state = result.state;
Azure is the #2 overall Cloud provider and, as expected, it's the best choice for most Microsoft/Windows-based solutions. That said, it does offer many types of Linux VMs, with quite similar abilities as AWS/GCP. The various types are not easy to use as on AWS/GCP though, for some reason even enterprise accounts start with zero quota on many types, so I had to request quota increases to even test tiny instances.
，推荐阅读新收录的资料获取更多信息
Fixed time budget. Training always runs for exactly 5 minutes, regardless of your specific platform. This means you can expect approx 12 experiments/hour and approx 100 experiments while you sleep. There are two upsides of this design decision. First, this makes experiments directly comparable regardless of what the agent changes (model size, batch size, architecture, etc). Second, this means that autoresearch will find the most optimal model for your platform in that time budget. The downside is that your runs (and results) become not comparable to other people running on other compute platforms.。业内人士推荐新收录的资料作为进阶阅读
Anthropic 旗舰模型 Claude Opus 4.6 成功率仅为 90.6%，排名第七，落后于多款中端模型。。新收录的资料对此有专业解读
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