{"slug": "show-hn-universal-blindspot-detector-ai-meta-analysis", "title": "Show HN: Universal Blindspot Detector (AI meta-analysis)", "summary": "An independent developer and a few interns released the Universal Blindspot Detector, a free, no-signup tool that reframes AI conversations to surface assumptions the user may not have known they held, and asks users to upload exported conversation files in specific formats. The tool's premise is that output quality depends on context available for decisions, and the developer claims that iterating during planning and strategy phases lets them one-shot execution output even with local models such as Qwen 3.6/3.8, spending far less time in the expensive execution phase. The developer said the project was built while doing indie game development and costs them money to run.", "body_md": "This is a rough \"blindspot detector\" for AI conversations.\n\nBasically, the tool reframes conversations and tries to find the smallest moves the user COULD have made to have surfaced unknown assumptions for whatever problem they were solving.\n\nThe goal is to show the value of using AI as a reflective surface to search for \"unknown unknowns\" or \"things YOU didn't know you needed to know\".\n\n-------------------------\n\nThe basic premise is this:\n\nFor any given AI conversation, the quality of the output (for the specified goal) depends on the context from which decisions can be made.\n\nYou can learn to surface assumptions (the AI's AND your own) and get better output from informed decisions. Best of all, when operating in this way, you have to actively engage the problem and strategy to accomplish this, so you don't lose understanding or creative agency.\n\nWhen you iterate during problem definition or strategy planning, it's cheaper and more efficient, too.\n\nFor example:\n\n[# of Planning Phase Iterations] x 1,000 tokens [# of Strategy Phase Iterations] x 3,000 tokens [# of Execution Output Phase Iterations] x 5,000,000 tokens\n\nUsually when we are solving problems with this method, we one-shot the output even with local models like Qwen 3.6/3.8, spending FAR less time in the expensive execution phase.\n\n---------------------------\n\nAnyway, please have a look! Totally free, no signup, you just need to export conversations in specific formats and upload them.\n\nThis was made by myself and a few interns in a barn while doing indie game development ... and costs us money, so please don't abuse it. <3\n\nComments URL: [https://news.ycombinator.com/item?id=50287887](https://news.ycombinator.com/item?id=50287887)\n\nPoints: 1\n\n# Comments: 0", "url": "https://wpnews.pro/news/show-hn-universal-blindspot-detector-ai-meta-analysis", "canonical_source": "https://trooperloops.com/upload", "published_at": "2026-10-29 12:29:57+00:00", "updated_at": "2026-10-29 12:47:39.521288+00:00", "lang": "en", "topics": ["ai-tools", "large-language-models", "artificial-intelligence"], "entities": ["Universal Blindspot Detector", "Qwen 3.6", "Qwen 3.8"], "also_reported_by": [], "alternates": {"html": "https://wpnews.pro/news/show-hn-universal-blindspot-detector-ai-meta-analysis", "markdown": "https://wpnews.pro/news/show-hn-universal-blindspot-detector-ai-meta-analysis.md", "text": "https://wpnews.pro/news/show-hn-universal-blindspot-detector-ai-meta-analysis.txt", "jsonld": "https://wpnews.pro/news/show-hn-universal-blindspot-detector-ai-meta-analysis.jsonld"}}