skysight-inc/HackerNews huggingface dataset:
This is not how LLMs work. An LLM doesn't "scour through its vast training data" at response generation time to "look for patterns — specifically, the most probably sentence/sentences that fit the question," nor does "probability" in terms of LLMs refer to "frequency — how many times something appears in its training data."

LLMs don't even have access to their training data at response generation time. And responses aren't created by "voting" on how many times it's seen a particular phrase (and it doesn't operate on sentences: it operates on word fragments, aka tokens).

I'd recommend Andrej Karpathy's "Neural Netowrks: Zero to Hero" YouTube lecture series (https://karpathy.ai/zero-to-hero.html), but here's a pretty condensed overview: the way LLMs work is they serially generate tokens when you ask them a question (generating tokens is referred to as "inference"). During training, we start with a random set of values for the model parameters (the "weights") and ask the model to predict the probabilities for token sequences. At the beginning of the process, it's usually very wrong, unless you won some unbelievably lucky universal dice roll. But we edit the values of the weights based on how right or wrong the model's predictions were by using backpropagation + a loss function to determine which parameters most influenced a particular prediction, and use gradient descent in that highly-dimensional space to perturb the parameters according to some value determined by an optimization function. Doing this zillions of times repeatedly is how we end up with the final values for the parameters — aka the model weights — it's not by "counting votes" or even "counting sequences," it's by calculating which parameter values are the best ones to predict the token probabilities. The frequency of tokens appearing in the dataset (or even the frequency of sentences) isn't directly computed, although extremely-high-frequency sequences might be memorized — but it can't memorize all of them, because the models are much smaller than the training set.

The theory for why this works so well is that since the models don't have enough space to memorize the entirety of the massive training set (i.e. Llama 3.1 8b is about 16GB, but was trained on the entire internet which is many orders of magnitude larger than that), the best values for the parameters are actually ones that create some sort of underlying world model for why that token sequence probability was predicted. That's very different than "counting votes," or counting sentence frequencies. Even if you disagree with this hypothesis for why the models work, you have to admit there's some underlying meaning being parsed out: it's not just memorization, even if it does sometimes memorize useful facts (useful, at least, to predict probabilities during training time). It simply can't memorize its training set: the training data is way too large compared to the size of the model, and it doesn't get to cheat and look at the training data when asked a question.
.. unless the compiler spots UB anywhere near the function call, in which case all bets are off.
In the USA, most steel is recycled. Most of the rest of the world uses basic oxygen furnace by wide margin.
Today, we're open-sourcing our SynthID text watermarking tool
This article seems to be quoting a lot of individual comments, rather than giving a concise summary of the situation.
I think we can point to specific choices and policies that made it suck. That’s probably more useful information since it can serve as a warning to others. I’ll start by reframing the question here:

“Which specific choices did Google make that ended up ruining a previously stellar product?”
(Subtext being, what can we all learn NOT to do when we’re in similar positions facing similar choices)
You can either have a sustainable economy, or real-world constraints will force a need for sustainability on you, whether you are ready or not.

And the only thing that can exponentially grow without any limitations is a cancer. (Until it kills the host, that is.)
Very cool! I already can see a lot of "this is already solved by playwright/cypress/selenium/deterministic stuff" in the comments.

Over nearly 10 years in startups (big and small), I've been consistently surprised by how much I hear that "testing has been solved", yet I see very little automation in place and PMs/QAs/devs and sometimes CEOs and VPs doing lots of manual QA. And not only on new features (which is a good thing), also on happy path / core features (arguably a waste of time to test things over and over again).

More than once I worked for a company that was against having a manual QA team, out of principle and more or less valid reasons (we use a typed language so less bug, engineers are empowered, etc etc), but ended up hiring external consultants to handle QA after a big quality incident.

The amount of mismatch between theory and practice in this field is impressive.
Irreversibly, like all political concepts, is a fiction that humans choose to believe in rather than a law of physics. If social security became insolvent while the US was distracted by some other future national crisis, I'm certain that the people of the future would find a way to cut the pension while staying within the letter of the law (probably via inflation or https://www.youtube.com/watch?v=TRgRz3nSG7o).
In some cases. But the point is other countries enforce lower prices, and they make up the margins in the US. On the one hand it is obscene but on the other we do see a pretty regular supply of miraculous new drugs. Something clearly needs done but the system does make sense (whether or not its optimal).
So much effort and so many resources wasted on such a pointless purpose.
I don't think the dev would leave the top left without a mine until one is moved there, as that would always be a safe square to click first.

