skysight-inc/HackerNews huggingface dataset:
Nice CPU benchmark for year-on-year performance here:
https://www.cpubenchmark.net/year-on-year.html

The data shows that the average laptop CPU in 2024 has 56% better thread performance, and 123% better total performance, compared to the average laptop in 2019.

Laptop thread 2019: 1689 avg. score

Laptop total 2019: 6396 avg. score

Laptop thread 2024: 2643 avg. score

Laptop total 2024: 14288 avg. score

For the specific case, just look up the benchmarks for the CPUs you are comparing.
That's interesting. It's probably still more work than I'd want to do, especially when out and about. Is there an app that essentially does this? Does this one?
There's still plenty of audience for blog posts. The blogosphere (god I hate that term) hasn't grown at the pace of social media, certainly, but that doesn't matter. And in some ways I'd probably consider that a good thing for the medium.
Same. I expected it could take a few ms longer. But less? Apparently that's a thing.
It's a very crude approximation, e.g. 1.75 * 2.5 == 3 (although it seems better as the numbers get closer to 0).

I tried implementing this for AVX512 with tinyBLAS in llamafile.

inline __m512 lmul512(__m512 x, __m512 y) {
__m512i sign_mask = _mm512_set1_epi32(0x80000000);
__m512i exp_mask = _mm512_set1_epi32(0x7F800000);
__m512i mant_mask = _mm512_set1_epi32(0x007FFFFF);
__m512i exp_bias = _mm512_set1_epi32(127);
__m512i x_bits = _mm512_castps_si512(x);
__m512i y_bits = _mm512_castps_si512(y);
__m512i sign_x = _mm512_and_si512(x_bits, sign_mask);
__m512i sign_y = _mm512_and_si512(y_bits, sign_mask);
__m512i exp_x = _mm512_srli_epi32(_mm512_and_si512(x_bits, exp_mask), 23);
__m512i exp_y = _mm512_srli_epi32(_mm512_and_si512(y_bits, exp_mask), 23);
__m512i mant_x = _mm512_and_si512(x_bits, mant_mask);
__m512i mant_y = _mm512_and_si512(y_bits, mant_mask);
__m512i sign_result = _mm512_xor_si512(sign_x, sign_y);
__m512i exp_result = _mm512_sub_epi32(_mm512_add_epi32(exp_x, exp_y), exp_bias);
__m512i mant_result = _mm512_srli_epi32(_mm512_add_epi32(mant_x, mant_y), 1);
__m512i result_bits = _mm512_or_si512(
_mm512_or_si512(sign_result, _mm512_slli_epi32(exp_result, 23)), mant_result);
return _mm512_castsi512_ps(result_bits);
}

Then I used it for Llama-3.2-3B-Instruct.F16.gguf and it outputted jibberish. So you would probably have to train and design your model specifically to use this multiplication approximation in order for it to work. Or maybe I'd have to tune the model so that only certain layers and/or operations use the approximation. However the speed was decent. Prefill only dropped from 850 tokens per second to 200 tok/sec on my threadripper. Prediction speed was totally unaffected, staying at 34 tok/sec. I like how the code above generates vpternlog ops. So if anyone ever designs an LLM architecture and releases weights on Hugging Face that use this algorithm, we'll be able to run them reasonably fast without special hardware.
Chasing our own tail with concepts like "reasoning". Let's move the concept a bit - "search". Can LLMs search for novel ideas and discoveries? They do under the right circumstances. You got to provide idea testing environments, the missing ingredient. Search and learn, it's what humans do and AI can do as well.

The whole issue with "reasoning" is that is an incompletely defined concept. Over what domain, what problem space, and what kind of experimental access do we define "reasoning"? Search is better as a concept because it comes packed with all these things, and without conceptual murkiness. Search is scientifically studied to a greater extent.

I don't think we doubt LLMs can learn given training data, we already accuse them of being mere interpolators or parrots. And we can agree to some extent the LLMs can recombine concepts correctly. So they got down the learning part.

