完整作品展
技术栈
60 projects
Wetter 支持本地计算和自定义聚合模式的天气预报和可视化应用。
__mp · HN
I built my own weather prediction / visualization app: https://wx.rsp.li (on-device temperature lapsing, on device interpolation modes, user-selectable aggregations, etc…). One of these days I should do a writeup. I also asked Claude to build a photo gallery for me https://places.pascalspoerri.ch (HDR, map support, similar images)
集中管理客户反馈，通过投票排序，并分享交互式产品路线图。
@himanshu_b20 · X
Imgkits - 免费 AI 图像视频编辑器，在浏览器中即时工作。
Imgkits — 全能 AI 图像、视频编辑器：使用 Imgkits 创建令人惊艳的照片和视频 - [更多功能](https://www.imgkits.com/create/image)
一次存储共享记忆，在AI系统和团队中复用。
Repeater22746 · HN
ContextVault – Shared memory layer for your AI and your team
旋转地球发现世界最晴朗、最温暖或最潮湿的地方，涵盖3800多个目的地。
@Flightmussy · X
用 FieldCam 整理工地照片和视频，添加 GPS 和时间戳，自动生成 PDF 报告。
@12squaredAI · X
| @fieldcam_app
Flint是为AI代理设计的可视化语言,用于创建交互式数据可视化。
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
实时追踪 Indian Railways 列车，包含实时地图和 PNR 状态检查。
@japangor · X
I wanted FlightRadar24 for trains — no one was tracking Indian railways in real time, so I built it myself.