So if the probability of finding a mine at any given spot is given by p, then the probability of finding a mine in the top left during gameplay for cases where one does not click it first (in which case it is 0?) is 1 for the case where you clicked on a mine first with probability p, and then p for the remainder.

So the total probability p' is p * 1 + (1 - p) * p, or 2p - p^2.

Wikipedia says

> Beginner is usually on an 8x8 or 9x9 board containing 10 mines, Intermediate is usually on a 16x16 board with 40 mines and expert is usually on a 30x16 board with 99 mines; however, there is usually an option to customise board size and mine count.

8x8 10 mines p = 0.16 p' = 0.29 ratio = 1.84
9x9 10 mines p = 0.12 p' = 0.23 ratio = 1.88
16x16 40 mines p = 0.16 p' = 0.29 ratio = 1.84
30x16 99 mines p = 0.21 p' = 0.37 ratio = 1.79

I was curious to see the concrete effects for no reason other than to procrastinate.
your software has real people in it?
Boeing's New CEO Calls for 'Culture Change' as Strike Vote Looms
"As Kelly Ortberg presented his strategic vision, more than 33,000 striking employees of the aerospace manufacturer were expected to vote on a new contract."
Same. TIM's designer/coder Kevin Ryan actually made a spiritual successor called Contraption Maker, it's pretty fun and still has regular updates: https://store.steampowered.com/app/241240/Contraption_Maker/
Meanwhile every vlogger going to China says among he first things you get taught is how to use a vpn to avoid the great firewall…
Tokyo Metro on market fast track with 45% jump in debut
AI Model Rankings on Human Preference from Not Diamond's Arena Mode
I don't think that's quite it. They had it on their website before this, that opus 3.5 was coming soon, now they've removed that from the webpage.

Also, Gemini ultra 1.0, was released like 8 months ago, 1.5 pro released soon after, with this wording "The first Gemini 1.5 model we’re releasing for early testing is Gemini 1.5 Pro"

Still no ultra 1.5, despite many mid and small sized models being released in that time frame. This isn't just an issue of "the training time takes longer", or a "skew" to release dates. There's a better theory to explain why all SoTA LLM companies have not released a heavy model in many months.
One thing to keep in mind when looking at geekbench is that it's lacking Vector/SIMD optimizations for RISC-V. Most current processor lack RVV support as well, but it makes the comparison a bit meaningless. Geekbench also probably doesn't enable extension besides rv64gc, so no bitmanip extensions.

Let's take two very similar cores, the SiFive P550 @1.4GHz and a Cortex-A72 @1.5GHz: https://browser.geekbench.com/v6/cpu/compare/123?baseline=74...

Notice that the A72 is a lot faster in some of the benchmarks. Looking at the manual suggests that all of them are to a varying degree SIMD optimized for arm Neon: https://www.geekbench.com/doc/geekbench6-benchmark-internals...

The most ISA agnostic benchmark you can compare is the clang one, as it just compiles clang, which doesn't benefit much from dedicated instructions that aren't enabled for RISC-V. Notice how there the P550 outperforms the A72.

---

That compared un-optimized code, but let's compare hand optimized ARM Neon vs hand optimized RISC-V RVV:

This time I'm comparing a Cortex-A72 with the SpacemiT X60:

* Cortex-A55: in order 1.8GHz, dual issue neon (two 128-bit execution units)

* SpacemiT X60: in order 1.6GHz. Has RVV with 256-bit vectors, but two 128-bit execution units with a weird layout:

only EX1: shift, bitwise, compare, mask-ops, merge, gather, compress

EX1&EX2: int/float arithmetic, including mul&div

The cores should be quite comparable, although the A55 is in a lot better SOC (my phone) than the X60 core and should be slightly faster, all else equal.

