@prologic Here's mine.

@lyse I am a big fan of "obvious" math facts that turn out to be wrong. If you want to understand how reusing space actually works, you are mostly stuck reading complexity theory papers right now. Ian wrote a good survey: https://iuuk.mff.cuni.cz/~iwmertz/papers/m23.reusing_space.pdf . It's written for complexity theorists, but some of will make sense to programmers comfortable with math. Alternatively, I wrote an essay a few years ago explaining one technique, with (math-loving) programmers as the intended audience: https://www.falsifian.org/blog/2021/06/04/catalytic/ .

@lyse Still melting!

@sorenpeter Sorry, I realized that shortly after posting. Here's another attempt to post the images:

@bender I try to avoid editing. I guess I would write 5/4, 6/4, etc, and hopefully my audience would be sympathetic to my failing.

Anyway, I don't think my eccentric decision to number my twts in the style of other social media platforms is the only context where someone might write 1/4 not meaning a quarter. E.g. January 4, to Americans.

I'm happy to keep overthinking this for as long as you are :-P

@bender @prologic I'm not exactly asking yarnd to change. If you are okay with the way it displayed my twts, then by all means, leave it as is. I hope you won't mind if I continue to write things like 1/4 to mean "first out of four".

What has text/markdown got to do with this? I don't think Markdown says anything about replacing 1/4 with ¼, or other similar transformations. It's not needed, because ¼ is already a unicode character that can simply be directly inserted into the text file.

What's wrong with my original suggestion of doing the transformation before the text hits the twtxt.txt file? @prologic, I think it would achieve what you are trying to achieve with this content-type thing: if someone writes 1/4 on a yarnd instance or any other client that wants to do this, it would get transformed, and other clients simply wouldn't do the transformation. Every client that supports displaying unicode characters, including Jenny, would then display ¼ as ¼.

Alternatively, if you prefer yarnd to pretty-print all twts nicely, even ones from simpler clients, that's fine too and you don't need to change anything. My 1/4 -> ¼ thing is nothing more than a minor irritation which probably isn't worth overthinking.

@prologic I'm not a yarnd user, so it doesn't matter a whole lot to me, but FWIW I'm not especially keen on changing how I format my twts to work around yarnd's quirks.

I wonder if this kind of postprocessing would fit better between composing (via yarnd's UI) and publishing. So, if a yarnd user types 1/4, it could get changed to ¼ in the twtxt.txt file for everyone to see, not just people reading through yarnd. But when I type 1/4, meaning first out of four, as a non-yarnd user, the meaning wouldn't get corrupted. I can always type ¼ directly if that's what I really intend.

(This twt might be easier to understand if you read it without any transformations :-P)

Anyway, again, I'm not a yarnd user, so do what you will, just know you might not be seeing exactly what I meant.

@prologic I wrote 1/4 (one slash four) by which I meant "the first out of four". twtxt.net is showing it as ¼, a single character that IMO doesn't have that same meaning (it means 0.25). Similarly, 3/4 got replaced with ¾ in another twt. It's not a big deal. It just looks a little wrong, especially beside the 2/4 and 4/4 in my other two twts.

@bender It's the experience of an ordinary person in a strange place where memories are disappearing with the help of the Memory Police. The setting feels contemporary (to the book's 1994 publication date) rather than futuristic, except for some unexplained stuff about memories.

@prologic Done. Also, I went ahead and made two changes: changed hexadecimal to base64 for hashes (wasn't sure if anyone objected), and changed "MUST follow the chain" to "SHOULD follow the chain.

@movq Yes, the tools are surprisingly fast. Still, magrep takes about 20 seconds to search through my archive of 140K emails, so to speed things up I would probably combine it with an indexer like mu, mairix or notmuch.

@prologic Thanks for writing that up!

I hope it can remain a living document (or sequence of draft revisions) for a good long time while we figure out how this stuff works in practice.

I am not sure how I feel about all this being done at once, vs. letting conventions arise.

For example, even today I could reply to twt abc1234 with "(#abc1234) Edit: ..." and I think all you humans would understand it as an edit to (#abc1234). Maybe eventually it would become a common enough convention that clients would start to support it explicitly.

Similarly we could just start using 11-digit hashes. We should iron out whether it's sha256 or whatever but there's no need get all the other stuff right at the same time.

I have similar thoughts about how some users could try out location-based replies in a backward-compatible way (append the replyto: stuff after the legacy (#hash) style).

However I recognize that I'm not the one implementing this stuff, and it's less work to just have everything determined up front.

Misc comments (I haven't read the whole thing):

• Did you mean to make hashes hexadecimal? You lose 11 bits that way compared to base32. I'd suggest gaining 11 bits with base64 instead.

• "Clients MUST preserve the original hash" --- do you mean they MUST preserve the original twt?

• Thanks for phrasing the bit about deletions so neutrally.

• I don't like the MUST in "Clients MUST follow the chain of reply-to references...". If someone writes a client as a 40-line shell script that requires the user to piece together the threading themselves, IMO we shouldn't declare the client non-conforming just because they didn't get to all the bells and whistles.

• Similarly I don't like the MUST for user agents. For one thing, you might want to fetch a feed without revealing your identty. Also, it raises the bar for a minimal implementation (I'm again thinking again of the 40-line shell script).

