MurphyA/hacker-news huggingface dataset:
by benmmurphy | 2025-06-04 12:36:29+00:00
this can easily happen in a BEAM system. say you have some shared state you want to access. you create a gen_server to protect this shared state. a gen_server is basically a huge mutex. the gen_server is just a normal beam process that handles requests sent to its message queue and then sends a reply message back. lets say it can process a request normally in 20us. so a 15ms pause would stack up 750 messages in its message queue. now maybe this is not enough to generate a huge outage on its own but maybe as part of your handling you are using the message queue in an unsafe way. so when you check the message queue for a message the BEAM will just search the whole message queue for a message that matches. there are certain patterns the BEAM is able to optimize to prevent the whole message queue being searched (i think almost every pattern is unsafe and the BEAM only optimizes the gen rpc style message patterns) . but if you are using an unsafe pattern when you have a message queue backlog it will destroy the throughput in the system because the time taken to process a message is a function of the message queue length and the message queue length becomes a function of how long it takes to process a message.

Also, the great thing is you might not even have an explicit `receive` statement in your gen_server code. You might just be using a library that is using a `receive` somewhere that is unsafe with a large message queue and now you are burned. The BEAM also added some alternative message queue thing so you are able to use this instead of the main message queue of a process which should be a lot safer but I think a lot of libraries still do not use this. This alternative is 'alias' (https://www.erlang.org/doc/system/ref_man_processes.html#pro...) which does something slightly different from what I thought which is to protect the queue from 'lost' messages. Without aliases 'timeouts' can end up causing the process message queue to be polluted with messages that are no longer being waited on. This can lead to the same problems with large message queues causing throughput of a process to drop. However, usually long lived processes will have a loop that handles messages in the queue.
by Shmungus | 2025-06-04 12:37:15+00:00
Thanks, really appreciate the thoughtful questions and skepticism (and totally agree: a “perfect” hallucination detector would be a truth oracle).

To your points:

1. Overconfidence and precision You're right that filtering on overconfidence alone could tank precision. That’s why the tool doesn’t treat it as a strict red flag, it’s one of several signals, and the final hallucination score is a weighted combination of multiple metrics (confidence, density, contradictions, progression, etc.). Overconfident phrasing tends to correlate with hallucinations in aggregate, but the idea is never to penalize all confident answers, just to flag the ones where that confidence is unjustified by the context or content.

2. Detecting hallucinations that require world knowledge Absolutely, those are the hardest cases. This tool doesn’t solve that. Instead, it acts as a proxy evaluator:

Factual density gives a rough measure of “how many claims are being made”

Overconfidence vs. ambiguity highlights where a model might be bluffing

Logical coherence and contradiction checks flag when a model violates internal structure (not ground truth) But it won’t catch the subtle world-knowledge misses (like fake court cases or made-up API calls) unless you pair it with a grounded context or use external validators.

The long-term hope is: use this tool to raise suspicion, not declare judgment. It's a cheap sanity layer, a “weak oracle” that’s fast, pluggable, and good enough to catch the dumb stuff before you escalate to expensive validators or human review.
by sam-cop-vimes | 2025-06-04 12:37:38+00:00
Maybe not impossible, but very hard indeed. Imagine this was the start of the search engine era. Everyone jumped on Google as it was hip and they promised not to be evil. We all know how that panned out.

What we need is open source implementations which governments can take and provide for their citizens. Yes I know many will laugh at this idea, but in principle it is the right thing to do. If these services become a part of everyday life, it is the responsibility of government to make it available to their citizens in a way that is not harmful.
by potato3732842 | 2025-06-04 12:38:13+00:00
>Cheap fossil cars with shitty automatics can be quite stressful to drive

This is a self inflicted problem. They're programmed for fuel economy (we're talking a small fraction of an MPG here) at the expense of drivability. The might even get worse fuel economy in practice because drivers learn you gotta floor them to tell the compute "no I'm serious, give me the ponies".
by numpad0 | 2025-06-04 12:38:24+00:00
> Porn just isn’t a necessity even if some folks really enjoy it.

