Moore's law is "the observation that the number of transistors in an integrated circuit doubles about every two years". For a while clock speed was a proxy for that metric, but it's not the 'law' itself.

Yeah, but today number of cores is the rough proxy for that metric.

How do you operate in that world if "multithreading isn't the answer"?

Modern CPUs contain a lot more computing units than cores. For a while, hyperthreading was thought to be a useful way to make use of them. More recently, people have turned to advanced instruction sets like SSE and AVX.

Those things aren’t mutually exclusive. Also firstly, I suspect there’s more “low hanging fruit” in making more software make use of more cores. We’re increasingly getting better languages, tooling and libs for multi threading stuff, and it’s far more in the realm of your average developer than writing SIMD compatible code and making sure your code can pipeline properly.

Threads are of course appropriate to implement high-level concurrency and parallelism. But for fine-grained parallelism, they are unwieldy and have high overhead.

Spreading an algorithm across multiple threads makes it more difficult for an optimizing compiler to find opportunities for SIMD optimization.

Similarly to how modern languages make it easier to safely use threads, runtimes also make it easier to take advantage of SIMD optimizations. For example, recently a SIMD-optimized sorting algorithm was included in OpenJDK. Apart from that, SIMD is way less brittle at runtime than GPUs and other accelerators.

> So what does it say?

It depends on the definition of tenure... if tenure is defined as a closed interval (i.e. defined only for developers who've joined and then left) then it means that, of the developers who have departed, none had wanted to grow with the company, or the company had let them go. For a startup company this might not be a good sign.

If tenure is defined as the length of time the developer had worked, OR, has worked so far, then it means they have a core group of developers and aren't growing the development team particularly quickly. Again, this might not be a good sign for a startup.

> then it means that, of the developers who have departed, none had wanted to grow with the company, or the company had let them go. For a startup company this might not be a good sign.

What. Of course that "of the developers who have departed" all have left/been fired, that's a tautology. How is it "not a good sign"? Or is it supposed to be a bad sign that there even is a person who is not working there anymore?

I think parent meant:

Assuming tenure is defined ONLY for those who have already left, 2 years is a bad sign. For a 3 year old company, if that definition is used, it is indeed pretty bad. It means that _of the people who are leaving_, people stay a couple of years, then bounce. This means people stick around long enough to get past the warmup of new employment, get used to your stack and tech, then bounce for greener pastures.

The very important metric this leaves out though is what percentage of the company actually left. If there's 7 people in this 3 year old company and only one person left last year . . . that says almost nothing. Any single person can leave for a great variety of reasons. You'd need a decent sample for this to matter.

The flip side definition of "tenure" is worth considering though: if the average tenure of 2 years includes people still working at the company, there are a lot more variables to content with before you can know anything. A 3 year old company could have an average employee of it have been working there for 2 years and not a single person who joined the company having ever left (e.g. if at year 1 there was a decent amount of hiring). I think this is probably why parent wanted to restrict the definition (and why it's worth thinking about this angle) - because otherwise the company ramp up and trajectory and hiring patterns become hidden variables and you can't glean anything out of the tenure numbers on their own.

I still don't get what's bad about that. So what would be a good tenure for a 3 year old company if 2 years is bad? 2.5 years? 1 year? 6 months?

Another part of the problem is that companies aren't incentivised to provide parents with the controls they need. Why can't I block an entire channel in YouTube Kids if I know that they make inappropriate content? I have to block every single video? I would hope any legislative efforts focus on incentivising sensible features such as that, but I'm gonna assume I already know the answer to that wish.

Don’t use System Settings to find passwords, open Keychain Access instead, it’s much more direct for searching.

From the article:

   Personally, I think the dichotomy between hypothesis-testing and likelihood-quantification is a false one. The “P=0.05” cutoff we use to “reject” a hypothesis is an arbitrary one. When I read papers, I never “accept” or “reject” hypotheses but rather consider likelihood quantification as a measure of the weight of evidence or a distance of the data from some null hypothesis, as measured by some statistic. I encourage everyone else to consider this probabilistic worldview when viewing our paper: we aimed to quantify probabilities of this system occurring in nature, and P-values were convenient and commonly understood ways of communicating quantiles.

This paragraph does a lot of lifting. Conflating p-values and probabilities is the science equivalent of a code smell.

Though p-values are probabilities.

They are the probability that the data seen (or more extreme) in the experiment were generated given the null-hypothesis is true.

Now, of course to fully understand the p, you also have to understand the null hypothesis. And yeah, sometimes it is misspecified. (by e.g testing out many null-hypotheses and only showing the more interesting ones, or accidentally creating a bad unlikely null-hypothesis which may allow for many uninteresting alternative hypotheses.)

Obligatory p-value snippet from the ASA:

   P-values do not measure the probability that the studied hypothesis is true, or the probability that the data were produced by random chance alone.

Ronald L. Wasserstein & Nicole A. Lazar (2016) The ASA Statement on p-Values: Context, Process, and Purpose, The American Statistician, 70:2, 129-133, DOI: 10.1080/00031305.2016.1154108

To follow that up (so people know what they actually are), what p-values represent is the likelihood we would observe our data, given the null hypothesis.

Setting a cutoff of .05 is saying “if there’s less than a 5% chance we’d see this data, assuming the null hypothesis, then we can assume that the null hypothesis is false”

But this statement only applies to a 'naive' (or first) statistical analysis or test on the dataset. Once the researcher starts changing their assumptions in response to the results they're seeing, they're p-hacking and p-values are no longer meaningful. In addition, once you have multiple researchers looking at the same dataset with different assumptions, and you factor in publication bias, the p-value also loses meaning.

Well, yes and no. The p-value still means the same thing, but when you take a dataset and go looking for any result that is under a certain threshold, you’ll probably find it. “Unlikely” events happen all the time!

What your comment is highlighting is an issue with bad experimental design. (And, obviously, with our publication regime)

A p-value is strictly _a_ probability.

I'm an idiot.

Because the bank's relationship with its customers, central banks, and society as a whole, is codified in law and not in code. So if you find a legal loophole that's different from hacking the bank to get something the legal system doesn't entitle you to. When the code is the law, a hack is a loophole.

There seems to be an ongoing misimpression here that code is, in fact, law, as though our existing legal system doesn’t have something to say about that.

Cryptocurrencies and smart contracts exist within our society as well, they're not subject to an alternative reality.

They're only barely being subject to society's laws though I think is the point.

Just because you personally (or even everyone you personally know) don't want something doesn't mean that no one wants it. It's popular for a reason.

That's nowhere near what the point of my comment was.

People wouldn't have to be living like cavemen to combat inflation if politicians weren't spending trillions.

And no it's not popular, it's a puff piece from the WSJ that tries to put the onus on citizens not the politicians.

It pays really well to shift blame, I wonder is someone somewhere has an excel spreadsheet with the ROI for propaganda expenses. Like Greta pointing the finger to western countries while ignoring the biggest polluters and the countries that mostly contribute to plastic in the oceans.

I think a Ring Modulator might have some equivalence. Depending on the frequency you set it to the ability to accurately detect the frequency of the input notes can diminish quite drastically.

I'm having a hard time parsing your argument. Could you make this concrete, what are your values and what are the values that are irreconcilable with yours?