The other part that is kind of comical is that the software factories keep growing quality gates and automated tasks that need to run for every commit, PR, deploy, etc. Then someone goes "oh, but now it's slowing us down", so a new thing gets added which decides when it's appropriate to run the gate, and on and on endlessly until it's a gigantic soup of actions that are running which provide negligible, and more frequently negative value over the software lifecycle. People are making big expensive messes of agents and acting like it's galaxy brain stuff.

I find it a bit comical. AI tools are so easy to pick up, and it's unlikely you've pushed some highly critical features which couldn't have waited for a few months.

Token maxing really is a thing if your token budget is unconstrained.

Tokenmaxxing is just the new pr-maxxing. Proxies truly are the root of all evil.

That sounds both very fun and very stressful.

This made me laugh https://www.reddit.com/r/GeminiAI/comments/1wh0qxq/comment/p...

Talking to my friends working there, they said they were surprised when that happened and they didn't find Claude quality to be that much better than Gemini when using agy. So maybe after all the issue is not mainly the model, but harness and custom tooling.

They give claude usage on antigravity and aren't them a investor on antrhopic?

What in the world is happening?

Life uhhh... finds a way.

it's circular revenue. they can claim tokenmaxing on one side, and huge revenues on the other.

Accurate training data?

At best you produce some noisy signals that are going to have a tiny impact if even that.

And that's on a personal plan where you didn't opt out of sharing usage data.

Business plans offer zero data retention. This is a non-issue.

Only if all you care about is developing models. I assume the rest of the business would rather just use whatever's best in class regardless of who made it, so I'm sure it's not that straightforward of a decision.

Or let them use Claude, track everything, and use that data to train your own models.

If you assume that noisy general usage data enables good RL, particularly compared to curated RL training sets.

I am not convinced that's the case.

Copilot

That's odd. I've been aware of that word for decades.

Wikipedia has the introduction of the term "dogfood" to the corporate world as a Microsoft internal memo....

... in 1988.

https://en.wikipedia.org/wiki/Eating_your_own_dog_food