So isn't speech! Not everyone need privileges to talk about human rights or democracy. China's doing fine with neither(officially).

By the way, unofficially and anecdotally, coastal Chinese prostitution as well as porn seem to be getting bigger and more wicked faster. I wonder which comes first: puritanism, or erosion of industrial base. This whole slow movement could be just a symptom than culprit.
by berdario | 2025-06-04 12:38:37+00:00
> if this in fact is the first recorded death that is directly linked to the car driving itself

There have been 51 such deaths

https://en.wikipedia.org/wiki/List_of_Tesla_Autopilot_crashe...

> There seems to be some kind of weird double standard where we let people get drivers licenses and run around causing X% accidents per year

I'm all for safe self driving, but the double standard here it's what's allowing Tesla to continue unpunished.

The autopilot/FSD algorithm/AI/implementation is relied upon as if it was a driver... But when a driver kills someone the driver is (or should be) prosecuted, and shouldn't go back immediately on the road before proving that they can drive a vehicle safely.

For humans you can blame "errors of judgement"... A person might not always reliably behave exactly in the same way when in the same situation (in both good and bad ways, e.g. distraction)

But FSD instead means that a Tesla car using the same FSD version, in the same road and sky conditions, will always behave in the same way...

If a human would cause an accident on that Arizona road in the same way, you could take their driving license away, and know that other road users don't risk being maimed by them anymore.

The double standard here instead allows for thousands of other Teslas, with the same FSD version, to drive on the same road... Knowing full well that it's only a matter of time before the same conditions happen again, and a different Tesla would replicate the crash, endangering other road users.
by Gracana | 2025-06-04 12:38:57+00:00
I wonder what happened.

IMO their shredder does not look very safe. It's a device that can cause digital amputation and entanglement and operators are exposed to it 100% of the time they're using the machine, which would classify it as a high-risk hazard. It really should have guarding to make the hazard inaccessible, like a longer infeed chute and a start/stop interlock that prevents it from running unless there's a bin underneath. I suspect it's going to be hard to argue that procedure & policy were appropriate ways to address the risk.
by diego_moita | 2025-06-04 12:39:01+00:00
The biggest problem I have with Claude Code is that if, for some reason, the past discussion is not in the terminal then there is no way to review it. This is crucial to understand why my project went one way and not the other.

Aider might be less polished but at least it keeps an .md file with the chat history. That's why I am back to Aider + claude-opus-4.
by furkanonder | 2025-06-04 12:39:11+00:00
Location: Turkey

Remote: Yes

Willing to relocate: Yes

Technologies: Python, C, Django, Django REST Framework, PostgreSQL, Redis, Celery, eBPF, XDP

Résumé/CV: https://pypy.trade/

Email: furkanonder@protonmail.com

Backend Developer & Systems Engineer with extensive experience in Python development, specializing in Django and Django REST Framework. Currently working on interest-free vehicle repair system, implementing third-party integrations including payment gateways and API services. Designed and optimized database queries for complex financial calculations, developed scalable APIs following best practices for performance and security, and implemented robust task management systems using Celery and Redis.

CPython contributor and Python Triage team member actively involved in the development of Python's reference implementation, with deep understanding of interpreter internals and virtual machine architecture. Interested in compiler development, systems programming, and infrastructure roles that leverage both high-level Python expertise and low-level C/systems knowledge.

Specialized in systems-level programming with hands-on experience in eBPF/XDP for network packet processing, ARM bare metal programming, and Linux kernel development. Extensive Linux server administration background including email/DNS server deployment, database clustering, and backup system implementation. Experience in cybersecurity product development and network infrastructure management. Built experience with container orchestration using Kubernetes and Argo CD, implemented comprehensive testing strategies with pytest, and collaborated effectively in Agile development environments.
by captainbland | 2025-06-04 12:40:28+00:00
The only real wild card to me is state sponsorship. Everywhere else I expect such a capital intensive tech industry to approach winner (or maybe duopoly) takes all over time.

