Every developer has been there. It's 2am, your CI pipeline is red, and you're staring at a wall of error logs trying to figure out which of the 47 things that could be wrong is actually wrong. That pain is what made me build FailSense — an AI debugging assistant that ingests error logs and returns ranked, actionable fixes using Llama 3.3. Here's an honest breakdown of what I built, the mistakes I made, and what I'd do differently. ~40% reduction in debugging time · ~99% uptime on AWS · 2 services, one pipeline The problem with debugging + LLMs The naive approach is obvious: dump the error into ChatGPT and hope for the best. It kind of works. But it breaks down quickly when: Your error spans multiple files and stack frames The root cause is buried 3 levels deep in a dependency You need ranked fixes, not a monologue You want this in your own pipeline, not a chat UI So I decided to build something purpose-built for error log analysis — with structured output, confidence-ranked fixes, and a real deployment.…