skysight-inc/HackerNews huggingface dataset:
I wish the morality section were more direct - either admitting that the author has no issue with "grey areas" (a euphemism if I've ever seen one) or that money outweighs their moral concerns. That would feel more intellectually honest.

When it comes to the morality of work, I think it's okay to be selfish with your ideals - whether that means prioritizing money over ethics or refusing to engage with something that conflicts with your values, regardless of your actual personal impact. I have no problem accepting that it's not my responsibility to make ICE more ethical.
That's why this is more of an attack on the Hacker News demographic, not Grandma.

If you had a real startup doing real things in healthcare, this is an intentional spoke in your wheels.
Laws are just words
> Only 2% of the population has “let’s go after BigTech” on their priority list according to the article.

> Breaking up BigTech doesn’t make their lives better.

That's conjecture. What people think about tech regulation does not in any way correlate to how the regulation works in practice. Most Americans probably didn't understand why Microsoft was a monopoly, but that didn't mean they weren't abusing monopoly power.

One might even argue there's an correlation between resistance to regulation and the importance of that regulation.
Unlikely. I wrote this myself. Of course it is in a private github repo.
Desperate product owners resort to desperate measures to juice metrics.
Not even a funny reply let alone helpful.
o3 mini has been coming for a while, and iirc was "a couple of weeks" away a few weeks ago before R1 hit the news.
Cambridge researchers developing brain implants for treating Parkinson's disease
This is a good idea, will add this to my cli tool :)
What? Karp is on record claiming Palantir prevents terror attacks which would otherwise force a right wing movement in Europe.

https://www.theregister.com/AMP/2024/02/28/palantir_boss_fii...

I think your definition of right wing is confused with authoritarian.
It's also a bit like saying that medical school is an elitist institution that only benefits the medical class. :-)
Cryptography isn't like that, it doesn't boil down to 10 simple steps. If you want ten, it's happening at higher level

1. Never invent your own primitives. Only professional cryptographers do that in algorithm competitions like AES, SHA-3, or PHC.

2. For interoperability, use BoringSSL, LibreSSL etc. Hire developer who knows how to do this. Or, hire a professional applied cryptographer to build TLS from low level primitives if you have a reason it's needed.

3. For closed ecosystems, use opinionated, misuse-resistant, high level libraries like libsodium, or libraries binding to it https://libsodium.gitbook.io/doc/bindings_for_other_language...

4. Use kernel CSPRNG to generate all secrets.

5. Have the implementation audited by professionals. E.g. Kudelski Security or NCC Group.

6. Have a bug bounty program that compensates well, and that allows disclosure of the attack.

7. If you're in a market where the competition has open source clients, you should have an open source client too, so that anyone can verify your cryptography is correctly implemented.

8. Keep anything that's WIP, under "research prototype, do not use in production" banner. That's not a cone of shame. If Dan Boneh can do it (https://crypto.stanford.edu/balloon/), so can you.

9. Realize that 90% of the work in cryptography is key management. Use OS keyring when possible.

10. Deploy end-to-end encryption everywhere you can. Data is a toxic asset. https://www.schneier.com/blog/archives/2016/03/data_is_a_tox...

So yeah, sorry, no "Use XChaCha20-Poly1305 or AES-GCM only", or "Use X25519/ed25519" or "remember to hash the X25519 shared secret". That's too in-depth for a ten point list. At that level, it's more of a top-ten book list.
Correction - everyone with HSV-1 outbreaks could in my opinion benefit from prophylactic antivirals.

It's worth noting that HSV-1 seroprevalence is extremely heterogeneous. Many nations have rates over 90%, which some Northern European nations have rates of 40% - suggesting broad immunity or reduced transmission. Although this could also be a function of different amounts of research and testing.
I think the benefit is not just from regular people having access - but other researchers who do need/trust the data.
Seems like with the last update (the italicized summary notifications) it's gotten every so slightly better. But yes until now my friends and I left it on just to enjoy how absurdly wrong the summaries were.
Grandma goes to the doctor.
> But those have their own issues! It’s a very difficult problem, and it all matters what you are protecting and what your threat model is.

it's all a trade-off - those challenge/response systems are better, but they also have more moving parts. There's more bits in the system to go wrong.