My view is that right now, because of the willingness of corporations and other investors to swallow short term (but massive) losses on this, we're basically in AI fiscal fantasy land.

The question we should be asking is how do we get access to these local models in the first place? It's all based on the work of these hyper expensive base models as the best small models are quantisations and distills. None of this works as soon as the profit motive comes into play and companies start gatekeeping effectively, which they will.
by gwbas1c | 2025-06-04 12:40:48+00:00
Then figure out how to structure the assignment to make students show their work. If a student doesn't understand the concept, it will show in how they prompt AI.

For example, you could require that students submit all logs of AI conversations, and show all changes they made to the code produced.

IE, yesterday I asked ChatGPT how to add a copy to clipboard button in MudBlazor. It told me the button didn't exist, and then wrote the component for me. That saved me a bunch of research; but I needed to refactor the code for various reasons.

So, if this was for an assignment, I could turn in both my log from ChatGPT, and then show the changes I made to the code ChatGPT provided.
Ask HN: Test-Driven Physics?
by behnamoh | 2025-06-04 12:41:22+00:00
In software engineering we often have a suite of tests and we make sure any changes in the code base still passes all (most) of these tests.

Are these such tests for physics? For example, someone comes up with a new theory (Relativity++) and wants to make sure this is consistent with the rest of our knowledge about physics. Should they make this verification manually, or is there a set of tests we put this new theory into to see if it passes them?

Example: Let's say Relativity++ is formulated in a certain way. Then we would probably want to make sure it doesn't violate the conservation of energy, objects moving ≤ c, etc.
by spwa4 | 2025-06-04 12:42:21+00:00
How about: it's flopPY, every second?

Seriously brains are neither analog nor digital, they use spike-trains. Very comparable to "clockless" digital circuits. To what we use in chips, synchronized tick-based calculation: it's not comparable. Judging by human but especially animal reaction times: one way to quantify it is to say it has about 10000 flop per second. technically the human head has 2 speeds: one type of cells with 1000 synapses, that can calculate about 10 times per second (the "animal brain" or reptile brain, the cortex). And there are cells with 10000 synapses that fire on average 1 time per second (the "human brain", the neocortex), which should be roughly the same capacity. Of this network type it is known that more synapses means more accurate, more long-term planning. Faster firing means faster responses. Reptiles are stupid, but despite reptiles being cold blooded we mammals have zero chance to respond in time to an attack. It's not happening. And yes, cats have a built-in trick that gives them a fighting chance, but is only ever going to work in small animals (you need muscles powerful enough to throw yourself several body lengths into the air, and you need to be small and light enough that you survive being thrown in the air several body lengths without coordination, and land without injury. Both properties that humans, or any animal 1m or bigger will never have. And of course, that reflex is an incredible source of youtube videos)

The problem with spike trains is that it's tough to say if a zero signal means anything. On the one hand, all zeros means the cell isn't using energy, and that is incredibly efficient (nanowatt, not even multiple nanowatts). Everything about your mind is designed to almost always be all zeros. A spike means milliwatt power usage for .15-.2 seconds after the spike. Given the amount of neurons, our brain would rapidly cook itself if the average firing rate even just double, in fact that is exactly what happens with epilepsy patients.

The above calculations only apply if all zeros means the network isn't doing anything. If that assumption is wrong, you should probably multiply those figures with the temporal accuracy of the spikes, which is incredible, 3-4 nanosecond. So you'd have to multiply the figures by 300 million, at which point the human mind still is 1000 times stronger than even a full stargate deployment. That sounds incredible, but it really isn't.

