MurphyA/hacker-news huggingface dataset:
by The5thElephant | 2025-05-06 19:02:13+00:00
Funny enough I also got diagnosed with gout once in my 20s. I have always had somewhat bad toes/bunions (probably partially genetic, and partially wearing only tight soccer shoes as a kid) and I went to a wedding wearing some new leather shoes that I hadn't broken in yet. The next day I woke up with a fever and horrific pain in the sides of my toes. Went to doctor and they did some tests and were also seemingly surprised at the results indicating gout. They asked me to come back in a week to double check, and by then my symptoms were gone and the tests no longer indicated gout.

Our bodies are such strange mechanisms.
by resters | 2025-05-06 19:02:14+00:00
While malpractice insurance exists for human docs and lawyers, there is not really any difference between an ai-powered lawyer drawing up a contract, an ai-powered doc reviewing a chart and recommending next steps, and a self-driving car making a turn.

The most obviously "lethal" case (cars) is already in large scale rollout worldwide.

At scale, self-driving car "errors" will fall under general liability insurance coverage, most likely. Firms will probably carry some insurance as well just in case.

LLMs already write better prose than 95% of humans and models like o3 reason better than 90% of humans on many tasks.

In both law and medicine there are many pre-existing safeguards that have been created to reduce error rates for human practitioners (checklists, text search tools (lexis nexis, uptodate, etc.), continuing education, etc.) which can be applied to AI professionals too.
by echelon | 2025-05-06 19:02:36+00:00
Teamsters statement:

https://teamster.org/2025/05/teamsters-statement-on-presiden...

IATSE statement:

https://iatse.net/iatse-on-president-trump-movie-tariff-anno...

tl;dr - the Teamsters love this. IATSE likes it, but wants to make sure their Canadian membership isn't hurt.
by klank | 2025-05-06 19:02:42+00:00
I'd love a further description of how you can have a program with the entire text of wikipedia that only ever outputs 5. It is not immediately obvious to me how that is possible.

Assuming the text of wikipedia is meaningfully used in the program, of course. A definition of "meaningful" I will propose is code which survives an optimization loop into the final resulting machine code and isn't hidden behind some arbitrary conditional. That seems reasonable as a definition of a program "containing" something.
by rietta | 2025-05-06 19:03:18+00:00
My lay scientist feel is still that Pluto is a planet, or at the very least have been grandfathered in. The New Horizons reporting 10 years ago just solidified this in my mind. The geological processes are more telling than having cleared the orbital neighborhood. That would just mean that get far enough out and nothing can be a planet no matter how massive. All that said, this massive of an object that far out is very interesting. It would be a very, very cold out there. Would even a gas giant be a solid at that distance? Are there any geological processes possible?
by JoshTko | 2025-05-06 19:04:13+00:00
Thank you for pinpoints my confusion/disconnect on what lack of improvement that I was sensing. There was an improvement on pacing, and cadence, yes, but that was not the main challenge with Victors accent. Visually I'd say victor improved by at most 5% and not 50% as indicated by the visualization. In some regards it was even harder to understand than the original due to speed and cadence without improvement in core pronunciation.
by mattlondon | 2025-05-06 19:05:26+00:00
AIs aren't intended to be used as calculators though?

You could say that when I use my spanner/wrench to tighten a nut it works 100% of the time, but as soon as I try to use a screwdriver it's terrible and full of problems and it can't even reliably so something as trivially easy as tighten a nut, even though a screwdriver works the same way by using torque to tighten a fastener.

Well that's because one tool is designed for one thing, and one is designed for another.
by zokier | 2025-05-06 19:06:03+00:00
If "turkish lower case dotted i" would be separate codepoint, that still wouldn't cause ambiguity like you describe. It would just mean that "U+0069 latin small letter i" would not be (directly) transcodable to the legacy Turkish character set. But that wouldn't really be any different from other similar homoglyph situations, for example "U+0430 cyrillic small letter a" does not transcode to ASCII and that is business as usual. U+0069 not being transcodeable to some legacy encoding is not really a round-tripping problem, vast majority of Unicode codepoints are not transcodable to any single legacy encoding. Round-trip compatibility is really only concern when going from legacy-unicode-legacy; it is naturally expected that not all strings will be able to roundtrip unicode-legacy-unicode.
by squigz | 2025-05-06 19:06:57+00:00
I guess I'm confused by your position here.

