lapinot

In contrast to some powerpoint, what is gained here is that you get to extinguish a couple species at every render. But it's ok because you'll get a bonus and donate 1% to the WWF.

The practice of "theory building" is especially endangered by the current LLM uptake. Not because LLMs are good at it, because they are bad at it. Worse, they sometimes actively hinder theory building, because it usually rests on actually having deep experience with some techniques: 1: you do something over and over, 2: it becomes a second nature, you just get the feel for it, 3: you intellectualize this intuitive understanding, 4: you discover a new structure, or a better way to express/teach/learn/use an existing one.

Theory building was always kind of niche, or at least less prestigious (in comparison with solving well-known problems). Now, because the mad problem solvers are pulling on the blanket so much harder, the marginalization is getting worse.

Indeed, and based non-extensive, one sample approximate average testing, my own bank works like a charm on GOS.

In the error monad NaN = NaN (or Nothing = Nothing or None = None, depending on your terminology) because mathematical equality is an equivalence relation. There are many foundational debates about equality, but whether or not it is an equivalence is never the question.

The root of the problem, completely overlooked by OP is that IEEE 754 comparison is not an equivalence relation. It's a partial equivalence relation (PER). It does have its utility, but these things can be weird and they are definitely not interchangeable with actual equivalence relations. Actual, sane, comparison of floating points got standardized eventually, but probably too late https://en.wikipedia.org/wiki/IEEE_754#Total-ordering_predic.... It's actually kinda nuts that the partial relation is the one that you get by default (no, your sorting function on float arrays does not sort it).

> just involves lecturing about the same material over and over again

It doesn't, specifically not in primary education. Also definitely not for subjects with any kind of amount of practical work. Let's see.. foreign languages, sports, physics, chemistry, biology...

Which part of these relevant quotes understand? Or perhaps have counter-arguments?

> shows how low our appreciation of pedagogues has fallen

> Dropout rates for these classes hovered around 90 percent

> learning is a social experience

> an expert is watching you and cares whether you succeed or fail

And you can reliable type text without looking.

You forgot privacy invasive tracking.. I guess some people don't understand irony!

I beg to differ. Tried to use a kaios nokia 800 tough. Firefox/KaiOS using a stupid kind of webview for every UI, you get two consequences: first, everything is ugly: wrong padding everywhere, complex layout with non-integer number of list items fitting on screen (this is important, vertical list is the primary widget on small screen dumbphones). And second: everything is sluggish. My true "series 30" nokia launches app instantly, whereas i'm sure the SoC is order of magnitudes slower.

So the UX definitely shows complete absence of culture of design in this kind of space (eg gameboy UI design, classic dumbphone UI, ...). Small screens need very simple widgets (most importantly grid and vertical list), hand made paddings and proper space optimizations, most likely some bitmap font tailored to the resolution.. You can't just slap some dynamic layout and expect it to work nicely on such a small screen. Everything vectorized will be both costly and of dubious rendering quality, etc..

And of course these things only have "decent", but definitely not "1 month scale" battery life. I guess LTE is the real culprit here.

> so not exactly the kind of thing you'd notice making a document

By "user interface" I mean (like Martin, in the linked video) the surface language, the way you interact with TeX, its syntax and how it presents itself to the user, the programming model. So definitely the kind of thing you'd notice making a document. Leaky abstractions and footguns are basically everywhere when you write a big LaTeX document.

> I mean TeX is basically an overpowered lambda calculus with a focus on text markup and generation.

No, lambda calculus has capture-avoiding substitution, aka hygienic macro expansion if you will. TeX has naive substitution. By the way, macro expansion is typically CBN, which is very much a rare and weird evaluation order (yes, Haskell, i know). TeX is much closer to some kind of assembly language for a virtual machine.

The user interface / language design. See talk "LaTeX: It's Not You, It's Me" by Martin Haug (cofounder). TeX's language design is at the level of Basic's. Which is in fact not surprising given the timeframe.

Speed and nice error messages in (La)TeX, and to some extent ease of programming, are entirely doomed because of fundamental design choices. Being based on unhygienic macro expansion means that there is only one way to evaluate (the slow way), there will never be incremental compilation (everything can possibly be stateful in horrible ways), there will never be good error messages because there's basically no AST information anywhere (begin/end is a joke).

Regarding ecosystem: tons of undecipherable LaTeX packages are basically one-liners (ok, 10 liners) in typst. I know it from experience: I've written my PhD manuscript in typst. So perhaps one reason why there are so many (basically frozen) packages in LaTeX is because they are so hard to write and maintain.

edit: of course, being only a few years old, typst is nowhere near as solid as TeX, but you can already use it for a lot of things and its a breeze to use.

This! My first actual project (besides some testing) has been my phd manuscript, and after ~2 days i actually had a tufte-style 1.5 column layout going, written from scratch. And its probably like ~250 lines.

There are some rough edges still, the dom model and advanced programming stuff is not quite there yet (user-defined elements, user-defined settables, advanced layout like chaining blocks for laying text flows). But like the quality of the user interface is several orders of magnitude better than (La)TeX.

