The author demonstrates two practical techniques for programming with System One models (fast classifiers that pick from multiple choices): tiered goal-setting for sequential decision-making, and tournament sampling for choosing among many options. He shows these approaches working in Doom gameplay and Wikipedia navigation tasks.

• Tiered goals work better than single-pass decisions: periodic prompts asking the model to choose short-term goals (e.g., "kill enemies" vs "collect armor") make it perform more intelligently than just reacting to immediate game state every 200ms.
• Tournament sampling beats confidence scoring when picking from many options: feeding 100 links at a time, then narrowing down, found the optimal path in seconds, while trying to score all 1,000+ links failed badly.
• System One models offer a practical alternative to tool calls for real-time systems where you need predictable latency and don't need full language generation flexibility.

System One models are stripped-down LLMs that only output multiple-choice answers, trading flexibility for speed and predictability. The author shows two practical techniques for building real-time systems with them: layered goal-setting for sequential decision-making and tournament sampling for choosing among many options.

• You can convert any LLM into a fast classifier by batching single-token outputs with structured prompts—no model retraining needed, just inference-level changes.
• Tiered goals (asking the model to pick short-term objectives before making immediate decisions) dramatically improves performance in tasks like game-playing by giving the model more compute to reason about strategy.
• Tournament sampling—splitting large choice sets across multiple rounds rather than trying to rank everything at once—works better than absolute scoring because LLMs judge relative quality more reliably than absolute confidence.

TypeSafe AI released Jev, a new model class designed for automation that outputs type-safe structured decisions instead of text, running 40-200x faster and cheaper than existing LLMs on decision-making tasks. Unlike traditional language models, Jev can't hallucinate, always provides confidence scores, and costs nothing for output tokens.

• Jev achieves comparable intelligence to frontier LLMs on decision tasks while being 40-200x faster (70-500ms vs 3-329 seconds) and drastically cheaper ($0.042 per billion input tokens, free output tokens vs $0.20-$10 per million input tokens for LLMs).
• The model uses a new training method called Reinforcement Learning for Calibrated Decisions (RLCD) that optimizes for accurate probability estimates rather than human preference, and generates all outputs in parallel instead of sequentially, eliminating hallucinations and type errors.
• Jev trades away general text generation to specialize in structured outputs that slot directly into software workflows as fuzzy decision rules—classifying, routing, scoring, and branching without requiring human validation or parsing.

TypeSafe AI released Jev, a new type of AI model designed for automation and structured decision-making rather than text generation. It's 40-200x faster and 444x cheaper than existing large language models for specific tasks, with guaranteed type-safety and calibrated confidence scores instead of hallucinations.

• Jev generates all outputs in parallel rather than token-by-token, achieving 70-500ms response times versus 3-329 seconds for frontier models, while outputting structured data instead of strings
• The model uses a new training method called Reinforcement Learning for Calibrated Decisions (RLCD) that optimizes for epistemically honest probability estimates rather than human preference, making it reliable enough to embed in production software workflows
• Pricing is $0.042 per billion input tokens with free output tokens, versus $0.20-$10 per billion for existing models, with claims backed by publicly available workflow evaluations showing performance across complex automation tasks