๐ Pincher โ vector scoring
115 lines ยท vector-extraction primitive
Developer hand-tunes vector thresholds with regex + manual review. With JEV: developer defines rubric, JEV scores each vector in 150ms. Workload: 1M vectors/month ร 200 tokens = $8.40/month on JEV vs $1,600/month on GPT-4 (190x cheaper). Developer iterates by adjusting rubric, not code.
Read spec โ
๐ค Lever-Runner โ agent orchestration
94 lines ยท agent-orchestration primitive
4-agent psyche: JEPA pre-classifies, JEV scores, LLM narrates top-3, runner executes top-1. Reduces hallucination-induced actions by 80%. Cost: $2.73/month vs $5,000/month for full LLM-everywhere. Agents with auditable decision trails.
Read spec โ
๐ Plato โ teacher-student architecture
131 lines ยท Socratic dialogue for AI training
JEV classifies each student question by depth + topic + confidence. Teacher LLM only for "deep" questions. 10x reduction in teacher-LLM calls (90% handled by JEV). Cheap pre-filter for educational AI.
Read spec โ
๐
Sunset-Ecosystem โ simulation substrate
123 lines ยท simulation substrate
Each tick: JEPA predict โ JEV validate โ LLM narrate โ next tick. Disagreement = fork in simulation log. Use case: 1000 parallel simulations, JEV flags the 5 that diverged. $84/month for fully validated, narrated, audited simulation.
Read spec โ
๐ AI-Writings โ canon substrate
141 lines ยท documentation + canon substrate
JEV classifies each tap with category + depth + novelty + witness-worthiness. Automatic canon indexing with typed metadata. Witness-log gains semantic dimension. $0.21/month on JEV vs $16/month on GPT-4 (76x cheaper).
Read spec โ