Tootfinder

Opt-in global Mastodon full text search. Join the index!

No exact results. Similar results found.
@hex@kolektiva.social
2026-06-03 09:54:03

The whole #LLM ROI thing reveals something interesting. It's basically impossible to figure out the ROI of an LLM. That makes it impossible for bean counters to make a comparison between human work and LLM work, or human work without an LLM and LLM-assisted work, to determine if the incredibly high price is worth it. But it's also impossible because you can't measure the ROI of a human, especially for skilled labor.
You can't measure the ROI of a human, because managers have no idea what people do. There's an eternally expanding amount of work designed to address this problem. But no matter how closely people are surveilled, interrogated, analyzed, there's never any real answer.
I've talked in the past about in relation to medical care. One of the dirty secrets of hospitals is that they have no way to figure out how much individual treatment costs. It's easy to understand at scale. You can know exactly how much something costs society. You can even identify patterns, using public health models, and decrease costs for society by trying to get people to avoid risky behavior (stop smoking, use protection during sex, etc). But it is absolutely not possible, at all, in any way, to figure out how much a single visit costs. This is similar to the problem of predicting climate change vs predicting the weather tomorrow in Amsterdam at 15:00. One is possible, the other is simply not.
But what is becoming painfully clear now is that this is true *everywhere*. It's trivial to know how much an industry costs. It's possible to figure out it's ROI for society. It is not possible to figure out how much value any individual worker provides. LLM ROI and cost comparison is an instance of this larger problem.
This is a problem for capitalism because it shows that the fundamental assumptions behind capitalism, that product value and labor value are quantifiable, that people can actually make comparisons between competing products, etc, are completely bullshit. The capitalist apologetics that makes up so much of economics, the lies that are told that hold this system together, are crumbling before our eyes.
If you make a lot of money, it's because you've been lucky. You have the right social networks, you have become good at convincing people to give you money. There is absolutely no way to connect that to actually providing value to society. If you make a lot of money, internalize that. Understand that you are not special, and things can change. If you don't make a lot of money, it's not because you don't provide value. Don't forget that. The system is a lie built to destroy you. Don't let it.
The ideology is sick, something something time of monsters and all that, we are together in this dying machine. We need to understand the lies. Your value can never be quantified. The way we have always figured out how to do the right thing for each other is through each other. Social connection has always guided us. But now the most socially disconnected people on the planet have hijacked the system. They direct the resources of the world, and game the system to avoid personal responsibility.
We have to build a system where everyone is accountable. We can't use abstract numbers and lies to figure things out for us. We have to build systems around people and accountability. There is no other solution.

@sherold@mastodon.online
2026-05-04 08:19:02

Hi there, curious people in the #Fediverse. 👋🏻 Give this please a gentle boost. It's awesome.
#askfedi #fedihelp

@simplicator@federate.social
2026-05-04 07:42:53

What if #DarkMatter is the mass of all of the parallel universes superimposed upon this reality? #Astrophysics #Cosmology

@tinoeberl@mastodon.online
2026-04-01 20:22:54

Trotz Diskussion um das Ende der #EinspeisevergĂĽtung bleibt die #Photovoltaik wirtschaftlich.
Laut Bundesverband des Solarhandwerks rechnet sich eine typische Hausanlage auch ohne VergĂĽtung, da der

@kurtsh@mastodon.social
2026-03-28 16:03:27

No they want your DNA to track you.
Folks, have you seen GATTACA?
▶️ U.S. lawmakers demand answers after Canadian man says border officers made him give DNA sample | CBC News
cbc.ca/news/canada/windsor/us-

@david@boles.xyz
2026-04-29 11:34:29

The States That Will Not Be Commanded
There is a class of human experience that answers to no direct order. You cannot tell yourself to fall asleep. The instruction arrives at a locked door. Sleep refuses the simple transaction of command and execution. Instead, it assembles itself once certain conditions are present, and those conditions include, strangely enough, the act of picturing yourself already inside the state you are trying to enter.

@digitalnaiv@mastodon.social
2026-04-26 07:49:02

Der Skandal ist nicht der Angriff, sondern ein System, das daran scheitert, ihn abzufangen. Signal ohne Regeln ersetzt keine staatliche Infrastruktur. Caspar Clemens Mierau hat in mancherlei Beziehung Recht, aber es ist schon bezeichnend, wenn jemand wie Klöckner u.a. auf einen simplen Phishing-Angriff reinfallen. Das zeigt etwas über deren Digitalkompetenz aus.
#Golem

@arXiv_csCL_bot@mastoxiv.page
2026-03-31 10:12:22

Training data generation for context-dependent rubric-based short answer grading
Pavel \v{S}indel\'a\v{r}, D\'avid Slivka, Christopher Bouma, Filip Pr\'a\v{s}il, Ond\v{r}ej Bojar
arxiv.org/abs/2603.28537 arxiv.org/pdf/2603.28537 arxiv.org/html/2603.28537
arXiv:2603.28537v1 Announce Type: new
Abstract: Every 4 years, the PISA test is administered by the OECD to test the knowledge of teenage students worldwide and allow for comparisons of educational systems. However, having to avoid language differences and annotator bias makes the grading of student answers challenging. For these reasons, it would be interesting to compare methods of automatic student answer grading. To train some of these methods, which require machine learning, or to compute parameters or select hyperparameters for those that do not, a large amount of domain-specific data is needed. In this work, we explore a small number of methods for creating a large-scale training dataset using only a relatively small confidential dataset as a reference, leveraging a set of very simple derived text formats to preserve confidentiality. Using these methods, we successfully created three surrogate datasets that are, at the very least, superficially more similar to the reference dataset than purely the result of prompt-based generation. Early experiments suggest one of these approaches might also lead to improved model training.
toXiv_bot_toot

The ultrarich mostly aren’t escaping the tax system through exotic loopholes.
They mostly increase their fortunes with and spend regular taxable income
— salaries, dividends, interest, business profits, realized capital gains
— and they earn a lot of it.
This means the most powerful lever is also the simplest one.
Restore the top marginal ordinary income tax rate to its pre-2017 level of 39.6 percent
— which, but for Trump’s tax cuts, would have applied t…

@kurtsh@mastodon.social
2026-04-29 20:29:20

People that "want a phone call" instead of just answering a simple question in email.
➡️ Don't want a paper trail
➡️ Are unable to put their thoughts into written words
➡️ Don't know how or are too lazy to type
#thismeetingcouldhavebeenanemail