Text: Hey HN! I'm Rohan, co-founder of GitArsenal. We built an AI agent that automates the painful "clone, install, build, test" workflow that eats up 60-80% of developer time. The problem: existing AI coding tools like Cursor and Copilot are great at writing code, but they break down at execution. Try to clone and run an unfamiliar repo and you'll hit dependency conflicts, missing env vars, obscure build errors - all the friction that AI can't currently handle. GitArsenal solves this by training specialized agents on setup execution traces. We achieved 40% accuracy on Terminal-Bench (outperforming many frontier models) and we're working with GitHub and Microsoft research teams on SetupBench validation. We're using this as infrastructure for better AI coding agents - if your agent can't reliably execute code, it can't validate its own work. We're partnering with Modal and E2B for execution environments. Hit #6 on Product Hunt recently and seeing strong traction from teams at Hugging Face, Prime Intellect, and others who need to spin up repos constantly. Our approach is to build both the product and the dataset - setup execution traces are a unique moat since they capture real-world repository complexity that synthetic data can't replicate. Currently raising our pre-seed round ($500K) and would love feedback from the HN community. What repository setup pain points have you hit that AI should be solving?