Failpoint

The Inspiration (The "Why") Honestly? I got tired of AI sugar-coating every single time. I was working on a project late at night, and I knew deep down that my state management was a mess and my core logic had a huge hole in it. I pasted it into ChatGPT, and it just said, "Looking good! Here are a few small clean-up tips." It didn't tell me the truth: that my project was going to crash the moment I added a second user. I realized we don't need more "helpful" AI; we need an AI that acts like that one senior dev who isn't afraid to tell you your architecture is trash. I wanted a tool that would find the "Failpoints" before I wasted weeks building a house of cards. The Problem I’m Solving The biggest problem isn't syntax, it's logic gaps and bad ideas. • The "Close to the Code" Bias: When you are building, you get tunnel vision. You can't see the structural flaws because you are too focused on making the buttons click. • Sugarcoated AI: Most LLMs are tuned to be polite and encouraging. They are great for writing code, but they are terrible at auditing it for failure. • Privacy Anxiety: People want a quick check without feeling like their entire codebase is being sucked into a training database forever. **How the Approach Evolved** At first, I just wanted a simple code linter. But as I worked on it, I realized that good code can't save a bad idea. The approach shifted from "Let’s find bugs" to "Let’s find failure paths." I started focusing on the context. I realized the AI needed to understand the intent behind the project. • Version 1 was just checking for clean code. • Version 2 (Current) actually looks at your logic and your project goals to see if they match up. I also doubled down on the brutal tone. I found that when the AI is blunt and "pessimistic," it actually forces you to think harder about your architecture. It’s a reality check that helps you build something that actually lasts.