Show HN: Kapso – Iterative learning from enterprise data
Hey HN! We're Alireza and Majid building Leeroo ( https://leeroo.com/ ). Previously we’ve built AI systems for products with millions of users, including at LinkedIn and Meta. We’re now focusing on building AI systems for enterprise data. Today, we're open-sourcing Kapso ( https://github.com/Leeroo-AI/kapso ), an engine that runs experiments, deploys the best solution, and learns from every run. The nature of data work is iterative: investigate, form hypotheses, experiment, and improve based on
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来源记录
指标快照
points · 37 votes
2026-11-13 01:16:57.591Z
cumulative since publication
研究分析
变化记录
来源描述：Hey HN! We're Alireza and Majid building Leeroo ( https://leeroo.com/ ). Previously we’ve built AI systems for products with millions of users, including at LinkedIn and Meta. We’re now focusing on building AI systems for enterprise data. Today, we're open-sourcing Kapso ( https://github.com/Leeroo-AI/kapso ), an engine that runs experiments, deploys the best solution, and learns from every run. The nature of data work is iterative: investigate, form hypotheses, experiment, and improve based on
版本与成本证据
来源票数: 37 votes (2026年11月13日 UTC; cumulative since publication)；前次同口径记录为 37（2026年11月13日 UTC），两个观测值不等于需求增长。 可供比较的近期同类记录：“Show HN: Autoresearch, but for Weight Space Programming” (Show HN, 2026年11月13日)；“[分享创造] 如果用命令行访问 V 站来摸鱼，隐蔽性会高一些吗？” (V2EX, 2026年11月13日)。主题相近仅用于导航，未核验其是否属于同一需求或独立证据。
适用与受影响对象
先明确变化影响的现有工作流，再判断是否升级。发布热度不证明效果提升；接口、价格或可用性变化也可能增加迁移工作。
缺失的比较信息
待补齐：新旧行为、受影响套餐与地区、生效时间、兼容性、回退路径及同一工作量下的成本。
采用与迁移验证
保存发布或政策原文，对新旧方案运行同一个任务并比较结果与完整账单；若涉及停服，先核验截止时间再安排迁移。