skysight-inc/HackerNews huggingface dataset:
> That means, counterintuitively, that kids who are good at math should be spending less time on math, not more.

Maybe it's the right thing to do, but you're not going to get a lot of support for letting the kids who are good at school go home after an hour. All the students are expected to spend the same amount of time sitting in a math class. Might as well try to have something for them to do other than throw paper airplanes after ten minutes and distract the kids who need the whole hour.

Anyway, I suggested 2x the pupils for the fast math class, so that the normal math class got more resources. If you've got smaller schools, it's harder to allocate resources, and that's understandable, but a lot of schools have enough kids to push the ratios around to get more attention to the kids that need it.

It's unnecessary for all high school graduates to have taken AP Calc; but it's a waste of students' time for those who could have made it through that not to have had the opportunity. Just like it's a waste of students' time for them to be in classes that are beyond their current level so much that they can't catch up and then just passing them because there's no alternative, so they just zone out. Admittedly, the daycare function of school doesn't really care about wasting students' time, but that's only part of the function of school.
Is there any news on what datasets llama 2 or chatgpt for that matter were trained on?
But the only way to make people equal is to sabotage, sabotage and sabotage. And, of course, if parents have the means, this won't work. This is just robbing a lot of lower class kids from any chance of getting higher, just because not everyone will make it.

That's stupid.

Of course sabotaging kids is also slightly cheaper, and creates the need for more administrators, the ones making these decisions, to decide exactly what is "equitable".
A primary trust indicator of any research result is the reputation of the journal publishing the paper. Better journals demand more, not just in terms of the impact/insight of the results, but also the rigor of the experimental methodology used and how well the data was curated and the confounding variables identified and isolated.

And of course, the criteria for observational-based research differ a lot from mechanism-based, especially since the former can't control for nearly as many variables. The same goes for simulations or interpretive modeling, where experimentation is minimal.

Research studies differ quite a lot in how precisely the mechanism of action is identified, isolated, tested, and results interpreted. IMHO, blackening the trustworthiness of all of science is unhelpful, especially when some models and methods are surely more trustworthy and replicable than others. Better to identify and catalog specific sources of error (or imprecision) in order to remedy them than to just toss the baby.
Thalidomide is sold in the USA as a treatment for several conditions- it's a highly effective drug and is mostly safe within the target population.

(I point this out because most people only tell the very first part of the thalidomide story).
Kudos and congrats to Todd Miller and the OpenBSD folks! [1]

[1]: https://man.openbsd.org/strlcpy.3
For a lot of the sections (esp. the Tone of motion section) - I have to ask: why?
As someone who has built the tech behind an online ordering platform just for pizzerias (Slice), this is all exactly on point.
Due to my VPN, the weather widget is always wrong.
> However, I feel like it needs to be said: Microsoft makes bad products[1]! They're overpriced, insecure, slow (it's astounding to me how slow their web properties are), and hard to use. Easily some of the worst UI I've ever seen and, what's worse, they've been like that my entire career. They keep slapping lipstick on the same pigs and claiming to have reinvented themselves.

Some think tank should make an analysis how many billions of dollars are lost in productivity across the globe if you compare the current MS Office stuff to an alternate of "the Windows 2000-era stuff, but with security patches" or "the FOSS office stack, with more money behind it".

Anyone wanna estimate how many digits that number has?

(Teams, while being ungodly slow, at least has the value proposition of being an integrated product - persistent chat, file sharing and voice / video calls - that really didn't exist in 2000)
> But that has been challenged by uncertainty principles, observer effect and all that.

Any system which allows universal computation necessarily has subsystems that cannot make perfect predictions about the full system. This doesn’t even require quantum mechanics; humans would run up against this problem in a fully classical universe. Loosely put, since we’re part of the system ourselves, we can’t gather all of the information necessary for perfect prediction without interfering with whatever we’re trying to predict.

