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This is a play website I built out with my 6 year old to show her that you can also build for the web (not just consume!). The idea was to make a bunch of small mini games (more coming) where you can learn some basic chess strategy. In this game you play a knight vs 3 kings who try to trap you. Try to survive for 15 moves.<p>Once you get bored of that, there's the classic radioactive pooping knights [1] ;). It's also wrapped up into an app [2] if that's better :)<p>Very keen for feedback :)<p>1. <a href="https://minichessgames.com/#/play/pooping-knights" rel="nofollow">https://minichessgames.com/#/play/pooping-knights</a>
2. <a href="https://play.google.com/store/apps/details?id=com.minichessgames.newapp">https://play.google.com/store/apps/details?id=com.minichessg...</a>
Shelfarr is a tool for cleaning and renaming audiobook libraries. It scans local files, matches available metadata, and produces consistent folder and filename structures, including series handling. All changes are previewed before applying, with support for dry runs and custom templates. The focus is on predictable behavior and user control rather than full automation.
AI generates complete novels with cover art, ready for print Text: I built a tool that generates complete, print-ready books from a single concept. Enter your idea, pick a genre and length, and you get:
Full manuscript PDF (formatted for print with title page, copyright, chapters)
Editable DOCX file
AI-generated cover art
Print-ready cover PDF with spine
The whole thing takes a few minutes. Output is sized for standard trim sizes so you can upload directly to KDP or other print-on-demand services. Built with Claude for the writing and image generation for covers. Priced starting at $19. Would love feedback on the concept and output quality. <a href="https://printreadybook.com" rel="nofollow">https://printreadybook.com</a>
Hi HN,<p>I built this tool to solve the "flakiness" problem in UI testing. Existing AI agents often struggle with precise interactions, while traditional frameworks (Selenium/Playwright) break whenever the DOM changes.<p>The Approach: Instead of relying on hard-coded selectors or pure computer vision, I’m using a multi-agent system powered by multimodal LLMs. We pass both the screenshot (pixels) and the browser context (network requests, console logs, etc) to the model. This allows the agent to:<p>"See" the UI like a user and accurately map semantic intent ("Click the Signup button") to precise coordinates even if the layout shifts.<p>The goal is to mimic natural user behavior rather than following a predefined script. It handles exploratory testing and finds visual bugs that code-based assertions miss.<p>I’d love feedback on the implementation or to discuss the challenges of using LLMs for deterministic testing.