> I feel like the problem seems to me to be behavior, not a technology issue.

Yes, it's a behavior issue, but that doesn't mean it can't be solved or at least minimized by technology, particularly as a technology is what's exacerbating the issue?

> It's fundamentally about trust in people.

Who is lacking trust in who here?
by mfrye0 | 2025-05-06 19:07:12+00:00
Congrats on the launch!

How do you dedupe entities, like companies and people? I've noticed ChatGPT tends to provide "great" results when asking about different entities, but in reality it just groups similar sounding entities together in its answer.

For example, I asked ChatGPT about a well known startup. It gave me a confident answer about how much they raised, their current status, etc. When looking at the 3 sources they cited though, it was actually 3 different companies that all had similar sounding names that it just grouped together to form its answer.

Basically, how do I trust the output of your system?
by impresburger | 2025-05-06 19:07:58+00:00
Do you mean being able to wrap the created model in a scikit-learn Pipeline? This isn't something we've thought about and we haven't explicitly built support for it, though we could.

As of now, I think you could relatively easily wrap the plexe model, which has a `predict()` method, in a scikit-learn Estimator. You could then plug it into a Pipeline.

What do you have in mind? How would you want to use this with scikit-learn pipelines?
by mcv | 2025-05-06 19:08:04+00:00
Yeah, that's absolutely the tricky part. I think it makes sense to prioritize also by how long someone has been a registered donor, but then you've got to make sure not to disadvantage young adults. Maybe you build up priority over a period of 5 years?

There's probably a dozen other factors that you should also take into account, and that I'm sure doctors are already taking into account (like life expectancy, quality of life, compatibility, etc). But I see those as implementation details that can be worked out by the experts.
by mvdtnz | 2025-05-06 19:08:05+00:00
I truly do not understand how people are getting worthwhile results from Gemini 2.5 Pro. I have used all of the major models for lots of different programming tasks and I have never once had Gemini produce something useful. It's not just wrong, it's laughably bad. And people are making claims that it's the best. I just... don't... get it.
by lurking_swe | 2025-05-06 19:08:37+00:00
many problems with your argument. I will enumerate just a few off the top of my head.

- if a child was born with poor planning from the parents, it’s too late and unhelpful to say “i told you so”. Feeding a disadvantaged child raises their chances of doing well in school and reduces their chance of being a much larger burden on your tax dollars down the line. Are you saying we should save $5 on this child now so we can spend $100 in the future (subsidizing their social security payments, maybe jail time, maybe homeless, etc)? Sounds like a smart business move! (sarcasm) Feeding kids is good ROI.

- it’s possible to get pregnant even when being responsible. No protection is 100% except for abstinence your entire life.

- you can “plan” for these expenses all you want. Sometimes a spouse dies. Sometimes a person needs to spend money to support their aging parents, AFTER their child is born. Or a divorce ruins one’s finances. Or someone loses their good job due to layoffs. Life is not a simple path, not sure what gave you that illusion.

- congrats you don’t have children. Guess what? When you’re old and need a diaper change in a nursing home, who do you think will be wiping your ass? One of the nurses… one of those kids that is not yours. Who will be delivering food to the grocery store? That’s right, one of those other kids. Who will be your doctor? Right again! One of those other kids.

- it’s easy to be smug until you’re unlucky in life. What if tomorrow you’re hit by a drunk driver and you go bankrupt trying to pay for major medical bills. Suddenly it’s not so fun saying nobody deserves a handout. At the end of the day, this mindset is an empathy problem.

It seems like _most_ of my fellow americans have an empathy problem. Are you all dead inside? Anyway…it’s useful to invest in our society. Not everything is about me me me.
by conartist6 | 2025-05-06 19:09:57+00:00
As a competitor in that key segment I don't feel locked out. I could almost jump for joy that this very weak-tea move is the most they can do with that much money. They're just quintupling down on the technology of 50 years ago. There's no threat to me at all here as a creator of from-first-principles IDE technology.
by nottorp | 2025-05-06 19:10:13+00:00
I have the hardware for a new home server waiting to be set up (as in, I don't need a new home server i'm just messing around, so once in a while i log in and configure one more service).