• For "who follows" lists: why must the long, random tokens be only valid for a limited time? Do you have a scenario in mind where they could leak?

• Why can't feeds be served over HTTP/1.0? Again, thinking about simple software. I recently tried implementing HTTP/1.1 and it wasn't too bad, but 1.0 would have been slightly simpler.

• Why get into the nitty-gritty about caching headers? This seems like generic advice for HTTP servers and clients.

• I'm a little sad about other protocols being not recommended.

• I don't know how I feel about including markdown. I don't mind too much that yarn users emit twts full of markdown, but I'm more of a plain text kind of person. Also it adds to the length. I wonder if putting a separate document would make more sense; that would also help with the length.

@prologic

There's a simple reason all the current hashes end in a or q: the hash is 256 bits, the base32 encoding chops that into groups of 5 bits, and 256 isn't divisible by 5. The last character of the base32 encoding just has that left-over single bit (256 mod 5 = 1).

So I agree with #3 below, but do you have a source for #1, #2 or #4? I would expect any lack of variability in any part of a hash function's output would make it more vulnerable to attacks, so designers of hash functions would want to make the whole output vary as much as possible.

Other than the divisible-by-5 thing, my current intuition is it doesn't matter what part you take.

1. Hash Structure: Hashes are typically designed so that their outputs have specific statistical properties. The first few characters often have more entropy or variability, meaning they are less likely to have patterns. The last characters may not maintain this randomness, especially if the encoding method has a tendency to produce less varied endings.

2. Collision Resistance: When using hashes, the goal is to minimize the risk of collisions (different inputs producing the same output). By using the first few characters, you leverage the full distribution of the hash. The last characters may not distribute in the same way, potentially increasing the likelihood of collisions.

3. Encoding Characteristics: Base32 encoding has a specific structure and padding that might influence the last characters more than the first. If the data being hashed is similar, the last characters may be more similar across different hashes.

4. Use Cases: In many applications (like generating unique identifiers), the beginning of the hash is often the most informative and varied. Relying on the end might reduce the uniqueness of generated identifiers, especially if a prefix has a specific context or meaning.

@quark It looks like the part about traditional topics has been removed from that page. Here is an old version that mentions it: https://web.archive.org/web/20221211165458/https://dev.twtxt.net/doc/twtsubjectextension.html . Still, I don't see any description of what is actually allowed between the parentheses. May be worth noting that twtxt.net is displaying the twts with the subject stripped, so some piece of code is recognizing it as a subject (or, at least, something to be removed).

@xuu Thanks for the link. I found a pdf on one of the authors' home pages: https://ahmadhassandebugs.github.io/assets/pdf/quic_www24.pdf . I wonder how the protocol was evaluated closer to the time it became a standard, and whether anything has changed. I wonder if network speeds have grown faster than CPU speeds since then. The paper says the performance is around the same below around 600 Mbps.

To be fair, I don't think QUIC was ever expected to be faster for transferring a single stream of data. I think QUIC is supposed to reduce the impact of a dropped packet by making sure it only affects the stream it's part of. I imagine QUIC still has that advantage, and this paper is showing the other side of a tradeoff.

@prologic One of your twts begins with (#st3wsda): https://twtxt.net/twt/bot5z4q

Based on the twtxt.net web UI, it seems to be in reply to a twt by @cuaxolotl which begins "I’ve been sketching out...".

But jenny thinks the hash of that twt is 6mdqxrq. At least, there's a very twt in their feed with that hash that has the same text as appears on yarn.social (except with ' instead of ’).

Based on this, it appears jenny and yarnd disagree about the hash of the twt, or perhaps the twt was edited (though I can't see any difference, assuming ' vs ’ is just a rendering choice).

@bender I'm not a yarnd user, but automatically unfollowing on 404 doesn't seem right. Besides @lyse's example, I could imagine just accidentally renaming my own twtxt file, or forgetting to push it when I point my DNS to a new web server. I'd rather not lose all my yarnd followers in a situation like that (and hopefully they feel the same).

@prologic @bender Exponential backoff? Seems like the right thing to do when a server isn't accepting your connections at all, and might also be a reasonable compromise if you consider 404 to be a temporary failure.

@prologic The headline is interesting and sent me down a rabbit hole understanding what the paper (https://aclanthology.org/2024.acl-long.279/) actually says.

The result is interesting, but the Neuroscience News headline greatly overstates it. If I've understood right, they are arguing (with strong evidence) that the simple technique of making neural nets bigger and bigger isn't quite as magically effective as people say --- if you use it on its own. In particular, they evaluate LLMs without two common enhancements, in-context learning and instruction tuning. Both of those involve using a small number of examples of the particular task to improve the model's performance, and they turn them off because they are not part of what is called "emergence": "an ability to solve a task which is absent in smaller models, but present in LLMs".

They show that these restricted LLMs only outperform smaller models (i.e demonstrate emergence) on certain tasks, and then (end of Section 4.1) discuss the nature of those few tasks that showed emergence.

I'd love to hear more from someone more familiar with this stuff. (I've done research that touches on ML, but neural nets and especially LLMs aren't my area at all.) In particular, how compelling is this finding that zero-shot learning (i.e. without in-context learning or instruction tuning) remains hard as model size grows.