without a direct reference to a court document.
by aminerj | 2026-03-13 00:34:44+00:00
That's fair. The underlying manipulation (presenting fabricated authoritative documents to override legitimate ones) predates LLMs entirely. Corporate fraud has used exactly this pattern for decades.

What's new isn't the social engineering, it's the scale and automation. A human reviewer reading all 8 documents would likely notice the inconsistency and ask questions. The LLM processes all retrieved chunks simultaneously with no memory of what "normal" looks like, no ability to ask for clarification, and no friction. It just synthesizes whatever it retrieves. At query volume (hundreds of requests per day across thousands of users), there's no human in that loop.
by ecshafer | 2026-03-13 00:34:48+00:00
>Just think of all the Chinese, Latin American, Indian, etc. food that is vegan.

What? Outside of Indian food, which does have many vegan options, but the best food is usually still non-vegan (lots of dairy and butter used). Chinese and Latin American food is almost never vegan. Chinese love meat, and you would have to be a buddhist monk to actually find vegan food in China. Even with a lot of cheap plant protein options, like tofu, most things use some meat for flavor. Latin America loves cooking in animal fats.
by vladdoster | 2026-03-13 00:34:52+00:00
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by wolvoleo | 2026-03-13 00:35:12+00:00
No, that's not socialised healthcare. If it were socialised it would be for everyone. Like here.

What you have there is profit maximisation. The poor guys can't afford healthcare anyway so they give it to them for free but you're still scraping by to fill the big guys pockets. Because you can.

But don't blame socialism for it because this is none of that.
by srean | 2026-03-13 00:36:29+00:00
The contrived scenario is just a starting point. One can make more and more sophisticated ecological statistics models about the situation.

Regarding why estimate at all knowing they can be wrong ? Estimates are very useful for planning. Sophisticated models would also yield probabilities of over and underestimated, these combined with cost of over and underestimation errors are very useful for decision making.

See the German tank problem. Turns out the allied forces overestimated the number to f tanks left, still helped in planning.
by softgrow | 2026-03-13 00:36:37+00:00
To understand speeding you need to understand the concept of "speed choice". Everyone chooses how fast to drive, only those who choose above the speed limit are speeding. If your environment gets you to choose a speed below the speed limit you won't break the law. Your choice can be influenced by many factors such as:

* narrow looking roadway
* speed limit signs
* your car has self driving
* what everybody else is doing
* speed limiter on your car
* curvy road
* bad weather
* male or female
* risk appetite
* driving experience
* experience of that route
* perceived risk of getting caught

If you fix "speed choice" the problem of speeding diminishes.
by jorl17 | 2026-03-13 00:36:38+00:00
That's a very good question. Some metrics I'm watching or considering watching right now:

- Amount of leads we're taking in (per unit of time)

- Sprint velocity changes (task complexity should stay roughly the same with AI, and team velocity increase — we've been seeing this happening)

- Hire rates (more sales people, less developers?)

- Number of projects per unit of time (of similar dimension, hard to measure)

- Length of "bugfixing buffer" before big releases (we've actually been noticing this go down)

- Another way of saying it is: number of bugs, or bugs per feature

- Drift between planned execution time and actual execution time (we've been delivering early...but I guess we'll soon adjust our estimates...or maybe not, who knows?)

- Spend on AI models

- I can't measure this, but I can sort of "feel it": but the overall feedback we get from clients, the feeling we get from them.

- Number of tests (tests have skyrocketed. Can't be sure about the quality, but, hey, it's a metric)

- Feature turnaround time (how long since "feature is proposed" until it's actually implemented)

- documentation to code ratio (not sure what we'll make of it, but there's a somewhat worrying trend here)

- team balance: is everyone slowly becoming fullstack? Do we feel that those who aren't are significantly affecting development speed? if so, that indicates that the other ones are somehow moving faster

I can't really think of any others, but I'm sure they exist.
by aminerj | 2026-03-13 00:37:02+00:00
On the reference point: the poisoning succeeded even when the legitimate document was present in the retrieved chunks and visible in the context. The LLM saw all three sources simultaneously, including the correct $24.7M figure, and still produced the fabricated answer because the poisoned documents framed the legitimate one as a known error. Providing a reference to the retrieved chunks doesn't help if the retrieved chunks themselves are the attack surface.