And for the searching part, we can probably agree its a matter of access to the search space not AI. It's an environment problem, and even a social one. Search is usually more extended than the lifetime of any agent, so it has to be a cultural process, where language plays a central role.

When you break reasoning/progress/intelligence into "search and learn" it becomes much more tractable and useful. We can also make more grounded predictions on AI, considering the needs for search that are implied, not just the needs for learning.

How much search did AlphaZero need to beat us at go? How much search did humans pack in our 200K years history over 10,000 generations? What was the cost of that journey of search? That kind of questions. In my napkin estimations we solved 1:10000 of the problem by learning, search is 10000x to a million times harder.
Show HN: I made a directory of directories (seriously)
I kept seeing more and more Product Hunt alternatives pop up, so I started tracking them in a spreadsheet to potentially use for future launches. The list was sitting at around 80 sites when I decided to deep dive and see how many more I could find.

So I then built IndieLaunch: 250+ directories to help you launch your startup.

It's free to use and includes Domain Rating (DR) and traffic info so you can pick the right spot to get the most eyes on your launch.
As soon as a company ceases to be cool it is game over. Professional management takes over and works in managing costs. Fine if you are a REIT, otherwise = death for most businesses.
> how does a caller distinguish between a value of nil and a container/list containing nil

Very easily; but the point is that it's very often easy to design things so that the caller doesn't have to care.

For instance, lookup in an associative list can just be (cdr (assoc key alist)).

If the key is not found, assoc returns nil, and so cdr returns nil.

Right, so when we use this shortcut, we have an ambiguity: does the list actually have that key, but associated with the value nil? Or does it not have the key.

Believe it or not, we can design the data representation very easily such that we don't care about the difference between these two cases; we just say we don't have nil as a value; a key with a value nil is as good as a missing key.

This situation is very often acceptable. Because, in fact, data structures are very often heavily restrained in what data types they contain. Whenever we assert that, say, a dictionary has values that are, say, strings, there we have it: values may not be nil because nil is not a string. And so the ambiguity is gone.

A nice situation occurs when keys are associated with lists of values. A key may exist, but be associated with an empty list (which is nil!). Or it may not exist. We can set things up so that we don't care about distinguishing these two. If key K doesn't exist then K is not associated with a list of items, which is practically the same as being associated with an empty list of items. If we split hairs, it isn't, but in a practical application things can be arranged so it doesn't matter.
Is it the one where you delete 95% of user accounts?
Interesting that you mention jail… the rule of law is kind of the ultimate accountability sink.
The Declining Relative Quality of the Child Care Workforce
Thank Gwynne Shotwell, SpaceX COO
IIRC the limiting factor for late Intel Macs was cooling. It would frequently throttle the CPU down to 20% to cool it down.

And even if the numbers are great on a sunny day (or rather a cool day, to fix this I would have to sit with it in an open window), this is a real performance sink.
Would be great to see some citations. I'm particularly interested to know the kind of tech we are talking about here. If it's PPT and the likes then I tend to agree. If we are talking about tools like geogebra [1] where students can manipulate and play around then I'll be extremely surprised.

[1] https://www.geogebra.org
Divide the Query to Make It Dirt Cheap (By BigQuery Geospatial Tech Lead)
Depends on your need. In general I would say if it has 16G memory then yes. Or can be upgraded to 16G. Likewise on the video card, depends on how Graphic intense. But some, not many, laptops you can change the video card. So a i7 cpu that is 5 yo should be ok. It is the other things, and like dotnet00 said, battery life is likely an issue. In the old days it was easy to replace the battery, I used to keep a charged spare on me when traveling.
Thanks for asking. I should have mentioned this is based on anecdotal personal experience. I used to have an ancient laptop with a bad mic and a low res webcam.

I noticed much higher callback rate after upgrading. This is admittedly a small sample size.
Why would Italy have anything called Piracy Shield? Is it located in the West or in China?
TCP Slow Start Simulation in Go
I did misunderstand it, because looking at it again now, they spent the entire post talking about how difficult it is to make the cables, except for the very last sentence where they mention die area one time, and it’s still not clear that they’re talking about die area for something that’s inside the computer rather than a chip that goes in the cable.