I tried the latest Ubuntu and it seems to be targeted at either containers or desktops. Everything I wanted to set up networking wise was a pain for my little non standard configuration.

Ended up wiping it and installing Debian instead.

As for this Rust thing, first question that comes to my mind is what features are missing from this new godly impervious to hackers by default implementation.
by Rendello | 2025-05-06 19:11:19+00:00
That reminds me of another crazy weather event that you rarely hear about, the 1972 Iran Blizzard:

> Southern Iran received as much as 7.9 metres (26 ft) of snow, burying at least 4,000 individuals. According to contemporary reports by the newspaper Ettela'at, the city of Ardakan and outlying villages were hardest hit, with no survivors in Kakkan or Kumar. In the northwest, near the border with Turkey, the village of Shaklabad and its 100 inhabitants were buried. According to some experts, about 200 villages were buried under the snow and completely erased from the map.

https://en.wikipedia.org/wiki/1972_Iran_blizzard
by atoav | 2025-05-06 19:11:49+00:00
I think Result<Option> is the way to go. It describes precisely that: was it Ok? if yes, was there a value?

I could imagine situations where an empty return value would constitute an Error, but in 99% of cases returning None would be better.

Result<Option> may feel clunky, but if I can give one recommendation when it comes to Rust, is that you should not value your own code-aesthetical feelings too much as it will lead to a lot of pain in many cases — work with the grain of the language not against it even if the result does not satisfy you. In this case I'd highly recommend just using Result<Option> and stop worrying about it.

You being able to compose/nest those base types and unwraping or matching them in different sections of your code is a strength not a weakness.
by openWrangler | 2025-05-06 19:12:14+00:00
It's not just you - OSS toolstacks can be sprawling and involve long manual processes while costs from most enterprise vendors are too steep for fully mapped observability.

Coroot is an open source project I'm working with to try and to tackle this. eBPF automatically gathers your data into a centralized service map, and then the tool provides RCA insights (with things like mapped incident timeframes) to help implement fixes quicker and improve uptime.

GitHub here and we'd love any feedback if you think it can help: https://github.com/coroot/coroot
by colechristensen | 2025-05-06 19:12:35+00:00
>They view it as a shortcut to problem solve and it isn't

Oh but it is, used wisely.

One: it's a replacement for googling a problem and much faster. Instead of spending half an hour or half a day digging through bug reports, forum posts, and stack overflow for the solution to a problem. LLMs are a lot faster, occasionally correct, and very often at least rather close.

Two: it's a replacement for learning how to do something I don't want to learn how to do. Case Study: I have to create a decent-enough looking static error page for a website. I could do an awful job with my existing knowledge, I could spend half a day relearning and tweaking CSS, elements, etc. etc. or I could ask an LLM to do it and then tweak the results. Five minutes for "good enough" and it really is.

LLMs are not a replacement for real understanding, for digging into a codebase to really get to the core of a problem, or for becoming an expert in something, but in many cases I do not want to, and moreover it is a poor use of my time. Plenty of things are not my core competence or anywhere near the goals I'm trying to achieve. I just need a quick solution for a topic I'm not interested in.
by mbesto | 2025-05-06 19:12:45+00:00
To date, LLMs can't replace the human element of:

- Determining what features to make for users

- Forecasting out a roadmap that are aligned to business goals

- Translating and prioritizing all of these to a developer (regardless of whether these developers are agentic or human)

Coincidentally these are the areas that frequently are the largest contributors to software businesses successes....not wether you use NextJs with a Go and Elixir backend against a multi-geo redundant multi sharded CockroachDB database, or that your code is clean/elegant.
by kschrader | 2025-05-06 19:12:58+00:00
Hi all, we started an experiment a few months ago to see if we could build an actual useful PM agent for software teams, and I think that we've come up with something pretty cool. Korey can take your ideas, flesh them out, and then break them down into tasks that you (or your AI) can build.

