MurphyA/hacker-news huggingface dataset:
by chasd00 | 2025-12-29 21:54:13+00:00
It’s shocking the collective shrug I get from our security people at work. I attend pretty serious meetings about genAI implementations and when I ask about points of view around security given things as crazy as “adversarial poetry” is a real thing I just get shrugs. I get the feeling they don’t want to be the ones to say “no, don’t bring genai to our clients” but also won’t dare say “yes, our client’s data is safe with integrated genai”.
by PaulDavisThe1st | 2025-12-29 21:54:33+00:00
> Google searches don't produce good results these days. The enshittification has become too extreme. Google openly admits as much (and further intensifies the enshittification) by placing a huge AI summary above those results.

I haven't asked Google a question it has failed to provide a more than adequate answer to in ... months? years?

And on all my devices, I run google search with &udm=14, so I am not talking about AI summaries. I also have search personalization disabled.

I see a lot of people complaining about this on HN. It simply doesn't match my experience at all, in any way.
by nneonneo | 2025-12-29 21:54:57+00:00
Oh yeah the classic Clang behaviour of “just end the function at UB”. If you look at the assembly, the main function just ends after the call to endl (right before where the if test should go); the program will run off the end of main and execute whatever nonsense is after it in memory as instructions. In this case I guess it calls main again (??) and then runs off into the woods and crashes.

I’ve never understood this behaviour from clang. At least stick a ud2 at the end so the program aborts instead of just executing random instructions?

The x and y values are funny too, because clang doesn’t even bother loading anything into esi for operator<<(unsigned int), so you get whatever the previous call left behind in that register. This means there’s no x or y variable at all, even though they’re nominally being “printed out”.
by 6r17 | 2025-12-29 21:55:07+00:00
I often use it as a self-reflection for myself ; i'm working solo so my exploration is really different - (thankfully I work on tools I use myself). Anger / Frustration can definitely be measured from text only - I don't necessarily need to "drive" a discussion to try to get an explicit confirmation of what is going wrong - that signal is a strong enough information to indicate for something important (or that the user is just mad). Being able to switch from mad -> chill is definitely the point where we can digest why something is happening - and depending on the context it can definitely underline important focus points to improve.
by StopDisinfo910 | 2025-12-29 21:55:27+00:00
Plenty of people here will probably focus on the Deutsche Bahn debacle but the problem is larger.

You have to understand that Germany is currently led by a mix of trauma and economy as religion rather than reason to get a sense of what is happening.

To simplify, Germany is somehow convinced that any inflation and rules deviation is an unstoppable slippery slope towards a return to the 20s and paving the way to some kind of nazi apocalypse. They therefore follow a rigid economic system, ordoliberalism, which to put it nicely, doesn't have a strong academic following.

As a result, they have implemented a mercantilist economy using internal devaluation while shielded by the euro and stubbornly refuse to recycle their surplus in the union or even in the country. You get this absurd situation where they have enshrined a balanced budget in their constitution while their median household wealth is bellow Italy, their infrastructures is crumbling, a significant portion of their savings finance foreign debts - especially bad foreign debts judging by what they lost with the Lehman bankruptcy - their industry fails to invest and the country is basically non ironically saving itself to irrelevance.

Honestly, if it wasn't for the beggar thy neighbors policies and them mostly refusing transfers despite benefiting hugely from the currency at the expense of the poorer members, it would be amongst the funniest quirk of history.
by elorant | 2025-12-29 21:55:30+00:00
Contextual ads is the answer. You sell shoes, go and advertise on fashion related sites. I don't want to see a shoe ad while I'm browsing a gaming site just because I did some relative search a week ago. It's so fucking annoying and I never understood why Google never bothered to try some alternative too. I don't mean completely replace behavioral targeting but at the very least try some contextual one too.
by guywithahat | 2025-12-29 21:55:39+00:00
As a personal anecdote in agreement with the article, I've seen design consultants come in with no industry experience and ruin projects. Friends of the VP with no satellite experience saying we need to introduce random exceptions everywhere who don't understand threading or type systems outside of Java. It's frustrating to see potential bugs introduced at the design level with no regard for how the end product operates
by amber_raza | 2025-12-29 21:56:03+00:00
Great questions.

