Tootfinder

Why AI can't possibly make you more productive; long
#AI and "productivity", some thoughts:
Productivity is a concept that isn't entirely meaningless outside the context of capitalism, but it's a concept that is heavily inflected in a capitalist context. In many uses today it effectively means "how much you can satisfy and/or exceed your boss' expectations." This is not really what it should mean: even in an anarchist utopia, people would care about things like how many shirts they can produce in a week, although in an "I'd like to voluntarily help more people" way rather than an "I need to meet this quota to earn my survival" way. But let's roll with this definition for a second, because it's almost certainly what your boss means when they say "productivity", and understanding that word in a different (even if truer) sense is therefore inherently dangerous.
Accepting "productivity" to mean "satisfying your boss' expectations," I will now claim: the use of generative AI cannot increase your productivity.
Before I dive in, it's imperative to note that the big generative models which most people think of as constituting "AI" today are evil. They are 1: pouring fuel on our burning planet, 2: psychologically strip-mining a class of data laborers who are exploited for their precarity, 3: enclosing, exploiting, and polluting the digital commons, and 4: stealing labor from broad classes of people many of whom are otherwise glad to give that labor away for free provided they get a simple acknowledgement in return. Any of these four "ethical issues" should be enough *alone* to cause everyone to simply not use the technology. These ethical issues are the reason that I do not use generative AI right now, except for in extremely extenuating circumstances. These issues are also convincing for a wide range of people I talk to, from experts to those with no computer science background. So before I launch into a critique of the effectiveness of generative AI, I want to emphasize that such a critique should be entirely unnecessary.
But back to my thesis: generative AI cannot increase your productivity, where "productivity" has been defined as "how much you can satisfy and/or exceed your boss' expectations."
Why? In fact, what the fuck? Every AI booster I've met has claimed the opposite. They've given me personal examples of time saved by using generative AI. Some of them even truly believe this. Sometimes I even believe they saved time without horribly compromising on quality (and often, your boss doesn't care about quality anyways if the lack of quality is hard to measure of doesn't seem likely to impact short-term sales/feedback/revenue). So if generative AI genuinely lets you write more emails in a shorter period of time, or close more tickets, or something else along these lines, how can I say it isn't increasing your ability to meet your boss' expectations?
The problem is simple: your boss' expectations are not a fixed target. Never have been. In virtue of being someone who oversees and pays wages to others under capitalism, your boss' game has always been: pay you less than the worth of your labor, so that they can accumulate profit and this more capital to remain in charge instead of being forced into working for a wage themselves. Sure, there are layers of manservant caught in between who aren't fully in this mode, but they are irrelevant to this analysis. It matters not how much you please your manager if your CEO thinks your work is not worth the wages you are being paid. And using AI actively lowers the value of your work relative to your wages.
Why do I say that? It's actually true in several ways. The most obvious: using generative AI lowers the quality of your work, because the work it produces is shot through with errors, and when your job is reduced to proofreading slop, you are bound to tire a bit, relax your diligence, and let some mistakes through. More than you would have if you are actually doing and taking pride in the work. Examples are innumerable and frequent, from journalists to lawyers to programmers, and we laugh at them "haha how stupid to not check whether the books the AI reviewed for you actually existed!" but on a deeper level if we're honest we know we'd eventually make the same mistake ourselves (bonus game: spot the swipe-typing typos I missed in this post; I'm sure there will be some).
But using generative AI also lowers the value of your work in another much more frightening way: in this era of hype, it demonstrates to your boss that you could be replaced by AI. The more you use it, and no matter how much you can see that your human skills are really necessary to correct its mistakes, the more it appears to your boss that they should hire the AI instead of you. Or perhaps retain 10% of the people in roles like yours to manage the AI doing the other 90% of the work. Paradoxically, the *more* you get done in terms of raw output using generative AI, the more it looks to your boss as if there's an opportunity to get enough work done with even fewer expensive humans. Of course, the decision to fire you and lean more heavily into AI isn't really a good one for long-term profits and success, but the modern boss did not get where they are by considering long-term profits. By using AI, you are merely demonstrating your redundancy, and the more you get done with it, the more redundant you seem.
In fact, there's even a third dimension to this: by using generative AI, you're also providing its purveyors with invaluable training data that allows them to make it better at replacing you. It's generally quite shitty right now, but the more use it gets by competent & clever people, the better it can become at the tasks those specific people use it for. Using the currently-popular algorithm family, there are limits to this; I'm not saying it will eventually transcend the mediocrity it's entwined with. But it can absolutely go from underwhelmingly mediocre to almost-reasonably mediocre with the right training data, and data from prompting sessions is both rarer and more useful than the base datasets it's built on.
For all of these reasons, using generative AI in your job is a mistake that will likely lead to your future unemployment. To reiterate, you should already not be using it because it is evil and causes specific and inexcusable harms, but in case like so many you just don't care about those harms, I've just explained to you why for entirely selfish reasons you should not use it.
If you're in a position where your boss is forcing you to use it, my condolences. I suggest leaning into its failures instead of trying to get the most out of it, and as much as possible, showing your boss very clearly how it wastes your time and makes things slower. Also, point out the dangers of legal liability for its mistakes, and make sure your boss is aware of the degree to which any of your AI-eager coworkers are producing low-quality work that harms organizational goals.
Also, if you've read this far and aren't yet of an anarchist mindset, I encourage you to think about the implications of firing 75% of (at least the white-collar) workforce in order to make more profit while fueling the climate crisis and in most cases also propping up dictatorial figureheads in government. When *either* the AI bubble bursts *or* if the techbros get to live out the beginnings of their worker-replacement fantasies, there are going to be an unimaginable number of economically desperate people living in increasingly expensive times. I'm the kind of optimist who thinks that the resulting social crucible, though perhaps through terrible violence, will lead to deep social changes that effectively unseat from power the ultra-rich that continue to drag us all down this destructive path, and I think its worth some thinking now about what you might want the succeeding stable social configuration to look like so you can advocate towards that during points of malleability.
As others have said more eloquently, generative AI *should* be a technology that makes human lives on average easier, and it would be were it developed & controlled by humanists. The only reason that it's not, is that it's developed and controlled by terrible greedy people who use their unfairly hoarded wealth to immiserate the rest of us in order to maintain their dominance. In the long run, for our very survival, we need to depose them, and I look forward to what the term "generative AI" will mean after that finally happens.

