@neha041187 · X

Point and figure chart US stock

@SlickFastHQ · X

@gimafore · X

Market intelligence on verified revenue, see how much it takes to get ROI when you acquire a startup

@Grow0nX · X

- Best X content creator friend/tool

@TridipBiswasdip · X

Check out what I just built with Lovable!

@GoWithFlow2026 · X

上线了一个 AI 八字排盘 🔮 🔗 和"直接把生日扔给 ChatGPT"完全不同: 1️⃣ 事实不靠猜:排盘、干支关系、刑冲合会等,全部由确定性规则引擎计算——LLM 只负责把判定结果写成人话，编不了盘。 2️⃣ 指令手写:分析指令是我根据《子平真诠》并结合自身经历逐条手写的命理逻辑。 🎁 免费演示,无需注册,点开即看完整报告: 觉得准,再测自己的($1.99 起)。欢迎反馈!

@OddsPlot · X

@tunebidapp · X

Congrats, Mark! 🎉 I can’t code, but I just launched TuneBid entirely through Vibe Coding. I’m using @DataFast_ for analytics and love how simple and transparent it is. Artists can submit a track from $5 and choose their best 60 seconds. 🎧

mjkl7896 · HN

Velprium - Time Workspace

@BBMax244185 · X

Check out what I just built with Lovable!

chenglong-hn · HN

Data visualizations are the bridge between user and data. But building AI agents that can generate visualizations reliably can be very tricky: - simple chart specs can be reliable, but generated charts are often of low quality due to reliance on system defaults; - complex chart specs with explicit details can produce good-looking charts, but they are verbose and agents can struggle with reliability We figured out it is a limitation on the language issue (not just AI capability thing) -- current visualization languages are a bit too low-level for AI agents, requiring them to explicitly make visual decisions that are supposed to be handled by a good compiler. Flint is a visualization intermediate language to address this issue, allow AI agents to solve this last-mile human-agent interaction problem. It provides a simple semantic-type based specification, and contains a layout optimization engine that can produce good-looking charts (filled with derived low-level details) from simple

@JohnSmistejbf · X

做了一个在线八字排盘工具。 输入出生时间，就可以快速生成八字命盘，查看四柱、五行、十神等信息。 欢迎反馈，我会继续优化。