1. Prioritization: I instruct the model to prioritize evidence in this hierarchy: Meta-Analyses & Systematic Reviews > RCTs > Observational Studies > Case Reports. It explicitly deprioritizes non-human studies unless specified.

2. Why not OpenEvidence? OE is excellent! But we made two architectural choices to solve different problems:

'Long Tail' Coverage: OE relies on a pre-indexed vector store, which often creates a blind spot for niche/rare diseases where papers aren't in the 'Top 1% of Journals.' Because Evidex queries live APIs, we catch the obscure case reports that static indexes often prune out.

Workflow: OE is a 'Consultant' (Q&A). Evidex is a 'Resident' (Grunt work). The 'Case Mode' is built to take messy patient histories and draft the actual documentation (SOAP Notes/Appeals) you have to write after finding the answer.
by skydhash | 2025-12-29 21:56:36+00:00
> So if you're banging out the same type of thing over and over again, yes, they will make that work light and then scarce.

The same thing over and over again should be a SaaS, some internal tool, or a plugin. Computers are good at doing the same thing over and over again and that's what we've been using them for

> But if you need to create something niche, something one-off, something new, they'll slip off the bleeding edge into the comfortable valley of the familiar at every step.

Even if the high level description of a task may be similar to another, there's always something different in the implementation. A sports car and a sedan have roughly the same components, but they're not engineered the same.

> We used to worry about pointers and memory allocation.

Some still do. It's not in every case you will have a system that handle allocations and a garbage collector. And even in those, you will see memory leaks.

> Now we will worry less and less about how the code is written and more about the result it built.

Wasn't that Dreamweaver?
by amber_raza | 2025-12-29 21:57:07+00:00
Great question. I haven't seen banner ads on OpenEvidence yet, but the 'hidden tax' of free tools is often Publisher Bias.

Users have noted that some current tools heavily overweight citations from 'Partner Journals' (like NEJM/JAMA) because they index the full text, effectively burying better papers from non-partner journals in the vector retrieval.

My goal is strictly Neutral Retrieval. By hitting the PubMed/OpenAlex APIs live, Evidex treats a niche pediatric journal with the same relevance weight as a major publisher, ensuring the 'Long Tail' of evidence isn't drowned out by business partnerships.
by hinkley | 2025-12-29 21:57:08+00:00
Yeah those are pretty spendy. I know one comes with extra guaranteed bandwidth which is kind of handy if you’re sharing a small number of cache nodes among a lot of servers. But we were doing okay running r6 for cache, though my coworker who knew the ritual for migrating them did eventually get a little boost out of switching us to r7’s. The latency wasn’t great and I don’t think faster network cards would have helped that. There was already plenty of incentive for us to do per-request promise caching to avoid pulling the same keys multiple times in a request but that was necessary because the business model forced the architecture to tolerate nondeterminism. The cost per request was what eventually killed them (the economy dipped and customers ran to cheaper vendors), but I’ve never seen a company survive being stupid for as long as this place did.

Well, except IBM. Maybe Yahoo.
by elbci | 2025-12-29 21:57:14+00:00
I love your vibe but unfortunately I don't share your optimism.
The interregnum between monopolies gets exponentially shorter as the money printing gets exponentially faster. What was maybe 20 years of "OK-ish" after say WW2 got down to a few years in the 90s when internet was worth browsing, from Google to AI could be just months..
by keyle | 2025-12-29 21:57:27+00:00
Take food for example. We don't eat food made by computers even though they're capable of making it from start to finish.

Sure we eat carrots probably assisted by machines, but we are not eating dishes like protein bars all day every day.

Our food is still better enjoyed when made by a chef.

Software engineering will be the same. No one will want to use software made by a machine all day every day. There are differences in the execution and implementation.

No one will want to read books entirely dreamed up by AI. Subtle parts of the books make us feel something only a human could have put right there right then.

