gradys

It can be the case that both:

- The physics of the universe can be completely modeled as computation, and

- It's possible to pose undecidable problems about the way the universe unfolds

This is intrinsic to the idea of undecidability even for Turing machines, e.g. "we equate computation with the functioning of Turing machines, but there are real processes executable in Turing machines that are undecidable".

IPOs pushing up housing prices

New Waymo car models rolling out

The laser thing in front of city hall

Pride month, parades this weekend

Antisocial guy harassing Scott Weiner on social media at Dolores Park

Lurie administration, after initially making progress on crime and cleaning up the streets seems to be stalling out

Supervisor Jackie Fielder back from long mostly unexplained absence that was maybe due to her illegally leaking confidential information

Both would be understood and are roughly interchangeable.

"Sequential" feels more appropriate to me for the task runner scenario where we wait for one task to finish before running the next.

"Series" suggests a kind of concurrency to me because of the electrical circuit context, where the outputs of one are flowing into the next, but both are running concurrently. Processes that are Unix piped into each other would be another thing that feels more like a "series" than a "sequence".

You might be using JSON mode, which doesn’t guarantee a schema will be followed, or structured outputs not in strict mode. It is possible to get the property that the response is either a valid instance of the schema or an error (eg for refusal)

Is it luck, or is scaling arithmetic genuinely a useful capability to offer the world?

I assume you know how to program in Python? I would start with just the client libraries of the model providers you want to use. LLMs are conceptually simple when treated as black boxes. String in, string out. You don't necessarily need a framework.

I’m not as familiar with the Baby Bells, so this is a surprising comparison to me. Bell Labs was famously so productive while it had the monopoly money hose, and not as much came from it after Bell was broken up.

What are the most noteworthy accomplishments of the Baby Bells?

Sounds more like Generation Augmented Retrieval in that case.

I don’t know of any public details on how they implement Context Caching, but that is presumably exactly what they are doing. Just caching the text would be a minimal savings.

The size of the cached internal state of the network processing the book is much larger than the size of the book. The resource that is preserved with caching is the compute required to recreate that state.

I’m guessing there was an instrumental reason for this, for instance to check that the model was listening before launching into what they wanted to demo