Move slow and fix things ;-)
My posts automatically disappear after a couple of weeks.
„By Wednesday morning, Anthropic representatives had used a copyright takedown request to force the removal of more than 8,000 copies and adaptations of the raw Claude Code instructions—known as source code—that developers had shared on programming platform GitHub.“
Because if there’s one thing GenAI companies absolutely don’t take lightly, it’s copyright.
https://www.wsj.com/tech/ai/anthropic-races-to-contain-leak-of-code-behind-claude-ai-agent-4bc5acc7
OpenBSD 7.8 // SMTPD // ERRATA 026
Date: March 27, 2026
Name: 026_smtpd.patch
Description: In smtpd(8), an LF character in the username or password could stop proc tables, causing a denial of service.
Link: https://cdn.openbsd.org/pub/OpenBSD/patches/7.8/common/026_smtpd.patch.sig
My biggest problem with the concept of LLMs, even if they weren’t a giant plagiarism laundering machine and disaster for the environment, is that they introduce so much unpredictability into computing. I became a professional computer toucher because they do exactly what you tell them to. Not always what you wanted, but exactly what you asked for.
LLMs turn that upside down. They turn a very autistic do-what-you-say, say-what-you-mean commmunication style with the machine into a neurotypical conversation talking around the issue, but never directly addressing the substance of problem.
In any conversation I have with a person, I’m modeling their understanding of the topic at hand, trying to tailor my communication style to their needs. The same applies to programming languages and frameworks. If you work with a language the way its author intended it goes a lot easier.
But LLMs don’t have an understanding of the conversation. There is no intent. It’s just a mostly-likely-next-word generator on steroids. You’re trying to give directions to a lossily compressed copy of the entire works of human writing. There is no mind to model, and no predictability to the output.
If I wanted to spend my time communicating in a superficial, neurotypical style my autistic ass certainly wouldn’t have gone into computering. LLMs are the final act of the finance bros and capitalists wrestling modern technology away from the technically literate proletariat who built it.
Human attention is a finite resource.
A lot of AI work _increases_ the amount of attention needed, just as it increases constant context switching (which, together with the myth of multitasking, we already know to be burn out catalysts).
Together with ill- (or un-)defined “productivity gains” and peer pressure induced FOMO, it’s no surprise that “Using AI leads to ‘Brain Fry’”.
As firms increasingly incentivize employees to build and oversee complex teams of agents—for example, by measuring and rewarding token consumption as a proxy for performance—people are finding themselves pushed to their cognitive limits. Participants in a recent study described a “buzzing” feeling or a mental fog with difficulty focusing, slower decision-making, and headaches. The authors call this phenomenon “AI brain fry,” defined as mental fatigue from excessive use or oversight of AI tools beyond one’s cognitive capacity. This AI-associated mental strain carries significant costs in the form of increased employee errors, decision fatigue, and intention to quit. The findings also show how AI-driven workflows can be designed to diminish burnout and point toward specific manager, team, and organizational practices to avoid mental fatigue even as AI work intensifies.
This week the European Commission published the draft for a guidance document for the Cyber Resilience Act (CRA). It is 70 pages, but contains some helpful examples and flowcharts, like this one, making it accessible even to Open Source folks with limited time.
Here: Quick guidance for the question if your FOSS component is in scope for the CRA, and if so, wether you're deemed a steward or manufacturer in regards of the component.
Jens Ohlig
BSD Security Advisories
Emily 🏳️🌈🏳️⚧️
Jan Schaumann
Mirko Swillus
mkb
I Forget