Databricks 为工程师全面部署 Astra 模型
wall-to-wall deployment of astra for engineers at databricks:
Databricks 给工程师用了 Astra，能帮工程师在复杂任务上提升效率，比之前的模型好，但日常任务还是用别的模型更划算。
Databricks 为工程师全面部署 Astra 模型。Astra 在复杂任务上显著优于其前代模型 Opus 5 和 Sol 5.6。使用 Astra 的工程师在编码上花费时间增加了约 60%。Astra 在中等/低复杂度任务上的提升不明显。Databricks 通过试点项目测试了 Astra 的效果。
wall-to-wall deployment of astra for engineers at databricks:
wall-to-wall deployment of astra for engineers at databricks: Patrick Wendell @pwendell Today we rolled out Astra to every engineer at Databricks (N=~3500). Some notes that may be helpful to others: 1. Astra unambiguously out performs our previous highest-end models (Opus 5, Sol 5.6) on highly complex tasks, especially those related to high level system design or long range horizontal tasks. 2. Engineers given Astra increased overall coding spend by around 60% compared to baseline. 3. It is not clear Astra meaningfully improves on medium/low complexity coding tasks compared to earlier models. We suspect those tasks are mostly saturated (i.e. perfectly executed) by existing models. 4. We learned above by piloting Astra with around 200 users to gain signal on both quality and cost. We use Unity Gateway to do cohort-based experiments for all new models. 5. We give engineers a sub-budget specific to Astra to encourage them to use Astra selectively on complex tasks while preferring lower cost models for everyday tasks. Our engineers are able to mix-and-match tools and models within their overall budget envelope (we also allow for increased budgets through various mechanisms). These budgets are defined in Unity Gateway and regularly revisited. Note: We do not have robust comparisons of Astra-vs-Fable because we have net yet rolled out Fable widely due to data retention policies. 🔗 View Quoted Tweet 💬 12 🔄 2 ❤️ 41 👀 4652 📊 10 ⚡
