MurphyA/hacker-news huggingface dataset:
by petsfed | 2025-04-10 18:23:56+00:00
That mandatory debriefing was part of the reason why the data was better. That's what I'm saying.

If the relevant Antarctic authorities were performing an end-of-season debrief with each and every employee, following the paradigm used for marine fisheries observers, they might go from "59%, but some punter can claim its actually 14%" to "actually its like 70%, and here's the mountains of data to prove it".

I specifically referenced the marine-fisheries example because, outside of grievous injury or death, the observers cannot leave until the captain of the boat they're narc'ing (your words) on chooses to return to port, and only if the captain of that boat returns to a port they can easily go home from. Which is a pretty serious problem when the harasser/assaulter is the captain. The analogy to Antarctica is pretty strong in that light.
by Symmetry | 2025-04-10 18:24:14+00:00
Life on Earth was dependent on chemosythesis for the better part of a billion years before photosynthesis was developed. Photosynthesis is really very complex so I think we can rule that out as an initial form of life.

Hydrothermal vents are a great source of chemical energy in the form of hydrogen, hydrogen sulfide, or other chemicals that can be reacted for energy where the come out into the sea. But you could imagine other sorts of chemical energy driven by other geologic or atmospheric processes that life might bootstrap from.
by tomxor | 2025-04-10 18:24:34+00:00
> I think the issue is that it's so tempting to lean on AI.

This is not the root cause, it's a side effect.

Student's cheat because of anxiety. Anxiety is driven by grades, because grades affect failure. To detect cheating is solving the wrong problem. If most of the grades did not directly affect failure, student's wouldn't be pressured to cheat. Evaluation and grades have two purposes:

1. Determine grade of qualification i.e result of education (sometimes called "summative")

2. Identify weaknesses to aid in and optimise learning (sometimes called "formative")

The problem arises when these two are conflated, either by combining them and littering them throughout a course, or when there is an imbalance in the ratio between them i.e too much of #1. Then the pressure to cheat arises, the measure becomes the target, and focus on learning is compromised. This is not a new problem, student's already waste time trying to undermine grades through suboptimal learning activities like "cramming".

The funny thing is that everyone already knows how to solve cheating: controlled examination, which is practical to implement for #1, so long as you don't have a disruptive number of exams filling that purpose. This is even done in sci-fi, Spok takes a "memory test" in 2286 on Vulkan as a kind of "final exam" in a controlled environment with challenges from computers - it's still using a combination of proxy knowledge based questions and puzzles, but it doesn't matter, it's a controlled environment.

What's needed is a separation and balance between summative and formative grading, then preventing cheating is almost easy, and student's can focus on learning... cheating at tests throughout the course would actually have a negative affect on their final grade, because they would be undermining their own learning by breaking their own REPL.

LLMs have only increased the pressure, and this may end up being a positive thing for education.
by vacuity | 2025-04-10 18:24:59+00:00
In fact, Barrelfish is based on running a microkernel per core, and makes good use of this design to better adapt to hardware diversity.

I understand why Linux develops everything in one place. This makes it far easier to manage. However, it is far more difficult to configure and specialize kernels. (Saw a paper where core operations of default Linux had gotten slower over the years, requiring reconfiguration.) Or to badly paraphrase Ingo Molnar: aim for one of two ideals in operating system design: the one that's easiest for developers to change/maintain and the one that maximizes performance.
by noahl | 2025-04-10 18:25:13+00:00
There are some semantic differences compared to POSIX filesystems. A couple big ones:

- You can only append to an object, and each object can only have one writer at the time. This is useful for distributed systems - you could have one process adding records to the end of a log, and readers pulling new records from the end. - It's also possible to "finalize" an object, meaning that it can't be appended to any more.

(I work on Rapid storage.)
by rfarley04 | 2025-04-10 18:25:20+00:00
To each their own! When I watch Ross ask how to beat up a woman in the street with eyes bugging out of his head there's a pretty big difference between a laugh track and no laugh track. Just like Garfield comics hit very different when you realize that John is actually talking to himself.