The hand optimized code is from simdutf and the gnuradio kernel library, which I ported both to RVV:

gnuradio kernels
A55 Neon vs X60 RVV
i16c mul: 674 vs 634 ms
i16c dot: 361 vs 415 ms
f32c conj dot: 1043 vs 900 ms
rotator2: 763 vs 918 ms
min index u32: 404 vs 156 ms
f32 interleave: 308 vs 742 ms
f32 log2: 1208 vs 789 ms
f32 sin: 2155 vs 2152 ms
f32 tan: 2962 vs 3152 ms
f32 stddev&mean: 609 vs 277 ms
f32 poly sum: 667 vs 266 ms
u8 conv k2: 10413 bs 7523 ms

simdutf: utf8 to utf16 utf16 to utf8
Neon A55 vs RVV X60 Neon A55 vs RVV X60
arabic.utf8.txt 0.25 vs 0.34 b/c 0.66 vs 0.95 b/c
chinese.utf8.txt 0.23 vs 0.28 b/c 0.51 vs 0.56 b/c
czech.utf8.txt 0.21 vs 0.35 b/c 0.67 vs 0.90 b/c
english.utf8.txt 0.81 vs 0.85 b/c 1.14 vs 1.63 b/c
esperanto.utf8.txt 0.34 vs 0.45 b/c 0.91 vs 1.12 b/c
french.utf8.txt 0.28 vs 0.35 b/c 0.86 vs 0.98 b/c
german.utf8.txt 0.35 vs 0.41 b/c 0.94 vs 1.10 b/c
greek.utf8.txt 0.24 vs 0.37 b/c 0.64 vs 1.03 b/c
hebrew.utf8.txt 0.21 vs 0.33 b/c 0.58 vs 0.92 b/c
hindi.utf8.txt 0.24 vs 0.29 b/c 0.48 vs 0.57 b/c
japanese.utf8.txt 0.23 vs 0.29 b/c 0.49 vs 0.57 b/c
korean.utf8.txt 0.21 vs 0.28 b/c 0.47 vs 0.55 b/c
persan.utf8.txt 0.23 vs 0.33 b/c 0.60 vs 0.80 b/c
portuguese.utf8.txt 0.29 vs 0.36 b/c 0.88 vs 1.02 b/c
russian.utf8.txt 0.23 vs 0.31 b/c 0.60 vs 0.84 b/c
thai.utf8.txt 0.29 vs 0.30 b/c 0.52 vs 0.60 b/c
turkish.utf8.txt 0.25 vs 0.38 b/c 0.73 vs 0.99 b/c
vietnamese.utf8.txt 0.18 vs 0.28 b/c 0.46 vs 0.54 b/c
Note: b/c is bytes/cycle, so bigger is better

As you can see the performance is very competitive between processors of a similar design point.
Wow are there issues with making things like chips that can even survive that? I remember when I was a scientist and 10,000g was some very high speed centrifuging that you better make sure everything like even glass and plastic can handle.
Real Life Tron on an Apple IIgs (2008)
> the only reasonable solution is a new language specifically designed for the purpose.

actual assembler is just such a language, as is being discussed here.
There is an LSP from Microsoft, it just doesn't report this sort of syntax error (presumably for the sake of people doing templating etc)
Both companies were founded by Yvon Chouinard
Agreed, and I think a lot of his reasoning falls apart if you look at his specific examples:

> What will I see if I click a link labeled "click here"? I have no idea. Instead, choose link text that describes the destination.

Pretty much all of the examples are of the form of "To do action XYZ, click here". Why aren't you considering the "do action XYZ" part of the sentence?

> It confuses search engines

No, it doesn't. As he said previously, "this is not 1995" - search engines also take into account the context around links and images.

Most importantly, though, these are all just this guy's feelings. While I think A/B tests definitely have limitations, this type of small change is the kind of thing that can absolutely be probed scientifically (i.e. A/B tests, formal user studies, etc.) If you're in a company where people are strongly arguing one side or the other of this debate, I'd just use the famous quote by Jim Barksdale: "If we have data, let's look at data. If all we have are opinions, let's go with mine."
Apple gross revenue is 10x the Qualcomm, and the difference in net income is even larger. Apple could easily afford these R&D costs.

Another obstacle, even if Qualcomm develops an awesome emulator / JIT compiler / translation layer, I’m not sure the company is in the position to ship that thing to market. Unlike Apple, Qualcomm doesn’t own the OS. Such emulator would require extensive support in the Android OS. I’m not sure Google will be happy supporting huge piece of complicated third-party software as a part of their OS.

P.S. And also there’re phone vendors who actually buy chips from Qualcomm. They don’t want end users to complain that their favorite “The Legendary Cabbage: Ultimate Loot Garden Saga” is lagging on their phone, while working great on a similar ARM-based Samsung.
> I don't think the dev would leave the top left without a mine until one is moved there, as that would always be a safe square to click first.

