ppeetteerr

Tesla’s move to austin had little effect on california. These people also hide their income so it doesn’t risk much in way of taxes.

As a hiring manager, I approve this message.

Good. Keep spending. Keep advocating.

CEOs are motivated by money and power. They will say anything to get either. If they truly wanted to slow down research, they would spend lobby money on the candidates who could enact laws.

Instead, they are partnering with some of the most disliked companies (x, meta) to further their goals.

There is a real possibility that either something you saw triggered the conversation and/or that you remember the ad because you spoke about it. It’s enough to linger on an ad for the platform to recognize your interest and serve you more ads like it.

Gruber was a good voice in the industry but this article misses the mark in a lot of ways.

A company the size of Anthropic would not voluntarily jeopardize their massive valuation if they didn’t feel the resulting output would maintain a similar level of quality as before. Is there a similar worry that their system prompt, which is injected at the start of every conversation also influences token generation in an artificial way?

If regulation will ruin Claude as a product, market forces will fill the void. There are also a ton of open weight models to choose from. It’s going to be okay.

I hate to be that guy, but I'm not reading a readme written by AI, nor using their tool. You don't care to put in a few hours to describe your project, I'm not going to bother learning about it.

Read through it an I'm curious whether setting the date and cmd on every system prompt call will cause the cache to invalidate.

I guess the cache would only be invalid if the day changed or the root directory, which would technically happen infrequently enough.

Isn't Apple about to license some variation of this from google for on-device AI? Maybe it’s their sales pitch to Apple and then they will lock it down.

Maybe it's my experience, but TPMs were often responsible for coordinating large org-wide or cross-org initiatives. It's prohibitively expensive to have TPMs on anything smaller.

Since engineering with AI is still very technical, I would wager that software engineers would stretch into less technical areas of software development rather than TPMs stretching into technical areas. I only say this as someone with experience with AI and I see how easy it is to write bad code with AI if you're not aware of what it's doing.

Yes, size and performance are not only problems for local LLMs, they are problems for frontier LLM companies like OpenAI and Anthropic. The latter still lose a ton of money on inference and advances in efficient, performant models helps their bottom line.

They are addressed but the core of the thesis is still wrong:

> This is the core problem: our entire evaluation infrastructure is structurally reactive. We measure the system after it has changed. We never predict the change.

That's kind of the point of evals.

The argument in the article is backwards. Evals test the stability and boundaries of a concept. They are not created before the concept has been prototyped (which the author acknowledges).

An eval is not somehow breaking silently due to some new capabilities in an LLM. It wouldn't be a good eval if it did. What it does is steer the LLM towards specific goals. If anything, an argument can be made that they restrict creativity and experimentation by narrowing goals.

If the argument is that evals need to written before some new behavior can be devised, that's incorrect. There are an infinite number of evals that test for things which cannot be done. Only when something has been demonstrated to work in a specific context, can an eval be written.

How does this compare to using Claude Web with connectors to build the same feature?

On a separate note, READMEs written by AI are unpleasant to read. It would be great if they were written by a human for humans.