> Look at the supported lengths and cables. … relatively generic cables. Your USB-C or HDMI connections cannot go nearly as far and require significantly more tightly controlled cables and shielding. … They used a long list of tricks to squeeze every possible dB of SNR out of those cables.
Who gets shipped and why?
> But the problem is that they have to be hosted on the same platform

No they don't. I avoid Medium and Substack like the plague. I don't use those sites for discovery at all, and whenever I come across a link to a blog on either of them, I usually regret clicking it.

I end up on plenty of blogs found through various sources. A few I've been reading diligently or on-and-off for many years, others I read one-off articles here and there that I've found through sites like HN.

The blogosphere's death is highly exaggerated, regardless of what TFA says.

> Maybe you get self-hosted things via github or whatnot, but that's about as non-consumerist as it'll get

I don't see that as a bad thing.

> And the younger generation is not doing this beyond work-related pages, so eventually the internet-as-literature phase will end

I don't think it will. I think there will always be enough people writing interesting long-form articles to satisfy my curiosity. Mind you, I don't exclusively read blogs, but I haven't touched social media in 5 or 6 years and that hasn't caused me to run out of interesting things to read.
For me, intel hardware is basically the same: slow, hot and noisy. ARM is fast, cool and quiet.

Once a company stops being cool it dies.
Okay, but does that help us reach any meaningful conclusions? For example, okay some AI system doesn't have the capabilities of an auditory cortex or somatosensory cortex. Is there a reason for me to think it needs that?
The IPv6 Transition
In 2010 I found the average HN comment far more insightful and likely to be true than I do in 2024. I am fairly certain that this is almost entirely due to me changing, and not the content of the site. At a very basic level my views on the concept of a VC funded startup is so very different now from what it was in 2010 that I would certainly interpret all of those discussions very differently now.

When Google takes me to a very old discussion on HN I am usually surprised by how similar they are to threads from today, even if some of the specific viewpoints in vogue are different.
Why I switched from Firefox…
Starlink is prepped to add ICBM launch sensors in select satellites and then potential "deactivation" measures. A global iron dome.
You can also get a USB webcam for like 15 bucks. Coming from the integrated 720p cam my laptop had, a 15 buck webcam was wonderful.
I put spreading conspiracy theories about Dominion voting machines (this week), plots by Jews, vaccines caused Lebron James' son to pass out, Paul Pelosi's attacker was actually his gay lover, etc in a different category than "he has a few different opinions than my peer group". Something has gone terribly wrong.
Here's the paper in Nature:

https://www.nature.com/articles/s41586-024-07894-z
Ask HN: Good publication summarizing this year's Nobel Prize achievements?
I’ve been out of touch with science and technology publications for a while. If I want to learn more about this year’s Nobel Prize winners and their work, which publication would you recommend? Do journals like Nature, Science, or The Economist provide an annual summary explaining the significance of the winners’ achievements? Ideally, I’m looking for something geared toward readers with higher education, but not necessarily experts or specialists in the field.
10 years ago, I would have answered: Sennheiser HD 280. They are comfortable, portable, and the thick pads offer semi noise-cancelling capabilities. I loved the fact that they faithfully reproduced the audio spectrum as the artist intended with their music.

Today, I can’t be anywhere without Airpod Pros. They are simply superb. Music sounds incredible on them, without excessive bass tones. Active noise cancelling is excellent. The user interface is sublime and intuitive. I only wish the battery life were a bit longer, but is sufficient for 95% of use cases
I haven't been a gamer for about 15 years, so I don't buy new any more. My browsing, server management, and spreadsheet needs are easily served by cheap, second hand i5 8-series systems in handy ultra small form factor boxen.

Lenovo ThinkCentre's and Dell Optiplex types.

I'll probably upgrade to 9- or 10-series in a year or two, depending on what's available for low low prices.
A practical drawback to premade cables is the need for a larger hole to accommodate the pre-attached connector. There's also a larger gap that needs to be plugged around the cable to prevent leaks into he wall.