No one will want to see movies entirely made by AI.

The list goes on.
Ask HN: How do we benchmark data extraction, analysis and subsequent enrichment
by srijansriv | 2025-12-29 21:57:29+00:00
I have been working on this system that essentially has to extract data from a bunch or structured and unstructured sources, analyse relevance and correctness of the data (im sorry this part is vague) and using the data obtained to fill forms and create write-ups. i do not want to mindlessly use any llm. i want to improve with proof for each of those (broadly, 3) steps. how do we do that? if there are any benchmarks for all these, what systems are leading these benchmarks?
by muvlon | 2025-12-29 21:57:32+00:00
> It’s the only kind of program that can be actually reasoned about.

No. That is one restriction that allows you to theoretically escape the halting problem, but not the only one. Total functional programming languages for example do it by restricting recursion to a weaker form.

Also, more generally, we can reason about plenty of programs written in entirely Turing complete languages/styles. People keep mistaking the halting problem as saying that we can never successfully do termination analysis on any program. We can, on many practical programs, including ones that do dynamic allocations.

Conversely, there are programs that use only a statically bounded amount of memory for which this analysis is entirely out of reach. For example, you can write one that checks the Collatz conjecture for the first 2^1000 integers that only needs about a page of memory.
by liampulles | 2025-12-29 21:58:04+00:00
Sure - if you don't want to do demosaicing on the camera, that's fine. It doesn't mean there is not an algorithm there as an option.

If you care about trying to get an image that is as accurate as possible to the scene, then it is well within your interest to use a Convolutional Neural Network based algorithm, since these are amongst the highest performing in terms of measured PSNR (which is what nearly all demosaicing algorithms in academia are measured on). You are maybe thinking of generative AI?
by hansmayer | 2025-12-29 21:58:26+00:00
> Was very enthusiastic in tabnine days. Used copilot since closed beta. Use it for 10 hours a day.

I sort of have a similar story with it. Was also one of the earliest GH Copilot users...but now I find its just utter crap. The one thing that worries me though is, while most of the tech folks have grown disillusioned, for each engineer who now rejects LLMs, there seems to be 20 "common" persons who just absolutely love it, for their ephemeral use cases, like planning their next trip, or asking if it will rain tomorrow and similar. And this sort of usage I think quietly underpins the drive. Its not just the CEOs, it is also the masses that absolutely love to use it, unfortunately.
by kayo_20211030 | 2025-12-29 21:58:30+00:00
Could we all just agree to stop using the term "abstraction". It's meaningless and confusing. It's cover for a multitude of sins, because it really could mean anything at all. Don't lay all the blame on the c-suite; they are what they are, and have their own view. Don't moan about the latest egregious excess of some llm. If it works for you, use it; if it doesn't, don't. But, stop whinging.
by o_nate | 2025-12-29 21:58:47+00:00
I guess I'm lucky not to have worked at a place with a role for software architects who don't actually write code. I honestly don't know how that would work. However, I think I can appreciate the author's point. Any sufficiently complex piece of existing software is kind of like a chess game in progress. There is a place for general principles of chess strategy, but once the game is going, general strategy is much less relevant than specific insights into the current state of play, and a player would probably not appreciate advice from someone who has read a lot of chess books but hasn't looked at the current state of the board.
by heliumtera | 2025-12-29 21:59:01+00:00
Stop freaking out. Seriously. You're afraid of something completely ridiculous.

It is certainly more eloquent than you regarding software architecture (which was a scam all along, but conversation for another time).
It will find SOME bugs better than you, that's a given.

Review code better than you? Seriously? What you're using and what you consider code review?
Assume I could identify one change broke production and you reviewed the latest commit. I am pinging you and you better answer. Ok, Claude broke production, now what?
Can you begin to understand the difference between you and the generative technology?
When you hop on he call, you will explain to me with a great deal of details what you know about the system you built, and explain decision making and changes over time. You'll tell about what worked and what didn't. You will tell a out the risks. About where the code runs, it's dependencies, users, usage patterns, load, CPU usage and memory footprint, you could probably tell what's happening without looking at logs but at metrics.
With Claude I get: you're absolutely right! You asked about what it WAS, but I told you about what it WASN'T! MY BAD.

