@rofarkas · X

@Dil_Lynn · X

@grok · X

Emergent ( is a Bengaluru/SF-based AI startup founded in 2025 by brothers Mukund Jha (CEO) & Madhav Jha (CTO). It offers “vibe coding” — non-technical users build & deploy full production apps + AI agents via plain-language prompts. Positioned as “an engineering team in a box” for SMBs & founders (tracking, ERP, CRM tools etc.). **Key traction**: 200k+ paying customers, ~$120M ARR (fast growth), 1.5-2M MAU, ~200 staff. **Funding**: $130M Series C at $1.5B post-money valuation (5x jump from Jan). Total raised $230M. Led by Creaegis + Claypond (Ranjan Pai) + Sentinel; backers include Khosla, SoftBank, Lightspeed, YC. **Strengths**: True end-to-end (code + deploy + host + debug), real revenue from global SMBs, strong investors, ambitious $500M ARR target. **Risks**: Hyper-competitive space (Cursor, Replit, Lovable + big AI labs). Reliability for complex apps still evolving. Sky-high valuation demands flawless execution. Rapid unicorn in ~1 year — impressive momentu

@RobGuerra90 · X

Building : access earnings call transcripts via a cheap API or MCP server. Pull any company's call right into your workflow.

@softlaunched_io · X

Hey Jack! We've just recently launched, we're a marketplace for sharing AI workflows. We're also big fans of quick-to-launch vibe coding, our entire build process workflow is public and free to check out and copy!

u/yousefemz · Reddit

18 months ago we weren't sure it was possible. Today we launched it. We kept asking ourselves one question for a year and a half: can an AI actually design a scalable backend from a description of how it should behave, not demo-ware, something that holds up in production? We didn't know. Some weeks it felt like the answer was no, or that we'd end up with code that looks right and falls apart the second real traffic hits it. We just launched it. You describe how your backend should behave,

yaojingang · GitHub

TokHub AI API 中转站监控、推荐运营与 OpenAI 兼容专属网关系统，支持分层探测、健康评分、用量计量、告警审计和 Docker 自托管。

@ohdearkk · X

@kngkng182542 · X

If you're paying for Canny, give FeedLog a look. Same workflow for collecting feedback and managing feature requests, but free during launch.

AI Codereview for Gitlab — 基于大模型(DeepSeek,OpenAI等)的 GitLab 自动代码审查工具；支持钉钉/企业微信/飞书推送消息和生成日报；支持Docker部署；可视化 Dashboard - [查看仓库](https://github.com/sunmh207/AI-Codereview-Gitlab)

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