@ts_floydTypeSafe just released a Blueprint for building x200 and x400 cheaper Agentic Loops with Jev I collected the best tips in a structured 14-page PDF on "How to ship an effective agent JEV LOOP": step 1 → meet Jev: a System One model. It doesn't generate text. It takes state + questions and returns typed answers with probabilities step 2 → learn the three question types: Choice picks one option, Score places it on a scale, Noul returns the probability something is true step 3 → batch questions per state: every question runs in parallel in one call, so extra questions barely add latency step 4 → split the loop: the LLM thinks and writes, tools act, Jev takes every bounded fork in between step 5 → route models with Jev: fast model for lookups, powerful model for architecture, picked from the latest message step 6 → guard every tool call: AutoModeMiddleware scores bash calls for risk and blocks them before they run step 7 → replace LLM-as-judge: correct, grounded and complete scored in parallel on the same trace step 8 → check the test: 5 frozen runs, 100 repeats per judge. Jev matched the human oracle on 500/500 decisions. Terra 99.8%, Luna 96.4%, Claude Sonnet 4.6 80% step 9 → do the math: 0.44s and $0.00035 per call. $0.34 total vs $28.17 for Claude Sonnet 4.6, with 92-913x lower variance step 10 → keep thresholds in code and a human in the loop: stable doesn't mean right. Claude gave the same wrong verdict every single time the result: the expensive model only does the work that needs it, and every decision around it runs in under half a second Send this PDF to your LLM before running your next agentic workflows, then explore how to become a Jev-native engineer in the article below

@ts_floydTypeSafe just released a Blueprint for building x200 and x400 cheaper Agentic Loops with Jev I collected the best tips in a structured 14-page PDF on "How to ship an effective agent JEV LOOP": step 1 → meet Jev: a System One model. It doesn't generate text. It takes state + questions and returns typed answers with probabilities step 2 → learn the three question types: Choice picks one option, Score places it on a scale, Noul returns the probability something is true step 3 → batch questions per state: every question runs in parallel in one call, so extra questions barely add latency step 4 → split the loop: the LLM thinks and writes, tools act, Jev takes every bounded fork in between step 5 → route models with Jev: fast model for lookups, powerful model for architecture, picked from the latest message step 6 → guard every tool call: AutoModeMiddleware scores bash calls for risk and blocks them before they run step 7 → replace LLM-as-judge: correct, grounded and complete scored in parallel on the same trace step 8 → check the test: 5 frozen runs, 100 repeats per judge. Jev matched the human oracle on 500/500 decisions. Terra 99.8%, Luna 96.4%, Claude Sonnet 4.6 80% step 9 → do the math: 0.44s and $0.00035 per call. $0.34 total vs $28.17 for Claude Sonnet 4.6, with 92-913x lower variance step 10 → keep thresholds in code and a human in the loop: stable doesn't mean right. Claude gave the same wrong verdict every single time the result: the expensive model only does the work that needs it, and every decision around it runs in under half a second Send this PDF to your LLM before running your next agentic workflows, then explore how to become a Jev-native engineer in the article below