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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.
Recently built something where simple domain-specific heuristics crushed a fancy ML approach I assumed would win. This has me thinking about how often we reach for complex tools when simpler ones would work better. Occam's razor moments.<p>Anyone have similar stories? Curious about cases where knowing your domain beat throwing compute at the problem.