Hey everyone,
I’ve been working on something I’d really appreciate feedback on — DinoDS, a modular training dataset suite for action-oriented AI assistants.
Most datasets today focus on making models better at chatting. But in real products, the harder problem is getting models to behave correctly — deciding what to do, when to retrieve, how to structure outputs, and how to execute workflows reliably.
That’s the gap we’re trying to address.
What DinoDS focuses on:
- Retrieval vs answer decision-making
- Structured outputs (JSON, tool calls, etc.)
- Multi-step agent workflows
- Memory + context handling
- Connectors / deep links / action routing
So instead of just improving how a model sounds, DinoDS is built to improve how it acts inside real systems.
We’re currently building this as a modular dataset suite that teams can plug into their training / eval pipelines.
Would love feedback on:
- What use cases this could be most valuable for
- Gaps we might be missing
- How teams here are currently handling behavioral / agent training
- What would make something like this actually useful in production
Also open to connecting with anyone working on similar problems or looking for this kind of data.
Check it out: https://dinodsai.com/
Cheers 🙌
submitted by /u/JayPatel24_
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