@prologic@twtxt.net How does yarn.social’s API fix the problem of centralization? I still need to know whose API to use.

Say I see a twt beginning (#hash) and I want to look up the start of the thread. Is the idea that if that twt is hosted by a a yarn.social pod, it is likely to know the thread start, so I should query that particular pod for the hash? But what if no yarn.social pods are involved?

The community seems small enough that a registry server should be able to keep up, and I can have a couple of others as backups. Or I could crawl the list of feeds followed by whoever emitted the twt that prompted my query.

I have successfully used registry servers a little bit, e.g. to find a feed that mentioned a tag I was interested in. Was even thinking of making my own, if I get bored of my too many other projects :-)

@movq@www.uninformativ.de Thanks, it works!

But when I tried it out on a twt from @prologic@twtxt.net, I discovered jenny and yarn.social seem to disagree about the hash of this twt: https://twtxt.net/twt/st3wsda . jenny assigned it a hash of 6mdqxrq but the URL and prologic’s reply suggest yarn.social thinks the hash is st3wsda. (And as a result, jenny –fetch-context didn’t work on prologic’s twt.)

@movq@www.uninformativ.de Thanks! Looking forward to trying it out. Sorry for the silence; I have become unexpectedly busy so no time for twtxt these past few days.

@prologic@twtxt.net Yes, fetching the twt by hash from some service could be a good alternative, in case the twt I have does not @-mention the source. (Besides yarnd, maybe this should be part of the registry API? I don’t see fetch-by-hash in the registry API docs.)

@movq@www.uninformativ.de I don’t know if I’d want to discard the twts. I think what I’m looking for is a command “jenny -g https://host.org/twtxt.txt” to fetch just that one feed, even if it’s not in my follow list. I could wrap that in a shell script so that when I see a twt in reply to a feed I don’t follow, I can just tap a key and the feed will get added to my maildir. I guess the script would look for a mention at the start of a selected twt and call jenny -g on the feed.

(@anth@a.9srv.net’s feed almost never works, but I keep it because they told me they want to fix their server some time.)

I guess I can configure neomutt to hide the feeds I don’t care about.

@movq@www.uninformativ.de Is there a good way to get jenny to do a one-off fetch of a feed, for when you want to fill in missing parts of a thread? I just added @slashdot@feeds.twtxt.net to my private follow file just because @prologic@twtxt.net keeps responding to the feed :-P and I want to know what he’s commenting on even though I don’t want to see every new slashdot twt.

@bender@twtxt.net Based on my experience so far, as a user, I would be upset if my client dropped someone from my follower list, i.e. stopped fetching their feed, without me asking for that to happen.

@bender@twtxt.net I’m not a yarnd user, but automatically unfollowing on 404 doesn’t seem right. Besides @lyse@lyse.isobeef.org’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@twtxt.net @bender@twtxt.net 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@twtxt.net 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.

@movq@www.uninformativ.de Variable names used with -eq in [[ ]] are automatically expanded even without $ as explained in the “ARITHMETIC EVALUATION” section of the bash man page. Interesting. Trying this on OpenBSD’s ksh, it seems “set -u” doesn’t affect that substitution.

Morphotrophic by Greg Egan is built around an idea for how life on Earth could have worked out differently. It gets increasingly strange and interesting as the story progresses. My partner and I finished it last night and thoroughly enjoyed it. The beginning is free online: https://gregegan.net/MORPHOTROPHIC/00/MorphotrophicExcerpt.html #scifi #reading

@prologic@twtxt.net I don’t know what you mean when you call them stochastic parrots, or how you define understanding. It’s certainly true that current language models show an obvious lack of understanding in many situations, but I find the trend impressive. I would love to see someone achieve similar results with much less power or training data.

@prologic@twtxt.net I thought “stochastic parrot” meant a complete lack of understanding.

@movq@www.uninformativ.de The success of large neural nets. People love to criticize today’s LLMs and image models, but if you compare them to what we had before, the progress is astonishing.

@prologic@twtxt.net Thanks. It’s from a non-Euclidean geometry project: https://www.falsifian.org/blog/2022/01/17/s3d/

@prologic@twtxt.net Thanks for the invitation. What time of day?

@prologic@twtxt.net Fair enough! I just added some metadata.

Thanks @prologic@twtxt.net! I like the way Yarn.social is making all of twtxt stronger, not just Yarn.social pods.

Does anyone care about the 140-char limit recommended by the #twtxt spec? I have been trying to respect it but wonder if it’s wasted effort.

I learned a #Toronto #hex club just started! I’ve played since ‘98 or ‘99, but rarely in person. https://www.hexwiki.net/index.php/Hex_clubs

@movq@www.uninformativ.de Thanks!

Hello twtxt! I’m James (or @falsifian@www.falsifian.org). I live in Toronto. Recent interests include space complexity, simple software, and science fiction.