I'm not sure why this is a surprise. Google most likely uses an efficient yet limited language model to give us search answers. When a search query does not have strong or specific enough context, the model will sample and find what most of the training material will cover. That is, the most likely average behavior covered in the training data. In this case, "hes never coming over dario" is such a generic sentence, so unless Google happens to have indexed what the author mentioned and ranked it high, the answer would appear natural to the AI. Or using the AI jargon, the answer appears to be properly aligned.

People say LLMs will let us build native apps instead of Electron, but is that missing the point? I thought people turned to Electron for a reason: building a fast, rich, collaborative Markdown editor on macOS is notoriously hard. Neither SwiftUI nor AppKit makes handling those rich interactions easy. AI can do wonders, but can it really overcome the inherent limitations of macOS's native UI frameworks?

I'm quite puzzled about why Anthropic is so hellbent on blocking other coding agents. It's not like Claude Code has any secret sauce, right? And doesn't Anthropic make monkey off API usage, and their magic is on the model side anyway?

Very true. Or further, access to capital is the moat. It is the very reason that we don't have a real open-source community that trains frontier models - individuals simply can't afford the training infrastructure, nor sufficient high-quality training data.

If it's true, it makes them evil. Especially Dario, who speaks about moral high ground and fate of humanity all day, yet it's not that different from a cult leader does.

Isn't this exactly what Dario wanted? He thought he knew what's best for the humanity...

This isn't unique to software engineering. In Renaissance Italy, mathematicians like Tartaglia and Fior hoarded cubic formula shortcuts like proprietary algorithms and challenged rivals to public math duels. Today, we solve cubic equations without a second thought. Special functions used to be a staple college course for physics and engineering majors. Are they still? The US military used to employ thousands of people just to calculate PDEs by hand. Do we need anyone doing that today?

Our only hope is that our society moves fast enough to create new demands and problem domains that genuinely require new systems and algorithms. Look at AI: it’s evolving rapidly, driving massive demand, and forcing the development of new systems and architectures. As a result, the lucky few[1] working at that frontier are having rewarding careers. Without frontiers like that, the rest of us risk becoming irrelevant.

[1] One unfortunate factor is that building AI now requires lots of capital for accessing GPUs, which means individuals in the open source community have a hard time working on it.

This is kinda vague. Correct at what scale? I wonder if there's a measurement on the correctness per scale, and hopefully the scale is not just CLOC.

Of course, I'm being naive here, as I've seen too many companies fail to achieve such basic ownership. So, curious what I have missed. Of if the ownership is not a clean DAG, well, it goes back to Gates, as he was responsible for both the org charts and the company culture.

It looks like our brains somehow have to experience the failures during a diagnosis and in gemerak perform this kind of pathfinding by themselves to truly understand the system. I don't know if this has to do with how our brains actually learn.

Take Anthropic for an example. Anthropic has successfully destroyed customer trust, at least for me. DHH in a recent interview mentioned that Claude refused to translate an article about immigration. Not summarize. Not editorialize. Translate! I think this reveals an unacceptable level of paternalism: Anthropic fundamentally believes that it possesses a moral authority superior to the people actually paying for the API. If such basic and mechanical translation is already too sensitive to touch, the goalposts have moved from safety into outright censorship. What prevents them from quietly deciding tomorrow that your proprietary business logic, financial data, or legal documents cross their invisible moral line?

Let alone how Anthropic treats Cursor and Figma - not that they are wrong as companies are free to compete legally, but nonetheless it shows that companies can't outsource their intelligence to a potential competitor.

I thought they could've bought just a single copy of each book and use the content to train their models. In that case, it falls into the fair use doctrine and they wouldn't need to pay the fine. And that will be way less expensive than the $1.5B price tag.