I don't "reflexively dismiss" all shows with a laugh track. Some of Friends is genuinely hilarious. But a lot of it is only funny, to me, when surrounded by others laughing.
by fpgaminer | 2025-04-10 18:25:40+00:00
https://gist.github.com/fpgaminer/8782dd205216ea2afcd3dda29d...

That's the model automation. To evaluate the prompts it suggests I have a sample of my dataset with 128 examples. For this particular run, all I cared about was optimizing a prompt for Llama 3.1 that would get it to write responses like those I'm finetuning for. That way the finetuning has a better starting point.

So to evaluate how effective a given prompt is, I go through each example and run <user>prompt</user><assistant>responses</assistant> (in the proper format, of course) through llama 3.1 and measure the NLL on the assistant portion. I then have a simple linear formula to convert the NLL to a score between 0 and 100, scaled based on typical NLL values. It should _probably_ be a non-linear formula, but I'm lazy.

Another approach to prompt optimization is to give the model something like:

I have some texts along with their corresponding scores. The texts are arranged in ascending order based on their scores from worst (low score) to best (higher score). Text: {text0} Score: {score0} Text: {text1} Score: {score1} ... Thoroughly read all of the texts and their corresponding scores. Analyze the texts and their scores to understand what leads to a high score. Don't just look for literal patterns of words/tokens. Extensively research the data until you understand the underlying mechanisms that lead to high scores. The underlying, internal relationships. Much like how an LLM is able to predict the token not just from the literal text but also by understanding very complex relationships of the "tokens" between the tokens. Take all of the texts into consideration, not just the best. Solidify your understanding of how to optimize for a high score. Demonstrate your deep and complete understanding by writing a new text that maximizes the score and is better than all of the provided texts. Ideally the new text should be under 20 words.

Or some variation thereof. That's the "one off" approach where you don't keep a conversation with the model and instead just call it again with the updated scores. Supposedly that's "better" since the texts are in ascending order, letting the model easily track improvements, but I've had far better luck with the iterative, conversational approach.

Also the constraint on how long the "new text" can be is important, as all models have a tendency of writing longer and longer prompts with each iteration.
by janalsncm | 2025-04-10 18:25:52+00:00
> Isaac Asimov describes artificial intelligence as “a phrase that we use for any device that does things which, in the past, we have associated only with human intelligence.”

This is a pretty good definition, honestly. It explains the AI Effect quite well: calculators aren’t “AI” because it’s been a while since humans were the only ones who could do arithmetic. At one point they were, though.
by joe_the_user | 2025-04-10 18:26:37+00:00
I recall Stanford's past AI Reports being substantial and critical some years ago. This seems like a compilation of many small press releases into one large press release ("Key take away: AI continues to get bigger, better and faster"). The problem is that AI went from universities to companies and the publications of the various companies themselves then went from research papers to press releases/white papers (I remember OpenAI's supposed technical specification of GPT-something as a watershed, in that actually involved no useful information but just touted statistics who context the reader didn't know).
by lxgr | 2025-04-10 18:26:49+00:00
Do you know if fitness trackers with integrated pulse oximeters can detect sleep apnea?

My Apple Watch has an oximeter (I bought it before that feature was pulled in the US), and there's a "sleep apnea notifications" section in the "Health" app, but I'm not sure if the absence of notifications implies an absence of a problem.

Would sleep apnea always be visible as a lower blood oxygen level when sleeping, or can there be negative effects even without that?
by fooker | 2025-04-10 18:26:55+00:00
Every piece of software in existence calls functions and composes functions, there's no reason to attach 'combinator' to everything.

I do not doubt the correctness of the term, just the point of naming it. The fact that people write parsers all the time using these concepts without using the name proves that the name was not a great one.

> It's a term that has existed in logic for over a century, from before computers existed.

Yes, that's a great reason to not overload mathematical terms for random things. Monads and monoids are a great example of when this goes a bit wrong.

