MurphyA/hacker-news huggingface dataset:
Launch HN: ParaQuery (YC X25) – GPU Accelerated Spark/SQL
by winwang | 2025-05-12 16:01:31+00:00
Hey HN! I'm Win, founder of ParaQuery (https://paraquery.com), a fully-managed, GPU-accelerated Spark + SQL solution. We deliver BigQuery's ease of use (or easier) while being significantly more cost-efficient and performant.

Here's a short demo video demonstrating ParaQuery (vs. BigQuery) on a simple ETL job: https://www.youtube.com/watch?v=uu379YnccGU

It's well known that GPUs are very good for many SQL and dataframe tasks, at least by researchers and GPU companies like NVIDIA. So much so that, in 2018, NVIDIA launched the RAPIDS program and the Spark-RAPIDS plugin (https://github.com/NVIDIA/spark-rapids). I actually found out because, at the time, I was trying to craft a CUDA-based lambda calculus interpreter…one of several ideas I didn't manage to implement, haha.

There seems to be a perception among at least some engineers that GPUs are only good for AI, graphics, and maybe image processing (maybe! someone actually told me they thought GPUs are bad for image processing!) Traditional data processing doesn’t come to mind. But actually GPUs are good for this as well!

At a high level, big data processing is a high-throughput, massively parallel workload. GPUs are a type of hardware specialized for this, are highly programmable, and (now) happen to be highly available on the cloud! Even better, GPU memory is tuned for bandwidth over raw latency, which only improves their throughput capabilities compared to a CPU. And by just playing with cloud cost calculators for a couple of minutes, it's clear that GPUs are cost-effective even on the major clouds.

To be honest, I thought using GPUs for SQL processing would have taken off by now, but it hasn't. So, just over a year ago, I started working on actually deploying a cloud-based data platform powered by GPUs (i.e. Spark-RAPIDS), spurred by a friend-of-a-friend(-of-a-friend) who happened to have BigQuery cost concerns at his startup. After getting a proof of concept done and a letter of intent... well, nothing happened! Even after over half a year. But then, something magical did happen: their cloud credits ran out!

And now, they're saving over 60% off of their BigQuery bill by using ParaQuery, while also being 2x faster -- with zero data migration needed (courtesy of Spark's GCS connector). By the way, I'm not sure about other people's experiences but... we're pretty far from being IO-bound (to the surprise of many engineers I've spoken to).

I think that the future of high-throughput compute is computing on high-throughput hardware. If you think so too, or you have scaling data challenges, you can sign up here: https://paraquery.com/waitlist. Sorry for the waitlist, but we're not ready for a self-serve experience just yet—it would front-load significant engineering and hardware cost. But we’ll get there, so stay tuned!

Thanks for reading! What have your experiences been with huge ETL / processing loads? Was cost or performance an issue? And what do you think about GPU acceleration (GPGPU)? Did you think GPUs were simply expensive? Would love to just talk about tech here!
by duped | 2025-05-12 16:01:34+00:00
I would argue that the prevalence of HDLs proves that DSLs are a good design for problem domains that scale in complexity. The alternative is point and click CAD, which has a ceiling on the scale of complexity you can reach.

> Honestly my sense is that it's just time to rip the bandaid off and generate synthesizable hardware from Python or Rust or whatnot. More syntax isn't what's needed.

People who think the problem is that they can't synthesize a program in hardware from something like Python completely misunderstand the purpose of an HDL. You do not write a program and press a button to get that program on hardware. You write a program to generate the design and verify its correctness. It is much less like writing an imperative or functional program and more like writing macros or code generation.

Now if you want to write a Python library for generating the underlying data for programming an FPGA or taping out circuits that's actually a good idea that people have tried out - the problem you run into though are network effects. Generating designs is easy, verifying and debugging them is very hard. All the money is in the tooling, and that tooling speaks HDLs.
by sdenton4 | 2025-05-12 16:01:37+00:00
LLMs are certainly not a jpeg or a database...

