@lologfts · X

built Apiarium, with one api key you can choose multiple Ai models, easy integration

ismaelyws · HN

Hey HN, I built an email deliverability SaaS with a RAG system. It's trained on all my knowledge and hundreds of real deliverability use cases from 4+ years experience as an Email Deliverability Specialist on Upwork. It solves a frustration problem a lot of businesses are experiencing. I'm looking for feedback so let me know in the comments.

@BuildsByHaris · X

Built ColdCraft. AI that writes your full cold outreach sequence in seconds. First touch, follow up, and close in one shot.

@Naman9761 · X

My Recent build (vibe coded) that real users are using: GitHub: Portfolio:

@marcin_u2 · X

-> here it is! claude finally knows everything gpt knows. and both of them know everything that's happened across your email, drive, slack, notion, github and granola :)

@mailthentic · X

g418572664 · V2EX

做了一个文档解析与记忆工具，专门辅助给传统行业做 AI 落地的老哥 现在 AI+的工作还挺常见的，就像大佬们说的，“所有行业的产品可能都会用 AI 重新做一遍”。最常见的就是各种 agent ，说要用 AI 赋能传统行业啥的，代替人类专家去处理海量的复杂资料、进行深度分析并做出决策。 举个例子，金融行业的“智能审计与尽调 Agent”。 过去，银行或投资机构想要给一家企业贷款或投资，需要人类审计师去读几十份、每份几百页的招股书和财务报表。现在虽然有了 AI ，但把文件一股脑全丢给它是不现实的，且不说烧 token 的问题，这些文档里有无数的跨行、跨列单元格表格，普通工具一拉，表格数据全串行了。如果 AI 把“第一季度利润”和“第二季度支出”的信息碎在一块，那得出的财务分析就完蛋了。 所以，现在要真想开发出一个能干活，还确保正确率的 agent ，就需要一个专业的、AI-native 的解析工具，把复杂的表结构和章节层级完整还原出来。我做的工具 Knowhere 就是干这个的： https://knowhereto.ai/?utm_source=v2ex 它能把复