MurphyA/hacker-news huggingface dataset:
by archagon | 2025-08-08 21:21:13+00:00
I'm in the process of writing up a blog post on how network shares on macOS are kind of cursed. Highlights:

* Files on SMB shares sometimes show up as "NDH6SA~M" or similar, even though that's not their filename on the actual network drive. This is because there's some character present in the filename that SMB can't work with. No errors or anything, you just have to know about it.

* SMB seems to copy accented characters in filenames as two Unicode code points, not one. Whereas native macOS filenames tend to use single Unicode code point accents.

* SMB seems to munge and un-munge certain special characters in filenames into placeholders, e.g. * <-> . But not always. Maybe this depends on the SMB version used?

* SMB (of a certain version?) stores symlinks as so-called "XSym" binary files, which automatically get converted back to native symlinks when copied from the network share. But if you try to rsync directly from the network drive instead of going through SMB, you'll end up with a bunch of binary XSym file that you can't really do anything with.

I only found out about these issues through integrity checks that showed supposedly missing files. Horrible!
by kj4ips | 2025-08-08 21:21:39+00:00
TL;DR: It's massively easier to run a few models really fast than it is to run many different models acceptably.

They probably are using some interesting hardware, but there's a strange economy of scale when serving lots of requests for a small number of models. Regardless of if you are running single GPU, clustered GPU, FPGAs, or ASICs, there is a cost with initializing the model that dwarfs the cost of inferring on it by many orders of magnitude.

If you build a workstation with enough accelerator-accessible memory to have "good" performance on a larger model, but only use it with typical user access patterns, that hardware will be sitting idle the vast majority of the time. If you switch between models for different situations, that incurs a load penalty, which might evict other models, which you might have to load in again.

However, if you build an inference farm, you likely have only a few models you are working with (possibly with some dynamic weight shifting[1]) and there are already some number of ready instances of each, so that load cost is only incurred when scaling a given model up or down.

I've had the pleasure to work with some folks around provisioning an FPGA+ASIC based appliance, and it can produce mind-boggling amounts of tokens/sec, but it takes 30m+ to load a model.

[1] there was a neat paper at SC a few years ago about that, but I can't find it now
by gbacon | 2025-08-08 21:21:52+00:00
It didn’t roll off the factory floor in 1958 with a moving map GPS. A common retrofit is a Garmin 430 that has a 2D bird’s eye view of airspace lateral boundaries. ForeFlight runs on iPhones and iPads; other electronic flight bag software runs on either iOS or Android devices. But you have to know what you’re looking at and what the rules are for different classes of airspace. In Class C or D, you only have to establish two-way contact, but Class B requires explicit clearance.
by kelnos | 2025-08-08 21:21:56+00:00
That depends on what you value, though. If local control is that important to you for whatever reason (owning your own destiny, privacy, whatever), you might find that trade off acceptable.

And I expect that over time the gap will narrow. Sure, it's likely that commercially-built LLMs will be a step ahead of the open models, but -- just to make up numbers -- say today the commercially-built ones are 50% better. I could see that narrowing to 5% or something like that, after some number of years have passed. Maybe 5% is a reasonable trade-off for some people to make, depending on what they care about.

Also consider that OpenAI, Anthropic, et al. are all burning through VC money like nobody's business. That money isn't going to last forever. Maybe at some point Anthropic's Pro plan becomes $100/mo, and Max becomes $500-$1000/mo. Building and maintaining your own hardware, and settling for the not-quite-the-best models might be very much worth it.
by mattmanser | 2025-08-08 21:21:57+00:00
Someone's take on AI was that we're collectively investing billions in data centers that will be utterly worthless in 10 years.

Unlike the investments in railways or telephone cables or roads or any other sort of architecture, this investment has a very short lifespan.

Their point was that whatever your take on AI, the present investment in data centres is a ridiculous waste and will always end up as a huge net loss compared to most other investments our societies could spend it on.

