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I'd say out of many generative AI observability platforms, Langsmith and Weave (Weights&Biases) are probably the ones most enterprises use, but there's definitely space for Langfus…

No but most models have their own libs you can use

I kind of agree but the same problem exists. If you add text-xl to a div and have nested divs and spans, they'll all be inheriting text-xl so for these nested elements, you also ha…

Just played with it and it's great! A good 2.0 release I think.

Is it on AI studio already?

A great option for Go projects where you aren't exploring your queries as changing and regenerating is cumbersome. Also, SQLC doesn't allow for dynamic query building so think abou…

Evaluate them how? Specifically the voice/audio or you're happy to evaluate them with the transcript with something like Modelmetry?

Do they build guardrails themselves or do they use an llm guardrail api like Modelmetry or Langwatch?

1k rps per bucket though ...

An improved and more interactive version of Google Sheets' explore tab. Looks good!

The "YC" part.

what exercises do you recommend for these auxiliary muscles?

Quick question, what do you use such an observability platform for? Can't be the price as you get it straight from Openai's dev dashboard. So what else do you use Languse for?

Nice to have: > Conditional use() (unline current hooks) > Support for Document Metadata > Support for stylesheets > Support for async scripts > Support for Custom Elements

Probably like what happened with Google Inbox... It's a greenfield canvas to try a lot of innovations, see which ones people use, then shut down the product and add these popular f…

Thank you for the thorough response!

What specific strategies does Hatchet employ to guarantee fault tolerance and enable durable execution? How does it handle partial failures in multi-step workflows?

Superset works wonders

Gemini 1 Ultra was also said to be on par with ChatGPT 4 and it's not really there so let's see for ourselves when we can get our hands on it.

Is this going to be only for consumer Gemini app or for API/Vertex too? The context window is..... Simply lovely.

1 million tokens?? This is wild and a lot of RAG can be removed.

Is this essentially implemented via RAG? New chat comes in, they find related chats, and extract some instructions/context from these to feed into that new chat's context?

You do have to instantiate that struct, and you can do it with.... a beautiful NewCreateUser(dep1, dep2, dep3, ..., dep20) *CreateUser {...}. This is essentially what he recommends…