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有人“发”“花”不分。经常说成：是金子总会花光的。不过好像也没错。
文库
- 1 告别 AI Demo 页面：构建支持淘内货品运营、可C端投放的会场 Agent Harness
- 2 3 个月用Harness重构90万行旧项目：实战经验与踩坑总结
- 3 如何把 Skills 变成 Agent 的训练场？小红书 AllSpark 提出 Skill2Env
- 4 淘特端侧模型从微调到应用的全链路实践
- 5 从 vmcore 到上游 commit：一次 systemd 事件循环缺陷的完整猎杀
- 6 基于AST与LLM的遗留代码重构：绞杀者模式自动化实践
- 7 3个月用Harness重写90万行旧代码：踩过的坑都在这
- 8 Inside Minerva: building scalable observability for eBay’s monetization platform
- 9 Meta’s Generative Ads Model (GEM): Scaling the Central Model and Compute Engine for Frontier Ads AI
- 10 大表治理之分表实践：带你手搓一个简单的数据库分表组件
- 11 CASTLE: training natural language search before the first real query
- 12 Reliable GPU compute for large training & inference jobs
- 13 Netflix Stratum Media Processing: Automated Container Right-Sizing
- 14 The Lifecycle of LLM-as-a-Judge: Building, Aligning, and Monitoring at scale
- 15 Running Apache Spark experiments in my sleep (and on a plane)
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