I think scale is less important than other considerations. For me the biggest motivator is when the use case prefers doing something exactly-once, the downside risk of failure is high, and robust failure handling is moderately complex. The canonical example I use is healthcare communications; others use payment/fulfillment.

Also having a unified control plane is handy even at a pretty small scale if your alternative is going full “cloud native” on hyperscaler microservices. The ability to see what’s happening when and where across a single workflow run is a dream compared to all the traditional logging approaches I’ve seen.

All except Gemini which can be rather important depending on your use case.

You mean the Gemini that is even behind the Chinese models?

Yes, the Gemini that might be “even behind the Chinese models” but is uniquely capable of “watching” video.

Do you remember how bad documentation was? I still have nightmares about Twisted.

Some projects have fantastic documentation. Qt comes to mind. The experience of writing Qt is a pleasure because I can always jump to the doc for whatever class I'm dealing with and find what I'm looking for immediately. Also every argument type is well-linked in every signature so it's easy to follow and understand everything you're dealing with.

And I have wonderful lucid dreams about MSDN.

Also, are you implying that docs became better in the AI era?

They’re asset millionaires and maybe gross revenue millionaires, but not typically net income millionaires.

Most millionaires are asset millionaires. Almost nobody has a million in their savings account.

Farms are some of the least liquid assets. Not because it's that hard to sell them but when your whole income depends on owning something you can't just decide to give it up. And the social networks you depend on for things like finding seasonal workers or helping out after a natural disaster break down if you move.

Similar here. Lots of places where we replaced Pandas with DuckDB for transformations. Also have scriptable custom dashboards running on top of BigQuery data pre-aggregated and extracted to parquet on GCS. It's way faster and the only limiting factor is your viz library. It was pretty easy to build and the only big gotcha I encountered was finding, somewhat counter-intuitively, that it's often best minimize partitioning.

> the only big gotcha I encountered was finding, somewhat counter-intuitively, that it's often best minimize partitioning.

For parquet, I think with partitioning, it's really important to be mindful of the ordering of the data within the parquet file and also the query patterns of the main use cases. A little hard to generalize well to every pattern I guess.

Depends on your definition of "customer."

The real biggest unlock is not using unlock as a noun.

How does a $25B offering have $85B in orders?

They won't all be fulfilled. I would imagine this is part of the price-discovery process.

You can’t interview effectively by limiting yourself to questions where the applicant knows what you want them to say.

In what way is “How are you using AI?” an objectively bad question?

Remember the context - this is while solving a whiteboard problem. Its bad in the same way asking candidate what their birthstone is - because any answer offer little or no signal about the odds of a candidates success at the company.

I'm curious to know why you think asking about AI usage is a good interview question.

You're fond of asking candidates an unrealistic hypothetical and you think that's revealing?

Yep. I sure do.