All squares are safe to click first, that's the point.
That 150 millions was essential, but not for what you think. The money was useful, but Apple cut enough jobs and projets that it did not need it. But Microsoft also committed to continuing to release Office for Mac at the same time.
Parallel molecular data storage by printing epigenetic bits on DNA
I would argue that the absolute majority of people don't come up with really novel ideas either (and I'm speaking of myself too). Most people just develop existing ideas, and maybe apply them in new contexts.
https://moonjump.app/

It’s a window into the small web. Just click a button to open a random site harvested from are.na, marginalia, etc. It used to just be a server but ever since I’ve made this TikTok-esque UX I’ve been kinda addicted to it
OpenRTB: Individual ad impressions put up for bid in real-time
Google Photos made a similar promise. They said "as long as you let us optimize your photos, the service will be free and unlimited". I started syncing my photos and I think 12 months later they announced the end of the policy.

I never paid so I'm not really that surprised but it was a quick about-face.
I remember trying to explain to someone that Java's List type was ordered, where they thought ordered meant sorted. Turns out, language is tough.
Did you actually expect anyone to deploy mmWave outside of stadiums, concert venues, and transit centers, and possibly a few blocks in NYC?

It's not usable spectrum for mass deployment.
Discussion (57 points, 4 hours ago, 120 comments) https://news.ycombinator.com/item?id=41923998
Sure - but that's not a portable language at all! Nor is it an especially good target for any kind of formal verification or even type-checking.
My impression is that actually solving this classic RPA problem with AI is exactly the raison d'etre of AI21Labs with their task specific models[1]. They don't have the biggest or best general purpose LLM, but they have an excellent model that's been pre-trained on specific types of business data and also made available for developers using simple APIs & "RPA-style" interfaces.

[1] https://www.ai21.com/use-cases
> They should lose the license & pay a fine, fuck this.

I've been saying this for years about DISH, they sat on nationwide mid-band AWS spectrum for something like a decade with only a single tower built in Colorado. They only started a network buildout after T-Mobile was forced to make concessions during Sprint merger. This is super useful/valuable spectrum in the 1.6/2.1ghz range, and it was just wasted. They also bid on 600mhz licenses they couldn't use and acted like the good guys when they leased it to other carriers during COVID.

The BRS/EBS (2.5ghz, band 41) spectrum was similarly a mess. The government gave tons of it to schools and nonprofits that would never use it, never had a need for it. They turned around and started their own market to license it out to companies that actually deployed it for LTE and 5G. In my area T-Mobile deployed more of this spectrum than the backhaul can even deliver. I can easily hit 1.4gbps between a few towers, 700mbps off a single tower (because that's the theoretical max after overhead on a 1gbps port). It's possible there's an argument to be made they were hoarding this, but they built it out, it's on-air and usable.

> T-Mobile shafting the American public super hard with this non-delivery is so insulting.

mmwave spectrum is just.... not that valuable? From a physics perspective it's blocked by too much and requires too much density to get effective deployment. They had a prior history of deploying tons of it, just not on the panels, they deployed it as radio backhaul between the towers in areas where fiber wasn't available. This spectrum should probably get light-licensed/flex-licensed because it's only really going to be needed/used in the most dense urban areas and for radio backhaul in challenging terrain.
Whoops - fixed. Thanks!

Edit: since https://news.ycombinator.com/item?id=41911099 was the first submission of the article, I'm going to merge the comments thither and re-up that one. But only the on topic comments. There is way too much dross in this thread—perhaps a fresh start will make for a healthier discussion.

(Sorry, l1n — not your fault at all, and I'm sorry we don't have karma sharing implemented yet.)
Darn tough socks still honor their lifetime warranty no matter how long passes, though obviously no socks can last forever. Generally reading online you find people mentioning you should be reasonable about it.
I can't comment on the morality of the situation, but it would certainly be foolish.

At a previous job, my team found ourselves in a similar situation. After being acquired into a very large company, where the official standard corporate development laptop did not support the tools we wanted to use and came bogged down with overhead from antivirus and other nonsense, it became difficult to get work done.