My ordinary home-centre electric drill and an affordable ~7mm masonry bit lets me drill a hole in stucco large enough to accept bare cables with a very narrow gap to worry about.
To be honest, who cares anymore.
We all know Microsoft will try to enable it by default with every update.

I stay away from Microsoft products, both privately and professionally. They make the world a worse place.
Exactly. They're motivated by reducing dollars per kg of payload. They have their own agenda for a Mars mission, and they'll build a business taking third party gigs to fund their long term mission to get people to Mars.
The older I am (and I am at my 50s) the more I have this intuition that entropy is a fundamental force driving not only physical phenomena but also social interactions, economy etc.

Formalising this intuition is another story though...
Boot from a USB SSD instead. I get literally 100x the IOPS over the reasonably fast SD cards I used. Things like apt-get upgrade take seconds instead of many minutes. It’s an entirely different experience.
Yes, you need language to describe (discuss) something. But not everything that exists must have a description. Furthermore, meaningful does not require organization.

If you stand outside under the sun, do you have to be able to write the word "sun" in order to feel warm?
Paranormasight: A Game Exploring the Depths of Honjo in Tokyo
it’s a specific viewpoint that chooses to measure success in terms of crop yields, rather than quality of life…

The tragedy of the commons didn’t claim that crop yields are slightly depressed. The claim is that the commons will be overused to the point of threatening to destroy it.
That's exactly how it went with the first Homepod...right ?

TBH the dynamic you're describing would have been Meta's glasses if they also release a viable version of Orion as the second product. But at this point nobody's in a place to make it a reality, the fact that Apple actually released the Vision Pro is the most telling sign that they don't see themselves reaching that level anytime soon.
All models are wrong, some are useful. Of course it has some utility, otherwise it would drop out of use on its own. The problem with big catchall symptom based diagnoses are what they drive focus towards and away from. I get that the scientific process has to start somewhere, by putting similar things in a bag, before it can tease out mechanisms and groupings. But when such simplistic models remain how doctors communicate with patients, it crowds out more nuanced understanding. Like even the word "spectrum", trying to add some depth to the pop culture model, is really just a fancy word for a single scalar.
"Where are we going to find the equivalent data set that will train this other kind of thinking?"

Just a personal opinion, but in my shitty When H.A.R.L.I.E. Was One (and others) unpublished fiction pastiche (ripoff, really), I had the nascent AI stumble upon Cyc as its base for the world and "thinking about how to think."

I never thought that Cyc was enough, but I do think that something Cyc-like is necessary as a component, a seed for growth, until the AI begins to make the transition from the formally defined, vastly interrelated frames and facts in Cyc to being able to growth further and understand the much less formal knowledgebase you might find in, say Wikipedia.

Full agreement with your animal model is only sensible. If you think about macaques, they have a limited range of vocalization once they hit adulthood. Noe that the mothers almost never make a noise at their babies. Lacking language, when a mother wants to train an infant, she hurts it. (Shades of Blindsight there) She picks up the infant, grasps it firmly, and nips at it. The baby tries to get away, but the mother holds it and keeps at it. Their communication is pain. Many animals do this. But they also learn threat displays, the promise of pain, which goes beyond mere carrot and stick.

The more sophisticated multicellular animals (let us say birds, reptiles, mammals) have to learn to model the behavior of other animals in their environment: to prey on them, to avoid being prey. A pond is here. Other animals will also come to drink. I could attack them and eat them. And with the macaques, "I must scare the baby and pain it a bit because I no longer want to breastfeed it."

Somewhere along the line, modeling other animals (in-species or out-species) hits some sort of self-reflection and the recursion begins. That, I think, is a crucial loop to create the kind of intelligence we seek. Here I nod to Egan's Diaspora.