Some people make me want to charge up the cattle prod. He's one.
Dr. Oz applauds blue states rolling back migrant health coverage
https://thehill.com/policy/healthcare/5361875-dr-oz-medicaid-medicare-migrant-health-coverage/

I still see no signs that indicate that the D-party has the ability to win seats in 2024 or win the presidency (or house or senate majority) in 2028.
I am perceiving the D-party eroding-itself away leaving individual candidates to run their own campaigns.
Although I am much in favor of the goals of much "identity" based campaign messaging I feel that emphasis on those has cost the progressive/liberal voice too many seats. Those messages, valid and important as they are…

Prime Day: Amazons Shopping-Fest dauert erstmals vier Tage
Erstmals wird Amazon seine als Prime Day bekannte Shopping-Feier über vier Tage abhalten. Los geht es am 8. Juli.
https://www.

I see @… has a similar reaction about a study that says something we want to hear.
Re that second remark, “The *point* of AI is to make cognitive abilities irrelevant,” well, that’s the thing that’s up for debate right now. Making cognitive abilities (and thus labor, and people) irrelevant is very much the marketing pitch of the hype bubble. That pitch is about cost cutting, and about fear and intimidation — an intoxicating, investor-frothing mixture of fantasy and terror.
It’s not the only vision of AI, however.
https://hachyderm.io/@datarama/114693869134389025

Kyler Murray wants Cardinals to be better at 'off-schedule plays' in 2025 https://www.nfl.com/news/kyler-murray-wants-cardinals-to-be-better-at-off-schedule-plays-in-2025

