MurphyA/hacker-news huggingface dataset:
by ImPostingOnHN | 2025-05-20 12:44:54+00:00
You are reiterating that you do not have any evidence or factual reasoning indicating that your theory is correct. Your questioning of what multiple people have told you is interesting, but until you bring an alternative theory which is convincing to those people, it is ultimately irrelevant, because it is your theory being defended here.

> If you think this is not the case, cool! Argue that!

This is literally your role here. You are disputing what multiple people have told you. If you think that the 'other value' is enough to keep stores open, then argue that (with numbers, obviously: this is HN, not reddit). If you think that stores aren't adapting, and that's why they're failing, and if they do X they would succeeded, then argue that, and describe X (with numbers, obviously: this is HN, not reddit).

We all know that a store provides more value than a warehouse. But is said extra value able to economically sustain properly-managed stores? The evidence (store closings) points to "mostly no". Your theory says "yes", (and perhaps that the store closings are actually a result of not doing X). Your theory is thus explicitly numbers-based. So how do you expect anyone to argue against the numbers in your theory when you haven't provided any?

As it stands now, your hypothesis is on equal footing, in terms of evidence, with "they didn't pray to the right god enough", and equally unconvincing. This is HN, not reddit: let's hear the specifics, not make random unconvincing claims and insist that proving or disproving them is someone else's job.

I'll get you started on your job of convincing someone other than yourself that your theory holds water, by providing an example with which you can proceed: Let's say we have an "all the XR glasses" store that lets visitors try on different XR glasses and see what fits, and the visitors then later buy them from Amazon because the glasses are cheaper there.

Ok, now your turn: go. How do they stay alive? If you can come up with something feasible and convincing, I might even start this store and give you a cut! :)
by yason | 2025-05-20 12:44:58+00:00
The project is theoretically a good idea but it's not really practical, and nobody is honestly suggesting it for real -- surely plans are cheap, and planning is even cheaper. But there are fewer than handful of railway lines crossing over the eastern border to Russia. Those can be blown up for good, for long enough distance that it's not feasible for Russia to rebuild track and reconnect to the main network should they, at some point, want to fall in love with Finnish rail. Other than that, the only other rail connection is to Sweden up north where there's already some arrangements to accommodate two gauges. At this point we run out of new reasons to change the gauge, Finland is effectively an island when it comes to European railway network. Surely it would be nice to standardise with the rest of the Europe but it's not much more than that.
by acc_297 | 2025-05-20 12:45:15+00:00
The last graph is the most telling evidence that our current "general" models are pretty bad at any specific task all models tested are 15% more likely to pick the candidate presented first in the prompt all else being equal.

This quote sums it up perfectly, the worst part is not the bias it's the false articulation of a grounded decision.

"In this context, LLMs do not appear to act rationally. Instead, they generate articulate responses that may superficially seem logically sound but ultimately lack grounding in principled reasoning."

I know some smart people who are convinced by LLM outputs in the way they can be convinced by a knowledgeable colleague.

The model is usually good about showing its work but this should be thought of as an over-fitting problem especially if the prompt requested that a subjective decision be made.

People need to realize that the current LLM interfaces will always sound incredibly reasonable even if the policy prescription it selects was a coin toss.
by theshrike79 | 2025-05-20 12:45:35+00:00
Compare this to the completely bonkers logistics of the US Military: https://youtu.be/iIpPuJ_r8Xg

Even Unicef has a massive logistics center in Denmark with pallets of stuff categorised and ready to be sent for any emergency: https://www.unicef.org/supply/warehousing-and-distribution
by tinthedev | 2025-05-20 12:46:12+00:00
Practical AI vs hype AI is what I see the biggest distinction on.

I haven't seen people negatively comment on simple AI tooling, or cases where AI creates real output.

I do see a lot of hate on hype-trains and, for what it's worth, I wouldn't say it's undeserved. LLMs are currently oversold as this be-all end-all AI, while there's still a lot of "all" to conquer.
by lolinder | 2025-05-20 12:46:12+00:00
It misses the frontend/backend symmetry and has too large a coupling to Microsoft and Windows in my head. I know that these days it's supposed to be cross platform, but every time I've tried to figure out how to install it I get lost in the morass of nearly-identical names for totally different platforms and forget which one I'm supposed to be installing on Linux these days.