However, as far as I’m aware, we haven’t yet closed all the loopholes related to superdeterminism, meaning quantum randomness might not be truly random in the sense that an observer completely outside of our universe could in theory procure an algorithm that predicts the exact eigenvalues for every quantum measurement we make. Since we’re inside the system, no luck for us though. But I like to make a subtle distinction between “unpredictable random” (max Kolmogorov complexity) and “truly random” (ungenerated via any Turing computable algorithm and unpredictable with infinite knowledge). The latter is a bit philosophically difficult to prove exists. The former is quite easy to prove exists; think about how a one time pad has max Kolmogorov complexity (assuming a good source of random noise) but it’s conditional Kolmogorov complexity with the key is much smaller.
To be fair on that, both the US and EU governments launched antitrust cases around that with the US case narrowly avoiding having the company split up and the EU ruling resulting in requirements the browser be decoupled, followed by half a billion in fines for not doing so well enough.

Not that the two situations are anything alike, but a "and look what happened with that" argument hardly points away from valid antitrust outcomes.
The cons are that RL sometimes requires more babysitting, more non-subject-matter (ie, optimization-method-specific) expertise, and can be a bit less... predictable... at inference time.

Also, LP often offers perfectly acceptable optimization and latency. The only case where this isn't true for offline problems is where LP times out or where relaxations of the problem are required to prevent timeout.

If I had to choose one method to learn, I guess I would learn RL. But it's a false choice. Given that I know both well, I'd reach for LP unless there was a good reason to use RL instead. And from a user perspective LP is much easier to learn than RL (see cons), so I think this is good general advice. Definitely worth having both tools on your belt if you plan on working on problems where either is a good fit.
8 x 4K monitors, I'm so jealous wow. I wish my work would give me even one, I'm developing on 2 full hd monitors :(

I even do some design work so it's honestly pretty bad.
Feed it as a prompt to Stable Diffusion or something and its the perfect attack on corporate greed.
what is the point of using cloudflare if they just turn off DDoS protection? Some more information on this part would be great
I mean, it applies to everyone. The more interesting question in this context is, which of the two groups does it apply to more?

If I have to put both the average CEO and the average epidemiologist on a spectrum of "more interested in my well-being" to "more interested in my pocketbook", I know where I'd put each one.
The answer is a bit over-enthusiastic, but it’s hard to say it’s incorrect.

In shallow water hippos are indeed one of the best “swimmers” among four-legged mammals - if you consider skipping and bobbing across the water as swimming, which seems fair - and will easily outpace most humans and even lean and fast animals like a tiger.
>When the means to achieve equality is to handicap the strong, we're going the wrong way.

A lot of people in the equity/equality movement have been following "the means justify the ends" for quite some time now and that approach has been growing in administrators. One of the reasons the supreme court ruled against affirmative action is the implementation of affirmative action in college admissions turned into shameless racism.
And then completely lost the plot and has been behind Chrome for the last ten years.
Prompt: 5 Ounce (Pack of 6) is $33. 4 ounce pack of 6 is 15. which is the better deal?

Llama 2:

The 5 ounce pack of 6 is the better deal. It costs 33for6packs,whichis33for6packs,whichis5.50 per pack. The 4

ounce pack of 6 costs 15for6packs,whichis15for6packs,whichis2.50 per pack. So, the 5 ounce pack of 6 is the

better deal.

ChatGPT4:

To figure out the best deal, we need to calculate the price per ounce for each option.

For the 5 ounce pack of 6:

Total ounces = 5 ounces/can \* 6 cans = 30 ounces
Price per ounce = $33 / 30 ounces = $1.10 per ounce

For the 4 ounce pack of 6:

Total ounces = 4 ounces/can \* 6 cans = 24 ounces
Price per ounce = $15 / 24 ounces = $0.625 per ounce

Therefore, the 4 ounce pack of 6 is the better deal, as it costs less per ounce.
I don't know lua. But the behavior you describe seems like a quirk of lua tables. Instead of having `table.get(key)` return a sentinel value, you can replace it with `table.has_key(key)` and `table.get(key)`.

