The second approach offers broader feature support, seen in projects like Cloud Hypervisor or QEMU microvm. Built for heavier and more dynamic workloads, it supports hot-plugging memory and CPUs, which is useful for dynamic build runners that need to scale up during compilation. It also supports GPU passthrough, which is essential for AI workloads, while still maintaining the fast boot times of a microVM.
从行业发展规律来看，单一药物模式的研发瓶颈日益凸显，多模态药物研发已成为突破成药困境、拓展治疗边界的重要方向。对于AI制药企业来说，通过更多元的成药路径，攻克传统研发难以突破的高难度靶点，是验证技术、算法能力的关键。
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如果回看2021 年—2025 年中国企业科创表现，我们也许会有答案。
Boris Cherny, creator of Claude Code, still starts 80% of tasks in plan mode today. But with each new model generation, the one-shot success rate after planning keeps climbing. I think we're approaching the point where plan mode as a separate human-in-the-loop step fades away. Not because planning doesn't matter, but because models are getting good enough to plan well on their own. Big caveat: this only works if you've done the work in levels 3 through 6. If your context is clean, your constraints are explicit, your tools are well-described, and your feedback loops are tight, the model can plan reliably without you reviewing it first. If you haven't done that work, you'll still need to babysit the plan.