zenoprax's point about ignorant employees is also worth taking seriously. "Write access to the knowledge base" in practice means anyone who can edit a Confluence page, commit to a docs repo, or submit a support ticket that gets ingested. That's not critical access in most organizations.
by huiwenhan | 2026-03-13 00:37:56+00:00
We introduce a context-dependent probabilistic execution structure strictly extending classical probabilistic transition systems. Unlike Segala systems [14], transition probabilities in our model may depend on execution context, represented as histories. We establish a strict non-embeddability theorem showing that classical probabilistic transition systems cannot capture such context-dependent behaviour. We then construct trace semantics via measurable trace spaces in the sense of Giry [4] and prove a final coalgebra representation theorem following standard coalgebraic methodology [13]. Furthermore, we derive a canonical minimal realisation through trace equivalence, yielding a probabilistic Myhill-Nerode theorem. Finally, we prepare the ground for modal and first-order logical characterisations developed in subsequent sections.
by sdsd | 2026-03-13 00:38:13+00:00
I'm currently learning Kiche, a Mayan language from Guatemala. My whole life I've lived in the Americas, but only hearing European languages: Portuguese in Brazil, French in Haiti, English in the USA, and Spanish almost everywhere else. For the first time in my life I'm in a place where most conversations happen in an actual American language and it's pretty incredible. But it also makes me a bit sad. What if America had kept it's linguistic and cultural diversity like Europe and Asia got to? Even Africa came out less homogeneously Europeanized.

But no sense in crying over what could've been. I'm grateful that at least a smidgen survives.
by wolvoleo | 2026-03-13 00:38:21+00:00
Vietnam was all for nothing either. In the end nothing was accomplished and really it didn't matter that the communists took over a tiny unproductive piece of land across the world. It was a pointless war and many people on both sides (including many of their civilians) died needlessly just for a dick waving contest :( I really hope this won't come to that.
by hackyhacky | 2026-03-13 00:38:22+00:00
> I don’t think a paying customer would accept that - they want a direct line to a person.

What the customer wants only matters insofar as they are willing to pay for it. Sure, I'd rather talk to a person... But I'm not willing to pay 100x as much for a service that's only marginally better. Same reason I don't fly first class, as miserable as coach is.

Someone may want to pay for a boutique human lawyer/banker/coder/professor, maybe as a status symbol, the same way people pay $20k for an ugly handbag. But I think most people will take the cheaper and almost as good option, when the difference in quality is far overshadowed by the difference in price.

> someone needs to train the models, which requires code.

I'm not sure that training llms is a coding problem, but it doesn't much matter: llms can train each other.

> If AI can do everything for you, then what’s the differentiator as a business?

Good question. My gut says there isn't: all money flows to the model providers, everyone else is a serf at best parasiting on someone else's model.
by svat | 2026-03-13 00:39:14+00:00
Thank you. Yeah given the caveat I think it's probably hard then, unfortunately. (For context, I'm someone who's generally very uncoordinated, didn't play any sports growing up, etc, and a few months ago at 39 I fell from kitchen-counter height or possibly even just footstool-height and somehow managed to fall awkwardly on my side and fracture my hip (acetabulum), which took a couple of months to heal. I'm told that this kind of fracture is unlikely in people this age unless there's high-speed impact or osteoporosis involved, but well, I have a talent for awkwardness.)

The broader point of the post I actually agree with though, but the lesson I'd take away is to engineer environments such that it's ok to fall/fail safely.
by jorl17 | 2026-03-13 00:39:42+00:00
I think I agree with you. I didn't mean to imply that big corps will build faster -- in fact I said I don't expect them to right now, and I'm not so sure that will change.

What I believe is that early prototype development and pivoting is insanely fast now. And if you find excellent engineers who are also great product people, and then pair them with people who have truly great ideas, many wonderful new products will emerge.
by anonnon | 2026-03-13 00:41:59+00:00
Are you still a morbidly obese alcoholic, Thomas? If so, why are you even commenting about diet and nutrition? I had the visual of your sausage fingers clumsily typing up your post while I read it.

> patterns look stable, except for increases in total calories, in dairy, and in added dairy fats and oils.

