I’ve been using AI coding tools for a while, and one thing that always bugged me was how inconsistent the results were. I could describe the same project twice and get two totally different outcomes. Sometimes it’s gold, sometimes it’s garbage. Occasionally I’d get a surprisingly great result, and other times, total junk. The problem wasn’t that the AI was bad. It was that I only had one shot per run, like drawing a single card from a random deck. You get stuck with local optimums, never the real best outcome. I even paid out of my own pocket to test Atoms’ race mode, which bears a striking resemblance to Claude’s earlier concept of “BON: Best of N.” Instead of one run, it spins up multiple parallel versions of the same project idea, compares their performance, and lets you pick the best one to build on. Instead of random spikes of wasted runs, it became a predictable linear growth: more runs, better chance to pick the best version. However, running four models at once consumes significantly more credits. Unless you divide the cost by four, haha. My overall practical experience is that it reduces time and trial-and-error costs, but the monetary cost isn’t necessarily lower. In fact, it might even increase due to the higher complexity of projects. Tbh if your budget is under $100 I wouldn’t really recommend using Atoms’ race mode. Perhaps other products have this mode too? I’d waste hours and credits re-running the same thing before, chasing that one good generation. It feels like gambling with AI. Any way to improve this? Has anyone else experimented with multi-run setups or modes like this? submitted by /u/Total_Hyena5364
Originally posted by u/Total_Hyena5364 on r/ArtificialInteligence