We estimate that it conservatively saved us about 100 hours of alignment in April alone, and it's getting better all of the time.

Here for questions/comments if anyone has them.
by mynameisash | 2025-05-06 19:13:06+00:00
It depends on where you're coming from. For me, Rust has replaced a lot of Python code and a lot of C# code, so yes, the Rust compilation is slow by comparison. However, it really hasn't adversely affected (AFAICT) my/our iteration speed on projects, and there are aspects of Rust that have significantly sped things up (eg, compilation failures help detect bugs before they make it into code that we're testing/running).

Is it a serious problem? I'd say 'no', but YMMV.
by bsnnkv | 2025-05-06 19:13:34+00:00
> Software doesn’t stay solved. Every solution you write starts to rot the moment it exists. Not now, not later, but eventually. Libraries deprecate. APIs change. Performance regressions creep in. Your once-perfect tool breaks silently because libfoo.so is now libfoo.so.2.

Since I started using Nix flakes to build everything and pin to specific versions this largely stopped being a problem for me. I happily run things I last touched many, many years ago without worrying about stupid stuff like this ^

> Burnout does not just come from overwork. It comes from overresponsibility.

I remain unconvinced by this, and the more years I rack up, the more I recognize the pattern of burnout having a direct relationship to alienation of labor.
by jackienotchan | 2025-05-06 19:15:28+00:00
AI crawlers have lead to a big surge in scraping/crawling activity on the web, and many don't use proper user agents and don't stick to any scraping best practices that the industry has developed over the past two decades (robots.txt, rate limits). This comes with negative side effects for website owners (costs, downtime, etc.), as repeatedly reported on HN (and experienced myself).

Do you have any built-in features that address these issues?
by Epa095 | 2025-05-06 19:15:29+00:00
In 1989 average NATO military spending was 4% of GDP, in 2014 it was dropped to 1.4%. The peace dividend is real, but it don't explain everything.

European countries had cheap higher education and healthcare in 1989 as well, even with higher military spending (and higher than USA has today, which is roughly 3.37%). Estonia and Poland both spend more than the US, and they both manage cheap higher education and public healthcare. This indicates that it's also a choice of priorities, where European countries seem more willing to increase taxes for these kind of services.

Personally I think it has something with the sense of belonging, where Europeans belong to a country, most of them with less than 30 million people. Americans are one in 350 million, so it makes sense to me that it's harder to feel 'part' of the same tribe as the other 350 million people.

But you are right that Europe will have to face some hard choices, both because we get less support from the US, and because Russia is more aggressive than before. Personally I am quite convinced that in 5-10 years most European countries will still have cheap higher education and healthcare, but maybe higher taxes.
by ElevenLathe | 2025-05-06 19:15:32+00:00
I work in gaming and this is very much the same attitude, though it is starting to change: with things like Xbox Game Pass, there are now theoretically revenues to be skimmed from older releases via subscription revenues, so there is at least lip service paid to proper archiving of working files and source code. It's still tough to make the case not to phone home or rely on publisher-hosted services.
by Seattle3503 | 2025-05-06 19:15:38+00:00
Absolutely, the compile times are the biggest drawback IMO. Everywhere I've been that built large systems in Rust eventually ends up spending a good amount of dev time trying to get CI/CD pipeline times to something sane.

Besides developer productivity it can be an issue when you need a critical fix to go out quickly and your pipelines take 60+ minutes.
by humbledrone | 2025-05-06 19:15:52+00:00
1. Anukari runs up to 16 entire copies of the physics model for polyphony, so 16 * 1024 * 48K (I should update the blog post)

2. Users can arbitrarily connect objects to one another, so each object has to read connections and do processing for N other entities

3. Using the full CPU requires synchronization across cores at each physics step, which is slow

4. Processing per object is relatively large, lots of transcendentals (approx OK) but also just a lot of features, every parameter can be modulated, needs to be NaN-proof, so on

5. Users want to run multiple copies of Anukari in parallel for multiple tracks, effects, etc

Another way to look at it is: 4 GHz / (16 voice * 1024 obj * 4 connections * 48,000 sample) = 1.3 cycles per thing

The GPU eats this workload alive, it's absolutely perfect for it. All 16 voice * 1024 obj can be done fully in parallel, with trivial synchronization at each step and user-managed L1 cache.
by freddydumont | 2025-05-06 19:16:11+00:00
"Nefarious" is indeed a great pick.