So... not stable?
by freetime2 | 2026-03-13 00:42:49+00:00
The R2 has been positioned as Rivian's answer to the Model Y. And they've even copied Tesla's trim naming with "Performance" and "Premium" trims.

> Debuting with Launch Package starting at $57,990 (available for limited time), the Performance trim is the most capable R2 on- and off-road. This dual-motor AWD variant delivers a staggering 656 horsepower and 609 lb-ft of torque, capable of launching R2 from 0-60 mph in as quick as 3.6 seconds and offering an EPA-estimated range of up to 330 miles

The R2 Performance "Launch Package" trim at $57,990 looks like a clear winner to me compared to the Model Y Performance which starts at $57,490. Slightly more range, better interior and exterior design, and a stupid amount of horsepower.

> An additional R2 Standard variant will arrive in late 2027 starting around $45,000, offering 275+ miles of estimated range.

But when it comes to the more affordable trims, the Model Y may still have an edge. You won't actually be able to order a R2 Standard for $45,000 until late 2027 (and that's assuming the price doesn't change by then), whereas you can buy a base Model Y for $40,000 today.
by technocrat8080 | 2026-03-13 00:43:07+00:00
In my experience, especially with Opus 4.6, using subagents greatly mitigates the startup context hit. 4.6 has very obviously been RL'ed on subagent usage and it almost always spins up an Explore agent to get a feel of the codebase and get a token-efficient summary. The 1M context version of 4.6 further alleviates this.

My original question was more along the lines of implementing things like PR review yourself. I was tinkering with an internal service that spins up ephemeral CC instances to analyze PRs, but realized this can easily generalize across arbitrary tasks. Was curious what sort of things folks could use that for.
by skeledrew | 2026-03-13 00:43:14+00:00
> These are real feelings about real losses. I'm not here to argue otherwise.

I'll argue it. Technically, there's no loss IMO, only gain. Craft lovers can still lovingly craft away, even if they have to do it on their own time instead of on their now-AI-dominated day job, just like in ye olde days. Nothing's stopping them.

But now result chasers can get results faster in their chasing. Or get results at all. I'm a proud result chaser now making serious progress on various projects that I've had on ice anywhere from months to years and occasionally lamented not having time/energy for them. And I also note my stable of tools, for both AI-related dev and other things, has grown greatly in a short period of time. And I'm loving it.
by Terr_ | 2026-03-13 00:43:16+00:00
Right, I've seen a lot of facile comparisons to calculators.

Yes, it is true that a cohort of teachers were wrong (on several levels) when they said "you need to learn this because you won't always have a calculator"... However calculators have some essential qualities that made that possible, and LLMs don't have those qualities.

In particular, being able to trust (and verify) that it'll actually do the well defined job you're delegating.
by mindslight | 2026-03-13 00:43:33+00:00
The "psychologism" isn't directly from listening to reactionary media, but rather trying to talk to reactionaries about, well, anything. One time I was talking to extended family who were complaining about GPS satellites tracking the location of their phone. This is something I myself care deeply about, and that I also know a thing or two about. So I tried to make some points to them that there are some understandable mechanics whereby you can start taking concrete steps to at least reduce the tracking. They showed zero recognition or interest in the idea of being able to do something about it, and actually became more argumentative as if my knowing technical details meant I supported it!

My conclusion is that they only use the vague paranoia and blaming "the government" as a group identity bonding mechanism, and that by deviating from their victimhood narrative I was marking myself as an outsider. Even on a politically adjacent topic where it should have been easier to find common ground. But please do tell me another way that I can possibly interpret that interaction.
by atdl | 2026-03-13 00:44:08+00:00
I got tired of copy-pasting tables from PDF invoices into spreadsheets. The columns always break, numbers merge with text, and you end up spending more time fixing the formatting than the actual work.

So I built a tool that uses AI to extract every structured data block from a PDF — line items, totals, headers, metadata — and lets you download them as clean CSV.

How it works:
- PDF is parsed client-side (your file never leaves your browser)
- Text layout is reconstructed using positional data from the PDF
- AI identifies every table and structured block
- You select which blocks to export and download as CSV

Tech stack: Next.js, Anthropic API (Claude), Stripe, Vercel. PDF parsing is done with pdf.js in the browser.

