Diogo Almeida announces Jev, a new frontier AI model trained with a method he calls RLCD and positioned as a decision-optimized, composable intelligence for code+AI workflows. He frames Jev as dramatically cheaper and faster than current LLMs, citing performance figures of 20-200x faster and 40-400x cheaper, with output tokens described as permanently free. Specific pricing given is $42 per billion input tokens ($0.042 per MTok) and an example run rate of roughly 10 calls/sec costing about $7/hour. Jev is named after Jevons paradox and is presented as the shortest path to an AI-driven economic revolution, though Almeida emphasizes trade-offs: Jev cannot generate free-form text.
The technical pitch centers on replacing sequential computation with parallel architectures - an architectural leap Almeida likens to the Transformer over RNN shift - enabled by RLCD to deliver real-time decision-making at scale. Demonstrations include a Wikipedia-link navigation game that highlights fast, low-hallucination choice among high-cardinality options. Release materials link to a technical blog, an early-access waitlist, and community channels for deeper discussion; the announcement stresses composability, operational cost reductions, and suitability for programmatic workflows rather than conversational text generation.
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