Perhaps that’s true, but it’s a price tag that’s a lot harder to swallow for many orgs than $200/mo, and would require some hard justification for how your increased productivity contributes to the business bottom line.

I’ll agree that you can probably do that with hard numbers. I am skeptical that most $200/mo users could.

Turns out when you have the choice of accepting lower payment per patient for Medicare or having a lot fewer patients, you choose the lower payment per patient.

I would expect the same situation here. Doctors would grumble, but no one would force them to accept patients on whatever “Medicare for all” would be called. Nothing other than market forces.

A number of things would probably have to change, including the cost of medical school. But the system right now is expensive and essentially unsustainable. So change is inevitable.

Actually, I spent a considerable amount of time in my doctorate and postdoc doing this.

Any kind of MCMC sampling of a simple model tends to be bound by the rate you can draw variates.

Examples of this include: Gillespie simulations of chemical kinetics, Ising and Potts lattice models (including their roughly bazillion variations), and anything resembling bootstrap or permutation sampling.

Just because your problems aren’t bound by the rate of drawing uniform variates doesn’t mean that these problems don’t exist. It just means that you have a narrow view.

As someone who has spent considerable time working in these areas, I still appreciate advances.

It’s a cute way of saying that you can only do 2-3 new things.

The post is written for an engineer at a startup as a reminder that although it’s green field development, you only have so much runway, so it’s better to focus on what matters instead of trying some new tech because it seems cool.

If you’ve ever had to estimate your stories/tickets/etc in “story points” or “T-shirt sizes” then “innovation tokens” is roughly the same.

If you haven’t had to do that, you’ve lived a charmed life.

> Engineers should understand requirements, risks, tradeoffs, and potential gains.

Ideally. But I’ve worked with plenty of engineers who get far too excited by shiny new tech and overvalue its potential while undervaluing its risk.

Hell, I’ve been that engineer in my misspent youth. The post resonates with many of us because it describes hard-won wisdom of our mistakes.

> New technology may be right for that. Novel approaches may be right for that. "Novel" or "New" are only proxies and they're weak.

Yeah maybe, but unless you’re working on a problem that the tech directly solves, it’s pretty unlikely.

> What if you know NodeJS really well? Or MongoDb? What if you have empirical, verifiable reasons for why they fit better?

In 2015, MongoDB was a dumpster fire (which is still kind of true) and node.js was still kind of new and had enough rough edges that most teams were probably better off choosing some other language/framework.

> I'm a bit tired of "simple" and "boring" and other nonsense words in this field taking up the air in the room that should be spent evaluating solutions on their actual merits.

“Boring” and “simple” are ways to convey that it’s good to be risk averse. It’s a bit of rhetorical flourish that helps drive the point home: choose what you work on carefully because you have limited runway and should spend that runway working on the problems that matter for your business, not new tech that’s orthogonal to it.

But the core question of “how does this behave as N -> \infty?” is asymptotic behavior (ie: limits) which were developed for calculus and are very much part of the foundational calculus canon.

I loved graphing calculators until I learned tools like Mathematica and Matlab. Still waiting for the Mathematica version of LLMs.

Agents / loop engineering / whatever is hot with the AI Twitter kids still isn’t it.

Given that the term is explicitly mentioned in the Chicago Manual of Style, I suspect that this is much more of unicode trying to capture existing behavior, even if rare. And I think it's important to err on the side of capturing what people did, even if it's from an bygone era.

> How many people actually use this thing? 10 loud people with power using it does not justify putting it into the open standard.

It's definitely more than 10. But even if it's 10, I disagree with your assessment. People used this; it's worth a codepoint. There are far stranger code points out there.

And I've seen far weirder things in open standards. Have you read the C99 standard? Are you aware of the three allowed integer representations? Of which maybe two were in use in 1999, and one only in use in relic hardware that might exist in maybe tens of sites.

As other people have commented, it's partly a relic of an era where things were typeset by hand. Some journals may also require this in their style guide; I don't know of any offhand but the list of journals is long and their choice of style is varied.

> I dunno, do people actually read through a citation list, in a manner where the redundancy would be relevant?

