Tootfinder

For mange er dette en stor dag, full av faner, slips og taler på Stortinget. For meg er det bare en helt vanlig mandag. Jeg kjenner vinden i håret når jeg sykler til og fra jobb, og ingenting i min rytme endres av hvem som sitter bak de tykke murene i maktens hus. Der teller de stemmer og kroner, men jeg teller solstråler, pedaltråkk og minutter av frihet. Penger og posisjoner fŸles som falske symboler i et system som har mistet forbindelsen til selve livet.
Jeg har en leilighet, men j…

Hab mir nun einen zweiten AP gekauft der nur via VPN ins Internet darf. Das GL-inet Gerät ist klein, aber die Leistung beeindruckt. Hat eine sehr gute GUI und darunter läuft ein OpenWRT. AdGuard z.b. kannst du einfach per klick installieren, VPN Einrichtung geht ebenfalls super einfach.
https://amzn.eu/d/3hj3EAC…

Jeg har fulgt Norsk Syndikalistisk Forbund en stund, og det fŸles som å stå på skuldrene til en lang kamp for ekte arbeiderkontroll og selvorganisering. NSF bærer historien til radikale arbeidere som siden 1916 har stått imot byråkrati, hierarkier og enhver form for politisk slippe-opp. Deres anarkosyndikalistiske tradisjon bygger på solidaritet nedenfra og direkte aksjon, noe jeg virkelig verdsetter.
I dag kan de virke små og marginale i det politiske landskapet, men for meg er de for…

Chemist Eye: A Visual Language Model-Powered System for Safety Monitoring and Robot Decision-Making in Self-Driving Laboratories
Francisco Munguia-Galeano, Zhengxue Zhou, Satheeshkumar Veeramani, Hatem Fakhruldeen, Louis Longley, Rob Clowes, Andrew I. Cooper
https://arxiv.org/abs/2508.05148

Chemist Eye: A Visual Language Model-Powered System for Safety Monitoring and Robot Decision-Making in Self-Driving Laboratories
The integration of robotics and automation into self-driving laboratories (SDLs) can introduce additional safety complexities, in addition to those that already apply to conventional research laboratories. Personal protective equipment (PPE) is an essential requirement for ensuring the safety and well-being of workers in laboratories, self-driving or otherwise. Fires are another important risk factor in chemical laboratories. In SDLs, fires that occur close to mobile robots, which use flammable…

potus wants the federal government to take control of 61.4 square miles (159.03 square kilometers) of DC.
The same federal government that failed to maintain control of a single car (~119 sq. feet/11 sq. meters) being operated by Big Balls, is going to take control of an entire city.
Sure Jan.
Bluster and military cosplay aren’t going to make us forget his name is all over the Epstein files.

Trump threatens federal takeover of Washington DC after attack on ex-DOGE worker
Two 15-year-olds have been arrested in Washington for an attempted carjacking and beating of Edward Coristine, a prominent member of the Department of Government Efficiency. The incident happened early Sunday in Logan Circle. Police say a group of teenagers tried to carjack Coristine and his significant other. Coristine was attacked until officers intervened. The attack has renewed calls from President Donald Trump for federal control over Washington, citing crime concerns. Trump threatened to …

Pseudo-likelihood produces associative memories able to generalize, even for asymmetric couplings
Francesco D'Amico, Dario Bocchi, Luca Maria Del Bono, Saverio Rossi, Matteo Negri
https://arxiv.org/abs/2507.05147

Pseudo-likelihood produces associative memories able to generalize, even for asymmetric couplings
Energy-based probabilistic models learned by maximizing the likelihood of the data are limited by the intractability of the partition function. A widely used workaround is to maximize the pseudo-likelihood, which replaces the global normalization with tractable local normalizations. Here we show that, in the zero-temperature limit, a network trained to maximize pseudo-likelihood naturally implements an associative memory: if the training set is small, patterns become fixed-point attractors whos…

Even if “AI” worked (it doesn’t), there’s many reasons why you shouldn’t use it:
1. It’s destroying Internet sites that you love as you use chat bots instead of actually going to sources of information—this will cause them to be less active and eventually shut down.
2. Pollution and water use from server farms cause immediate harm; often—just like other heavy industry—these are built in underprivileged communities and harming poor people. Without any benefits as the big tech companies get tax breaks and don’t pay for power, while workers aren’t from the community but commute in.
3. The basic underlying models of any LLM rely on stolen data, even when specific extra data is obtained legally. Chatbots can’t learn to speak English just by reading open source code.
4. You’re fueling a speculation bubble that is costing many people their jobs—because the illusion of “efficiency” is kept up by firing people and counting that as profit.
5. Whenever you use the great cheat machine in the cloud you’re robbing yourself from doing real research, writing or coding—literally atrophying your brain and making you stupider.
It’s a grift, through and through.

MIT says AI isn’t replacing you… it’s just wasting your boss’s money
https://www.interviewquery.com/p/mit-ai-isnt-replacing-workers-just-wasting-money

I don’t care what people think or say. I stand in solidarity with the @… and share their vision for a free, self-organized workers’ movement in Norway. Even if syndicalists are few, we’re here, and I proudly support their struggle for worker control and direct action.
I identify as a syndicalist because I actually enjoy working, but only when labor is organized…

Interactions across multiple games: cooperation, corruption, and organizational design
Jonathan Bendor, Lukas Bolte, Nicole Immorlica, Matthew O. Jackson
https://arxiv.org/abs/2507.03030

Interactions across multiple games: cooperation, corruption, and organizational design
It is socially beneficial for teams to cooperate in some situations (``good games'') and not in others (``bad games;'' e.g., those that allow for corruption). A team's cooperation in any given game depends on expectations of cooperation in future iterations of both good and bad games. We identify when sustaining cooperation on good games necessitates cooperation on bad games. We then characterize how a designer should optimally assign workers to teams and teams to tasks that involve varying arr…