#BuildInPublic# I am open-sourcing an AI simulation engine: SGO (Semantic Gradient Optimization) You build something. You think it's ready. But you have no idea how actual people will react, and that reaction sequence is your product's real roadmap. User research takes weeks and still misses scenarios. You can ask an LLM to role-play a buyer persona, sure, but you get back one data point shaped entirely by whatever role you made up beforehand. SGO takes a different route: simulate against census-aligned synthetic populations. NVIDIA open-sourced Nemotron-Personas-USA, a dataset of one million synthetic Americans built on top of US Census distributions. These aren't the "25-year-old tech worker" archetypes an LLM invents on the fly. They're construction workers in suburban Illinois, artisans in rural Texas, single parents in New York, each with hobbies, habits, and priorities that reflect real demographic distributions in age, education, occupation, income. Paste in whatever you want to optimize: a product landing page, a fundraising pitch, a blog post draft. One user ran his dating profile through it. SGO picks an optimization target and audience for you, then samples from the million-person pool, stratifies by segment, runs each persona through a counterfactual evaluation, and stacks up a ranked list of what to change first and why. About 30 seconds per run. Around $0.10 in API costs. Code is open-source. Live demo on HuggingFace Spaces. Also works as a standalone Skill you can drop into an inner loop with auto-research. HF Space: Things people have already run through it: resumes, business plans, app UX flows, billboard copy, logos, landing page layouts, dating profiles, and one dessert shop's name.