Instead of individually going rogue and potentially getting ourselves into trouble, our manager bought us all MacBooks we could use alongside our corporate machines. We were still doing all our work on company-owned hardware this way, operating over the company intranet, everything kosher and above-board: but we still got to work on machines which suited us.

Perhaps your manager can help you find a similar solution.
The paper says,

« We found binaries with issues in all optimization levels as depicted in Figure 3. We observe notable drops for LLVM's and GCC's -O0. We believe that fewer enabled optimizations also lead to fewer compiler-induced issues. We discuss in Section VII that optimizations are one of the reasons compilers introduce side channels; thus fewer optimizations could mean defensive programming techniques are less likely to get inter-fered with. However, we note that this cannot be considered a solution to compiler-induces issues as we still observe some issues with -O0. We discuss these further in Section VI-D. »
Amazingly, this has been the recommendation for at least the last 27(!) years: https://www.nngroup.com/articles/be-succinct-writing-for-the...

People who headed that advice benefited from better human usability and improved "SEO" performance due to keyword relevance.

Web tools should have warnings about "click here" text in the same way they do syntax errors.
It's an interesting area. I worked in that part of a company for a few years, and work with that area as a consumer.

Part of it is using Jenkins/GitLab/GitHub effectively, etc... but... there are also some real shortcomings to setting up elaborate, complicated systems with those tools.

As a first step, devs should have a sane, efficient incremental build setup for their project using CMake/maven/gradle/whatever that works well on their laptop (i.e., forget about all the centralized web-based apps). If they can't do that, no amount of Jenkins/GitLab/GitHub/ADO can help. If their build is slow on its own... then fix it! Running a bad/slow build on a centralized platform just makes things slow for everyone, and then there's no incentive for anyone to fix their own thing because everyone will stil be the victim of everyone else.

Only once that is sorted out, should things be hosted on Jenkins/GitLab/GitHub/ADO... and even then, using a bare minimum, simple, boilerplate setup, not some elaborate configuration that means that the software can not be built without Jenkins/GitLab/GitHub/ADO any more, and nothing can ever be migrated.
https://en.wikipedia.org/wiki/Sayer_Ji#GreenMedInfo
Actually, it was an attempt to reuse as much of the ARM design they got when they bought Nuvia moving to a different CPU architecture. They were worried about ASIC design not software code.
Debugging audio artifacts caused by... a serial port?
At Recall.ai, we built a 10,000-node cluster, processing over 1TB/sec of raw video in real-time.

After a major migration, we faced a strange audio issue that led us on a deep dive through our infrastructure.

The culprit? Not the audio code—but a hidden interaction with AWS’s virtual serial ports.

We wrote about our journey discovering the artifacts and finding a clean fix!
Maybe it is not supposed to be quantified. At least not in a monetary value.

This requirement that everything must have some currency value attached to it is a societal disease. We can extrapolate this to say that anything that increases the country GDP should be done.

I can think of many awful examples. Maybe it makes more monetary sense to uproot every last tree to extract lumber. Maybe it makes more monetary sense to just kill people with serious illnesses or disabilities instead of building more hospitals. Maybe it makes more monetary sense to just put every damn child to work in the mines or in an Amazon fulfillment center instead of sending them to school.

I can go on. As long as we can only measure the value of anything in USD, we can justify quite a lot of things that would be pretty awful for society as a whole.
Well, you do mention ascii generator hehe.

https://github.com/kyle10n/ascii_project
Neither is C, to be fair.
There is no platonic definition for words that make it wrong to use them in a different way.

Dictionaries are descriptive, not prescriptive.

If someone uses a term like "open source" in a way that is clearly different from how you understand the word, that doesn't make them wrong. All it does it highlight a different perspective.

You can attempt to tell them that they are wrong to use the term that way. It's changing the topic and distracts from the point, so if that's your goal, go for it. Most people don't react well to it. You can think that they should be okay with it, but people don't tend to react well to that either.

it's your choice.
Maybe he's not high level enough employee to have any say in the product roadmap, and he's behind on leadership planning?
I see lots of arguments that LLMs aren't really intelligent because they lack understanding and are "just doing autocomplete". But I never see any precise definitions of what "understanding" is, so it comes across as kind of a hand-wavy defense to make sure that human-like intelligence remains special and that we can say machines don't have it.
It's hard science as far as accessibility goes. A screen reader user wastes a lot of time if they have to differentiate what 10 different "click here" links mean.
This needs a [2022] tag /s