The google news snippets case is, in my non-lawyer opinion, the most obvious touch point. And in that case, it was decided that providing large numbers of snippets in search results was non-infringing, despite being a case of copying text from other people at-scale... And the reasons this was decided are worth reading and internalizing.

There is not an obvious right answer here. Copyright rules are, in fact, Calvinball, and we're deep in uncharted territory.
by btbuildem | 2025-05-12 16:01:39+00:00
How would you approach using them in a specialized discipline (think technical jargon, acronyms etc) where traning a model from scratch is practically impossible because everyone (customers, solution providers) fiercely guards their data?

A generic embedding model does not have enough specificity to cluster the specialized terms or "code names" of specific entities (these differ across orgs but represent the same sets of concepts within the domain). A more specific model cannot be trained because the data is not available.

Quite the conundrum!
by kaycebasques | 2025-05-12 16:01:44+00:00
Oh yes, this makes a lot of sense, thank you for the "nit" (which doesn't feel like a nit to me, it feels like an important conceptual correction). When I was writing the post I definitely paused at that part, knowing that something was off about describing the model as having a dimension that maps to gender. As you said, since the models are general-purpose and work so well in so many domains, there's no way that there's a 1-to-1 correspondence between concepts and dimensions.

I think your comment is also clicking for me now because I previously did not really understand how cosine similarity worked, but then watched videos like this and understand it better now: https://youtu.be/e9U0QAFbfLI

I will eventually update the post to correct this inaccuracy, thank you for improving my own wetware's conceptual model of embeddings
by bgwalter | 2025-05-12 16:01:55+00:00
The same president that is putting 145% tariffs on China could put 1000% tariffs on Internet chat bots located in China. Or order the Internet cables to be cut as a last resort (citing a national emergency as is the new practice).

I'm not sure at all what China will do. I find it likely that they'll forbid AI at least for minors so that they do not become less intelligent.

Military applications are another matter that are not really related to these copyright issues.
by fellowniusmonk | 2025-05-12 16:02:10+00:00
I think byroot got it exactly right with:

>First, I'm not convinced by the motivations:

>

>Avoiding name conflicts between libraries: Applications can require two different libraries safely which use the same module name.

>

>Is this a problem that happens on a regular basis? I believe Ruby has a pretty well established convention for libraries to expose a single module with a name that correspond to their gem name.

I really don't think we want to make it easier for newbies to alter gem naming conventions and run multiple versions of a gem within the same project, this sounds like a genuine nightmare to me. I've found from jumping in to fix broken and crippled rails projects for startups that the fuckup surface area is already high enough.
by dmurray | 2025-05-12 16:02:27+00:00
Isn't it the other way around? One revolution of your pedals gets one revolution of the wheel. Normally you'd get several revolutions out of it. So you need to pedal faster than you're used to, but each push is easier. It's like being in a very low gear suitable for climbing hills.

The penny-farthing solved this problem by having a very large wheel.
by lenerdenator | 2025-05-12 16:02:35+00:00
Or it could be that a massive influx of VC money into the for-hire transportation market allowed for a player (Uber) to more-or-less set prices as it found desirable.

OC has something they must do: get from point A to point B. If your choices for doing so have been restricted by anticompetitive behavior on the part of a market player, you will have to do so by a less consumer-friendly set of rules.
by kaoD | 2025-05-12 16:02:36+00:00
Related question: I'm coding a demo (as in "demoscene") and want to displace a plane SDF (think e.g. a heightmap) for raymarching.

This produces some visible discontinuities in the plane at larger slopes. My surface is still at f(x,y,z) = 0 but the rest of the field is completely messed up. From the little maths I know, that is no longer an SDF because among other issues, it no longer satisfies the Eikonal equation mentioned in the article.

Is there any way to fix this or am I SOL?
by GistNoesis | 2025-05-12 16:03:06+00:00
Time is irrelevant. What matters are units of computations.

When things are predictable they can be simulated fast : A spinning ball in the void can be simulated for 10^78 years in O(1).

When things are fuzzy, they can be simulated fast : A star made of huge number of atoms is not so different than another star made of a huge number of atoms. When processes are too complex they tend to all follow the law of large numbers which makes the computations memoizable.

