semicolon_storm

It’s truly strange that people keep citing the quality of Claude code’s leaked source as if it’s proof vibe coding doesn’t work.

If anything, it’s the exact opposite. It shows that you can build a crazy popular & successful product while violating all the traditional rules about “good” code.

Everything's more costly in Hawaii, including maintaining infrastructure

Sorry you got scammed, FIFA awarded me their 100m sprint gold medal so I'm the real fastest 100m runner in the world

It seems unlikely, athletes are consuming liters of oxygen per minute. Plus, the impact is at least partially offset by needing to carry the extra weight with you.

Different story for apnea sports like freediving where a little bit of extra oxygen goes a long way.

Don't hold your breath for that, Azure still has spotty IPv6 support

It requires mercury-198 as an input. The only quote I could find for mercury-198 is about $15K per milligram.

So now we just need to figure out how to make mercury-198 cheaply.

You can also have the most flexible system ever designed, but if the rest of your team doesn't understand it then good luck implementing that required use cases

And treating politics like team sports is the root of the problem

Not sure about that. A hash function suitable for security sensitive work, used properly, should make a collision so unlikely that you can basically forget it that it's even possible.

Think about it, that's what hashing passwords relies on. We don't store a plaintext password for a final check if the password hash matches, we count on a collision being basically impossible.

A hashmap is different, because it's using a much weaker hash function with far fewer security guarantees.

Plus, you're assuming the original values are even kept around for comparison. The cache key likely just mapped to something simple like a boolean or status flag.

The censorship is not present in the distilled models which you can run locally

Are you referring to the distilled models?

Perhaps because that's a strawman argument. "Scaling" doesn't mean double the investment and get double the performance. Even OpenAI's own scaling laws paper doesn't argue that, in the graphs compute increases exponentially. What LLM scaling means is that there hasn't been a wall found where the loss stops decreasing. Increase model size/data/compute and loss will decrease -- so far.

In security, industry standard seems to be about the same as military grade: the cheapest possible option that still checks all the boxes for SOC.