Maybe we'll invent AGI and he'll be proven wrong as they'll pay back themselves many times over, but I suspect they'll ultimately be proved right and it'll all end up as land fill.
by oblio | 2025-08-08 21:22:01+00:00
At a certain point Moore's Law died and that point was about 20 years ago but fortunately for MP3s, it happened after MP3 became easily usable. There's no point in comparing anything before 2005 or so from that perspective.

We have long entered an era where computing is becoming more expensive and power hungry, we're just lucky regular computer usage has largely plateaued at a level where the already obtained performance is good enough.

But major leaps are a lot more costly these days.
by alsetmusic | 2025-08-08 21:22:32+00:00
When I first got into homelabbing as a hobby, I built a massively overpowered server because I was highly ambitious.it mostly just drew power for projects that didn’t require all the horsepower.

A decade later, I like NUCs and Pis and the like because they’re tiny,low-power, and easy to hide. Then again, I don’t have nearly as much time and drive for offhand projects as I get older, so who knows what a younger me would have decided with the contemporary landscape of hardware available to us today.
by stego-tech | 2025-08-08 21:23:13+00:00
Hell, we haven’t even seen actual AI yet. This is all just brute-forcing likely patterns of tokens based on a corpus of existing material, not anything brand new or particularly novel. Who would’ve guessed that giving CompSci and Mathematics researchers billions of dollars in funding and millions of GPUs in parallel without the usual constraints of government research would produce the most expensive brute-force algorithms in human history?

I still believe this is going to be an embarrassing chapter of the history of AI when we actually do create it. “Humans - with the sort of hubris only a neoliberal post-war boom period could produce - honestly thought their first serious development in computing (silicon-based mircoprocessors) would lead to Artificial General Intelligence and usher in a utopia of the masses. Instead they squandered their limited resources on a Fool’s Errand, ignoring more important crises that would have far greater impacts on their immediate prosperity in the naive belief they could create a Digital God from Silicon and Electricity alone.”
by pchristensen | 2025-08-08 21:23:35+00:00
I didn't see that when it came out, heard it was a flop, and disregarded it for years. Then we watched it over winter break a few years ago, and it's basically the same as Phantom Menace - pretty good action, pretty good but dated effects, pretty ok but miscast lead actor, pretty clunky plot. It might have lost a ton of money, but it's like a B- movie at worst.
by ActorNightly | 2025-08-08 21:23:48+00:00
Right, exactly what I said. They don't make money by market making. They make money by charging transaction fees, and on an access basis to their "algorithms" which are designed against analyzing the complex futures that they are the market maker for.

The incentive for users to sign up with them is to get access to "better" pricing for whatever commodity they pair the buy/sell orders for - but remember these are futures so its all betting, and so the algorithms don't really mean anything.
by danpalmer | 2025-08-08 21:24:20+00:00
My read is that this is a mature response to an immature accusation. This guy is well known in the industry for being mature, responsible, and having high standards.

Given that context, and the context in which accusations are being made, the unfounded idea that he is corrupt due to links to China says a lot more about those spouting that idea than it does about him.

Refuting individual claims would be engaging with that which does not deserve engagement, whereas pointing out the shared goals (US economic/national security) is a good way to refocus the discussion.
by mjd | 2025-08-08 21:24:56+00:00
We already know the system is really bad at spelling. I have Claude configured to periodically remind me “By the way, I think there are ** n's in 'banana'”, so I don't forget what I am dealing with. It has never gotten this right.

But that doesn't mean that it is not extremely useful. It only means I shouldn't ask it to spell stuff.

If a human is unable to count the n's in 'banana' we expect them to be barely functional. Articles like this one try to draw the same inference about the LLM: it can't count 'n's, so it must not be able to do anything else either.