I have my share of issues with Parkrose Permaculture, but she has a lot of things I do strongly agree with. I can't stress enough that you never dehumanize your enemies. You can respond appropriately to violence. You can defend yourself from them by any means necessary. But you do not dehumanize them. You always limit your response to the minimum necessary to defend yourself.
There are a number of former Nazi skins who became antifascists after realizing they were wrong. Those folks tend to be some of the most dedicated because they feel a debt, and some of the most knowledgeable because they were there. Coming out of these types of cults, police included, is hard and takes time. A lot of us don't have the ability to work with them. But some do.
By repeatedly humanizing your opponent, you can break some of them. The #Seattle Police Department was not defunded but saw a massive reduction in numbers because their morale was destroyed. Some people will never change. Some people are broken and feel like they need the power. But if you change one person's mind, even give them something to think about, it's a crack. If even one cop quits, that's one less trained gun pointed at you in the future.
The 18 year old marines and federalized national guard troops out there are literally kids. A lot of them came from poor communities. They are being used in a way they haven't been trained to do, doing things they (should) have been told are not legal. They joined to get out of poverty, to go to college, or to "defend the American people" (regardless of how misguided that is). Few, if any, of them joined to abuse people. They will be especially open to persuasion.
Remind those troops that they are carrying out illegal orders, that they are being called on to violate their oath to protect the constitution, that they are suppressing the free speech of the fellow Americans they swore to defend. Remind them that the people they could be illegally arresting now are just like their parents, their neighbors, their families, the friends who didn't join. Remind them that this is the first step. They will be called on to kill Americans if they let this keep going.
Remind them ICE sleeps in hotels while they sleep on the ground. Remind them that their drunk and incompetent leadership thinks of them as disposable tools. Remind them that some of these people are out protesting *for them* against cuts to the VA and other services. Remind them that the people they're defending refuse to make college free so they can recruit from poor schools. Remind them that they will always be welcome when they're ready to join the side of freedom and justice.
When you dehumanize your enemies, you unify them. When you humanize your enemies, you can divide them. There is no weapon available to us right now so powerful as compassion.
https://youtu.be/YtWOYUDMsBw

Non-uniform finite-element meshes defined by ray dynamics for Helmholtz problems
Martin Averseng, Jeffrey Galkowski, Euan A. Spence
https://arxiv.org/abs/2506.15630

Non-uniform finite-element meshes defined by ray dynamics for Helmholtz problems
The $h$-version of the finite-element method ($h$-FEM) applied to the high-frequency Helmholtz equation has been a classic topic in numerical analysis since the 1990s. It is now rigorously understood that (using piecewise polynomials of degree $p$ on a mesh of a maximal width $h$) the conditions "$(hk)^p ρ$ sufficiently small" and "$(hk)^{2p} ρ$ sufficiently small" guarantee, respectively, $k$-uniform quasioptimality (QO) and bounded relative error (BRE), where $ρ$ is the norm of the solutio…

Minimizing Communication for Parallel Symmetric Tensor Times Same Vector Computation
Hussam Al Daas (STFC, Scientific Computing Department, Rutherford Appleton Laboratory, Didcot, UK), Grey Ballard (Wake Forest University, Computer Science Department, Winston-Salem, NC, USA), Laura Grigori (EPFL, Institute of Mathematics, Lausanne, Switzerland,PSI, Center for Scientific Computing, Theory,Data, Villigen, Switzerland), Suraj Kumar (Institut national de recherche en sciences et technologi…

Minimizing Communication for Parallel Symmetric Tensor Times Same Vector Computation
In this article, we focus on the parallel communication cost of multiplying the same vector along two modes of a $3$-dimensional symmetric tensor. This is a key computation in the higher-order power method for determining eigenpairs of a $3$-dimensional symmetric tensor and in gradient-based methods for computing a symmetric CP decomposition. We establish communication lower bounds that determine how much data movement is required to perform the specified computation in parallel. The core idea …

Don't Make It Up: Preserving Ignorance Awareness in LLM Fine-Tuning
William F. Shen, Xinchi Qiu, Nicola Cancedda, Nicholas D. Lane
https://arxiv.org/abs/2506.14387

Don't Make It Up: Preserving Ignorance Awareness in LLM Fine-Tuning
Existing work on mitigating catastrophic forgetting in large language model (LLM) fine-tuning has primarily focused on preserving specific data or tasks, while critically overlooking the degradation of essential capabilities instilled through safety alignment, particularly the model's ability to faithfully express ignorance. In this work, we show that this capability is significantly degraded during conventional fine-tuning, leading to undesired behaviors such as hallucinations. To address this…