When there's only two pieces of the system (compare salted hash of user-provided password against stored salted hash) there's very little room for errors to creep in. Your auditing will all be focused on ensuring that the user-provided password is not leaked/stored/etc after a HTTP sign-in request is rxed.

When using challenge/response, there is some pre-shared information that must synchronised between the systems (algorithm in use, key length, etc depending on the specific challenge/response system chosen). That's a great deal more points of attacks for malicious actors.

And then, of course, you need to add versioning to the system (algorithms may be upgraded over time, key lengths may be expanded, etc) that all present even more points of attack.

Compare to the simple "compare salted hash of password to stored salted hash": even with upgrading the algorithms and the key lengths there still remain only one point of attack - downloading the salted hashes!

It doesn't matter how much more secure a competing system is if it introduces, in practice anyway, more points of failure.

My takeaway after doing some cryptography for some parts of my career is that by choosing a hash function to be expensive in computational power and expensive is space and keeping the "user enters a password and we verify it" is still going to have fewer points of attack than "Synchronising pre-shared information between user and authenticator as the security is upgraded over the years".

Basically, the trade-off is between "we chose a quick hash function, but can at least upgrade everything without the client software noticing" and the digital equivalent of "It's an older code, sir, but it checks out" problems.
no, just being speculative... about that.
There's this thing called the internet that can help you find out how to change your default search engine. You might even get some AI suggestions to make it clearer.

Yes, the "fucking" trick is awesome too.
https://voicenotes.com/ is also kinda in the space.
It’s not uncommon for an ISBN to have been assigned multiple times to different books [0]. Thus “all books in ISBN space” may be an overstatement.

There’s also the problem of books with invalid ISBNs, i.e. where the check digit doesn’t match the rest of the ISBN, but where correcting the check digit would match a different book. These books would be outside of the ISBN space assumed by the blog post.

[0] https://scis.edublogs.org/2017/09/28/the-dreaded-case-of-dup...
The Chess Notation lesson[0] from Lichess was really helpful when I was learning the notation a few years ago.

If you have a physical chessboard it can also help to try to play out a game from it’s notation and stop every few moves to check your board’s state against the state of the actual game.

I spent a couple of afternoons doing this and it stuck. You can refresh with some Chess Vision training[1] from Chess.com.

If you are interested in learning or reading more about chess, it is worth learning Portable Game Notation[2] (PGN). If you’re interested from a more programmatic perspective the Forsyth–Edwards Notation[3] (FEN) is more common in software, and there’s also the Universal Chess Interface[4] (UCI) format that is mostly used for communicating with engines.

[0]: https://lichess.org/study/ezdKJ7Di/9NLD1jo2

[1]: https://www.chess.com/vision

[2]: https://en.m.wikipedia.org/wiki/Portable_Game_Notation

[3]: https://en.m.wikipedia.org/wiki/Forsyth%E2%80%93Edwards_Nota...

[4]: https://en.m.wikipedia.org/wiki/Universal_Chess_Interface
More channels > faster ram.

Some math:

DDR5 6000 is 3000mhz x 2 (double data rate) x 64 bits / 8 for bytes = 48000 /1000 = 48GB/s

DDR3 1866 is 933mhz x 2 x 64 / 8 / 1000 = 14.93GB/s. If you have 4 channels that is 4 x 14.93 = 59.72GB/s
Would be quite expensive when talking about potential petabytes of data.
Show HN: A classifier that learns new categories without retraining from scratch
I built a text classifier that can learn new categories on the fly without full retraining – solving a practical problem I kept hitting in production systems. Try it out:

from adaptive_classifier import AdaptiveClassifier

# Start with basic sentiment classes
classifier = AdaptiveClassifier("distilbert-base-uncased")
classifier.add_examples(
["Great product!", "Terrible service"],
["positive", "negative"]
)

# Later add a completely new class without retraining
classifier.add_examples(
["404 error", "API timeout"],
["technical", "technical"]
)

# It maintains accuracy on both old and new classes
print(classifier.predict("The API is down")) # [('technical', 0.82), ...]
print(classifier.predict("Best purchase ever")) # [('positive', 0.89), ...]

