SatvikBeri

For comparison, Amazon had a valuation of $500 million at IPO, roughly 4,000x less than Anthropic.

The mechanism is usually that you can exchange tokenized stocks for real stocks for some fee. That causes prices to be similar.

Another similar instrument is perpetuals, which are kept close to the price of the underlying by having people who hold long positions pay people who pay short positions, or vice versa, when the price deviates. (This is called funding.)

If you can actually do that effectively, many companies will happily make you a manager.

That really hasn't been my experience, LLMs have done well with Julia since Claude 3.7

I'd be really curious to see benchmarks of haiku vs lower effort on bigger models. My own evals found Fable 5.1 at low to be better than Opus 5 on high.

Similarly, you can defeat the Egg Dragon superboss in Lufia 2 by healing before attacking: it starts with 65,535 HP.

I started paying for phones with really good cameras once I had kids and I wanted to take lots of pictures with them. I definitely notice the quality difference in random, off-the-cuff photos (which is the vast majority), though if I have time to set up a bit, cheaper phones produce pictures that look the same to me.

One example that comes to mind is math textbooks – the early textbooks in a field are usually much worse than later ones that come along. To pick on one, I think most people who've read both would agree that the commutative algebra section of Lang's Algebra is much worse than Atiyah & McDonald's Commutative Algebra book, despite covering basically the same ideas, theorems, etc.

I hit it once when I asked a question about whether butterflies remember anything from their time as caterpillars. I've never hit it for coding, but I also don't really do much related to security.

> We can also imagine the application of these models for malicious purposes , including the following (or other applications we can’t yet anticipate):

  *   Generate misleading news articles

  *   Impersonate others online

  *   Automate the production of abusive or faked content to post on social media

  *   Automate the production of spam/phishing content

Seems like the prediction was pretty accurate.

It's trivial to try another agent. You can spend $20 for a monthly subscription and ask it to import all your settings from Claude Code.

I use Julia! I like it a lot, and add type parameters to all application code. But JET.jl does not feel anywhere close to the assurances I can get from a statically typed language (yet)

What statically typed language would you suggest for machine learning and large data pipelines? I don't love Python, but it has by far the largest ecosystem.

Every interview method has some glaring flaws, and I find you mostly have a choice of which flaws you pick.

On my current team, I care a lot about the ability to write fast code. The most important part of my process is a take-home designed to take 2 hours where the main goal is to solve a relatively easy problem as performantly as possible. Answers have varied from 0.2ms – 50ms.

Take-homes have some obvious disadvantages, but overall I find they're better at finding the people we're looking for than just about every other method. But I'm at a small company, hiring for a fairly specialized team. If the situation was different (e.g. I needed to hire 50 people/year) I'd use a much more standard process.

I'm a fairly moderate user, never hit any kind of usage limits, but I used 44 million cache create tokens and 1.5 billion cache read tokens, which ccusage estimates would have cost $990, and calculates the different categories separately.