@_srvsi_ · X

Make internet's fragmented and unstructured knowledge/content into personalised learning experience.

@Ebrahim_Rio · X

Most founders skip validation and pray. I automated the "worth building?" check. AI scans competitors, Reddit, and market data → Cook or Kill in 90 seconds. Killed? It surfaces the pivot the data actually backs.

@pavithra2870 · X

Building - less prompting, more work getting done. Lets connect 🚀 (do try out portraits category agents if you are at it )

@Bim_evaporates · X

Check out DMs open

u/RayanBuilds · Reddit

I’m 18 and built an AI chat app solo. Tear it apart (brutal feedback welcome) Built this solo this year at 18. It’s called Veris, an AI chat + writing assistant. I know, “another chatbot” 😭. So I gave it stuff the big ones don’t. Favorite feature: upload an image and pick a mode: Normal (it analyzes it) or Debate (it actually argues with you about it). Not selling anything. I just want to know: what would an AI have to do for you to use it daily? submitted by

@magazine_active · X

Check out what I just built with Lovable!

策引 — 全球市场技术分析工具，可以创建多个市场的模拟组合并做深度回测分析。同时正在开发 AI Agent 功能，可帮助用户使用大模型自动生成基于不同交易策略的模拟组合。

@GetAgentHail · X

AgentHail — a control layer for AI agents to request approval, execute work, and return verifiable results. Would you sign up or leave?

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

@OBSIDYNAI · X

Check out what I just built with Lovable!

@ShivamT_Pro · X

u/sebriz · Reddit

I built an institutional NFL data terminal that audits 44 situational factors against 10 years of market data [Open Beta] Hey everyone, I wanted to share a side project I’ve been building called SeeBet.ai . As someone who loves sports data, I got tired of generic broadcast hype, sports betting "gurus," and subjective narratives about things like "revenge games," travel fatigue, or short rest. I wanted an institutional, zero-hype terminal that simply audits physical reality against market