If you want to see incredible figures, figure out how much calculations natural selection does for a simple ocean-based bacterial species (assuming 1 cell division = one calculation, if you assume a more reasonable 1 allele combination = 1 flop, you're another 3-5 orders of magnitude higher). Bacteria do hundreds of orders of magnitude more thinking than all humans combined.
by mythz | 2025-06-04 12:42:23+00:00
Nice, only got the pro plan and didn't want to get Nickel and Dimed per usage so ended up ignoring claude code, but will give it another go now to see how far I can get with the Pro plan.

Turned out to be a blessing as it led me to discovering augmentcode.com which I'm now using. It's a monthly usage cap but has a unique context engine where it indexes your entire code base which gives its agents access to a lot more content about your code base which helps when adding features to match your existing implementations without needing to explicitly feed it context in every agent message. It still uses Claude Sonnet 4 [1] under the hood so the results are great.

Blows me away what AI agents are capable of these days, it's especially nice when they end up creating new Tailwind UIs (Vue) that's better looking than what I was even thinking about creating.

[1] https://www.augmentcode.com/blog/claude-sonnet-4-the-best-mo...
by II2II | 2025-06-04 12:42:59+00:00
To be fair to Microsoft, this was in their help system. They probably wanted to be as clear as possible, to avoid confusing people in an application that was supposed to help people. It is not as though they were removing icons from all applications. It was also a time when companies were exploring how to present GUIs and many people were much more timid about experimenting with computers. Where people today become frustrated with constantly changing interfaces, people then were more likely to fear breaking things.

You are also right about learning things, but also look at it from a different perspective: would a person have even realized that a hamburger menu did something 35 years ago, particularly with today's flat UIs?
by davidrupp | 2025-06-04 12:43:11+00:00
I agree that it wouldn't cause the doors to open any sooner for deplaning, but once they did, people could just ... leave. The people standing in the aisle with their luggage out of the compartment are the ones who started in the aisle seats. People in the other seats need to get out, reach up, pull down, get organized. Sometimes they have to salmon their way back from their actual seat to the compartment several rows behind them because that's where they had to stow their carry-on because the people seated in row 30 put their bag over row 16 when they boarded[1].

Similarly, for takeoff I agree that it wouldn't necessarily save time, net. But it would help with the frustration of standing in line, backed up on the jetway while everyone is struggling with setting up the initial conditions for the deplaning scenario I described above. At least people could get seated sooner and be comfortable for longer while they're waiting for takeoff.

[1] Based on a true story.
by mwest217 | 2025-06-04 12:43:12+00:00
Disclaimer: I work at Google but not on cloud. Opinions my own.

I think the reason this doesn’t get prioritized is that large customers don’t actually want a “stop serving if I pass this limit” amount. If there’s a spike in traffic, they probably would rather pay the money to serve it. The customers that would want this feature are small-dollar customers, and from an economic perspective it makes less sense to prioritize this feature, since they’re not spending very much relative to customers who wouldn’t want this feature.

Maybe if there weren’t more feature requests to get prioritized this might happen, but the reality is that there are always more feature requests than time to implement them, and a feature request used almost exclusively by the smallest dollar customers will always lose to a feature for big-dollar customers.
by layer8 | 2025-06-04 12:43:46+00:00
The name is a bit grandiose. It’s a fairly obvious application of (meta-)genetic programming to LLMs, which has been around for many decades.

https://en.wikipedia.org/wiki/Genetic_programming

It also reminds me of Core War: https://en.wikipedia.org/wiki/Core_War#Core_War_Programming
by ekr____ | 2025-06-04 12:44:23+00:00
Generally, ultra food preferences are really personal. The broad facts are: (1) you need a certain level of calories to keep going and it's more than you'd think (though see below) (2) running long distances is hard on your digestive system. The consequence is that it's a challenge to consume enough calories without causing enough gastric disruption that you then can't consume enough.

