用 Jev 批量校验课程内容的工作记忆负荷
作者用 Jev 跑课程内容的工作记忆检查，同样规模换成 Astra 要花约 25 万美元，用 Jev 只要约 2000 美元。
Jev has accelerated the timeline for personalized education by years. The biggest bottleneck in building personalized education with AI is validating the quality of the material it generates. For the courses I’m building, generating lessons is only ~5% of the content cost. The other ~95% is validation and improvement. One of the biggest costs is checking whether a lesson gives a student too much information to process at once, overloading their working memory. Running that analysis across all of our content with frontier models gets incredibly expensive. At our current scale, even if Astra used half as many tokens as Jev, it would cost us ~$250,000. With Jev: ~$2,000. I built the tool we’re using for the working-memory check and made it available here: https://github.com/AustinAWay/Working-Memory-Jev