@Henry_Yangs · X

u/Critical-Tea-6034 · Reddit

My friend and I built an AI that lives in his WhatsApp and remembers everything people tell him — I just came on as co-founder and we need ~30 honest testers before we spend money on ads Quick backstory. My buddy Bernardo is one of those people who builds things instead of talking about building things. A few months ago he got tired of forgetting stuff buried in his WhatsApp chats — client details, his friend's kid's name, that thing someone asked him to do three days ago — so he built an ass

Varamir

Bloomberg charges hedge funds $25k/year to see who actually supplies who. Varamir does it free. →

@wtAngat · X

u/Melodic-Living4805 · Reddit

Roast my Agentic Platform Been building this platform on the side for about a year would love to hear if the messaging is clear and what your first impressions are. Are you curious ? confused . impressed etc ? https://pindown.ai/ submitted by /u/Melodic-Living4805 to r/microsaas [link] [comments]

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

u/CodeElectrical7071 · Reddit

X’s API is expensive, so I built a simpler API for public X data I’ve been building ThreadQuery, an API for getting structured public X data into apps and AI workflows. It supports search, post lookup, profiles, user posts, threads, trends, followers, and following. I’m starting with a free beta while I test reliability with real usage. The first 100 verified users get 1,000 free credits. No payment integration yet. I’d love feedback, especially on incorrect data or missing endpoints:

u/dooddyman · Reddit

I launched 2 SaaS. First failed brutally. Second hit $5k MRR in 3 months. What worked and didn't. tldr: My first AI SaaS failed because I built blindly, outsourced marketing, and had no clear ICP. My second product hit $5k/mo in 3 months because I built an audience first, obsessed over SEO and distribution, and targeted a specific niche. I launched my first AI SaaS last year, kept it alive for about a year now. It's making less than $50 mrr and i'm thinking of closing it. Here's what went

@RasinMuhammedX · X

Declarative Synthetic Data Generation.

@MyCrystalSite · X

@thread_snoop · X

Give you claude/codex free access to search through reddit :)

aiobe · HN

I built Commonplace because I wanted to learn more about Google's Open Knowledge Format for knowledge sharing with human and agents. At the same time, Atlassian announced it will start using customer cloud data to train its AI models (opt-out is Enterprise-only), which made a self-hosted Confluence alternative feel timely. The idea: - Use a Git repository that follows Open Knowledge Format with plain Markdown - Commonplace is just a stateless frontend on top (nice UI, but no database, no backups