Shamar @Shamar@qoto.org

"Pseudo-decentralizzazione

Un pericolo differente e più subdolo è la "pseudo-decentralizzazione".

L'esempio più chiaro ed evidente è BlueSky, un social network commerciale che si finge decentralizato, mentre di fatto rende "Bluesky Social, PBC" un gatekeeper in condizione di decidere cosa ciascuno possa leggere.

Ma una forma più subdola di "pseudo-decentralizzazione" è causata da grandi fornitori di istanze gestite come masto.host o toot.io: è bello vedere fornitori professionali di applicazioni connesse al fediverso, ma il loro utilizzo da parte di istanze con centinaia o migliaia di utenti riduce nettamente la resilienza del sistema e l'autonomia delle comunità più piccole.
E se ti lamenti troppo dei rischi che queste aziende pongono per i dati personali degli utenti, verrai presto silenziato o bloccato da quegli amministratori che non hanno le competenze per amministrare in prima persona i server che forniscono agli altri."

tratto da Come creare il tuo social network (con Snac) di @giacomo

#fediverso #snac #decentralizzazione

https://encrypted.tesio.it/2024/12/18/come-creare-il-tuo-social-network.html

Breve guida pratica alla creazione di un’istanza del fediverso su un webhosting economico, senza bisogno di db o vps.

@alephoto85@fedi.bobadin.icu

Qui trovi la traduzione italiana https://encrypted.tesio.it/2024/12/18/come-creare-il-tuo-social-network.html

Spero davvero che #snac possa essere utile a decentralizzare il #fediverso anche in #Italia: ce n'è davvero bisogno!

Gajim’s port to GTK4 is almost finished. Currently we’re testing thoroughly to make the switch as smooth as possible.

We went ahead and made lots of small improvements, e.g. writing messages while offline, better styling for image previews, improved chat filters and more.

Gajim also improved its spam fighting toolkit: The next release will allow you to moderate all messages of a spammer at once.

If you like to support Gajim, please consider making a donation: https://liberapay.com/Gajim

I finally turned off GitHub Copilot yesterday. I’ve been using it for about a year on the ‘free for open-source maintainers’ tier. I was skeptical but didn’t want to dismiss it without a fair trial.

It has cost me more time than it has saved. It lets me type faster, which has been useful when writing tests where I’m testing a variety of permutations of an API to check error handling for all of the conditions.

I can recall three places where it has introduced bugs that took me more time to to debug than the total time saving:

The first was something that initially impressed me. I pasted the prose description of how to communicate with an Ethernet MAC into a comment and then wrote some method prototypes. It autocompleted the bodies. All very plausible looking. Only it managed to flip a bit in the MDIO read and write register commands. MDIO is basically a multiplexing system. You have two device registers exposed, one sets the command (read or write a specific internal register) and the other is the value. It got the read and write the wrong way around, so when I thought I was writing a value, I was actually reading. When I thought I was reading, I was actually seeing the value in the last register I thought I had written. It took two of us over a day to debug this. The fix was simple, but the bug was in the middle of correct-looking code. If I’d manually transcribed the command from the data sheet, I would not have got this wrong because I’d have triple checked it.

Another case it had inverted the condition in an if statement inside an error-handling path. The error handling was a rare case and was asymmetric. Hitting the if case when you wanted the else case was okay but the converse was not. Lots of debugging. I learned from this to read the generated code more carefully, but that increased cognitive load and eliminated most of the benefit. Typing code is not the bottleneck and if I have to think about what I want and then read carefully to check it really is what I want, I am slower.

Most recently, I was writing a simple binary search and insertion-deletion operations for a sorted array. I assumed that this was something that had hundreds of examples in the training data and so would be fine. It had all sorts of corner-case bugs. I eventually gave up fixing them and rewrote the code from scratch.

Last week I did some work on a remote machine where I hadn’t set up Copilot and I felt much more productive. Autocomplete was either correct or not present, so I was spending more time thinking about what to write. I don’t entirely trust this kind of subjective judgement, but it was a data point. Around the same time I wrote some code without clangd set up and that really hurt. It turns out I really rely on AST-aware completion to explore APIs. I had to look up more things in the documentation. Copilot was never good for this because it would just bullshit APIs, so something showing up in autocomplete didn’t mean it was real. This would be improved by using a feedback system to require autocomplete outputs to type check, but then they would take much longer to create (probably at least a 10x increase in LLM compute time) and wouldn’t complete fragments, so I don’t see a good path to being able to do this without tight coupling to the LSP server and possibly not even then.

Yesterday I was writing bits of the CHERIoT Programmers’ Guide and it kept autocompleting text in a different writing style, some of which was obviously plagiarised (when I’m describing precisely how to implement a specific, and not very common, lock type with a futex and the autocomplete is a paragraph of text with a lot of detail, I’m confident you don’t have more than one or two examples of that in the training set). It was distracting and annoying. I wrote much faster after turning it off.

