High Signal Podcasts Evidence ledger
Method
Browse
← Back to evidence

Evidence receipt / preference

Published · transcript-backed

Jason Liu: preference

19 Apr 2024 Latent Space High Agency Pydantic > VC Backed Frameworks — with Jason Liu of Instructor

“I think at some point, you know, tools like Guardrails and Marvin came out. Those are kind of tools that I use XML and Pytantic to get structured data out.”

— Jason Liu

Source trail

Everything needed to verify it.

Speaker
Jason Liu
Attribution
Verified speaker
Claim type
preference
Recorded
19 Apr 2024
Publisher
Latent Space

Transcript context

…You know, you had YouTube University and a journaling app, you know, a bunch of other explorations. But it seems like the most productive or the best known thing that came out of your time there was Instructor. Yeah. Written on the bullet train in Japan. I think at some point, you know, tools like Guardrails and Marvin came out. Those are kind of tools that I use XML and Pytantic to get structured data out. But they really were doing things sort of in the prompt. And these are built with sort of the instruct models in mind. Like I'd already done that in the past. Right. At Stitch Fix, you know, one of the things we did was we would take a request note and turn that into a JSON object that we would use to send it to our search engine. Right. So if you said like, I want to, you know, skinny jeans that were this size, that would turn into JSON that we would send to our internal search APIs. But it always felt kind of gross. A lot of it is just like you read the JSON, you like parse it, you make sure the names are strings and ages are numbers and you do all this like messy stuff. But when function calling came out, it was very much sort of a new way of doing things. Right. Function calling lets you define the schema separate from the data and the instructions. And what this meant was you can kind of have a lot more complex schemas and just map them in Pytantic. And then you can just keep those very separate. And then once you add like methods, you can add validators and all that kind of stuff. The one thing I really had with a lot of these libraries, though, was it was doing a lot of the string formatting themselves, which was fine when it was the instruction to models. You just have a string. But when you have these new chat models, you have these chat messages. And I just didn't really feel like not being able to access that for the developer was sort of a good benefit that they would get. And so I just said, let me write like the most simple SDK around the OpenAI SDK, a simple wrapper on the SDK, just handle the response model a bit and kind of think of myself more like requests than actual framework that people can use. And so the goal is like, hey, like this is something that you can use to build your own framework. But let me just do all the boring stuff that nobody really wants to do. People want to build their own frameworks, but people don't want to build like JSON parsing. And the retrying and all that other stuff.…

Stored transcript either side of the excerpt. The highlighted words are the published quote; the surrounding text is unedited source, never generated.

Named in this claim

Books, apps, tools, and people.

Search evidence