@ashkans_dev · X

An ETL tool:

u/sheshu29 · Reddit

My side project Hey everyone i have just created this website for developer who does not want to rely on ai agents so please visit and give me some feedback if you want any specific tools 🫠 submitted by /u/sheshu29 to r/SideProject [link] [comments]

@iASHeeesh · X

@akinkunmi · X

He trash-talked me and my amazing project ( unprovoked while all his projects are vibe-coded slop. That's why I named him King of Slop.

@JOFIMAN · X

Check out what I just built with Lovable! 🤗

@brryg_ · X

sure man, working on a social and visual news hub called heuron - lmk what you think!

@alabi_debo55272 · X

Just shipped SatioFlix! A seamless, immersive way to discover global recipes through short video reel. Built with: React + TypeScript Tailwind CSS v4 Vite + PWA support Live link: Feedback is highly welcome! 👇 #buildinpublic #webdev #reactjs

@SpecParse · X

Convert Complex PDF Tables into Styled Excel Workbooks

@il_deil · X

I’m building an interactive, ultra-minimalist portfolio platform strictly for architects & designers, because I tired of ads and clutter ruining portfolio on Issuu and same services. I made zero visual noise. Multiple viewing modes. Just your portfolio

@factfile0 · X

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

@jielove233 · X

最近看到 @dappOS_com 一个作品挺有意思 一个主流币交易共振信号Agent，通过 dappOS Coding 快速完成 链接： 这个案例的价值，不在于又做了一个行情页面，而是在于它展示了一种新的 AI Agent 构建方式 过去开发一个交易辅助工具，需要先处理数据接口，再搭建前端交互，编写指标逻辑，最后不断测试优化。 对于很多有交易想法的人来说，真正困难的不是没有策略，而是从想法到产品之间隔着一整套开发流程 dappOS Coding 改变的，就是这个过程 通过自然语言描述需求，就可以快速生成对应功能模块 像这个交易共振信号案例，本质上就是把市场数据、技术指标和信号逻辑结合起来，形成一个面向交易场景的辅助工具 它并不是替用户直接交易，也不是承诺预测市场 更重要的价值在于 帮助交易者降低信息处理成本 在 Crypto 市场里，行情 24 小时运行，大量数据、新闻、价格变化不断涌入 普通用户很难持续人工筛选有效信息 AI Agent 可以承担其中一部分重复工作： 监控市场变化 整理关键数据 输出信号参考 辅助交易决策 这也是为什么最近 AI + Crypto 的结合越来越受到关注 OKX 推出 AI 与交易生态结合 @BNBCHAIN 也在推动更多项目加入 BNB Agent Studio，鼓励开发者围绕交易、情感陪伴等方向构建 AI Agent 整个行业正在从AI 能聊天进入AI 能完成任务的阶段 而 dappOS Coding 展现出来的方向，是降低 Agent 创建门槛 以前一个想法可能停留在笔记里，需要开发团队才能实现 现在，更多开发者、交易者甚至普通用户，都有机会快速验证自己的想法 未来 Crypto 的竞争，不只是资产和协议之间的竞争，也会是 AI Agent 生态和应用数量的竞争 谁能让更多人低成本创造工具，谁就更有机会成为下一阶段基础设施的一部分 这个交易信号案例只是一个开始 AI Agent 正在逐渐成为连接用户、数据和链上应用的新入口