>It seems to me that no amount of renaming of terms will fix people's lack of curiosity or lack of desire to learn or understand.

Yes, I agree. Most people are just writing programs and solving problems, and moving on with their lives. It's weird to insist that they call something by a specific name if it's a simple idea that thousands of people have developed independently.
by Suppafly | 2025-04-10 18:27:02+00:00
>The solution is making all homework optional and having an old-school end of semester exam.

Not really. While doing something to ensure that students are actually learning is important, plenty of the smartest people still don't always test well. End of semester exams also tend to not be the best way to tell if people are learning along the way and then fall off part way through for whatever reason.
by famouswaffles | 2025-04-10 18:27:05+00:00
>This is the dimensionality mentioned in the adjacent post

LLMs are only a few years old but symbolic ai was abandoned for NLP, Computer Vision etc long before that. Why ? Because the alternative was just that bad and more importantly, never seemed to really budge with effort. Companies didn't wake up one morning and pour hundreds of millions into LMs. In fact, NNs were the underdog for a very long time. They poured more and more money into it because it got better and better with investment.

There is zero reason to think even more dimensionality would do anything but waste even more time. At least the NN scalers can look back and see it work in the past. You don't even have that.

>an LLM trained on descriptions of dogs is going to hallucinate when an otherwise sensible query about dogs doesn't match its training. As others have said more elegantly than I will, this points to a pretty different cognitive model than humans have; human beings can (and do) give up on a task.

It doesn't take much to train LLMs to 'give up'. Open AI talk about this from time to time. It's just not very useful with a tendency to overcorrect. And humans hallucinate (and otherwise have weird failure modes) all the time. We just call them funny names like dunning kruger and optical illusions. Certainly less than current SOTA LLMs but it happens all the same.

>I feel like I've had to say this a few times in threads now: none of this is to imply that Cyc was a success or would have worked.

The point is not about Cyc. They're hardly the only attempt at non-monotonic logic. The point is that they should work much better than they do if there's anything to it. Again, forget recent LLMs. Even when we were doing 'humble' things like spelling error detection & correction, text compressors, voice transcription boosters, embeddings for information retrieval, recommenders, knowledge graph creation (ironically enough), machine translation services, etc these systems were not even in the conversation. They performed that poorly.
by throwme0827349 | 2025-04-10 18:28:26+00:00
Love GMG, glad to see it at number one here. It's really quite amazing how much funnier and yet more profound it is without Garfield. If you like this, you might also enjoy Nietzsche Family Circus: https://www.nietzschefamilycircus.com/

I used to have one stuck to the door of my doom room. No one laughed. :(
by r00fus | 2025-04-10 18:29:03+00:00
There is rationality if you think like an autocrat - the goal is to crush dissent and any avenues of dissent so that people "self-censor" in fear.

Combine with upcoming extra-judicial detentions of citizens (we are already detaining legal immigrants in El Salvador), it's required to create a culture of fear and submission.

That it happens to be against a specific group of people is irrelevant - this is the tip of the spear.
by neonsunset | 2025-04-10 18:29:33+00:00
You can use newer LangVersions with older targets, including .NET Framework. A good deal of features can be made work by just setting LangVersion: 13 and doing dotnet add package PolySharp. But of course this won't bring missing span-based APIs, new ASP.NET Core and EF Core, etc.

However, the workplaces that adamantly refuse to upgrade also select for developers who may not be familiar with pattern matching or other features at all, and never read release notes.
by trod1234 | 2025-04-10 18:30:20+00:00
Unfortunately few young people actually learn and know this, and many workers are selected to exploit these type of blindspots many young people have for exploitation. This is true in tech especially.

Management often would choose the candidates who were young because they didn't ask questions, they followed directions, they didn't know their rights, and they were desperate for experience to kickstart their careers. They also would require legalese that would steal any potential competing ideas they ended up making outside of work through inventions agreements, and this trend still occurs today.

