CASE FILE / Robotics
Is Jarvis finally here
Is Jarvis finally here? TypeSafe just launched Jev, an API for structured decisions that can route models, rank search results and check other AI systems WITHOUT a chatbot in the loop. Imagine a Magic 8-Ball that actually works: a typed answer, with odds and a confidence score. This means they can: • Model routing: send each request to the right mode • LLM output checks: flag weak answers for review before users see them • Support workflows: sort tickets and decide which to automate or escalate And just realized TypeSafe reports ~150ms latency, ~100x lower cost & faster responses than LLMs. Early API access waitlist is open below.
Original post
Is Jarvis finally here? TypeSafe just launched Jev, an API for structured decisions that can route models, rank search results and check other AI systems WITHOUT a chatbot in the loop. Imagine a Magic 8-Ball that actually works: a typed answer, with odds and a confidence score. This means they can: • Model routing: send each request to the right mode • LLM output checks: flag weak answers for review before users see them • Support workflows: sort tickets and decide which to automate or escalate And just realized TypeSafe reports ~150ms latency, ~100x lower cost & faster responses than LLMs. Early API access waitlist is open below.
Chinese translation
贾维斯终于来了吗？ TypSafe刚刚推出了Jev，这是一个结构化决策API，可以路由模型、对搜索结果进行排名并检查其他人工智能系统，而无需聊天机器人参与循环。 想象一下一个真正有效的Magic 8球：一个输入答案，带有赔率和信心分数。 这意味着他们可以： ·模型路由：将每个请求发送到正确的模式 · LLM输出检查：在用户看到弱答案之前标记弱答案进行审查 ·支持工作流程：对门票进行排序并决定自动化或升级哪些门票 刚刚实现的TypSafe报告延迟约为150 ms，成本比LLM低约100倍，响应速度更快。 早期API访问等待列表在下面打开。
What this case shows
- Use JEV for fast discrete calls on robot state
- Threshold probability to act or halt
- See the original post for the sim or hardware demo