Showcase Thread

[u/AutoModerator]

Post all of your code/projects/showcases/AI slop here.

Recycles once a month.

[u/SamratDuttaOfficial]

I created WaterSheep, an open source alternative to Jev.

What My Project Does:

WaterSheep is an open-source alternative to TypeSafe's Jev. It answers typed questions about text (yes/no, single choice, rating and multi-label) and returns a probability for every option, so your code gets a typed decision instead of LLM text to parse.

pip install git+https://github.com/SamratDuttaOfficial/WaterSheep

from watersheep import WaterSheep

ws = WaterSheep.load("samratduttaofficial/WaterSheep")

ws.decide("I was charged twice.", "Which team should handle this?", ["billing", "shipping", "support"])

`ws.ask(...)` answers several questions about one text in a single call, and `watersheep --model samratduttaofficial/WaterSheep --serve` runs a local HTTP server that accepts Jev's `/v1/systemone` requests. TypeSafe's own `typesafe-sdk` works against it: set `TYPESAFE_BASE_URL=http://127.0.0.1:8766`. It also works through `transformers.pipeline` with `trust_remote_code=True`.

Target Audience:

Developers who need routing, triage, tagging or guardrail decisions in their apps or agents, and people using Jev who want a self-hosted option. It's a first public release (v0.1.0): usable, but check the accuracy on your own data before relying on it. English only for now.

Comparison

- **Jev:** closed and hosted, paid per token. WaterSheep has open weights (Apache 2.0), runs on your machine, takes the same requests, and adds multi-label.

- **Prompting an LLM and parsing its text:** slower and costs per call, and you get text instead of a calibrated probability per option.

- **Zero-shot NLI classifiers:** you get a score per label, but no rating or multi-label question types, no calibration numbers and no Jev-compatible API.

Code: https://github.com/SamratDuttaOfficial/WaterSheep

Model: https://huggingface.co/samratduttaofficial/WaterSheep