But it's a bad argument, and I'm tired of hearing it.
by simonw | 2025-08-08 21:25:47+00:00
The GPT-4o you talk to through ChatGPT and the GPT-4o you access via the API are different models... but they're actually both available via the API.

https://platform.openai.com/docs/models/gpt-4o is gpt-4o in the API, also available as three date-stamped snapshots: gpt-4o-2024-11-20 and gpt-4o-2024-08-06 and gpt-4o-2024-05-13 - priced at $2.50/million input and $10.00/million output.

https://platform.openai.com/docs/models/chatgpt-4o-latest is chatgpt-4o-latest in the API. This is the model used by ChatGPT 4o, and it doesn't provide date-stamped snapshots: the model is updated on a regular basis without warning. It costs $5/million input and $15/million output.

If you use the same system prompt as ChatGPT (from one of the system prompt leaks) with that chatgpt-4o-latest alias you should theoretically get the same experience.
Show HN: I made FiscalBud to send invoices fast and worldwide in 77 languages
by drex91on | 2025-08-08 21:26:03+00:00
hi! i built an app that takes the pain out of invoicing so you can send them faster and worldwide without a headache. i've always found invoicing to be a waste of time, switching between templates, calculating taxes, tracking different currencies, and keeping files organized. so i made FiscalBud :)

the idea from tools like stripe inspired me, but for invoices. it lets you create, customize, and send professional invoices to clients anywhere in the world in just minutes. it supports 8 currencies, 77 languages (you can choose the output data language and ui language separately), and works in 248 countries, so you can bill confidently on a global scale.

it comes with smart templates, automatic tax/subtotal/total calculations, localized csv exports, and cloud storage to keep everything organized. (coming soon) you can automate recurring invoices, payment reminders, and follow-ups.

it's built to be secure and privacy-focused, with encryption and compliance baked in. you can even send invoices directly via email using your own smtp settings, with automatically signed pdfs.

i've got plenty of ideas for making it even better, like deeper automation and more integrations with other tools you already use (including Stripe which is on the roadmap).

any feedback is much appreciated! :)
by ladberg | 2025-08-08 21:26:35+00:00
I think you're misunderstanding what that page is: it's not an advertisement to invest with the company, it's an advertisement to trade via/with the company in the same way you might otherwise go manually trade from a Bloomberg terminal (or any other method).

There is no way to invest in the company, and the only way of becoming a "customer" is to engage in trading.
by tracker1 | 2025-08-08 21:27:05+00:00
I switch to 45" UWQHD myself.. slightly taller with bigger pixels for work and enjoy it immensely. I've had a few issues where my vision got dramatically worse a couple years ago so even my 32" 1440p was a struggle at times.

With this display, it's similar to two 4:3 aspect displays in my typical use... my IDE or Code pinned on one side, my browser or another app on the right.

Overall, it's been pretty great.
by trod1234 | 2025-08-08 21:27:36+00:00
The major attack of concern described in the paper is the transparent early terminated encryption attack, and root trust signing that fall under effectively the same centralized hands at the AS level.

Where an AS level entity MITMs all outbound connections from a region in automated fashion for collection, before that traffic ever makes it to TOR or its destination.

It works for TOR, TLS, pretty much any protocol out there where key exchange or trust occurs; so long as the protocol is known and has distinct classifiable characteristics allowing computation to automatically do this.

There have been instances where public certs issued by a CA with the same domain names, but are issued from a root CA that is other than the legitimate site's root CA which are used for attacks. CT logs don't stop this either.

There is a lot of ephemeral content, and private information that can be both collected, and injected on a targeted basis if one has access to such junctions which the industry (Telecom) has proven time and again that they can't secure following basic practice; largely because mandates to backwards compatibility at the regulatory level.

Social credit, where invisible factors people don't control force those same people into poverty through targeted denial of service (communications for job hunting/social contacts), zersetzung, etc; that all would be a breeze to set up without any external indicator, or remedy using that attack.

What the target sees vs what everyone else sees would be quite different, and of course there would be people that gaslight and torture on top of it all (as a natural psychological defense mechanism of denial).