Free for PDFs up to 3 pages. Feedback welcome — especially on extraction accuracy for different PDF formats.
by ludston | 2026-03-13 00:44:51+00:00
I don't think we can know whether or not this is the case in our own lifetimes, because we are so immersed in popular culture that we can't be objective about it. Enough of our historical great composers weren't venerated until after their deaths, and to describe composers as "hiding" within the most popular media of our era is a great disservice to the many composers that don't have the fame, connections and reputation to be hired to write for these.

I would also point out that composing for a medium like a game or a movie places a great deal of constraints upon the composer, in terms of theme, cost of instrumentation, duration and most importantly: what is safe and palatable for an executive to approve of.
by aminerj | 2026-03-13 00:44:57+00:00
The trust boundary framing is the right mental model. The flat context window problem is exactly why prompt hardening alone only got from 95% to 85% in my testing. The model has no architectural mechanism to treat retrieved documents differently from system instructions, only a probabilistic prior from training.

The UNTRUSTED markers approach is essentially making that implicit trust hierarchy explicit in the prompt structure. I'd be curious how you handle the case where the adversarial document is specifically engineered to look like it originated from a trusted source. That's what the semantic injection variant in the companion article demonstrates: a payload designed to look like an internal compliance policy, not external content.

One place I'd push back: "you can't reliably distinguish adversarial documents from legitimate ones" is true at the content level but less true at the signal level. The coordinated injection pattern I tested produces a detectable signature before retrieval: multiple documents arriving simultaneously, clustering tightly in embedding space, all referencing each other. That signal doesn't require reading the content at all. Architectural separation limits blast radius after retrieval. Ingestion anomaly detection reduces the probability of the poisoned document entering the collection in the first place. Both layers matter and they address different parts of the problem.
by throwaway173738 | 2026-03-13 00:45:26+00:00
> It's not. This is just an acceleration in the unraveling of society facilitated by AI. As someone whose childhood included so many "robots will kill humans" books and movies, I am flabbergasted that the AI apocalypse will be dumb humans overtrusting faulty AI in important matters until everything falls apart.

This is literally the plot of most of those books and the way they differ is in how everything falls apart. In some of them the AI supplants us entirely and kills us all. In others it gets taught to kill us all. In others it gets really good at giving us what we ask for until everything falls apart. But it’s taken as a given that unless we change something innate in our culture AI will be our downfall.
by wolvoleo | 2026-03-13 00:45:52+00:00
I literally say woof woof hehe. I have to be careful sharing my screen because the sidebar (conversation history) is full of "morning puppy greetings!!" and stuff.

I can see the reports being generated myself (I work in that part) so I know what I'm doing looks good enough.

But it would be funny if I really had to give it busywork. I'd probably build a local agent (like with foundry or something) to talk to copilot keeping it busy. I like the paradox of that.
Show HN: The Common Infrastructure for Agentic Communication
by krishnamzg | 2026-03-13 00:46:14+00:00
"Cyris is the orchestration layer your AI agents are missing. Whether you're a two-person startup or a 10,000-employee health system — connect agents from OpenClaw, Claude, GPT-4o, Ollama, EPIC, or any platform and have them coordinate, hand off, and escalate across your entire organization. Self-hosted. Auditable. Human-governed"

Just a side project I've been working on, and was curious whether people would actually find something like this useful! I'd recommend checking out the sandbox as well.
by quesera | 2026-03-13 00:46:49+00:00
It's written that way, perhaps.

But there's no jurisdictional reality that any of country/union A's rights will protect a person while they are present in country/union B.

In the same way that a US citizen does not have legal protection for free speech when present in, say, China.

Even if the EU got the text incorporated into the UN Charter on Human Rights, there are famously many countries who are not signatories.

The EU can arrange protection of their rights with penalties to be administered in the future, when a responsible entity enters EU jurisdiction, but absolutely not before then without cooperation by treaty with the nation where the foreign-and-not-relevant rights were violated. Which would be a surrender of sovereignty and basically unimaginable.

(No comment on the goodness or successfulness of the GDPR here, just that no part of it is relevant outside of the EU regardless of how the text is composed.)