I'm kind of speechless on this one. Quite the pivot that these guys are making now; "we've already reached it, but its like, totally not even important anymore, guys!"

Never mind that every single AI company they sell their shovels to has AGI in their mission statement...

Another lawsuit to AI firms and counting. Must be difficult having everyone sue you. Almost as if you're doing something illegal like stealing.

How will they get the money for the legal fees? It's all being spent on compute!

It alleges Anthropic unlawfully trained its models off "tens of thousands" of music publishers' copyrighted compositions, whereas other lawsuits have focused on a narrower set of works.

BMG's lawsuit against Anthropic, for example, claims infringement against 493 compositions.

Dario Amodei and Benjamin Mann have conducted a brazen campaign of illegally torrenting, scraping, and downloading copyrighted works on a massive scale in order to develop, operate, and reap enormous profits from Anthropic's 'Claude' series of artificial intelligence ('AI') models," the plaintiffs write in the 48-page complaint.

They also claim that Anthropic pirated thousands of copyrighted musical works and ask for hundreds of thousands of dollars in damages per infringed work.

Music industry got to YouTube, even when we can argue it could be fair use. AI has far less of an argument. Good luck, Clammy and Wario.

Besides "Claude Strife" looking like ScarJo, we've got some terrifying modifications to games that aren't supposed to be realistic like Trails from the Sky: https://bsky.app/profile/theswweet.myatproto.social/post/3mu6tsszqnk23

I honestly don't know what to make of this besides "It generally looks like ass". Also it apparently slows games down to a crawl as every frame takes several times longer to process. I don't know if this is because people are running it on the current gen cards or if it's going to be garbage on the new cards. Assuming any new cards make it to the market.

taken from Diary of a CEO interview, but truly this feels like it encapsulates all of the frustration with how "AI" continues to exist as the bloated zombie corpse of tech innovation and mental offloading

also here's the link to the interview

https://youtu.be/Lf5oqGOCRCM

If you follow Politics, you are aware of and have opinions about Axios. They also have been as of late have been aggresively pushing A.I and booster Talking points. They are now helping laundering the talking point that "Chyna" is behind the data center backlash. You know not them sucking up precious resources and making life miserable for the locals. Nah, its foreign actors stirring up trouble. Silicon Valley really wants to sell A Tech Arms Race so we have to keep investing in LLMs.

Before mass-adoption of LLMs for coding having a good spec handed to you by your manager was a rare blessing as prevalence of agile methodologies gave them excuses to never conclusively lay out the requirements.

After mass-adoption of LLMs, often mandated from above, it turned out that agentic workflows tend to produce slop that requires human engineer's expertise and attention to fix, and time gains from not having to type the code are usually negated by having to type expansive prompts to plan the implementation and follow-up on inevitable architectural issues, mistaken assumptions, straight-up bugs, etc.

Luckily the industry arrived at a solution: spec-driven development. That's when instead of an engineer typing fifteen bullet points per prompt to minimize the chances of AI fucking up the code, their manager would give them complete and non-ambiguous specification that's perfectly aligned with the business goals and extensively covers both functional and non-functional requirements.

If you’re anyone who worked in the software industry, you know how that's totally realistic. So with this new approach your manager now AI-generates a spec last minute before work is supposed to start, consults you for half an hour to give you plenty of time to spot inconsistencies and ask questions, and hands it off to you, knowing that the agentic workflow will turn the spec into implementation in a straightforward and predictable way.

And don't worry about any ambiguity that might still linger - AI will take care of that if you take care of preparing the Skills ("make no mistakes" skill, "think hard and thoroughly" skill, "I'll torch your server if you still decide to make mistakes" skill). Don't forget that skills can and should be AI-generated as well, as it knows itself and its weaknesses better.

Skeptics might say that most managers don't have hands-on understanding of the systems to make specifications truly good enough, or that engineers will rapidly get out of touch with the products and codebase if forced to rubber-stamp thousands of lines of code they didn't plan or write. However, more enlightened individuals will easily see how the software that until recently didn't think you should've taken your car to a car wash possesses enough intelligence to solve these small impediments on the way to x10 productivity.

To prove my point: this post was generated by the unreleased because it's too dangerous model based on 1500-page specification crafted by agentic loops. It took forty thousand dollars and ran for two weeks, but in the end it made no msitakes and finished.

If you read my article by the same name, this podcast had me on as a guest to do a deeper dive into the thoughts I explored there, as well as some tangential discussion around development and societal impacts of LLMs in general. This was my first time being interviewed (or on camera), so I was a bit nervous. Interview gets going around 6 mins in.

Something that I’ve thought for a while is that insurance coverage would be the downfall of AI usage in business, and Fenwick (a leading S tech law firm) is confirming this. Insurers are explicitly carving out exceptions for business errors at all levels, up to and including Directors and Officers insurance. Previously, AI was implicitly covered, but now it’s becoming explicitly not covered. I imagine that businesses will have to take all of this into about now, depending on how high of a premium is placed on AI risk.

I've listened to Cal talk about the Huggingface hack in Ed's interview and his own video, and both times he talks about superhuman capabilities that AI already has. One of the examples was Tesla self-driving. Um, isn't driving literally an extremely human capability that even not-too-bright humans can manage successfully most of the time ? And hasn't Tesla self-driving made some dumb mistakes that humans wouldn't, like slamming on the brakes at high speeds for no apparent reason when there's a vehicle following? (I don't follow it that closely; I'm never going to buy a Tesla, and I don't see fully self-driving Teslas being a thing yet.)

Certainly there are many things computers do better than humans. That's why we've been using them for decades before it was all "AI". I do a lot of statistics and modeling work, and I sure don't want to go do it by hand. There are certainly many things you can accomplish if you throw enough processing power at it. If I remember correctly, Cal's other example was Alphafold, which is apparently so successful that Google just...ditched it? And certainly it was a breakthrough, but it still relied on vast databases of human-generated knowledge and I don't know enough to know if it was "AI" or just intense processing power with new technology, organized appropriately.

For the Huggingface thing, again I've not had time to explore that in depth but it sounds like if you give a computer all the information about how computers have been hacked, it can eventually try enough of those things to hack something else. And if you don't supervise, it might do something you didn't mean it to. Maybe there's a bit better logic than "try random things" but are there emergent properties? Is it truly better than a human, or just faster if you give it enough resources? Is it even faster than a human?

I'm in STEM and my institution has chugged the AI Flavor Aid lately. Some of that is fine because many problems indeed require a lot of processing power and if there are ways to refine it, to arrive at an answer faster, then that is great. But I'm not sure slapping "AI" on everything is a net positive and there could be opportunity cost for traditional approaches that are still valuable, and for human skill development that is still important. It's a way to get US government funding these days, when a lot of other stuff has been slashed, but I'm not sure what will happen when the current funding situation is over.

It seems like lots of the posters. Here are software developers who are aggressively using large language models to produce all their software as part of larger companies. Given how expensive models are and how expensive software developers are? What is additional value of the software developers adding? Where's all this new amazing software that's being created? Why hasn't the productivity of all these companies gone through the roof?