enraged_camel

>> Amazon didn’t borrow anywhere near this much.

Amazon also did not go from ~$1B revenue to $16B in one year.

I switched from Gmail to Fastmail over a decade ago now, and it has been fantastic. IMO it is one of the highest quality software products in the world. I know that's a bold claim. But from a user's perspective: it's blazing fast, the UX is excellent, the web interface works flawlessly on every device and browser, and I have not run into a single bug.

I haven't used GrokBot, but I have been sort of amazed by how popular it is. I don't have the numbers but even my friends who otherwise dislike Elon have at least given it a try, and several of them use it daily.

It makes me want to check it out just to see if there's some magic UX juice they put in there. Then again I also don't want to take any risks heh.

Specifically, it's because the verification loops are much longer in applied sciences. In biology you need to wait for wet lab experiments, clinical trials, regulatory steps, etc. before you can call something "solved".

Since sequential steps are slow, I think we're going to see massive increases in parallelization. I'm not sure how the regulatory frameworks will keep up honestly.

>> If I have a function add_nums that takes 2 numbers and adds them, there are like infinite inputs that can be tested.

That's what formal verification is for: you generate math statements from the code, and then prove them.

Testing is for covering the most common scenarios and likely edge cases. 100% coverage means every line of code in the app is executed when your test suite runs. It is useful as a bare minimum, but by itself does not guarantee that there are no bugs.

...no it's not? At max effort, it scores 50% on terminal bench vs. Opus 5.5's 70%.

People keep asking OpenAI for a mode that's as good as Opus 5.5, and they release this instead. Interesting strategy. Let's see how it works out for them.

>> You people drunk on LLM kool aid forget that some of us are old enough to remember those years. I do.

>> It didn't require hundreds of billions of capex. It required someone to set up shop with a PC, a serial card and a couple modems.

This is a bad faith comparison. Those small business owners who gained access to the internet benefited from decades of research by military and research institutions, as well as massive amounts of infrastructure spending by telcos. That spending really started to pick up near the end of 1995. Between 1996 and 2001, North America collectively spent $1 trillion on the Internet buildout.

>> How do you determine which one's ahead in what?

We are talking about discoveries the labs have publicly showcased.

>> They're trying to catch up to OpenAI which has been doing these a lot

Sorry, what? OpenAI is "ahead" in math. Anthropic is "ahead" in life sciences, and even have their own wet lab.

I think it goes beyond individual functions. AI is capable of building complex features in a coherent manner. But it has trouble realizing those features exist as part of a larger system and need to fit upstream and downstream workflows. IMO that's where human engineers come in.

>> AI rarely seems to write better code than humans.

This assertion is at the crux of a lot of these debates. Fundamentally it depends on what is meant by "better code", does it not?

Code that is bug free? Frontier models are inarguably better at that, because they can think of far more edge cases and test each one much faster than you can. You can argue that its code and surrounding tests are frequently overkill, but that's a dial you can tweak until you're happy.

Code that is performant? Frontier models are inarguably better at that too. They can create a benchmark harness and run it over and over to optimize the code they wrote while you're still poring over the docs on which method of iterating over a list is faster.

Code that is secure? Frontier models are also better at that, for the above two reasons: they can create complex security vulnerability plans and run swarms of agents to audit the code and attack the system that runs that code until it's like a god damn fortress.

Code that is easily understood by humans and fits coherently with the rest? I think humans are better at this still, because humans are definitionally the best at understanding what is easily understood by humans. I posit that this definition of "better code" is quickly going out of fashion though.

I don't care either way, but I do wonder how those stats look if you focus on powerful women, rather than all women.

>> In my experience it's the opposite - the model's design decisions always try to add complexity, even when it's not necessary.

This happens if the model does not have good context regarding what is being built, why and for whom.

For one of my side projects, my CLAUDE.md has a line that says "the users of this product will be non-technical people who find technology scary." And you know what? Claude always steers me away from complexity when working on that project.

Sorry, I'm not following. It's badly engineered because it changes frequently, or because it does not control spacecraft?