I tested GPT-4, Claude, and Gemini to find out where AI actually breaks.
Not in a blog post. In a research lab at Penn State — feeding all three models flawed science questions, logical fallacies, ambiguous prompts, and watching them fail in different ways.
That research taught me something that applies directly to startup software:
The most expensive mistakes happen before you write a single line of code.
LLMs fail when they're given the wrong inputs. MVPs fail for the exact same reason — not bad code, but bad assumptions baked in from day one.
I've seen this from both sides. At Penn State, I studied how AI systems break under real-world conditions. At SRMIST's Computing Lab, I built PyTorch models that helped farmers in India predict soil quality and crop disease — tools that had to actually work, for people who couldn't afford for them not to.
That's not agency work. That's pressure-tested engineering.
Here's what I've learned about building software that ships:
Scope is everything. My agricultural model started as one question: can we predict soil quality from weather data? Not ten questions. One. We got that right, then expanded. That's why it worked.
Real-time data beats elegant architecture. The farmers didn't care about my model's loss curve. They cared that it pulled live weather from OpenWeatherMap and told them what to plant. Usefulness over cleverness, every time.
The stack should match the stage. Python + React covers 90% of what early-stage products need — AI tools, data dashboards, web apps. I've built all three. You don't need microservices at 12 users.
I offer MVP development retainers at $250/month — a dedicated technical partner, not an agency ticket queue. I work with a small number of founders at a time, which means when you message me, you get me.
MS in CS from Penn State. Background in LLM research, predictive modeling, and full-stack development. I'll tell you what I think before I tell you what you want to hear.
If that sounds like the kind of person you want building your product — let's talk.