Within those broad parameters, you see many different strategies, with some ultrarunners preferring "real food", and others preferring engineered food (gels, sports drinks, etc.) Famously, Courtney Dauwalter is a fan of actual candy. Aid stations at a typical race will often have both of these, and at a longer race you'll often see cooked hot food like quesadillas, bacon, pancakes, etc., especially during the overnight sections where it can get quite cold. In general, people try to experiment and find what works for them, which can change as the race goes on.

Contra the OP, there's not much evidence that you need fat intake in an ultra for energetic reasons. Even someone with quite low body fat is carrying plenty of calories in fat to do an ultra. However, some people find that taking in fat helps keep their stomach in line, especially after hours of consuming mostly simple carbohydrates.

There's a huge amount of food engineering work on trying to design nutrition that doesn't cause gastric distress. For example, Maurten pioneered a sports drink and gels that use hydrogel to encapsulate the carbohydrate, which is supposed to reduce distress. In the past few years there has been a lot of interest in the elite ranks in "super high carbohydrate" fueling. The conventional wisdom was that most people couldn't process more than about 75g (300 calories) of carbohydrate an hour, but recently runners like David Roche have been pushing this to 90g or 100g with success, and you see products designed to let you do this easily.

One thing that the OP doesn't really covers is weight. In a typical ultra, you're never going that long between aid stations, so you can pick up food there. Even if you don't like the nutrition the race is providing you can often give them a "drop bag" containing your preferred products which will be delivered to an aid station where you can pick it up. However on an adventure run, you may well have to carry all of your nutrition all the way. The author of the OP suggests going to stores, but this doesn't really work in the backcountry. I've done 12+ hr runs in the Sierra [0] and the Grand Canyon where you just had to carry everything the whole way, and suddenly you become really conscious of the water content of your gels, which can easily be 50% water by mass.

[0] https://educatedguesswork.org/posts/northern-yosemite/ https://educatedguesswork.org/posts/tenaya-loop2/
by timr | 2025-06-04 12:44:30+00:00
> I don't really doubt that AI can put together your Nth Rails backend that does nothing of note pretty solidly

Ha. Funny you should say that....recently I've been using AI to green-field a new Rails project, and my experience with it has been incredibly mixed, to say the least.

The best agents can, more or less, crank out working code after a few iterations, but it's brittle, and riddled with bad decisions. This week I had to go through multiple prompt iterations trying to keep Claude 3.7 from putting tons of redundant logic in a completely unnecessary handler block for ActiveRecord::RecordNotFound exceptions -- literally 80% of the action logic was in the exception handler, for an exception that isn't really exceptional. It was like working with someone who just learned about exceptions, and was hell-bent on using them for everything. If I wasn't paying attention the code may have worked, I suppose, but it would have fallen apart quickly into an incomprehensible mess.

The places where the AI really shines are in boilerplate situations -- it's great for writing an initial test suite, or for just cranking out a half-working feature. It's also useful for rubber ducking, and more than occasionally breaks me out of debugging dead ends, or system misconfiguration issues. That's valuable.

In my more cynical moments, I start to wonder if the people who are most eager to push these things are 0-3 years out of coding bootcamps, and completely overwhelmed by the boilerplate of 10+ years of bad front-end coding practices. For these folks, I can easily see how a coding robot might be a lifeline, and it's probably closer to the sweet spot for the current AI SOTA, where literally everything you could ever want to do has been done and documented somewhere on the web.
by taylodl | 2025-06-04 12:46:09+00:00
It's not a mutually exclusive choice. That's the point.

Let me counter your collective auto collision/injury rate. Let's suppose the only injuries sustained in a year are the deaths of school children exiting a school bus. There's no rhyme or reason to why sometimes the driver assist mows down children. BUT, collectively, there are far, far fewer deaths. Say 1,000 school children are killed per year, but those are the only deaths. That's far less than the 40,000 Americans killed per year in auto crashes. So that's good, right? No. Of course not.

We want these systems to not make the same mistakes humans make and to not make mistakes humans wouldn't make. Do that, and your fatalities will decrease in an acceptable manner.