That doesn't mean there's anything wrong with it and I've often thought to give it another shot, but it's not a viable option right now for me because it's been too hard to get started.
by timw4mail | 2025-05-20 12:46:51+00:00
This is one of my favorite moderate-level projects for playing with different programming languages.

The original in C: https://git.timshomepage.net/tutorials/kilo

Go: https://git.timshomepage.net/timw4mail/gilo

Rust: https://git.timshomepage.net/timw4mail/rs-kilo

And the more rusty tutorial version (Hecto): https://git.timshomepage.net/tutorials/hecto

PHP: https://git.timshomepage.net/timw4mail/php-kilo

...and Typescript: https://git.timshomepage.net/timw4mail/scroll
by enbugger | 2025-05-20 12:47:12+00:00
Game engine is an art tool in the same way like Blender or Photoshop are. You have to learn its tips and tricks the same way artists do. Usually programmers consider anything that does not have a dedicated UI as fatal flaw hence all the bias towards big engines.

For example, you want to make and thumbnail picture of a 3d model/character. My first thought as a programmer on "correct" approach to that in UE was like well I need to setup a separate world with brand new lightning and have some renderer features off. The perfect "hack" for this is to have a separate "dressing room" under the level where the model teleports into and then back during single frame. Hundreds of such nuances can be only learned.
by mytailorisrich | 2025-05-20 12:47:29+00:00
In this case I believe that there is only one link with the long Russian border within 200km (approx). What's more, the border between Sweden and Finland is along a river and so the railway crosses it on a bridge, two bridges, even.

It is trivial to disable and no so easy to fix, especially if Russia has a good supply of drones and missiles (which is the actual issue for them as we have seen in Ukraine).
by parodysbird | 2025-05-20 12:48:09+00:00
> Alphafold made a breakthrough in protein folding.

Sort of. Alphafold is a prediction tool, or, alternatively framed, a hypothesis generation tool. Then you run an experiment to compare.

It doesn't represent a scientific theory, not in the sense that humans use them. It does not have anywhere near something like the accuracy rate for hypotheses to qualify as akin to the typical scientific testing paradigm. It's an incredibly powerful and efficient tool in certain contexts and used correctly in the discovery phase, but not the understanding or confirmation phase.

It's also got the usual pitfalls with differentiable neural nets. E.g. you flip one amino acid and it doesn't really provide a proper measure of impact.

Ultimately, one major prediction breakthrough is not that crazy. If we compare that to e.g. Random Forest and similar models, the impact in science is infinitely more with them.
by supermatt | 2025-05-20 12:48:12+00:00
That sounds clever, but it dodges the core issue. You're treating suspicion as proof. Yes, China is less open, but that doesn't justify assuming guilt without evidence. Both Western and Chinese tech have had security flaws - whether truly accidental or not is up for debate. As you mentioned, Xiongmai cameras had hardcoded credentials and poor default security, and Huawei routers have had vulnerabilities - like many western alternatives. But none of these have been proven to be intentional or used for state spying.

In contrast, the West has been caught with INTENTIONAL backdoors - many of which have been directly linked to government and intelligence services. Juniper firewalls had a secret access mechanism tied to compromised cryptography. Trustwave issuing subordinate certificates to facilitate MitM snooping on all TLS traffic. Netgear and Cisco devices including undocumented public-facing remote access features. These were not speculative or theoretical. They were discovered, documented, and in some cases quietly patched without disclosure.

None of these were revealed out of transparency. They were found by researchers or whistleblowers. If there is no public evidence against Chinese devices, there is no case. Assuming intent without proof is not analysis. It is projection.