I'm not sure about the ergonomics of the trade in lua. But in C# the ergonomics of `DBNull` are terrible. If it were just replaced with `null` everywhere, everything would just be better.

IMHO.
The impact of this, I think is that the LLM is right, hippos do swim, because that's how people commonly describe what hippos do, such that it gets into the training set
Is it possible to run the pretrained model on your own local hardware or is this really cloud only.
I thought the README was long enough that I wasn't going to go into those details, especially as 1) I am not an expert on the topic, 2) the choice of curve depends on your application, and 3) there are a lot of resources on the topic. I listed a few.

If you want to learn more, see "Space-Filling Curves An Introduction with Applications in Scientific Computing", listed as one of my sources, eg, "Algorithm 13.3: Matrix-vector multiplication based on Hilbert order matrix traversal" and "Matrix Multiplication Using Peano Curves".

One of the sources I cited is "Improved Data Locality Using Morton-order Curve on the Example of LU Decomposition" at https://ieeexplore.ieee.org/document/9378385 :

> Here, we look at the block factorization algorithm, where the LU decomposition performance depends on the performance of the matrix multiplication. In both cases, the LU decomposition and the matrix multiplication, such a matrix is traversed by three nested loops. In this paper, we propose to traverse such loops in an order defined by a space-filling curve. This traversal dramatically improves data locality and offers effective exploitation of the memory hierarchy. Besides the canonical (or line-by-line) access pattern, we demonstrate the traversal in Hilbert-, Peano and Morton order. Our extensive experiments show that the Morton order (or Z -order) and the inverse Morton order (or И-order) have a better runtime performance compared to the others.

I first learned about this issue from "The Anatomy of High-Performance 2D Similarity Calculations" at https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4839782/ :

> Certain self-similar space-filling curves provide a natural hierarchy and iteration order to optimize locality in a cache-oblivious algorithm. In particular, Morton ordering, based on the hierarchical Z-order curve, has been previously used to optimize cache locality in matrix multiplication,22 suggesting that it may also be useful for similarity matrices. .... Other space-filling curves, such as the Hilbert curve, also have excellent cache-locality properties; however, the calculations required to implement the Z-order curve are particularly simple.

I want to reproduce their result, then see how other traversal orders affect the timings. Careful reading of that paper shows they only used sizes which are a power of two, while real-world data sets aren't so convenient.

I couldn't find an off-the-shelf library with non-powers-of-two/rectangular generalizations, so I wrote one.
Lots of retail businesses have a sign that reads "If you don't receive a receipt, your purchase is free!" It enlists the customer as an auditor to make sure the employee uses the POS system, rather than pocketing the customer's payment. If the cash in the till doesn't match receipts at the end of the day, the proprietor knows something's wrong.

(Just in case you're one of today's lucky 10,000.)
This is due to the tokenization scheme. These LLMs can’t see individual characters, they see chunks of characters that are glued together to form tokens.

It’s impressive that they understand individual-character-level questions as well as they do.
What are you driving at? The rates for vaccinated versus not are a clear indicator that the vaccines helped. Hard to see any other way of interpreting that data.

You are correct that, if I did, in fact, have an early case of covid, I cannot be sure that the vaccine helped me with the later case. So, as far as that goes, my "evidence" is anecdotal at best and can't be taken fully as proof of anything.

You will have a hard time arguing against vaccines with the aggregate evidence above, though.
>but come high school it could be worse to have lower grades in more challenging courses, depending on how hard the GPA hit is.

At least when I was in high school, the regular, pre-AP and the AP classes each had a different grade scale. Core classes were a 1.0(70%)-4.0(100%), pre-AP were +1 (1-5) and AP were +2 (2-6). So in theory they were taking into account how difficult the classes were. Since this was in the DFW area, I'd expect something similar in Dallas.
I dove into language design a couple of years ago to try and build my own transpiled DSL. At first I tried reading about it, and quickly became overwhelmed. Took a step back and just started writing code that seemed like it would do what I wanted.