Technical implementation:

- Uses Elastic Weight Consolidation to prevent catastrophic forgetting when learning new classes

- Combines prototype memory with neural adaptation for efficient few-shot learning

- Memory-efficient: stores only representative examples using k-means clustering

- Fully deterministic results through careful seed management

- Built on PyTorch/HuggingFace, works with any transformer model

The key insight was combining EWC (usually used in reinforcement learning) with a prototype memory system. This lets the model learn new patterns while preserving its knowledge of existing categories.

Real-world use cases I've tested:

- Customer feedback classification where new issue types emerge over time

- Content moderation systems that need to handle new violation types

- Technical support routing where new product categories get added

pip install adaptive-classifier

GitHub: https://github.com/codelion/adaptive-classifier

Would love feedback from anyone who's dealt with similar challenges in production ML systems!
Yeah, the original script I rewrote was doing about 15 different operations depending on the user input/arguments, so I guess it indeed reached the point you're describing where there were a lot of common patterns. It's just that instead of being 15 separate scripts, it was one gigantic one with a lot of conditionals and case statements.
Video games are a good counter example. The others you've listed are fandom specific which is distinct from having a relationship with a brand per say.

I hadn't heard about camping for the 30 series, thats nuts.
Today is my first day on HN and I came here looking for the comments. I miss intelligent conversations and reasonable interactions and reddit is just a dumpster fire now (and arguably before). Even if you can find a reasonably mature sub you're still exposed to the muck raking of the front page overflowing into everywhere else.
Thanks for all the advice.
I'm that old guy who married his highschool sweetheart and never looked back. I'll be sticking with vim 5.7 until the day I die:

https://sources.vsta.org:7100/vim57/index
I arbitrarily chose $1k as the "cheap" cut-off. Two 3090 is definitely the most bang for the buck if you can fit them.
I have vague memories of a scene from the UK comedy show "The Thin Blue Line" where a bunch of bad -isms were followed by counter-examples of good -isms, but despite the episode itself being easy to find (the episode is called "Ism Ism Ism"), I can't easily find the scene within the episode.
I don't think the author harps on it just to say "I am very smart", they want to point out that the degree to which Palantir employees then were acquainted with the Western philosophical canon and thought was unusual.
Yes, that may be part of the same playbook. Those pesky foreign viruses are taking our ill! We must stop that! Glory to the White Death!
Why would I want a crippled version of ChatGPT that is only updated once per year alongside new iPhones?

Apple Intelligence feels useless and the iPhone has never felt so stagnant.
> Your point is weak, 35 is less then 4000

Ok, I admit, I could have spent more time crafting the argument. The point is, SpaceX has already built 35 of the Starship, and flown quite a few of them, all while in prototype mode. When they reach full production mode, they intend to build one Starship per day. You may retort that Musk's promises don't amount for much, but it is a fact that SpaceX launches more rockets than any other company in the world, private or government-owned. Just this year they've launched 13 times, which is almost one launch every other day.
Indeed 20th century fascist revolutions used the guise of law for their takeover. Following them would include banning the existence of other parties, changing the constitution to give all power to executive, cancelling elections, etc.

I assumed the poster was using facist to mean “bad authoritarian government” - not that trump is actually a disciple of Mussolini style philosophy.

Right now the democratic system is working as designed, minus the incredible power of the executive branch which has been built up since FDR. Obama pioneered this approach to executive order.
I have a small position in PLTR, and is doing well enough for me.
Interesting to see how quickly the US is going downhill. I’m sure everyone thought “the Roman Empire is too big to fail” yet here we are with a similar mindset about USA.
That’s wishful thinking… One can’t simply stop using USD for trade on a whim. Decoupling will take at least a decade if not longer.
Using uv as an Installer
I suggest to read the article because it's quite detailed about it
The problem with h2 compared to electricity is that it has abysmal round trip efficiency. It makes sense for a few, limited use cases and an excavator may be one of them - if they’re used in places where no electric grid is available. In almost all other use cases, batteries provide a better efficiency.
Looks like a great idea, but there doesn’t seem to be any way to try it for free, which is an immediate pass for me.
> You know the Linux kernel, and probably most of your userspace, are also written in C?