What you want is a way to prevent the universe from taking shortcuts in its computations. Luckily its quite easy. You have to make details important. That's where chaos theory comes to the rescue. Small perturbations can have big impacts. Bifurcations like tossing a coin in the air create pockets of complexity. But throw too many coins in the air and its just random and boring. Life exists on this edge where enough structure is preserved to allow enough richness to exist.

One way humans have found of increasing precision is the lathe, which lead to building computers. Build a big enough fast enough computer and you will run-out of flops faster than reaching the 10^78 endgame.

But you have to be smart, because computation being universal it means that if you are just building a big computer what matters will be what runs on it. And your universe can be reduced to a recursive endgame state of "universe becoming a computer running universe simulation of a specific type", which doesn't need to computed more than once and already was, or isn't interesting enough to deserve being computed.

That's why we live on the exciting edge before the Armageddon, boring universes having already been simulated. The upside being universe hasn't yet decided which endgame we may reach, because the phytoplankton aliens of k2-18b have not yet turned on their supercomputer.
by A_D_E_P_T | 2025-05-12 16:03:09+00:00
It's nonsense.

See this comment on their previous paper: https://arxiv.org/abs/2306.07628

The authors of the comment show that the "gravitational pair-production" rate used in the work in OP comes from truncating the covariant heat-kernel (proper-time) expansion of the one-loop effective action at second order in curvature, an approximation that is valid only in weak-field regions where all curvature invariants satisfy |R| · ℓ² ≪ 1 (where ℓ is the Compton wavelength). When that same expression is pushed into the high-curvature interior of a neutron star -- where the inequalities fail by many orders of magnitude -- the series is no longer asymptotic and its early terms generate a spurious imaginary part. Because the paper's entire mass-loss mechanism and lifetime bound follow from that uncontrolled imaginary term, its conclusions collapse.

Simply put, it doesn't even correspond to known experiments. It's entirely driven by a narrow artefact and has no physical basis.
by rixed | 2025-05-12 16:03:18+00:00
> transmission costs are half of your energy bill

Wait, what?

Wikipedia[0] seems to disagree:

> Long-distance transmission (hundreds of kilometers) is cheap and efficient, with costs of US$0.005–0.02 per kWh, compared to annual averaged large producer costs of US$0.01–0.025 per kWh

Do you maybe mean that half electrical energy dissipate between production plant and consummer? But that figure seems quite large compared to what I can find online, and this would not be a problem with "free fusion".

Care to explain?

[0]: https://en.wikipedia.org/wiki/Electric_power_transmission
by tuetuopay | 2025-05-12 16:03:47+00:00
I still don't understand why vendors like Asus bother developing their own (crappy) driver installation tool. It's always bad, takes developer resources, for something that's handled way better by Windows Update.

The cynical me imagines juicy telemetry to sell to advertisers.

The realist me imagines time gains by not needing to go through Microsoft's driver update validation process (like companies keep linux drivers out-of-tree to not cleanup their code).

It's probably both.
by A_D_E_P_T | 2025-05-12 16:04:38+00:00
That's almost the mainstream position in physics as of 2025 -- that cosmic inflation never stopped, that it produces universes beyond number, and we're in one pinched-off region of it.

You'd like this book: https://mitpress.mit.edu/9780262547222/an-infinity-of-worlds...
by nsteel | 2025-05-12 16:04:53+00:00
I don't think this is about simulation. Python requires an additional DSL layer in order to express parallelism. I've personally no interest in learning that, or anything like that stuck on top some other language that's similarly unfit for HDL purposes.

Modern VHDL isn't too far off what we need. I'd rather see more improvements to that. But most crucially, we need tooling that actually supports the improvements and new features. We don't have that today, it's an absolute mess trying to use VHDL '19 with the industry's standard tools. We even avoid using '08 for fear of issues. I can't speak to how far off SV is.
by ethagnawl | 2025-05-12 16:04:59+00:00
> You need a way to turn off the power.