Yes, they do. When you work on a Ph.D.-level dissertation, you end up having to learn your subfield pretty well: its major works, open questions, current major players, their ongoing arguments, etc. You start to notice citations cluster, and you can often find more than one opinion group. People you have to listen to (ie: your advisor / committee) will hand you articles with authors you don't know and who may come from labs that you don't know. One common way to start orienting yourself is to look at who they cite.

You pick up patterns: some people love to cite themselves and their friends, some people really try to give a detailed overview of the state of the field, and some people go out of their way to attack their rivals via citation. Sometimes the reference list has an author or work you don't recognize, and might have missed while glancing over the text. Mostly it's something mildly relevant and occasionally it's an exciting direction to look into.

The really long citation lists, where the triple emdash would make sense, mostly occur in fields outside science and engineering where knowledge is exchanged through chapters and books. Some fields expect a lot of detailed textual analysis which can involve a lot of careful citation of the same chapter or book over and over again.

While I've never worked in these fields, I've been friends and roommates with people in them, and I quickly learned that bibliographies are way more of a thing for them than I ever imagined.

You’ve clearly never had to deal with a lot of references. Author lists can cluster. If you’re reading, it can get tedious if the last 5+ references are to the same three authors. Let alone 20+ references.

This happens. In some writing it happens _a lot_.

It’s a common enough that it’s even explicitly mentioned in the Chicago Manual [0], one of the main style guides for academic writing in the US:

> Em dashes also substitute for something missing. For example, in a bibliographic list, rather than repeating the same author over and over again, three consecutive em dashes (also known as a 3-em dash) stand in for the author’s name.

[0]: https://www.chicagomanualofstyle.org/qanda/data/faq/topics/H...

You can do a full unpacking-via-lookup with a uint16[256] and then do bit shifting and masking to extract the individual trits, but using an extra byte in each entry (or 3 tables) would let you extract with just two shifts.

This starts to vary a lot with the microarchitecture, and there’s the added dimension of SIMD vectorization, so accurate timing in a realistic context becomes important.

Even if the current President was known for his strategic global thinking and ability to keep quiet about the “real goals,” other explanations seem more likely.

For all the current US administration has complained about the opposition being “socialist,” they’ve certainly gone all-in on the state partially owning private companies.

Almost like cries of “socialism” have become a dog whistle instead of what the term actually means.

Have you done any serious graphics programming? Even at the OpenGL 1.x level? What you’re saying just doesn’t make sense.

Just because you’re rotating and translating things in 3-space doesn’t negate that you have a stack of transforms that relate a point in world space to one on screen space and you want to be able to project from one to the other.

Nor does it make it any easier when you need to think about how to stack transforms to achieve effects like rendering a mirror.

I honed a lot of useful practical skill with linear algebra trying to get graphics to do what I wanted. And I say this as someone who’s spent the bulk of my career using linear algebra in the context of quantum mechanics, physical simulation, and ML-adjacent areas.

SVD is more complex but ultimately it’s just another useful decomposition of a matrix.

I’m not sure why you’re both negative and dismissive. Transformation matrices in graphics are a good and approachable way to get used to linear transformations, which turn out to be useful pretty much everywhere.

Whether or not that helps you with ML depends more on what you’re doing in ML. FAANG doesn’t have a monopoly on ML or on interesting work in ML.

This is the engineer’s take on things. I am entirely sympathetic to it.

I also think it missed a lot of what management values in consulting. At its best, you can offload a lot of things unrelated to your business to people who are experts. At its worst, you’ve offloaded the blame to a group of over-worked twenty-something’s with impressive degrees who have no idea what they’re doing, but who sound really fucking confident about it.

Can an internal team do it better? Probably. Will they be cheaper? Probably. Will they assuage management’s anxieties and deflect some/all of the blame? Nope, not at all.

I have never worked at a place where this was true. Either senior devs would pound through the tasks, or we’d cut them as unimportant.

The only reason we ever hired a junior was because we saw potential and thought they could grow into solid colleagues.

It tries to get you to imagine that advances in the last 50-100 years will project linearly into advances in the next 50-100 years.

This is not generally the way that science and medicine work. Even if you add in gobs of questionable data collected by companies with a bad track record of doing right by it.

They’re essentially trying to get you to believe that AI + your data will give you the kind of step change in medicine that we got from penicillin and X-rays/MRI/CT imaging. It’s a cheap rhetorical trick.