Tootfinder

The media focus on this one criticism is insane, just massive focus on ensuring everybody is quoted as being against Bob's chant.
The manufacturing of consent in plain view. No support of Bob allowed to be quoted, only condemnation. No focus at all on the horrors committed by the army itself, just condemnation and contrition from all corners.
And all so much louder than the coverage of death and hunger and destruction and genocide being committed by that army.
Our media environment is so focused and in lock-step, even though it's in theory free. Yet not a single newspaper saying, you know, the Vylan character has a point! Maybe we should disband a criminal army!
Got confused about the date in the show here, but the reading's about right. The bandwagon rolls on because it's a victory for everyone. The media gets to condemn someone, and they don't have to condemn Israel.
https://wordcloudtarot.com/readings/2025-07-01-gazaandglastonbury/

Just read this post by @… on an optimistic AGI future, and while it had some interesting and worthwhile ideas, it's also in my opinion dangerously misguided, and plays into the current AGI hype in a harmful way.
https://social.coop/@eloquence/114940607434005478
My criticisms include:
- Current LLM technology has many layers, but the biggest most capable models are all tied to corporate datacenters and require inordinate amounts of every and water use to run. Trying to use these tools to bring about a post-scarcity economy will burn up the planet. We urgently need more-capable but also vastly more efficient AI technologies if we want to use AI for a post-scarcity economy, and we are *not* nearly on the verge of this despite what the big companies pushing LLMs want us to think.
- I can see that permacommons.org claims a small level of expenses on AI equates to low climate impact. However, given current deep subsidies on place by the big companies to attract users, that isn't a great assumption. The fact that their FAQ dodges the question about which AI systems they use isn't a great look.
- These systems are not free in the same way that Wikipedia or open-source software is. To run your own model you need a data harvesting & cleaning operation that costs millions of dollars minimum, and then you need millions of dollars worth of storage & compute to train & host the models. Right now, big corporations are trying to compete for market share by heavily subsidizing these things, but it you go along with that, you become dependent on them, and you'll be screwed when they jack up the price to a profitable level later. I'd love to see open dataset initiatives SBD the like, and there are some of these things, but not enough yet, and many of the initiatives focus on one problem while ignoring others (fine for research but not the basis for a society yet).
- Between the environmental impacts, the horrible labor conditions and undercompensation of data workers who filter the big datasets, and the impacts of both AI scrapers and AI commons pollution, the developers of the most popular & effective LLMs have a lot of answer for. This project only really mentions environmental impacts, which makes me think that they're not serious about ethics, which in turn makes me distrustful of the whole enterprise.
- Their language also ends up encouraging AI use broadly while totally ignoring several entire classes of harm, so they're effectively contributing to AI hype, especially with such casual talk of AGI and robotics as if embodied AGI were just around the corner. To be clear about this point: we are several breakthroughs away from AGI under the most optimistic assumptions, and giving the impression that those will happen soon plays directly into the hands of the Sam Altmans of the world who are trying to make money off the impression of impending huge advances in AI capabilities. Adding to the AI hype is irresponsible.
- I've got a more philosophical criticism that I'll post about separately.
I do think that the idea of using AI & other software tools, possibly along with robotics and funded by many local cooperatives, in order to make businesses obsolete before they can do the same to all workers, is a good one. Get your local library to buy a knitting machine alongside their 3D printer.
Lately I've felt too busy criticizing AI to really sit down and think about what I do want the future to look like, even though I'm a big proponent of positive visions for the future as a force multiplier for criticism, and this article is inspiring to me in that regard, even if the specific project doesn't seem like a good one.

🚨 Encryption isn’t optional anymore. In EdTechSR Episode 342: Jason & I discuss why the FBI wants you on Signal, Google’s antitrust headaches, and AI tools that’ll blow your mind.
🎙️ https://edtechsr.com/2025/05/30/edtechsr-ep-342-encrypted-by-default/

EdTechSR Ep 342 Encrypted by Default
where technology news meets educational analysis Welcome to episode 342 (“Encrypted by Default”) of the EdTech Situation Room from December 18, 2024, where technology news meets educational analysis. This week, Dr. Jason Neiffer (aicentrist.com) and Dr. Wesley Fryer (wesfryer.com) unpacked the FBI’s unprecedented advice urging Americans to use encrypted messaging apps like Signal and WhatsApp, discussing the implications for personal privacy, national security, and school digital literacy…

One way to ensure I don't buy an item is to market it "as seen on Shark Tank" -- but that's probably just me. Another way to ensure I don't buy an item is to market it as having AI features (??), and apparently that's widespread.
https://futurism.com/customers-see-ai-…

Something Hilarious Happens When Potential Customers See That a Product Has AI Features
New research explores how customers react to products that are marketed as having AI — with striking results.

Cryptanalysis of LC-MUME: A Lightweight Certificateless Multi-User Matchmaking Encryption for Mobile Devices
Ramprasad Sarkar
https://arxiv.org/abs/2507.22674 https://

Cryptanalysis of LC-MUME: A Lightweight Certificateless Multi-User Matchmaking Encryption for Mobile Devices
Yang et al. proposed a lightweight certificateless multiuser matchmaking encryption (LC-MUME) scheme for mobile devices, published in IEEE Transactions on Information Forensics and Security (TIFS) (DOI: 10.1109/TIFS.2023.3321961). Their construction aims to reduce computational and communication overhead within a one-to-many certificateless cryptographic framework. The authors claim that their scheme satisfies existential unforgeability under chosen-message attacks (EUF-CMA) in the random oracl…

Comparing the current situation in #Blatten after the massive landslide is of course difficult. But you CAN compare official reactions with other (natural) disaster in, let’s say, Germany:
- A plan on how and where to rebuild 🏗️ the village will be announced on June 12. THIS YEAR!
- Single trees are removed from the “lake” and the landslide by helicopter 🚁 to ensure the water can run…

social.linux.pizza is doing invite-only in a few weeks.
This is to promote other, small instances and also encourage people to set up their own instance.
social.linux.pizza is here to stay, and with an added touch of organic growth :)

An Empirical Study on the Amount of Changes Required for Merge Request Acceptance
Samah Kansab, Mohammed Sayagh, Francis Bordeleau, Ali Tizghadam
https://arxiv.org/abs/2507.23640

An Empirical Study on the Amount of Changes Required for Merge Request Acceptance
Code review (CR) is essential to software development, helping ensure that new code is properly integrated. However, the CR process often involves significant effort, including code adjustments, responses to reviewers, and continued implementation. While past studies have examined CR delays and iteration counts, few have investigated the effort based on the volume of code changes required, especially in the context of GitLab Merge Requests (MRs), which remains underexplored. In this paper, we d…

Synopsis: Secure and private trend inference from encrypted semantic embeddings
Madelyne Xiao, Palak Jain, Micha Gorelick, Sarah Scheffler
https://arxiv.org/abs/2505.23880

Synopsis: Secure and private trend inference from encrypted semantic embeddings
WhatsApp and many other commonly used communication platforms guarantee end-to-end encryption (E2EE), which requires that service providers lack the cryptographic keys to read communications on their own platforms. WhatsApp's privacy-preserving design makes it difficult to study important phenomena like the spread of misinformation or political messaging, as users have a clear expectation and desire for privacy and little incentive to forfeit that privacy in the process of handing over raw data…