As a native French speaker, my favourites are those words that capture concepts better than their French equivalent.

Something as simple as "mind" for example. In French, you're stuck with "esprit" which also means "spirit" or "facultés mentales" which is quite annoying to use in conversation.
by godelski | 2025-05-06 19:16:15+00:00
> journalctl --grep is still much slower than grep on simple files

Idk what to tell you. You had a problem, showed the commands you used and the times it took. So I showed you a different way that took less than half the time to just dump and grep (which you said was faster)

My results don't match your conclusion.

> if you use ripgrep

I've been burned by ripgrep too many times. It's a crazy design choice, to me, to default filter things. Especially to diverge from grep! The only thing I expect grep to ignore are the system hidden files (dotfiles) and anything I explicitly tell it to. I made a git ignore file, not a grep ignore file. I frequently want to grep things I'm ignoring with git. One of my most frequent uses of grep is looking through builds artifacts and logs. Things I'd never want to push. And that's where many people get burned, they think these files just disappeared!

The maintainer also has been pretty rude to me about this on HN. I can get we have a different opinion but it's still crazy to think people won't be caught off guard by this behavior. Its name is literally indicating it's a grep replacement. Yeah, I'm surprised its behavior significantly diverges from grep lol
by mattlondon | 2025-05-06 19:16:16+00:00
The benchmarks (1) seem to suggest that o3 is in 3rd place after Gemini 2.5 pro preview and Gemini 2.5 pro exp (for text reasoning, o3 4th for webdev). o3 doesn't even appear on the openrouter leaderboards (2) suggesting is hardly used (if at all) by anyone using LLMs do actually do anything (such as coding) which makes one question if it is actually any good at all (otherwise if it was so great I'd expect to see heavy usage)

Not sure where your data is coming from but everything else is pointing to Google supremacy in AI right now. I look forward to some new models from Anthropic, xAi, Meta et al (remains to be seen if OpenAI has anything left apart from bluster). Exciting times.

1 - https://beta.lmarena.ai/leaderboard

2 - https://openrouter.ai/rankings
by cmrdporcupine | 2025-05-06 19:16:37+00:00
I worked in the chromium C++ source tree for years and compiling there was orders of magnitude slower than any Rust source tree I've worked in so far.

Granted, there aren't any Rust projects that large yet, but I feel like compilation speeds are something that can be worked around with tooling (distributed build farms, etc.). C++'s lack of safety and a proclivity for "use after free" errors is harder to fix.
Show HN: An agent to run your newsletter
by TheIndieBuilder | 2025-05-06 19:17:16+00:00
I've made an AI agent for Topical - the newsletter automation platform. You can chat to it from the UI but also over email! And MCP coming soon.

Topical is all about "human in the loop" newsletter automation, which means you are strongly encouraged to review, reword and edit your newsletter by hand before you send it to your subscribers. Nobody wants to read 100% AI slop! But 80/20 is usually fine because of how our tolerance changes as we read down a newsletter to the bottom.

This new agent makes adding your human input to the newsletter much easier as you can chat to the agent and describe changes you want to make. Either explicitly say "Make the intro say this..." or do the classic "reword it to sound more informatl" that all the AI tools have now. Try it out for free on the link above and let me know in the comments what you think.
by codingdave | 2025-05-06 19:18:04+00:00
You aren't missing a thing. LLMs produce code that is somewhat correct, but wrong enough that it needs correction. The people promoting "vibe coding" are saying that you can just tell it the concerns, and eventually it will land on something that works. Everyone else codes the corrections themselves. But AI simply does not produce working code without human interventions.

It also does not keep up to date with releases. That is not a valid expectation, and if you are working with leading-edge releases and libraries, LLMs will not help. On the same note, it cannot innovate - it will give you code that fits common conventions. Most of the time this works, but if you are doing truly novel work, its results will frustrate you more often than not.