Right. This is my concern, too and it's why you shouldn't _just pipe your generator into the dryer's outlet_ when the power goes out. The electricity will continue to feed back out into the grid and can hurt/kill line workers who aren't expecting power.

Reading between the lines here, though. Maybe the device has a buffer and some mechanism to detect when the grid goes down and will only send it back into the outlet if things are operating normally? This is very clever if it somehow works and is safe but, yeah, it also makes me queasy and is the reason it's only available for sale in Utah.
by lossolo | 2025-05-12 16:05:00+00:00
> It costs more to build a 1GW coal power plant than it does to build a 3GW solar power plant (the 3X is capacity factor compensation)

That “3X” figure assumes a high‐insolation region (CF ~25 %). In Central Europe, where solar CF is only ~12 %, you’d need about 5x the PV capacity to equal a 1 GW coal plant’s annual generation. How does scaling up to 5 GW of PV change the cost comparison vs a coal plant?
by thaumasiotes | 2025-05-12 16:05:00+00:00
> Visiting europe felt like a breath of fresh air in comparison—technically they have uber, but there was no point in using it because taxis were so prevalent, reliable, and cheap.

Taxis in Shanghai have been largely replaced by app-related taxis. I have no particular knowledge of whether this moved prices up or down.

But there are two obvious changes:

1. It's now possible to get a taxi when it's raining.

2. The app drivers feel free to smoke.

Point 2 is a negative, but point 1 is a huge positive. How do European taxis respond to the rain?
by gruez | 2025-05-12 16:05:45+00:00
>Okay, so now we have AI. We can ignore the algorithm entirely and how it works, because it's not relevant. There is a large amount of data that it operates on, the weights of the model and so on. You _can_ with the correct prompts, sometimes generate a copy of a copyrighted work, to some degree of fidelity or another.

Suppose we accept all of the above. What does that hold for human learning?
by kridsdale1 | 2025-05-12 16:05:53+00:00
Your post reminded me of circa-2017 when I emerged from the subway station, wearing my Facebook shirt (which was a normal thing to do for years) but now noticed the sneers of hatred and disgust from people on the street. They’d look at my chest, recognize, then look in my eyes and nonverbally insult me.

That was the end of techy swag wearing for me.
by fakedang | 2025-05-12 16:06:43+00:00
> We're at a point where even "free hot water" is not competitive with solar for power generation.

You're making the obvious mistake here of equating 1 GW solar with 1 GW of any other source with a 95-99% baseload capacity. To achieve the equivalent result, you'll need to have at least >2 GW actual solar power to equally compare the two.

Granted, in most developed places, solar still beats coal, but this is why in many developing economies with ample coal resources, it makes more sense economically to go with the coal plants.

Take any other resource, say hydel or geothermal - solar and wind quickly go down in economic efficiency terms compared to these, in most cases almost doubling or tripling in costs.
by yjftsjthsd-h | 2025-05-12 16:08:01+00:00
> Why? That sounds dumb.

Good, you understand the situation perfectly.

> And (assuming you're correct), how does Armbian deal with that / get around it?

You'll notice that if you try to download it from https://www.armbian.com/download/ , nearly every board has a different download image; this is because every one of those images embeds its own boot chain. There are efforts (in some projects, I'm not aware of armbian doing this) to build some amount of early bootloader per-board (often uboot), and just make the install steps something like "install this per-board thing, then install the real OS using a standard image" but that's less common and doesn't work super well when that initial bootloader has to go on the same storage device as the main OS.
by kaycebasques | 2025-05-12 16:08:30+00:00
> were any direct applications to tech writers discussed in this article

No, it was supposed to be a teaser post followed up by more posts and projects exploring the different applications of embeddings in technical writing (TW). But alas, life happened, and I'm now a proud new papa with a 3-month old baby :D

I do have other projects and embeddings-related posts in the pipeline. Suffice to say, embeddings can help us make progress on all 3 of the "intractable" challengs of TW mentioned here: https://technicalwriting.dev/strategy/challenges.html
by flkiwi | 2025-05-12 16:08:47+00:00
Gig companies violate a lot of things, though workers' rights may well be at the top of the list. They are profoundly unaccountable. Doordash driver screws up your order (or takes part of it)? They'll credit you via an automated system. It is never, ever, ever the fault of Uber, Lyft, or the rest when something goes wrong, and there are no mechanisms for you, as a customer, to express anything other than transactional frustration other than not using the platform entirely. Given there is real value for me in having someone else do my shopping sometimes, it's unfortunate it's essentially my sole risk if something goes wrong.

