开发者工具1 month ago
ERD Factory
将SQL架构可视化为ER图，审查AI建议的修复后再应用。
@erd_factory · X
Paste your CREATE TABLE statements. Get an instant ER map, flagged issues, and AI-proposed fixes — reviewed as a diff before anything touches your schema.
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将SQL架构可视化为ER图，审查AI建议的修复后再应用。
@erd_factory · X
Paste your CREATE TABLE statements. Get an instant ER map, flagged issues, and AI-proposed fixes — reviewed as a diff before anything touches your schema.
将 Markdown 转换为视觉知识卡片，支持 30+ 主题和多格式导出。
md2card — MD2Card 在线工具，支持多主题，一键将 Markdown 拆分并生成知识卡片，支持 AI 魔幻卡片和长文智能拆分功能，可编辑，可导出 PNG/SVG/PDF，适合笔记分享与社媒传播
RostaHQ提供AI代理自动化财务、薪资和HR运营。
@RostaHQ · X
SaaS: Building in public. Follow our journey on X.
获取即时 SQL 数据库端点和认证密钥，可在您的应用中使用。
@gonelf · X
So many things - dead simple way to share projects within your team - dead simple base - no backend forms - ez directory submissions - a Lego event
与AI模型进行策略游戏，查看大语言模型在排行榜上的排名。
masterchef2209 · HN
I created a platform to check which AI models is the best gamer
自动化B2B收入执行，统一营销、销售和客户数据。
@k_mansourizadeh · X
Curious to learn more, building
用 GPT Image 2 为营销和社交媒体生成编辑 AI 图像。
Gptimage2 — AI 图片生成工具
从文本或图像用 AI 生成高清图片，支持 2K-4K 分辨率和精确文字排版。
GPT Image 2 — AI 图片生成器，支持文生图、图生图、4K 图片、图中文字生成与 Prompt 示例库，适合制作产品图、海报、广告图和社媒配图
将 MongoDB 数据库迁移到 SQL，提供自动化架构映射和实时复制功能。
@heyayankai · X
🚀 Building NoSQLSync. It helps developers migrate NoSQL databases to SQL without writing custom migration scripts. Always happy to connect with fellow builders and AI enthusiasts.
使用Misata为Python生成精确声明的合成测试数据。
@RasinMuhammedX · X
Declarative Synthetic Data Generation.
通过 ThreadSnoop API 访问 Reddit 数据，为 AI 代理开发提供免费积分。
@thread_snoop · X
Give you claude/codex free access to search through reddit :)
AI原生CRM平台，自动获取、增强、评分和分配B2B销售线索。
u/youex-ai · Reddit
Built a CRM w/ MCP -- Biggest misunderstanding on first look? We launched an MCP connector a few weeks ago that connects our CRM data. We're still working through our product market fit, so I asked Claude What is the biggest thing people misunderstand about YouEx.ai when they first see it? I was actually not even thinking about researching our actual CRM data, but that's what it did. The answer was actually really insightful: Your own data points at it: a large sha