When Your LLM Becomes Your Twin (and Starts Judging Your Code) 🤖👀

I built an LLM Twin one weekend, convinced that following a clean FTI setup would be smooth, elegant, and maybe even impressive enough to make me look like I knew what I was doing. First came data, which I promised would be clean but quickly turned into logs, broken CSVs, and random files I kept anyway because deleting them felt like admitting defeat. Then I moved to features, skipped the heavy setup, used a vector DB, and confidently called it a “logical feature store,” hoping the name alone would carry the architecture. Training was where things got serious, because the GPU started working harder than I ever had, and I just watched it like that was part of the plan. Finally, I deployed it, thinking everything was ready, until the first user asked, “Why is this slow?” and suddenly all my clean design ideas became very quiet. So I asked my LLM Twin, hoping for something helpful. It answered: “Because you built it that way.” That’s when I realized I didn’t build an AI assistant. I built