I’m aware. It would be nice were that not the case. One of my favourite userspace programs uses C, but just to build a Lisp runtime!

> Why do you say systemd is a disaster solely based on its use of the C language

I wrote ‘part of the problem.’ Systemd is a disaster in part due to its use of C. Its bugs are in part because C is not a language which is really suitable for abstraction and reasoning over abstractions.

I do think that what systemd does is more complex than the parts of a kernel which require assembler or C. I wasn’t constructing a full argument, just opining. I think that the sheer weight of C required to do anything complex makes it very difficult to reason about what that code is doing. Languages higher on the abstraction curve make it easier to reason about abstractions. Reasoning about abstractions helps prevent design bugs. And also languages with good abstraction capabilities tend to help avoid implementation bugs.
> TacticalCoder 14 minutes ago [dead] | root | parent | prev | next [–]

>

> TFA says it can bump the spec to 768 GB but that it's then more like > $2500 than $2000. At 768 GB that'd be the full, 8 bit, model.

> Seems indeed like a good price compared to $6000 for someone who wants to hack a build.

> I mean: $6 K is doable but I take it take many who'd want to build such a machine for fun would prefer to only fork $2.5K.

.

I am not sure why TacticalCoder's comment was downvoted to oblivion. I would have upvoted if the comment wasn't already dead.
Yup, three is definitely value to the code not doing things it doesn't need to. I also find it clearer if the should-never-happen null check is in the form of an assertion. You know anything being tested in an Assert is a should never happen path, you don't need to consider why it's being done.
I agree but also found your comment funny in the context of LLMs. People love getting facts straight out of their models.
Yeah, Yudkowsky saying many years ago that it would be better for one person to be tortured for 50 years than for 3^^^3 people to get a dust speck in their eye was pretty much everything I needed to know to realize this group was going to be a problem. And things just get worse from there.
https://archive.is/8BFtL
Holy crap, you can sleep only one side of your brain?! How would anyone think of even doing this?
> What is a bit weird about AI currently is that you basically always want to run the best model,

I think the problem is thinking that you always need to use the best LLM. Consider this:

- When you don't need correct output (such as when writing a blog post, there's no right/wrong answer), "best" can be subjective.

- When you need correct output (such as when coding), you always need to review the result, no matter how good the model is.

IMO you can get 70% of the value of high end proprietary models by just using something like Llama 8b, which is runnable on most commodity hardware. That should increase to something like 80% - 90% when using bigger open models such as the newly released "mistral small 3"
H2 has higher losses in converting energy into hydrogen and hydrogen back in to energy though. And currently like 99% of hydrogen is obtained via fossil fuels, not split from water. Hydrogen could at some point be the equivalent of electricity and batteries, but currently it isn't any different than using straight fossil fuels.
Some benchmarks showing the advantage over traditional approaches:

Traditional classifier adding a new class:

- Requires full retraining (~30-60 minutes on typical dataset)

- Needs all historical data

- Uses 2-3x more memory during training

This approach:

- Adds new class in seconds

- Needs only examples of new class

- Memory usage stays constant

- Maintains 95%+ accuracy on existing classes

The code is well-documented and tested. I've included detailed examples showing:

- Batch processing for large datasets

- Multi-language support

- Model persistence

- Custom transformer models

Happy to share more details about the architecture or specific implementation challenges!
I use VSCode with the C# LSP, but I prefer to immediately see where a name comes from by reading it, rather than hovering over it. That's why I prefer to avoid global.