Compromised communications under such type of attacks are madness inducing.
by ryao | 2025-08-08 21:27:56+00:00
At the heart of inference is matrix-vector multiplication. If you have many of these operations to do and only the vector part differs (which is the case when you have multiple queries), you can do matrix-matrix multiplication by stuffing the vectors into a matrix. Computing hardware is able to run the equivalent of dozens of matrix-vector multiplication operations in the same time it takes to do 1 matrix-matrix multiplication operation. This is called batching. That is the main trick.

A second trick is to implement something called speculative decoding. Inference has two phases. One is prompt processing and another is token generation. They actually work the same way using what is called a forward pass, except prompt processing can do them in parallel by switching from matrix-vector to matrix-matrix multiplication and dumping the prompt’s tokens into each forward pass in parallel. Each forward pass will create a new token, but it can be discarded unless it is from the last forward pass, as that will be the first new token generated as part of token generation. Now, you put that token into the next forward pass to get the token after it, and so on. It would be nice if all of the forward passes could be done in parallel, but you do not know the future, so you ordinarily cannot. However, if you make a draft model that is a very fast model runs in a fraction of the time and guesses the next token correctly most of the time, then you can sequentially run the forward pass for that instead N times. Now, you can take the N tokens and put it into the prompt processing routine that did N forward passes in parallel. Instead of discarding all tokens except the last one like in prompt processing, we will compare them to the input tokens. All tokens up to and including the first token that differ, that come out of the parallel forward pass are valid tokens for the output of the main model. This is guaranteed to always produce at least 1 valid token since in the worse case the first token does not match, but the output for the first token will be equal to the output of running the forward pass without having done speculative decoding. You can get a 2x to 4x performance increase from this if done right.

Now, I do not work on any of this professionally, but I am willing to guess that beyond these techniques, they have groups of machines handling queries of similar length in parallel (since doing a batch where 1 query is much longer than the others is inefficient) and some sort of dynamic load balancing so that machines do not get stuck with a query size that is not actively being utilized.
by sixdimensional | 2025-08-08 21:28:03+00:00
Sorry to here about your bad experience. I do understand it, as when the hype cycle causes perfectly good things to be thrown away or messed up for no practical reason, it really bugs me too.

In this case, it's so easy for some to say everything else is bad if it is not AI, or you have to include AI "because it's the future".

I just wanted to clarify that I am very pragmatic in my approach to new tech and AI.

I used to work on enterprise ERP, MRP, sales and support systems (Oracle, Salesforce, ServiceNow, and more)... not always by choice.

At one point a project came up to rebuild our core customer experience portal across our business. I got to build with the rule based chatbots to implement an assistant in that web app.

Not easy, but it worked well enough, especially for simple scenarios. For example, imagine a field support engineer being able to pull up the exact page in a big technical document for some obscure part by just asking for what they need, in seconds.

Or, being able to reorder some quantity of part by just asking for it, or checking the quantity of same.

These are time savers, especially if you have a voice to assistant interface too. So you can just type something, or ask for what you need without typing.

Tablets were common for manufacturing, inventory or service staff. Being able to pull up our interface and just say what they needed and get prompted for simple things - there were TONs of pragmatic small wins there.

Current LLM just make that easier than the old rigid rule based way we had to code those assistants (at the price of going wrong sometimes).

I care about efficiency, pleasant user experience, and pragmatics.. not AI for the sake of AI.

All that to say, if a drop down box works better for some use case, instead of AI - or even a rule based chatbot - hell yeah! Save on them tokens.
by gbacon | 2025-08-08 21:28:10+00:00
No need to anxious. Your CFI is there to keep you safe and prevent anything wildly dangerous while you’re teaching yourself to fly.

Look outside, and learn what correct looks like. References on the ground are already giving you gobs of information. The feel of the yoke and the sounds from the engine are also giving you continuous clues about what the airplane is doing. No sim I’ve seen reproduces all of those additional channels of information.

Where simulators are really helpful is with procedural flying, like practicing instrument approaches. You can’t log your desktop sim for currency, but advanced training devices are good enough in the FAA’s eyes.
by WarOnPrivacy | 2025-08-08 21:29:08+00:00
Research indicates that kids who are exposed to more than two hours a day of recreational screen time ...