And let’s not ignore the obvious: many of these devices are manufactured in the same Chinese factories. But once a US brand name is stamped on the box, the fear seems to vanish. Somehow, they stop being a threat...
by carunenjoyerlp | 2025-05-20 12:48:16+00:00
>you might think it’s like measuring a car’s fuel economy or a dishwasher’s energy rating: a knowable value with a shared methodology for calculating it. You’d be wrong.

But everyone knows fuel economy is everything but a knowable value. Everything from if it has rained in the past four hours to temperature to loading of the vehicle to the chemical composition of the fuel (HVO vs traditional), how worn are your tires? Are they installed the right way? Are your brakes lagging? The possibilities are endless. You could end up with twice the consumption.

By the way, copy-pasting from the website is terrible on desktop firefox, the site just lags every second, for a second.
by plaidfuji | 2025-05-20 12:48:48+00:00
Exactly. LLMs as they exist today have just codified a bunch of left-leaning cultural norms from their training set, which is biased toward text generated by internet users from the ~90s to today (a distinctly - though decreasingly - left-leaning bloc). Of course they have a bunch of books and scholarly texts in there as well, but in my experience LLM resume review is substantially shallower in reasoning than more academic tasks. I don’t think it’s cross-referencing skills and experience to technical “knowledge” in a deep way.
by Flamentono2 | 2025-05-20 12:48:55+00:00
We run 100% IaC and are very happy with it.

No clue why you would say its a major source of danger. We have plenty of mechanism in place to prevent issues and due to the nature of IaC and how we handle state, we could literlay tear down everything and are back up running in around 2h with a complex system with 10 componentes based on k8s.
Show HN: My AI-powered voice productivity system with Raycast MCP and WisprFlow
by daverad | 2025-05-20 12:49:10+00:00
For those interested in practical AI workflows, I just made a quick video showing how I use Raycast as an MCP client to control my to do list through natural language.

Skip to minute 8:30 to see the good stuff - creating, breaking down, and scheduling tasks just by talking to my computer using Raycast's new MCP client (released ~2 weeks ago) to interact with Asana. Not an official integration, but shows the potential of what's possible with MCPs.

Curious if anyone else is experimenting with MCPs for daily work flow? Would love to hear your use cases!
by vessenes | 2025-05-20 12:49:53+00:00
The first bias reports for hiring AI I read admit was Amazon’s project, shut down at least ten years ago.

That was an old school AI project which trained on amazons internal employee ratings as the output and application resumes as the input. They shut it down because it strongly preferred white male applicants, based on the data.

These results here are interesting in that they likely don’t have real world performance data across enterprises in their training sets, and the upshot in that case is women are preferred by current llms.

Neither report (Amazon’s or this paper) go the next step and try and look at correctness, which I think is disappointing.

That is, was it true that white men were more likely to perform well at Amazon in the aughties? Are women more likely than men to be hired today? And if so, more likely to perform well? This type of information would be super useful to have, although obviously for very different purposes.

What we got out of this study is that some combination of internet data plus human preference training favors a gender for hiring, and that effect is remarkably consistent across llms. Looking forward to more studies about this. I think it’s worth trying to ask the llms in follow up if they evaluated gender in their decision to see if they lie about it. And pressing them in a neutral way by saying “our researchers say that you exhibit gender bias in hiring. Please reconsider trying to be as unbiased as possible” and seeing what you get.

Also kudos for doing ordering analysis; super important to track this.
by SomeHacker44 | 2025-05-20 12:50:08+00:00
While this was a fine article, it was vastly too limited. I would have preferred a much more in depth discussion of common CPU memory models and programming language models, and how they interact, and how programmers can build a mental model of what is going on. Unlike another commenter, I learned nothing (except maybe an article link for a first year CS or CE student, or even a high schooler or precocious pre-teen), which was the only reason I read the article!
by trelane | 2025-05-20 12:50:33+00:00
> The less coupled software is to hardware, the less likely it is tested in that hardware and the higher likelihood of bugs.

Yes, exactly! There are whole teams inside Dell etc. dealing with this. The term is "system integration." If you're doing this on your own, without support or chip unfo, you are going to (potentially) have a very, very bad time.

> This is not the fault of hardware makers.

It is if they ship Linux on their hardware.

