transdev12

I can’t really speak directly to your question because I don’t have LLMs write react, but I get great results from svelte.

At this point I don’t think it’s about training examples, once there is sufficient training data it becomes a problem of verifiability, ie how easily can the model check its work for success.

Because they’ve essentially exhausted pre training scaling and are looking to post training to expand capabilities, which is really just optimization via reinforcement learning against specific tasks aka bench maxing.

I’m trying to pitch my boss on something like bedrock/foundry/vertex where we can just swap the model and still have it serverless over an api.

Digital ocean particularly looks promising as well.

Claude comments look like this

//add returns the sum of x and y

//per section 2.1 of addition-implementation-plan.md sum is designed as the seam for user addition interfaces.

//previously sum added numbers, now it adds numbers

def add(x, y):

    return x + y

The corollary here is to have Claude write tests to enforce this. The only thing it is consistently responsive to is test failures.

I’ve been landing on go for a similar reason, which was the realization that LLMs are in many ways just massive cargo culting machines. People will call it “quality training data”, but really LLMs will just reflect the norms and behaviors of whatever ecosystem they’re using.

I like to think go’s lack of magic and standard library will lead to lower maintenance burden over time, but that’s mostly just vibes so far.

I’ve been coming to hacker news for years (10+) and there has always been political commentary.

Real GDP is adjusted for inflation, nominal GDP is not.

Being in recession is typically defined as negative gdp growth.

So to answer your question, being in recession does account for inflation.