I've walked away from several MSP jobs in the past because they took approaches like this and was grateful for my past experience and sticking to my principles.

The hard sell pressure, requirements to agree before they'll send you the paperwork (which require conditional agreement pending review), and unwillingness to be reasonable (i.e. your given it on a saturday and it must be agreed to by Monday (few lawyers can review things over the weekend in that timeframe), followed by a withdrawal of any prospective offer for simply asking if parts of the employment contract were negotiable given valid concerns are just a few major red flags I regularly look for in employers today.

If the employer is going to rescind an offer because you ask questions, for me its not just them doing that. Its them triggering a revocation of my conditional acceptance and its mutually rescinded, and I make that clear in my final communication and never apply for those companies again.

Eventually the lack of talent available at these places, through their own actions, will have an impact and drive them towards insolvency.
by Suppafly | 2025-04-10 18:30:25+00:00
>So how do I answer my child when he asks "Why should I learn to do X if I can just ask an LLM and it will do it better than me"

Realistically it comes down to the idea that being an educated individual that knows how to think is important for being successful, and learning in school is the only way we know to optimize for that, even if it's likely not the most efficient way to do so.
by tylertreat | 2025-04-10 18:31:12+00:00
I thought Crossplane v2 was only a design proposal at the moment: https://github.com/crossplane/crossplane/pull/6255

But I guess there is an actual preview implementation now? https://docs.crossplane.io/v2.0-preview/

The comparison on Kro would definitely be good to include as there are quite a few similarities. I can write up more on how it compares in a bit.
by happytoexplain | 2025-04-10 18:31:57+00:00
We certainly do not have to choose between those two extremes.

More importantly, you said:

- One side wants nobody to care about our differences, and that erases identity.

- The other side wants to celebrate everyone's differences, and that reinforces identity.

This seems unrealistic, and the wording is starkly one-sided (in terms of painting one side as "wrong" and the other as "right"). You're splitting on the right metric, but the wrong dimension.

One side wants nobody to care about our differences in negative contexts ("X people are bad in some way", "Y people are better than X people"), and wants to celebrate our differences in positive contexts (cultural exposure, diversity, etc). However, some of the details are often unrealistically idealistic.

The other side wants to neither ignore nor celebrate differences. This side is simpler, and has real benefits, but makes more room for the human tendency to express difference-based negativity, which ranges from understandable to horrifying. However, it doesn't suffer from as much of the complication of idealism.
by dceddia | 2025-04-10 18:32:17+00:00
I have the weirdest calendar problem: if someone sends an invite to my Fastmail account, it gets swallowed up by Gmail. It never shows up in Fastmail, and in the calendar invite it appears as if it was sent to my Gmail address even though it wasn’t. It’s the strangest thing and I don’t know how to stop it or even what’s causing it (Fastmail support also had no idea).
by sbalnojan | 2025-04-10 18:32:32+00:00
I’m currently in a product role, but I used to lead marketing—and when our new (and very fresh) marketing lead asked for some structure, I realized I’d never actually written down the process I relied on back then.

So I did. This is the full 6-week focus plan I would’ve wanted in my first B2B SaaS marketing job. It's heavy on frameworks, light on fluff, and painfully honest about where we lost time by doing too much too soon.

Figured it might help others in similar spots.
by ajross | 2025-04-10 18:32:47+00:00
It's only surprising when you phrase it like that. Real bugs are interactions. I mean, sure, if you're asked to review a pull request like "patient_info: Use google to track MRI data", it's a no brainer.

But no doubt the analytics blurb was stuffed into a generic "top_level_page" generator or whatever. And it happens that the PHI wasn't firewalled by design, and pulled it in by default. Or it was firewalled, but then someone forgot and migrated the "patient_info_page" templates to a generic framework, etc...