Regarding imports, I guess I could do something like `using c = my.namespace.ClassWithMyStaticMethods`, but I suppose it's not idiomatic in C#.
It's not for terminal UI apps; it's for web apps themed to look like terminal UI apps.
it's also true for people in cgs (centimeters, grams, seconds) sports like running, cycling, and lifting. if you look at the best in the world (pogacar, ingebrigtsen, blummenfelt) they don't look like fitness models. they look surprisingly normal. carrying "extra" body fat is helpful for training recovery, hormone levels, mood, and sleep. if the sport requires getting lean to hit a weight target for a specific event, athletes can do that, but they shouldn't stay ultra lean during the majority of their training.
Sounds cool. Is there any demo video? I didn’t see one
1) I don't believe that this happened, people lie/embellish stories on the internet all the time.

2) I believe people have the right to free association. I wouldn't fire someone for saying retarded, but people like forever_wario have the right to live their values.

3) Using politically controversial language in white collar working places that are traditionally woke or woke leaning is asking for trouble.
"Dad bod" is a very silly term. Any "dad bod" talked about by the popular media is almost always accompanied by height and extensive musculature. Mahomes is in the low end of "athleticism" in the NFL, but is 99th percentile on all metrics in the general male population.

In other words: "if you are extremely tall and/or extremely strong, you don't need be shredded to still be athletic". Who would have thought?
A couple comments have mentioned that whatever issue my post raises, are minor compared to the simplicity of the language. But the current implementation of GenericWriter + AnyWriter (with some performance pitfalls) seems more, not less, complicated. Also, neither of these, nor anytype, lend themselves to _simple_ documentation, so that seems like another strike against the simplicity-argument.

As for anytype specifically, in simple cases where it's being used in a single function, you can quickly figure out what it needs.

But in non trivial cases, the parameter can be passed all over the place, including into different packages. For example `std.json.stringify`. Not only does its own usage of `out_stream: anytype` take more than a glance, it passes it into any custom `jsonStringify` function. So you don't just need to know what `std.json.stringify` needs, but also any what any custom serialization needs.
They failed because they didn't fold the glorious Nippon steel themselves.
I’m an epidemiology professor and I write a weekly “weather report” outbreaks [1]. These communication and data blackouts are coming at a bad time. We’re having an unusual flu season—activity has rebounded unexpectedly. I’ve been having to scramble for data, last week by visiting each state health department website. It’s really troubling and consequential.

[1] https://caitlinrivers.substack.com
Conversely:

“Just because you look the part, doesn’t mean you can play, ask Manute Bol.” - Sage Francis

I'm not sure Manute is actually a great example but it's still a good line.

When I was playing sports back 25 years ago, I heard the expression "looks like Tarzan, plays like Jane" more than once.
Wonder if drivers were adopted under newer low-level languages (Rust/Zig/Odin/C3/Hare etc..) if that would boost participation rates for newer kernel device driver builders..
I wonder how this relates to Hollywood, namely the well-known shift towards men having bodybuilder level physiques, even in roles where it doesn't really fit the character.

If you're in sport, you know that highly performant bodies don't look like that almost ever. They're too specialized to have the physique that prioritizes aesthetics. Road cyclists with their lanky frames, powerlifters have fat over their absurdly strong cores. High level marathoners look like they might blow over in a stiff wind.

The most interesting thing about the article to me was the mention of kinesthetic skill. This is a huge differentiating factor IME in mountain biking. Some people do not have the meta visualization to understand why what they did worked or didn't, why they cleared that rock, why their weight transfer caused the front to wash out. The feedback loop doesn't exist, and they can't progress.
I'm not sure Linux is the best comparison; it was specifically created to run on standard PC hardware. We have user access to AI models for little or no monetary cost, but they can be insanely expensive to run.

Maybe a better comparison would be weather simulations in the 90s? We had access to their outputs in the 90s but running the comparable calculations as a regular Joe might've actually been impossible without a huge bankroll.
Super cool. Love that you can zoom all the way in and suddenly it looks like a bookshelf.

When I got down to the individual book level, I found several that didn’t have any metadata- not even a title. There are hyperlinks to look up the ISBN on Google books or World Cat, and in the cases I tried WorldCat had the data.

So… why not bring the worldcat data into the dataset?