I ended up writing a parser that built an AST, even though I didn't know what an AST was at the time. It's a huge boost to your confidence when you get around to reading the literature and it describes exactly the solution you came up with, like "yes I'm on the right track and I'm not as stupid as I thought I was."
If everything you contribute to the product is tracked in Jira, you should consider your career trajectory. Managers are humans. They rely on a synthesis of data and social cues to form an opinion about how much to invest in your growth.

As automation becomes more common, forming relationships with colleagues and managers will become more important. As money becomes more expensive, someone, somewhere will be looking at a spreadsheet drawing a red line for who to cut. That decision won’t be made using just # of JIRA tickets closed.
Just a hunch but the scientists who don't approve are the ones who aren't getting grants? I am all for private funding of basic science research, and if there are billionaires putting cash bounties on problems they would like to be solved then tough cookies for the scientists who want to work on other things. The idea of governments funding science is new; wealthy patrons funding all manner of science inquiry (and doing the research themselves as well) was the norm for centuries. Personally I would rather see someone who has made something of themselves in business decide who gets scientific grants rather than a bureaucrat and history has many examples of a scientist obtaining patronage to perform research in areas that weren't originally of interest to the donor. Perhaps scientific advocacy and PR is an under-staffed field in science?
Using that interpretation of the definition, you could argue that walking through knee-deep water is swimming.
And this is one place where I think Azure does better... I mean, the UI/UX sucks, but at least you can do everything in the UI without loosing too many brain cells. Google and AWS seem to require automation tooling to make sense, which IMO makes all of it harder to learn in practice. I tend to like learning some of those things visually first, then automate out of necessity as opposed to the other way around.

Not that I'm tethered to Azure... I've used a combination of dedicated server, DigitalOcean and Cloudflare for my personal stuff... though just started delving into the Cloudflare (edge compute) stuff.

There are definitely ups and downs to them all.
European here, I went to school from 8:20 till 16:05. You guys really have school starting at 10:00?
Who takes care of the kids after parents go to work and before school starts?
Statement of Support for Meta’s Open Approach to Today’s AI
For testing:

- we have a dedicated dev environment for analysts to experience a dev/test loop. None of the pipelines can be run locally unfortunately.

- we have CI jobs and unit tests that are run on all pipelines

Observability:

- we have data quality checks for each dataset, organized by tier. This also integrates with our alerting system to send pagers when data quality dips.

- Airflow and our query engines hive/spark/presto each integrate with our in-house lineage service. We have a lineage graph that shows which pipelines produce/consume which assets but it doesn't work at the column level because our internal version of Hive doesn't support that.

- we have a service that essential surfaces observability metrics for pipelines in a nice ui

- our airflow is integrated with pagerduty to send pagers to owning teams when pipelines fail.

We'd like to do more, but nobody has really put in the work to make a good static analysis system for airflow/python. Couple that with the lack of support for column level lineage OOTB and it's easy to get into a mess. For large migrations (airflow/infra/python/dependecy changes) we still end up doing adhoc analysis to make sure things go right, and we often miss important things.

Happy to talk more about this if you're interested.
PFSD is out of print and PDF is freely available

http://www.nobius.org/dbg/practical-file-system-design.pdf

http://www.nobius.org/dbg/
Consideration -> Implementation -> Verification is a super low friction way to improve how LLMs do at coding tasks (or most tasks really).

-

Me: We want to swap keys with values in a Ruby hash, what are some considerations?

LLM: When swapping keys with values in a Ruby hash, there are a few considerations to keep in mind. First, it's important to note that swapping keys and values can change the order of the hash, which may affect the behavior of your code. Additionally, if the hash contains duplicate keys, swapping the keys and values may not have the desired effect. It's also worth noting that swapping keys and values does not affect the hash's size or the number of keys it contains. Is there anything else you would like to know?

