4 ms·

This is a very promising idea - a model that takes arbitrary text input (which can be a complex json), plus a set of questions (yes/no, multiple-choice, or scor

This is a very promising idea - a model that takes arbitrary text input (which can be a complex json), plus a set of questions (yes/no, multiple-choice, or score) and quickly (milliseconds) and cheaply ($0.042/MTok) answers those questions. Unfortunately, none of this is explained in the announcement, but the documentation [0] is pretty good. [0]: https://docs.typesafe.ai/concepts/how-to-build-with-system-one https://docs.typesafe.ai/concepts/how-to-build-with-system-o...

18al 18d ago
API example[0] makes it clear how it'd be used: from typesafe_sdk import Choice, Noul, Score, TypeSafeClient with TypeSafeClient() as client: response = client.system_one( state={"document": "I was charged twice. Please fix this ASAP."}, questions={ "billing": Noul(instructions="Is this ticket about billing?"), "tone": Choice( instructions="What is the customer's tone?", criteria={"calm": None, "frustrated": None, "angry": None}, ), "urgency": Score( instructions="How urgent is this ticket?", criteria=["can wait", "this week", "today"], ), }, ) print(response.nouls["billing"].noul) print(response.choices["tone"].choice) print(response.scores["urgency"].score) [0]: https://docs.typesafe.ai/sdk/python https://docs.typesafe.ai/sdk/python
- lostmsu 15d ago
  [flagged]
leobuskin 18d ago
An example with manual combinatorial exclusion in “not_for” field made me cry, this is a wild hybrid of code logic, textual definitions, and AI blackbox. It’s a cool idea, but the “glue” layer is too boilerplate-ish
niutech 14d ago
The concept isn't new: zero-shot classification using e.g. Facebook BART has been available for years. And there is already an open source model Laya: https://laya.convaiinnovations.com/ https://laya.convaiinnovations.com/

18al 18d ago

API example[0] makes it clear how it'd be used: from typesafe_sdk import Choice, Noul, Score, TypeSafeClient with TypeSafeClient() as client: response = client.system_one( state={"document": "I was charged twice. Please fix this ASAP."}, questions={ "billing": Noul(instructions="Is this ticket about billing?"), "tone": Choice( instructions="What is the customer's tone?", criteria={"calm": None, "frustrated": None, "angry": None}, ), "urgency": Score( instructions="How urgent is this ticket?", criteria=["can wait", "this week", "today"], ), }, ) print(response.nouls["billing"].noul) print(response.choices["tone"].choice) print(response.scores["urgency"].score) [0]: https://docs.typesafe.ai/sdk/python https://docs.typesafe.ai/sdk/python

lostmsu 15d ago
[flagged]

leobuskin 18d ago

An example with manual combinatorial exclusion in “not_for” field made me cry, this is a wild hybrid of code logic, textual definitions, and AI blackbox. It’s a cool idea, but the “glue” layer is too boilerplate-ish

niutech 14d ago

The concept isn't new: zero-shot classification using e.g. Facebook BART has been available for years. And there is already an open source model Laya: https://laya.convaiinnovations.com/ https://laya.convaiinnovations.com/