drzivil · HN

Drag boxes around and watch a layout engine infer the Flexbox (no AI)

@PeakVisibilitys · X

🚀 AI-powered SEO audits and AI search optimization. Would love some feedback!

@WebScore_now · X

@benvspak · X

@iamdeepak89 · X

@isreadyai · X

We help check if AI agents can read your site 💚 Free scan, no signup: — 32 checks in 5 seconds across GPTBot, ClaudeBot, Perplexity & more.

@thinking_555 · X

🚀 我做了一个超有意思的网站！ 「S」—— 一个极简、纯粹、能让你安静思考的地方。 没有广告、没有推送、没有干扰，只有干净的内容和思考空间。 感兴趣的朋友欢迎来坐坐 👉 #极简 #思考 #个人网站 #极简搜索

u/holios89 · Reddit

Built an AI SaaS that turns your notes/articles/PDFs into study flashcards — looking for harsh feedback (and bugs) Hey folks, Solo/small side project here: Deckly ( deckly.one ) — an AI-powered flashcard generator for spaced-repetition learning (Anki-compatible export). You give it text, a PDF, or an article link, it generates a deck of flashcards (Q&A, Cloze, or multiple choice), optionally with TTS audio and images per card, and you can study it right in the browser or export to `.apkg`.

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

Loerei · HN

I found that describing where a broken UI is and taking screenshots for AI agents really sucks. I’m too lazy to explain an indescribable visual bug or capture a millisecond-long flash. I also don’t want to remember which file defines an element, whether it's right in the .tsx or a problem with the Parent Styles in .css. And even if you can point out the exact file, your agent still has to dig through thousands of lines of code to know what on earth you're yapping about. In a 7700-file monorepo like Cal.com, simply giving the right file in the prompt for your agent saves about 68.9% of execution time and 94.2% of the tokens your agent needs to digest. HoverSource gives all the needed information and further pushes it to -88.5% time and -94.5% tokens, basically skipping the entire digging session and jumping straight to reasoning and executing. The save is linear to how big your codebase is. I want to save tokens, but don’t want to spend my time and energy digging manually, and a junio

@fortsignal1 · X

@itz_sayan_03 · X

Ever wondered how cinematic scroll-driven websites are built? 👀 Meet SectionFlow—an open-source React library with 60+ ready-to-use section transitions, a one-command CLI, reusable sections, and optimized performance. 🚀