So, after giving it a fair try, I have concluded that it is both a net decrease in productivity and probably an increase in legal liability.

Discussions I am not interested in having:

You are holding it wrong. Using Copilot with this magic config setting / prompt tweak makes it better. At its absolute best, it was a small productivity increase, if it needs more effort to use, that will be offset.
This other LLM is much better. I don’t care. The costs of the bullshitting far outweighed the benefits when it worked, to be better it would have to not bullshit, and that’s not something LLMs can do.
It’s great for boilerplate! No. APIs that require every user to write the same code are broken. Fix them, don’t fill the world with more code using them that will need fixing when the APIs change.
Don’t use LLMs for autocomplete, use them for dialogues about the code. Tried that. It’s worse than a rubber duck, which at least knows to stay silent when it doesn’t know what it’s talking about.

The one place Copilot was vaguely useful was hinting at missing abstractions (if it can autocomplete big chunks then my APIs required too much boilerplate and needed better abstractions). The place I thought it might be useful was spotting inconsistent API names and parameter orders but it was actually very bad at this (presumably because of the way it tokenises identifiers?). With a load of examples with consistent names, it would suggest things that didn't match the convention. After using three APIs that all passed the same parameters in the same order, it would suggest flipping the order for the fourth.

#GitHubCopilot #CHERIoT

"In the #Fediverse you mostly find people minding their own business, talking with real-world friends, sharing their own contents and occasionally joining interesting conversations with strangers."

Try to ask yourself if such a model could be (re-)applied to scientific research as well.

#fediverse How to run your own social network (with Snac): a rather interesting reading, both theoretically and practically...

Finally out a little #tutorial about running your own #fediverse instance on cheap shared hostings with @grunfink@comam.es's #snac.

https://www.tesio.it/2024/12/18/how-to-run-your-own-social-network.html

#permacomputing #retrocomputing #hacking #cgi

In case you missed our latest WIN: The EDPS found that the EU Commission illegally targeted citizens with advertising using sensitive personal data about their political views.

https://noyb.eu/en/political-microtargeting-eu-commission-illegal

Now that I have a new public #fediverse home at my own domain name with #snac, I wonder if I should migrate my old public account from @Shamar@qoto.org or I should keep both accounts indipendent.

Suggestions welcome!

@dzwiedziu @pluszysta wspaniałe algorytmy gigantów technologicznych nie tylko odwrócą uwagę od tego jak korpo i CEO unikają podatków, ale jeszcze sprawią, że dodatkowo zarobią na reklamach jakie wyświetlisz w czasie tej jałowej awantury o cycki 🫠 capitalism at its finest

If "generative AI" would be a good tool to simplify the process of interacting with your organization your whole process is deeply fucked.
https://tldr.nettime.org/@tante/113661291675296679

#Framasoft ha appena rilasciato una prima versione di una app di #PeerTube per Android e iOS. Qui gli articoli di presentazione in inglese: https://framablog.org/2024/12/10/peertube-mobile-app-discover-videos-while-caring-for-your-attention/
e in francese: https://framablog.org/2024/12/10/peertube-sur-mobile-un-univers-de-videos-qui-prend-soin-de-votre-attention/ Un motivo in più per sostenere @Framasoft : https://soutenir.framasoft.org/en/ #Fediverso #video #contributopia @informapirata @peertube @devol @scuola@poliverso.org @scuola@a.gup.pe @lealternative @maupao @opensource

We're starting a sprint to look at all the issues preventing #ReproducibleBuilds in all the apps we ship. Most of the issues are simple fixes in the upstream code, like unsorted outputs or timestamps included in the build.
You can help make the #FreeSoftware #Android ecosystem be more reproducible! See the failures here and help us report them upstream: https://verification.f-droid.org/failed.html

Imperdible - Brillante @pluralistic sobre el asesinato de Thompson

https://pluralistic.net/2024/12/09/radicalized/

Embodied, produttore del robot sociale Moxie per bambini autistici, chiude per difficoltà finanziarie, interrompendo il funzionamento del dispositivo

I proprietari di Moxie devono affrontare il dolore di spiegare ai propri figli la situazione, dato che il robot smetterà di funzionare senza possibilità di assistenza o rimborso. Emerge il problema della sostenibilità economica dei dispositivi AI e i rischi di relazioni illusorie tra bambini e tecnologia

https://aftermath.site/moxie-robot-ai-dying-llm-embodied

@eticadigitale

A deeply personal piece by my wife Ayesha and I about the hell insurance companies have put us through to get the life saving care our daughter needs to live.

We're sharing because her story is sadly all too common. I have a platform & I intend to use it:
https://www.qasimrashid.com/p/when-insurance-rejects-life-saving