There are 3 syntactic modes in typst: markup, code and math. In markup, everything is literal, unless you put a `#` sigil like `#expr` in which case `expr` is parsed in code mode. In code mode everything is an identifier, as usual in programming. In math its a bit of an ugly tradeoff but its ok: single-letter things are parsed as literals but multi-letter tokens are parsed as identifiers. Finally, in code you can enclose in `[...]` to parse in markup mode. So typically, your document will be mostly in markup mode and you will encounter stuff like `#something[An argument]`, which is a function call to which you pass one content-typed argument.

So above, `y` is parsed as literal, while `dt` is parsed as an identifier, hence function call.

Datalog is a nice query language but it is far more limited than prolog or general purpose logic programming.

Sure, but people don't owe them respect. illegal != immoral.

I'm gettin it now! :)

I guess you meant 2 operations and 3 laws?

I got interested into the hashgraph algorithm quite early and wrote a toy implementation in python (in fact discovering an error in the paper in the process). Unless something changed it's entirely useless for open-membership internet-scale consensus. As I remember, the processing time of a message at a node is linear in the number of nodes and same for the local storage at a node, meaning it's not very scalable. Moreover, the nodes must somehow a priori agree on the list of participants of the consensus process, again something which is not realistic for internet-wide consensus. The protocol is quite neat and not too hard to implement but it's similar in scope to paxos/raft: consensus inside an organization, where some things are a priori agreed upon.

Always infuriating to see that people always focus on the his pre-70s (hardcore math) or post-80s period (borderline mysticism ramp up). In the 70s he was most politically active and _definitely_ not delirious in any sense of the word and in fact according to Leila Schneps this is one of the few periods of his life he described as happy, the "sunday of his life" [1]. I translated the '72 CERN talk, its baffling how relevant it is, to this day [2].

[1] french, https://www.youtube.com/watch?v=V8BbFTEyvIw

[2] https://github.com/Lapin0t/grothendieck-cern

Demonstrating how some languages and some compilers are bad at tasks such as writing constant-time crypto routines is fine. Concluding that all compilers and non-asm languages are bad is a non sequitur. Just because you don't want non-branching code to change into branching code doesn't mean you should have to do register allocation by hand. Write simple domain-specific compilers and languages people.

Every heard about https://typst.app/?

> This has nothing to do with science and is really a point about the division of labor/economics.

This is not a critic of the idea science, ie some kind of pursuit of knowledge using any reasonable means. It is a critic of the modern institution that academic science currently is. As such, yes, some critics are in fact more generally applicable than just for science (as you say, division of labor). But these are particularly visible in science and have specific consequences in this context.

For a bit more background on Grothendieck's position on science, I've recently translated Grothendieck's talk on science at CERN, which was quite hard to find at some point: https://github.com/Lapin0t/grothendieck-cern/.

See other similar funny stuff like electric drill sound simulation using phaseplant: https://www.youtube.com/watch?v=R1Ifle4q7Tw

> Of the people who see your math paper, 90% will only read the title. Of those who read on, 90% will only read the abstract. Of those who go still further, 90% will read only the introduction, and then quit.

My personal experience is usually quite different. Perhaps i'm very weird but i like to think i'm nothing special. I mostly read papers when searching for something specific (referral by someone in a discussion, searching for a definition, a proof). I almost never read the introductions, at least not in my first pass. My first pass is usually scanning the outline to search which section will contain what i'm searching for and then reading that, jumping back and forth between definitions and theorems. I usually then read discussion/related work at the end, to read about what the authors think about their method, what they like or dislike in related papers.

Abstract and introduction i only read when i have done several such passes on a paper and i realize i am really interested in the thing and need to understand all the details.

I very much hate this "be catchy at the beginning" and its extremist instantiation "the quest for reader engagement". Sure you should pay attention to your prose and the story you're telling. But treating reader of a scientific paper as some busy consumer you should captivate is just disrespectful, scientifically unethical and probably just coping with current organizational problems (proliferation of papers, dilution of results, time pressure on reviewers and researchers). Scientific literature is technical, its quality should be measured by clarity and precision, ease of searching, ease of generalization, honesty about tradeoffs. Not by some engagement metric of a damned abstract.

The tone is probably flamebait, but the content is on topic imho. 15 papers before getting a phd?! Being a phd myself and having serious issues with a lot of the academic practices, reading the title i thought i would identify with the author and situations, which i didn't. I don't think this kind of experience is representative with the common "struggle with papers" among young researchers i have seen around me.

Depends on the "algorithm"! At this point "algorithm" in the context of social media doesn't mean anything sensible. A very common "algorithm" used to order messages in email inboxes is by "read/unread" status, which very much helps you catch up efficiently. Chronological sorting is very limited, in comparison with the myriad of other ways you could organize such data.

The objectives of the CAP are very much industrial and monetary in their orientation, seeing agriculture as a resource extraction activity and definitely not as any kind of land or biodiversity management. First goal is particularly crispy in this regard (number must go up, machine good, human labor bad).

> 1. increase productivity, by promoting technical progress and ensuring the optimum use of the factors of production, in particular labor;

> 2. ensure a fair standard of living for the agricultural Community;

> 3. stabilize markets;

> 4. secure availability of supplies;

> 5. provide consumers with food at reasonable prices.

How is fighting against dark patterns, opaque and arbitrary moderation decisions (among other things) an attack on (your very american conception of) free speech?