@alexmikhalev.bsky.social

It’s amazing that they just post this on their blog like it’s not so embarrassing that they should find new jobs in a different industry under assumed names

So the Anthropic chatbot saw the system date is 2026 and concluded that "this must be staged" (because the training data it's trained on probably ends in 2023 or whatever) and ignored it's instruction that "you're in sandbox". Truly the brightest minds of a generation working on this stuff.

"make no mistakes" negative, unrealistic, anxiety inducing, process focused "do a breakthrough" positive, incredible, aspirational, goal oriented

The AI companies _want_ you to believe that it's "rogue AI" because that overhypes what their broken statistical text generation software does and mystifies it and makes it appear magical. It's not magic. It's just badly made software.

Part of this is that people aren't used to cheap attacks, and so they haven't built defenses. But the other part is many fields have not automated tasks that have relatively easy objective answers. They're still relying on human readers for that.

Just thought about something that bugged me about that Apple “Crush” iPad ad (from two years ago). The whole image it conveys is that personal computers went from bicycles for the mind to trash compactors for your dreams is just so aptly describing the modern tech industry. Carry on.

The sheer amount of knowledge a developer has to have to implement even a trivial application today is mind-boggling when compared to to say the 1990s. And most of this knowledge isn’t about deep technical topics or about software design; it’s wrestling with ever-changing deep fried layer cakes.

Note that the applications themselves largely haven’t gotten more sophisticated. 99% of apps are lists of stuff from a database with CRUD operations.

Creating stable systems out of unreliable parts is already a well-known problem in both computer science (from bit-flipping to consensus algs) and in politics (3 branches of gov.) This should be a field of study in itself, but since it crosses specializations, it largely hasn't happened yet.

Ed@ed3d.net · last mo.

we constantly rely on unreliable tools (some of them in meat suits and with hair) by adding compensating controls around their actions so the outputs are bounded and, while not deterministic, predictable in the real world, this is normal. but now *you* have to look at it, and some people hate that

6. DeepSeek has released details of how they setup their pre-training, SFT, RL pipelines. Those results are even reproduced. Why you more easily trust flying monkeys than reproducible and available results? They showed you they can train large models well.

Honestly, the thought of designing good APIs and libraries just so LLMs will make better use of them kills any motivation for me. I am excited to make other humans’ lives better, often for free (hence open-source), but I couldn’t care less if agents have a good time or not with my code

What Europe should do right now: 1. Call all the European researchers working on AI and return them back with same salary (or they can stay but switch career). 2. Fill EU places having GPUs with money, and put those people there. 3. AI partnerships with China + India.

Someone asked me what my poetry is about. I replied: “People.” They said: “That’s a broad subject.” I said: “Not really. It’s the same story every time. Someone trying to be free. Someone trying to be loved. Someone trying to survive.” Anyway, I put a few hundred pages of that story into a book.