There is some pushback happening. A significant US grocery chain has recently launched its in-house grocery delivery where I live, and they have been astonishingly good. They have actual customer service, employees driving liveried vehicles. It's a weird throwback and it works.
by Intralexical | 2025-05-12 16:09:37+00:00
> LLMs are certainly not a jpeg or a database...

Their weights are derived from copyrighted works. Evaluating them preserves the semantic meaning and character of the source material. And the output directly competes against the copyrighted source materials.

The fact they're smudgy and non-deterministic doesn't change how they relate to the rights of authors and artists.
by pkd | 2025-05-12 16:09:42+00:00
This was great and the photos were good too!

I have a similar sort of fascination with a structure closer to me: Habitat 67 in Montreal. I have at various points considered buying a unit there but practicality prevents me from doing so each time. I don't know how long I'll resist.

https://en.wikipedia.org/wiki/Habitat_67
by ETH_start | 2025-05-12 16:10:05+00:00
You’re basically saying "92% accurate" devices will cause harm, so let’s ban them outright. Even if the failure was death, we should allow it. It's a free country. We let consenting adults do all sorts of dangerous stuff — base jumps, do weird diets, drink alcohol, smoke, etc — yet somehow medical device regulation treats personal risk tolerance as if it’s off-limits.

But the failure scenario is not death. It's an unnecessary hospital visit. And you STILL want it banned. If someone wants early heart-attack warnings at 92% accuracy, that should be their call.

As for costs, a few thousand false alarms might clog ERs and bill wallets, but big deal. That’s the price of getting these things in the wild and learning what actually works. More wearables in real world situations equals massive real-world data, faster device improvement, economies of scale, and ultimately, over the long run, millions of prevented heart attacks — plus orders-of-magnitude lower costs down the road. I’d take a handful of extra ER visits any day if it speeds up innovation and saves lives.
by ProllyInfamous | 2025-05-12 16:10:49+00:00
I used to gig for a bay area startup which connected runners with task seekers (had recently added a bunny to its name...). This was a long time ago, well before they changed their matching system to be more algorythmic/generic.

Honestly, my twentysomething brain thought I was making $bigtime$, when in reality I was whoring my life away underbidding even the least-sane of co-giggers. The company consistently sided against its 1099 contractors, often ending arrangements without justification nor input from its workers.

To this day, I do not use gig platforms, and despise when family rents in AirBNB neighborhoods (I think quite possibly the worst for humanity Y-Combinator-funded idea). After my couple years scrapping by the bayarea, I eventually completed an IBEW apprenticeship and became a full-time electrician (the antithesis of gig work).

Although I still hope things have improved for current gig-workers, I know they likely have not.
by artski | 2025-05-12 16:11:24+00:00
Basically what I mean by it is for example a repository appears to be under a permissive license like MIT, Apache, or BSD, but actually includes code that’s governed by a much stricter or viral license—like GPL or AGPL—often buried in a subdirectory, dependency, or embedded snippet. The problem is, if you reuse or build on that code assuming it’s fully permissive, you could end up violating the terms of the stricter license without realising it. It’s a trap because the original authors might have mixed incompatible licenses, knowingly or not, and the legal risk then falls on downstream users. So yeah essentially a plain old license violation which are relatively easy to miss or not think about
by permo-w | 2025-05-12 16:11:24+00:00
(in the UK) it really depends on your bank and even the type of card you use. the debit card chargebacks I did when I was with Natwest were always very simple. fill out a form, send it off, get a response by email. for the one CC chargeback I did I think it required a call and a lot more trouble. when I tried a (debit card) chargeback with a different bank, it was an incredible amount of trouble and then I think they rejected it anyway