@Bit_LittleP · X

刷到一个男生邀约女生的AI约会软件，很有意思 于是，我做了一个女生邀请男生版 Oops, It’s a Date 👉 有人这样邀请你，你会心动吗？😻🫶

@TheEduardoRFS · X

I vibe coded a demo for grading, not in a great shape but it's fun to play a bit.

TeachAny — 使用 TeachAny AI 工具，简化您的教学 - [更多功能](https://www.teachany.com/tools)

@promptcarrot · X

@mkappworks · X

I am building lets connect

@7spiritz · X

Check out what I just built with Lovable!

LearnFlux — AI 驱动的智能学习助手，为你生成个性化学习材料，让你以更少时间掌握更多知识。

@harryjwang · X

I've used many of the major conference systems. Plenty still have docs as an unsearchable static site — or worse, as PDFs that stopped tracking the product three versions ago. Somehow, still true in 2026. We took an AI-first path with instead. Our docs are generated from the codebase itself. A custom Claude Code skill writes each page and retakes the screenshots whenever a feature changes — so the docs don't drift from the product. Every page still gets reviewed by a human before it ships. Now you can talk to them, too. We just shipped "Ask AI" over our docs: This feature also turned out to be a nice teaching case for agentic RAG vs. classic RAG: • Classic RAG: chunk → embed → vector DB → retrieve once, by fixed rules • Agentic RAG: hand the model a search tool and let it drive — write a query, read the results, rewrite, retry At more than 100 pages, still no vector database needed. And every answer cites the exact page it came from. #Age

@fugteapp · X

which empowers nontechnical users to be able to customize code generated by AI or developer, and be able to share and embed it to the website they are already using

u/safi1409 · Reddit

I built a learning path builder tool powered by AI — maps your knowledge gaps and builds a focused learning path Hey everyone, sharing a side project I've been working on. The problem: When you're self-learning a technical field, you don't know what you don't know. Most people either follow a generic roadmap or keep consuming content without a clear picture of their actual gaps. I personally have completed multiple courses which reiterate the same thing because I was not sure if I had act

@getazrivo · X

Building - an AI assistant that doesn't just chat, it ships. Plan Mode maps out multi-step builds and produces real artifacts: live web pages, documents, spreadsheets, slide decks. It can also assemble a curated team of specialists to debate decisions.

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