Security is really, really hard. And one of the worst responses to a mistake like this is to tut-tut about how obvious a mistake and how easy to avoid it was. Believing that secure software is a matter of "not making mistakes" makes you more likely to write such bugs and not less, because you won't take the time to establish clear boundaries from the start.
by ivraatiems | 2025-04-10 18:33:02+00:00
No idea about Apple Watch, but wife, definitely. My wife noticed it. (She's also a doctor, but anyone can identify the sound of choking and gasping, which is what it sounds like.)

I can't give medical advice, but: Record yourself on your phone overnight and listen back. If you hear loud snoring with gaps or pauses in between, and especially any sort of choking, gasping, or coughing sound, and you don't remember doing it... definitely look into it further.
by beloch | 2025-04-10 18:33:16+00:00
What we used to think of as "AI" at one point in time becomes a mere "algorithm" or "automation" by another point in time. A lot of what Asimov predicted has come to pass, very much in the way he saw it. We just no longer think of it as "AI".

LLM's are just the latest form of "AI" that, for a change, doesn't quite fit Asimov's mold. Perhaps it's because they're being designed to replace humans in creative tasks rather than liberate humans to pursue them.
by ivraatiems | 2025-04-10 18:34:30+00:00
I'm not a doctor, can't give medical advice. This is my understanding:

> Would sleep apnea always be visible as a lower blood oxygen level when sleeping, or can there be negative effects even without that?

It would not always be visible, no, if your AHI was low enough. It is still damaging even if your blood oxygen is okay, because you are waking up to keep it that way. You're not getting enough sleep as a result.

You might try the recording trick I mentioned below to listen for sounds of it, but if you're unsure at all, I'd consider seeing a sleep doc.
by perching_aix | 2025-04-10 18:34:32+00:00
Windows key + dot, then type in warning / if it was among the last ones you used, use the arrow keys, and hit Enter to insert it.

If you use whatever OS other than Windows, I'm sure there are similar flows available if you search for it. And since it's just Unicode, I'm sure there are numpad based keybinds available too.
Show HN: BrandifyIt – AI-Powered Platform for Automated Content (Crowdfunding)
by kameni_nt | 2025-04-10 18:34:32+00:00
Hello HN,

I’m excited to share our work on BrandifyIt – an AI-powered platform designed to radically streamline the digital content creation process. We built BrandifyIt to tackle the endless challenges of manual content generation by leveraging cutting-edge machine learning algorithms. The idea is simple: use AI to produce creative, on-brand copy fast, efficiently, and at scale.

What it does:

Automated Content Generation: Our system learns your brand’s tone and style to generate high-quality, tailored content that resonates with your target audience.

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Data-Driven Personalization: Real-time analytics help adjust content dynamically based on audience engagement metrics and trending data.

Creative Dashboard: A user-friendly interface lets you easily adjust parameters and fine-tune content outputs to your precise requirements.

We’re currently in a crowdfunding phase via FundRazr (link in comments) to support further development, enhance our infrastructure, and gather early adopter feedback. I’d love to hear your thoughts on our approach, any technical feedback on the AI implementation, and suggestions for improvement. We're especially interested in how similar tools might scale or integrate with existing workflows and any potential pitfalls you foresee.

Thanks for checking it out, and I look forward to your feedback!
by anon6362 | 2025-04-10 18:34:39+00:00
That's the rub. It provides scalability for very large organization, of which, there are few. It's similar to running OpenStack. Meta also has some projects like this, such as buck2 which lacks the really good virtual FS acceleration stuff (eden). Megacorp FOSS tend to skew towards offering whizbang features that are incomplete, complicated, poorly documented, and require a lot of extra work.
by janalsncm | 2025-04-10 18:34:49+00:00
I have soured on UBI because it tries to use a market solution to deal with problems that I don’t think markets can fix.

I want everyone to have food, housing, healthcare, education, etc. in a post scarcity world. That should be possible. I don’t think giving people cash is the best way to accomplish that. If you want people to have housing, give them housing. If you want people to have food, give them food.

Cash doesn’t solve the supply problem, as we can see with housing now. You would think a rise in the cost of housing would lead to more supply, but the cost of real estate also increases the cost of building.