I think they just want Apple devices to train their computer-use models in a realistic environment.

10000 realistic encironments

I mean an agent probably only uses one mac at a time. But they're obviously training tons of agents concurrently.

You can use stuff like Litestream to continuously back up your SQLite DB to a cheap storage bucket. It's practically set and forget too.

FYI I use my Codex models with Claude code and they work pretty great. It can even pick up on existing conversations w/ Opus and then resume w/ OAI models.

I use my OAI subscription on my Claude Code. I get the benefit of the Claude Code interface with the intelligence of OAI models.

https://prabal.ca/posts/claude-code-chatgpt-subscription/

> in a single working day those trucks would shift rocks equivalent to the weight of the Empire State Building.

Oh. My. God.

Reposting it here ... I wrote this more intuitive explanation. I think you might find it helpful too!

https://prabal.ca/posts/google-long-context-cheaper/

I wrote this more intuitive explanation. I think you might find it helpful!

https://prabal.ca/posts/google-long-context-cheaper/

They're weighted differently. Currently it's set to: reposts 2.0x, replies 1.5x, bookmarks 1.2x, likes 1.0x, and clicks 0.5x. These are guestimates based on public info.

It's real embeddings (MiniLM, truncated to 128-dim), so the cosine similarities between tweets are genuine, but it's obviously not X's actual trained model. The relative distances are meaningful, though.

It should technically work (everything is being routed via LiteLLM under the wraps) with Mistral, but it's untested haha. I don't think Mistral's lost the race; it's just that it doesn't seem to be that popular relative to Gemini/Anthropic/OpenAI, so I didn't bother testing it.

Hope you got a chance to try this out!