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TypeSafe AI’s Jev is presented as a fast, low-cost general-purpose classifier that can produce outputs matching a specified schema without extensive task-specific training. Its launch 33 days earlier drew attention through claims of a “System One Model,” Reinforcement Learning for Calibrated Decisions (RLCD), and outputs that “cannot hallucinate.” The author argues that this claim means schema-conforming output rather than logically correct output, and says Jev can still be wrong. The author also says Jev’s hype declined rapidly as users assessed its limits, while open-weight alternatives may offer better performance-to-cost ratios for some tasks. Jev can reportedly run locally at the edge with quality comparable to frontier LLMs in some uses. The author characterizes recent AI progress as a series of incremental advances: instruction tuning, function calling, coding tools, agents, larger context windows, multimodal models, tool protocols, orchestration, web access, memory systems, and verification loops. The author argues that major unresolved problems include abstract reasoning, continuous real-time learning, capable embodied robotics, and the energy and infrastructure costs of operating AI at scale. Current AI systems paired with tools and automation can still cause harm without reaching artificial general intelligence, the author says, citing potential uses in surveillance, cyber intrusion, warfare, and disease generation. The author urges skepticism toward marketing based on name-dropping, jargon, ambiguous technical disclosures, isolated benchmarks, and emotionally charged publicity.