-

Me: Let's implement the swap

LLM: Sure! Here is an example of how you can swap the keys and values of a Ruby hash using the each method:

hash = {a: 1, b: 2, c: 3}

hash.each do |key, value|
hash[value] = key
end

# Output:
# {1: "a", 2: "b", 3: "c"}

This code iterates over the hash using the each method, and for each key-value pair, it sets the value of the key to the value of the previous key. Is there anything else you would like to know?

-

Me: Did we miss anything?

LLM: No, that covers the basics of swapping keys and values in a Ruby hash. Is there anything else you would like to know?
That's an important bit of context I think :)

Air mass sensor failure can often be determined by unplugging the thing completely, O2 sensor failure could be the cause of your problem but if it only starts to fail at very high revs I'd check the fuel system first to make sure that there is enough flow.

Those can get clogged up pretty badly, especially in vehicles that have been standing for a while with ethanol based fuel in them, it takes forever before that gunk clears out without some work, if at all.

If it really is the O2 sensor (of which you usually have two) then I'd suspect the pre-cat one first, it runs in a much hotter environment and is more critical to the engine working properly.

Another option is the crank sensor, they may not give enough signal at a high number of revs so you start to miss if enough pulses fail (one or two in an otherwise consistent signal isn't going to cause the PLL to lose lock but if it is more erratic then it will and then revs would drop back to a regime where the sensor is still working reliably).

Good luck fixing that.
I have been running my own services for years, all self-hosted, with different VPS providers: A2Hosting, Digial Ocean and now Contabo. I use ISPConfig and host about 25 domains and 200 email addresses. You have to configure SPF, DKIM, DMARC, reverse PTR address, but nothing impossible. Encountered some problems with some providers blocking the IP range of my VPS provider (Microsoft in particular), but if you send an email and explain you have a new server configured form scratch and carefully configured, they add an exception for your IP. Check your IP addresses are not list in any black list and you are done. I use Rspamd and I had to add some domains to the whitelist ... There are many servers out there (even of medium sized companies, very badly configured) that don't even respect the minimum requirements like a public registered and valid server name for the smtp server. I avoid any external SMTP service ... they can read all your emails ... There are many ready-to-use solutions, such as mailcow, which simplify the management of the mail server.
I don't doubt it. I also heard that when a Linux dev find a bug that might be a vulnerability, they often don't bother adding it to the CVE database (and say as much in discussions with the other kernel devs) which casts doubt on the usefulness of counting CVEs as a way to estimate how secure Linux is.
You are comparing those numbers directly, but that's not how you compare those numbers.

First the population size, as per the article, was half what it is now. So you can either double the asian flu deaths or half the covid deaths to account for this.

That still leaves a huge gap, but this is where demographic analysis comes in.

Usually you see this as age adjusted deaths.

In this case, the population of the US has been aging for a long time so you would expect more deaths from covid than the Asian flu for an equally deadly virus.

The other demographic change that is important, especially when talking about covid is rates of obesity, which is something else that has grown over time in the US population.

That's a really long winded way to say you can't just compare raw numbers.

The article doesn't give an explanation of how they came to their conclusion, but if you take population size, age distribution, and obesity rates into account, I could see the adjusted numbers being close.
That's a terrible system, it doesn't represent gaps in performance. If the first model is orders of magnitude better than the second, that system still says "99% as good" or whatever.
I think you and parent/GP all agree? A thing can be anti competitive, and a strategic failure.
I also am curious about this statement. If a problem becomes too complex for linear programming solutions, how easy is it to know that a reinforcement learning solution is actually a global optima and not just local?
Buried the lead: you can preorder a Framework Laptop 16 starting today (2023-07-18).

Everyone who can afford to invest in a sustainable laptop that can be upgraded instead of thrown out should consider it.