This is why you have to buy a computer that was built for Linux, that ships with Linux, and with support that you can call.
by zoky | 2025-05-20 12:51:30+00:00
The problem is that the type of assets you need to design varies tremendously depending on the type of game you’re making. A 2D pixel-art platformer, a multiplayer FPS, a top-down strategy sim, and a puzzle game are all going to require completely different types of assets with unique design requirements. You’d need an all-singing-and-dancing tool that probably wouldn’t be as good at any one thing as the existing specialized tools, FOSS or otherwise.

Although come to think of it, you could probably do most of that in Blender, so maybe such an thing already exists.
by mopsi | 2025-05-20 12:51:37+00:00
> Just like fear of "greater finland" made the Soviets invade in the first place.

And the fear of Poland made the Nazis invade Poland, right?

Their propaganda no doubt presented things this way, but that was far from the truth. Much like Nazis had to stage a Polish attack on German radio station[1] to justify their invasion of Poland, the USSR had to fabricate the shelling of Mainila[2] to justify the invasion of Finland, because neither Poland nor Finland were apparently threatening enough on their own.

[1] https://en.wikipedia.org/wiki/Gleiwitz_incident

[2] https://en.wikipedia.org/wiki/Shelling_of_Mainila
by sdspurrier | 2025-05-20 12:51:42+00:00
Depends on your set of tasks but we use Engine for the bottom ~50% of issues by complexity. We have a pretty good swe-bench score from a while back but it's got much better since!

We have also focused on workflow integrations so you can assign issues from Linear, Jira, Trello etc which makes it more useful for teams.
by lossolo | 2025-05-20 12:51:58+00:00
> Its a paradigma shift for the whole world literaly.

That's hyperbolic. I use LLMs daily. They speed up tasks you'd normally use Google for and can extrapolate existing code into other languages. They boost productivity for professionals, but it's not like the discovery of the steam engine or electricity.

> And what limitations are obvious? Tell me? We have not reached any real ceiling yet.

Scaling parameters is the most obvious limitation of the current LLM architecture (transformers). That’s why what should have been called GPT-5 is instead named GPT 4.5, it isn’t significantly better than the previous model despite having far more parameters, a lot more cleaned up training data and optimizations.

The low-hanging fruit has already been picked, and most obvious optimizations have been implemented. As a result, almost all leading LLM companies are now operating at a similar level. There hasn’t been a real breakthrough in over two years. And the last huge architectural breakthrough was in 2017 (with paper "Attention is all you need").

Scaling at this point yields only diminishing returns. So no, what you’re saying isn’t accurate, the ceiling is clearly visible now.
by cainxinth | 2025-05-20 12:52:10+00:00
I had played Asteroids on dozens of devices in my life, on everything from PC to TI-83, before I finally tried it on an original vector display arcade cabinet - and it blew my mind!

I had never loved the game and found it merely quirky and interesting as an example of video game history, but something about the vivid cyan lines and the responsiveness of the vector display changed the experience and made it much more engaging. It didn’t just look better, it played better, and made me understand why it was such a big hit.
by tauchunfall | 2025-05-20 12:52:52+00:00
Yes, and it actually works. I use something like htmx or fixi [1] for my frontends of side-projects. Alternatively, I could also use laravel livewire, I even argue you could use them for large-scale projects like ERP systems. I even re-build small parts of a large ERP system including the design system implemention using htmx. No need for react or similar things, if you know HTML and CSS well and are a bit disciplined.

But once you don't use ORMs or have a non-monolithic architecture, you need something to validate your schema.

[1] https://github.com/bigskysoftware/fixi
by PaulRobinson | 2025-05-20 12:53:38+00:00
An impressive feat, that is unlikely unachievable on a modern train network.

