Before you press play
What you’ll find in the video
- Jev is designed as a machine-native System One model mapping outputs to programming control-flow primitives like enums, booleans, and sorting thresholds rather than conversational chat.
- Developers should decompose AI workflows into small, discrete, and measurable decision queries using structured JSON state instead of stuffing tasks into massive system prompts.
- Decoupling multi-agent coding systems from a single shared KV cache allows parallel agents to coordinate state through external software memory and structured subtask hierarchies.
Worth knowing
Auto-generated English captions reviewed with Gemini. Diogo notes Jev is fundamentally empirical, does not guarantee deterministic outputs across queries, and explicitly rejects public benchmarking scores in favor of private workflow evaluations.