A couple of thoughts.

The klaxons about kids+phones+harms are always louder than the researchers who come along later and look at the same data with a less narrow lens.

Over and over and over we find that phones are not the demon that is portrayed.¹ And there are much better reasons for youth mental health issues.

In the last 60 years we have wholly eradicated the adult-free time and spaces where kids learned their most critical social and problem solving skills. Instead, we permanently trap kids in a series of adult-curated, adult-curated boxes.

If our goal was to ruin youth mental health, this is about as pervasive a method as we could orchestrate. Conversely, phones are often how youth cope with this crippling, disastrous loss.

¹ https://www.npr.org/2019/08/27/754362629/the-scientific-deba...
by ubermonkey | 2025-08-08 21:29:50+00:00
>Spoken like someone who's never used a really good screwdriver.

Again, no. I have a very fine set of tools available for jobs large enough when it'll matter.

You just seem really invested in the idea that the SAK sucks, when it just doesn't. It's entirely sufficient for most small jobs. That there exist BETTER options for larger jobs does not mean the SAK sucks.

>I'd just hate to see anyone inadvertently misled into overspending on a broadly unsafe "all things to all people" social statement play, the exact equivalent in taste and quality of a Mont Blanc fountain pen,

I have absolutely no idea what you mean to be saying with this sentence.

Are you arguing that Victorinox knives are expensive and dangerous? That's a wild thing to say. The one in my pocket was like $35, and it's safe as houses.

Are you arguing they're somehow equivalent to high-end fountain pens? That's even crazier, since perfectly delightful fountain pens exist at a fraction of the cost of a Mont Blanc (which, IME, is a pen that people who care more about brands than nibs buy).

Maybe take a breath, and try again?
by ashton314 | 2025-08-08 21:29:56+00:00
I don't get that emotional when watching movies. I cried a little when the parachutes opened in Apollo 13.

As a kid I had a book detailing hundreds of space missions—mostly probes, obviously—but my favorite mission to read about was Apollo 13. Just incredible.

Maybe when Jim got to heaven, the first place the angels took him to was where he would have landed on the moon.
by torginus | 2025-08-08 21:29:59+00:00
I wonder if it's feasible to hook up NAND flash with a high bandwidth link necessary for inference.

Each of these NAND chips hundreds of dies of flash stacked inside, and they are hooked up to the same data line, so just 1 of them can talk at the same time, and they still achieve >1GB/s bandwidth. If you could hook them up in parallel, you could have 100s of GBs of bandwidth per chip.
by CamperBob2 | 2025-08-08 21:30:03+00:00
Are you talking about the guy in Temecula running two different auctions with some of the same photos (356878140643 and 357146508609, both showing a missing heat sink?) Interesting, but seems sketchy.

How useful is this Tesla-era hardware on current workloads? If you tried to run the full DeepSeek R1 model on it at (say) 4-bit quantization, any idea what kind of TTFT and TPS figures might be expected?
by ilaksh | 2025-08-08 21:30:11+00:00
DeepSeek came up with several significant optimizations, not just one. And master's students do contribute to leading edge research all the time.

There have really been many significant innovations in hardware, model architecture, and software, allowing companies to keep up with soaring demand and expectations.

But that's always how it's been in high technology. You only really hear about the biggest shifts, but the optimizations are continuous.
by altairprime | 2025-08-08 21:30:24+00:00
The difference between “I earned a tidy profit for me and my investors, as well as for my non-investor workers” and “I earned the maximum profit possible for me and my investors, moreso by denying workers as much of those profits as possible” compounds detriment at the difference in profit and wage growth rates, which steadily decreases the maximum profit possible to all firms due to household purchasing powerlessness. Eventually they’ll reach a lower maximum profit than could have be reached if they’d shared more profits as worker wages rather than as investor payouts. Oops.