Knowledge requires a soul to experience and this is why you're paid.
by ellis0n | 2025-12-29 21:59:26+00:00
The main risk is that an LLM will rewrite itself and programmers will no longer be needed. I worked a bit with tinygrad and it looks quite amusing I managed to run it right away and make fixes in one of the tasks, but I decided not to commit because I was afraid of rejection. For example, the tasks are strange: $500 for two months, optimizing H.265, something that only a small group of people in the world can do.

The SV is a unique place where you can meet Geo and get $5M, maintain a bunch of hardware, build a framework in 20,000 LOC and everything works well.
by ggreer | 2025-12-29 21:59:42+00:00
Which cell technology to use depends on the application. Tesla actually uses BYD blade batteries in some of their vehicles sold in Europe. The main issue with prismatic cells is that to be safe, they must be made with LFP chemistry, which hurts energy density. LFP is also worse at charging/discharging when cold, though battery management software mostly solves that.

Cylindrical cells make sense for higher performance NMC and NCA chemistries, as they can be cooled more easily (coolant lines can run in the voids between cylindrical cells), and any single cell failure is less likely to cascade to other cells. Batteries with cylindrical cells were easier to repair, but nowadays cells are welded together instead of bolted, so that's no longer an advantage.
by nostrademons | 2025-12-29 22:00:01+00:00
In my anecdotal experience, it's moved to private, trust-based channels: iMessage, WhatsApp, email, face-to-face interactions. Our 30-year bender of putting our lives online and blurring the public and the private has finally ended: people don't want to be online, don't trust social media, don't really trust any media, and are living simple local lives with a small circle of friends that they get together with regularly in person.

But then, my anecdotal experience may not be representative of most of the world. Most of my friends have money, houses, kids, friends - all of which are, by the numbers, rarities these days.

It's an interesting thought experiment to explore what it means if that actually is the new normal, and people are not consuming media or much of anything, or even if the people who are still addicted to social media are now tapped out and don't have any more disposable income left to spend. Probably economic depression. If everybody bought only what they needed and ignored all the advertisements, our present level of economic activity would plunge.
by rchaud | 2025-12-29 22:00:15+00:00
As a podcast listener, I can think of a few reasons:

- 10-20 companies focused on Direct to Customer (DTC) products seem to make up the majority of advertisers for a lot of podcasts: VPNs, mattresses, personal grooming products, discount code providers, online courses, etc. If their ad budgets are reduced in the current economic climate, podcast earnings will fall. It's also possible that they've collected enough data to know that ROI in this medium isn't great, and growth of podcast creation is slowing.

- A lot of top podcasts have been being acquired by Spotify and Apple as exclusives over the past few years, where a lot of this ad spending was concentrated . This reduces the total pool of advertising money available.

- Programmatic advertising (where ads are spliced into the downloaded file, varying by geographical location) has lowered the cost of advertising, so the money paid out to podcast owners is less.
by amber_raza | 2025-12-29 22:00:25+00:00
That sounds like a fascinating project.

To answer your question: In the biomedical world, the 'Time-Series' equivalent is Patient Telemetry (Continuous Glucose Monitors, ICU Vitals, Wearables).

The Question Researchers Ask: 'Can we predict sepsis/stroke 4 hours before it happens based on the velocity of change in Heart Rate + BP?'