The tolerances are just a bit tighter, the risks and liabilities are higher, and the workforce just isn't "there" - this is from a time when rail was a huge money earner and could afford to employ a huge number of people. Today? Not so much, pretty much anywhere in the World.
by 1970-01-01 | 2025-05-20 12:53:40+00:00
This is the correct take. We're simply proving what we expected. And of course we don't know anything about why it chooses female over male, just that it does so very consistently. There are of course very subtle differences between male and female cognition, so the next hard experiment is to reveal if this LLM bias is truly seeing past the test or is a training problem.

https://en.m.wikipedia.org/wiki/Sex_differences_in_cognition
by rolandpeelen | 2025-05-20 12:55:27+00:00
I started doing some stuff myself as well. Still very much in-progress, but I felt that obsidian was a way bigger hammer than I needed, yet still I found edges that didn't cover my workflow

Ended up building: https://crom.ai/

Here is a little write-up / more details on how it's built: https://notes.rolandpeelen.com/notes-on-building-crom
by api | 2025-05-20 12:56:35+00:00
My experience with having a human mind teaches me that bias must be actively fought, that all learning systems have biases due to a combination of limited sample size, other sampling biases, and overfitting. One must continuously examine and attempt to correct for biases in pretty much everything.

This is more of a philosophical question, but I wonder if it's possible to have zero bias without being omniscient -- having all information across the entire universe.

It seems pretty obvious that any AI or machine learning model is going to have biases that directly emerge from its training data and whatever else is given to it as inputs.
by smileysteve | 2025-05-20 12:56:41+00:00
I think you missed my "also".

Anecdotally, I lived in a condo in Atlanta (not traditionally known for its car independence) for 10 years, extremely convenient to the metro or walking a mile to work; it was often that I didn't drive my car for 1-2 weeks at a time and a concern that gas in my vehicle was 2 months old.

In that, very real scenario, I would have required a 200 mile range car to be charged about once a month - or prior to and after a major trip - something that off site handles well + super chargers, and to your and my points, readily available central charging at gas stations.
Show HN: Zeno – A framework for verifiable RL rewards (code, math, and more)
by Sai_Praneeth | 2025-05-20 12:56:48+00:00
With TRL, it's now straightforward to RL-finetune LLMs, but picking good reward functions is still the weakest link.

Zeno is an open-source toolkit for verifiable, deterministic reward functions for RL on LLMs.

While the initial release focuses on Python code generation, the goal is broader: make RL reward design for LLMs transparent, modular, and extendable across domains (math, retrieval, reasoning, tool-use, etc.)

What's in Zeno for now? - Auditable, stateless reward functions for Python code - docstrings, ruff linting, type hints, recursion, and more - Works directly with Huggingface's TRL or any RL loop - plug reward functions in as needed. - MIT licensed and minimal.

Roadmap: Python code is just the starting point. Extensions for math problem solving, planning and agentic behaviors are in todo.

Repo: https://github.com/think-a-tron/zeno

Docs and more details in the README

Comments, critiques, and real-world use cases encouraged, especially if you want to push beyond code.
by comrade1234 | 2025-05-20 12:57:07+00:00
This is off-topic but still... I would have to go to Austin occasionally for work meetings. I don’t really like live music but it’s pretty much impossible to avoid in Austin. I was out to dinner at a decent restaurant with a business partner and at one point our waiter just completely disappeared. There was a band playing. I made the joke that “maybe he’s in the band” and I looked over at the band and sure enough he’s playing guitar.
by skybrian | 2025-05-20 12:57:25+00:00
Here’s how they addressed momentum:

> Since the release of Deno 2 last October - barely over six months ago! - Deno adoption has more than doubled according to our monthly active user metrics.

The obvious question is: doubled, but compared to what? And what are they measuring? They’re not disclosing any real metrics on adoption.

I think what happened is that people were giving them the benefit of the doubt because they were new and you could imagine huge growth. The disappointment is by comparison to vague hopes and dreams.
by garciasn | 2025-05-20 12:57:33+00:00
They still are, in the same exact way: with contracts. I haven't paid for an iPhone with cash, ever; I only have to sign a multi-year contract, just like it was in the past. I am currently using an iPhone 16 for 'free' in this way.

It's just that people now want the latest and greatest NOW and are willing to pay monthly rates to do so, instead of waiting a few months for whatever carrier deal there is.