Why Jev Is Changing How We Build With AI with Diogo Almeida - #779
TypeSafe's Jev model uses RLCD to deliver reliable machine-native intelligence for software automation
TypeSafe CEO Diogo Almeida argues that text-optimized LLMs are poorly suited for real-world automation decisions, and introduces Jev, a model built around calibration and reliability via reinforcement learning from calibrated decisions (RLCD). The core thesis is that AI systems should become more engineered — specialized models for specific decision types rather than one general model. This is an early-stage signal in the emerging debate over LLM-native vs. purpose-built intelligence layers in agentic software architecture.