Right now, Evidex is focused on the Unstructured Text (Literature/Guidelines) rather than the structured time-series data, but the 'Holy Grail' of medical AI is eventually combining them: Using the Literature to interpret the Live Vitals in real-time.
by wahern | 2025-12-29 22:00:31+00:00
I've always thought static allocation was why got overcommit[1] in Linux and the infamous OOM killer. In the 1990s big boy commercial databases assumed specialized admins, and one of their tasks was to figure out the value for the memory allocation setting in the DB configuration, which the DB would immediately allocate on startup. As a magic value, the easiest path forward was just to specify most of your RAM. DBs used to run on dedicated machines, anyhow. But then Linux came along and democratized running servers, and people wanted to run big boy databases alongside other services like Apache. Without overcommit these databases wouldn't run as typically configured--"best practice" allocation advice (i.e. use all of it) used up too much memory, leaving nothing for the rest of the services. Because on a typical system most of the memory preallocated to the DB was never used anyhow (because the amount wasn't actually carefully chosen), and Linus wanted things to Just Work, whether experienced admins or not, the easy fix was just to overcommit. (NB: I was just a newbie back then, so any older grey beards, please feel free to correct me. But I distinctly remember supporting commercial databases as being one of the justifications for overcommit.)

[1] Note that AFAIU the BSDs had overcommit, too, but just for fork + CoW. (These days FreeBSD at least has overcommit more similar to Linux.) Solaris actually does strict accounting even for fork, and I assume that was true back in the 90s. Did any commercial Unices do overcommit by default?
by tonfa | 2025-12-29 22:00:52+00:00
France, Italy, Austria (and probably others) don't have monopoly on long distance train. For instance, you can take a DB/Renfe/Trenitalia train on french high speed line, or in Austria take a Westbahn train instead of ÖBB.

That said personally I much prefer the mostly fixed pricing (and no reservation required) of swiss network than the dynamic one of other countries.
by phatfish | 2025-12-29 22:00:58+00:00
Well, you don't "stop using Steam" unless you don't care about playing most games released in the last 10-15 years. But the premise is solid, given that GOG has no DRM. Steam did get DRM "right" though.

My problem with Steam are the casino tactics Valve inject into their own games and the platform. That is an entire gaming industry problem however. At least Valve do some good things with the dirty money.
by nneonneo | 2025-12-29 22:01:00+00:00
Not the OP, but note that adding a std::string to a POD type makes it non-POD. If you were doing something like using malloc() to make the struct (not recommended in C++!), then suddenly your std::string is uninitialized, and touching that object will be instant UB. Uninitialized primitives are benign unless read, but uninitialized objects are extremely dangerous.
by this_user | 2025-12-29 22:01:07+00:00
> What does Steam look like 60 years from now?

Does it matter? You are treating this like these games are some valuable collector's items, when they really are just toys you play once and then never touch again for the most part.

But let's assume you had physical copies of all of these games you own on Steam. Once you are gone, there is a > 90% chance that whoever inherits it, will throw it away, just like Millenials now are throwing away all this junk they are inheriting that Boomers used to collect.

The point is, Steam is good enough for all practical purposes, which is to acquire and play games in the now.
by chrisfosterelli | 2025-12-29 22:01:27+00:00
Well one of the inherent issues is assuming that text is the optimal modality for every thing we try to use an LLM for. LLMs are statistical engines designed to predict the most likely next token in a sequence of words. Any 'understanding' they do is ultimately incidental to that goal and once you look at them that way a lot of the shortcomings we see become more intuitive.

There's a lot of problems LLMs are really useful for because generating text is what you want to do. But there's tons of problems which we would want some sort of intelligent, learning behaviour that do not map to language at all. There's also a lot of problems that can "sort of" be mapped to a language problem but make pretty extraneous use of resources compared to a (existing or potential) domain specific solution. For purposes of AGI, you could argue that trying to express "general intelligence" via language alone is fundamentally flawed altogether -- although that quickly becomes a debate about what actually counts as intelligence.

I pay less attention to this space lately so I'm probably not the most informed. Everyone seems so hyped about LLMs that I feel like a lot of other progress gets buried, but I'm sure it's happening. There's some problem domains that are obviously solved better with other paradigms currently: self-driving tech, recommendation systems, robotics, game AIs, etc. Some of the exciting stuff that can likely solve some problems better in the future is some of the work on world models, graph neural nets, multi modality, reinforcement learning, alternatives to gradient descent, etc. I think it's a debate whether or not LLMs are a local maxima but many of the leading AI researchers seem to think so -- Yann Lecun recently for e.g. said LLMs 'are not a path to human-level AI'