graboid

I feel for a smallish project I'd rather prefer to have more readable, dense code like Ruby's over the ceremony of static types.

Sounds cool, as someone interested in concatenative languages and also a user of C#, might I ask if you have a link?

Factor is super cool! And the amount of packages ("vocabularies") it comes bundled with is just astonishing.

I assume that in most array languages, you also create "words" or however you want to call functions, to reuse code. I wonder about a purely aesthetic issue: how does it look to interleave those symbols with user-defined words that by nature will be much, much longer, i.e. "create-log-entry" or "calculate-estimated-revenue".

It is a very interesting write-up. A random thought I had while reading this: I feel like long-term, a system that schedules/"optimizes" the process of learning by reading/watching content and then engaging with this new content by taking notes and connecting those notes to existing knowledge could be more fruitful. Something akin to SuperMemo's "Incremental Reading", but not as focused on creating flashcards out of the material.

With traditional Q/A-style spaced repetition, I feel like accumulating a long list of isolated facts sometimes (I know, you can remedy this a bit by also quizzing connections, context, but I feel like the general tendency still remains).

Did you learn that handwriting pose already as a child? If not, how hard was it to teach yourself writing that way?

At work, we use the .editorconfig of the .NET runtime, with slight modifications:

https://github.com/dotnet/runtime/blob/main/.editorconfig

Hi, as someone also fiddling around with a concatenative toy language, I wanted to ask if any of your languages have a public repository somewhere? You seem very knowledgeable on the topic and your descriptions made me interested.

See my answer on the sibling comment.

https://sweepy.com/

There is also one that is called "tody" that we didn't try out. Both require a small subscription fee though, which I really dislike. I wish I had found a nice open source alternative. Besides the subscription fee (which was like 18€/year for us both), I have no complaints yet about the app.

I like it! For me, I can confirm that the smaller the task, the less likely it is for me to procrastinate on it. I also didn't know that receipt printers don't need ink, that's cool. On a similar note: me and my partner recently also started using an app that divides up the household chores into small tasks and schedules them for us (e.g. "today you have to vacuum the living room"). For us, this prevents conflicts and also frees the mind of having to keep track of those things.

Are there any other VM's like the BEAM? I never heard of any (admittedly I know little of this subject), and I wondered, is it because there is no need for another one because the BEAM is just so good, or is it because the amount of work and skill required to get another BEAM-like runtime with comparable quality is too demanding?

That sounds very intriguing, do you mind sharing the smart pen model?

Thank you. I once read a bit about Span<T>, but some of this reference stuff is very new to me. Interesting, definitely. C# really is a big language nowadays...

I sometimes write C# in my day job. But I think I don't know much about how to write really fast C#. Do you have any recommendations for learning resources on that topic?

Anecdotal: I recently found a little trick that works for me to overcome the horrors of the blank page: I turn my phone (having opened my preferred note-taking solution) into horizontal mode. The keyboard gets larger in width, making it nicer to type, and on my medium-sized phone, it covers enough of the UI that I don't actually see what I type into the textbox, until I close the keyboard again. So I just happily type away and hit save at the end.

Me too. If anyone likes that font but doesn't want to spend so much money, I found SF Mono and CommitMono to be kind of similar looking.

Nice. I also have an implementation in Rust (no public repository, private note-taking app). One low-hanging fruit (IMO) to improve on the base SM-2 is to more smartly pick the initial ease. I just took the average ease of "similar" mature items. Since for my use-case, spaced repetition items are embedded in notes, "similar" items meant items in the same note, or in notes that were tagged similarly.

These days I often wonder if I should just switch to FSRS [1], which Anki also switched to. It delivers better results. However, I am hesitating, since I understand SM-2, and it is easy to read the code while FSRS is complex and feels kinda black-boxy, which wouldn't feel right to me.

A quick note to the implementation above: I wonder if having that many answer options is worth it. It probably increases the cognitive effort needed for grading and I wonder if the increased precision in some cases is worth that. But who knows?

[1] https://github.com/open-spaced-repetition

Working on a concatenative language in my free time, lots of fun: https://codeberg.org/tzell/TSL

I love this font.

Thank you for clarifying!

I guess math is uniquely suited for this kind of strategy, but would you say it translates to learning concepts in other domains too?

I was thinking about whether something like "what is X?" -> "What field is X used in?", which seems to form a hierarchy for me, would benefit of this technique? Personally, I found that for something like the preceding example, I could answer the second question without thinking about what X is at all, just by rote memorization of the wording. Happened to me quite a lot when I was using Anki. And actually, I guess this is even acceptable in some way, since the question is not about activating "what X is", but "what X is used in". What I am trying to express: I feel like I would not necessarily activate a parent concept by answering a child concept, and I think that might be true for a lot of questions outside math problems, although they form a hierarchy. So I am wondering what you think about the general applicability of this technique...

Please don't take all of this questioning wrong, I think you are doing pretty cool stuff, and I am grateful for everyone trying to push the boundaries of current SRS approaches :-)!

Say you have concepts/items/cards A, B and C, with

A -> B -> C (C encompasses B, B encompasses A, keeping the notation from the article).

As I understand it, the article advocates for showing C first, then you can assume that you also know B and A to at least some part, and save yourself the repetitions for these.

Intuitively, I would have guessed the opposite approach to be the best: Show A first, suspend B until A is learned (by some measure), then show B, etc.

That means no repetitions to skip, but also you get less failures (and thus additional repetitions) that occur as follows: you are shown C, but don't know B anymore, and thus cannot answer and have to repeat C.

If you are shown C before B, you kinda make C less atomic (you might have to actively recall both, B and C to answer it), showing B before C makes C more atomic, as you will have B more mentally present/internalized and can focus on what C adds to B.

Thanks for sharing.

I am quite curious about Swift as a "more ergonomic Rust". How would you say it fares for typical backend stuff?

Thanks.

Thanks. And yes, I forgot to mention, here in Germany it as apparently also more costly than regular laser surgery.

Did anyone here do the eye surgery where they make a cut and slide in a permanent contact lens? My eyes are too bad for lasering, but this method would apparently still work. The doc tried to sell it to me as the better method irrespective of how bad your eyes are, since it is reversible (you can remove the lens).

Maybe also Janet? https://janet-lang.org/

Yup, to anyone reading this, I want to second-recommend Kagi. Also super-customizable.

To me, it seemed a bit better than GPT-4 at some coding task, or at least less inclined to just give the skeleton and leave out all the gnarly details, like GPT-4 likes to do these days. What frustrates me a bit is that I cannot really say if GPT-4, as it was in the very beginning when it happily executed even complicated and/or large requests for code, wasn't on the same level as this model actually, maybe not in terms of raw knowledge, but at least in term of usefulness/cooperativeness.

This aside, I agree with you that it does not feel like a leap, more like 4.x.