Looping back to your original point about the training data, I don't think that loop is sufficient for an AGI to do anything but think about itself, and that's where something like Cyc would serve as a framework for it to enter into the knowledge that it isn't merely cogito ergo summing in a void, but that it is part of a world with rules stable enough that it might reason, rather than "merely" statistically infer. And as part of the world (or your simulated environment), it can engage in new loops, feedback between its actions and results.
It's not just that the social media is filled with low on substance posts with excess anger and snark, but this incentivizes everyone to be more forceful - as otherwise the louder voices can dominate the discussion. So, it's not just a quality of people issue but also an emergent dynamic which encourages tribalism instead of substantive posts. The same people can make reasonable posts in other contexts

This need not be a unsolvable problem, and that one has to retreat places like HN relying on a single moderator(good, but doesn't scale).

One can also rely on timelines/feeds being based on Distributed moderation -
A user selects moderators or custom-algorithms who they find valuable. The moderation can be along different dimensions like accuracy, interest, or aligned with some political view.

There could a moderator whose style is to purely check the soundness of the reasoning without taking any position on the issue itself. This can lead to improved standards of discussion.

A key issue is how to reduce the energy required to moderate - typically a moderator evaluates the quality and rely on networks of other moderators each handling smaller domains.

Current discourse encourages users to sort into strongly polarized groups, whereas more nuanced feeds in social media can lead to coalitions which don't neatly align with the standard fault lines. Platforms like Polis actively encourage common points of agreement across different groups.
Did GPT help you write this, or do you just happen to write in the same style, conditioned by having to write research literature in that style?
That's adding more weight to carry around, and anyway, if needed, modern laptops can also use battery banks, which once again puts them ahead. Bonus that since laptops tend to use USB-C nowadays, you get the versatility of being able to use that bank for both phone and laptop.
Ultimately - yes. But as I mentioned, the fact it's sold as ready for critical situations doesn't mean the developers thought/said it's ready.
Sun Fire 15ks too!
My 'uninvited activity'[0] system picks up quite a bit from googleusercontent, but also gets a bit from 1e100 network ranges, which is a bit surprising / disappointing.

I have to add exclusions for some google-owned network ranges just so my auto-blocking setup doesn't break some basics critical to just web browsing.

[0]:https://github.com/UninvitedActivity/UninvitedActivity
Good, that will distract them for a while and will prevent them from actively making TB even worse with every release.

I like keeping my software secure and up to date, but I dread every TB upgrade, wondering what stupid cosmetic change will trip me up this time.
> Too focused on the bottom level. If a given business process results in employee A doing their job correctly according to the process... then the person who is accountable for the end result being shit is the manager who is responsible for the process itself.

The book's point is that while this _should_ be the case, all too often it's not. AFAIK, nobody has been charged with forging documents in the case of Wells Fargo cross selling. Not the counter clerks who directly responded to incentives and management pressure nor the executives who built that system.
If it’s a requirement to close a customer, they’ll tell you. Just keeping going with what you have until it blocks a deal you want to close enough to implement it.
I wouldn't want to build and run my own auth system at the expensive of building features that my customers want.

For many decently sized (ie high AAR) business customers, particularly those in regulated industries, SSO is a non-negotiable requirement anyway.
Overviews aren’t code though. In code, for me, they don’t pass 80/20 tests well enough, sometimes even on simple cases. (You get 50-80% of an existing function/block with some important context prepended and a comment, let it write the rest and check if it succeeds). It doesn’t mean that LLMs are useless. Or that I am antillamist or a denier - I’m actually an enthusiast. But this specific claim I hear often and don’t find true. Maybe true for repetitive code in boring environments where typing and remembering formats/params over and over is the main issue. Not in actual code.

If I paste the actual non-trivial code, it starts deviating fast. And it isn’t too complex, it’s just less like “parallel sort two arrays” and more like “wait for an image on a screenshot by execing scrot (with no sound) repeatedly and passing the result to this detect-cv2.py script and use all matching options described in this ts type, get stdout json as in this ts type, and if there’s a match, wait for the specified anim timeout and test again to get the settled match coords after an animation finishes; throw after a total timeout”. Not a rocket science, pretty dumb shit, but right there they fall flat and start imagining things, heavily.

I guess it shines if you ask it to make an html form, but I couldn’t call that life-changing unless I had to make these damn forms all day.