Jason Agostoni𝐃𝐮𝐦𝐛𝐐𝐮𝐞𝐬𝐭𝐢𝐨𝐧.𝐚𝐢 - "𝐉𝐮𝐬𝐭 𝐁𝐮𝐢𝐥𝐝 𝐈𝐭" 𝐁𝐞𝐜𝐨𝐦𝐞𝐬 𝐎𝐯𝐞𝐫𝐥𝐲 𝐎𝐫𝐠𝐚𝐧𝐢𝐳𝐞𝐝 𝐚𝐧𝐝 𝐏𝐫𝐞𝐩𝐚𝐫𝐞𝐝
"Let the flow guide me" seemed like a fun way to build a side project. That lasted about 10 minutes.
Turns out, even side projects benefit from structure. Especially when you're using AI coding agents that will happily generate code for whatever half-baked idea you throw at them. Without precise direction, AI coding agents will build you something half-baked every time. Some people vibe code, this guy needs absolute control.
𝐃𝐮𝐦𝐛𝐐𝐮𝐞𝐬𝐭𝐢𝐨𝐧.𝐚𝐢 - "𝐉𝐮𝐬𝐭 𝐁𝐮𝐢𝐥𝐝 𝐈𝐭" 𝐁𝐞𝐜𝐨𝐦𝐞𝐬 𝐎𝐯𝐞𝐫𝐥𝐲 𝐎𝐫𝐠𝐚𝐧𝐢𝐳𝐞𝐝 𝐚𝐧𝐝 𝐏𝐫𝐞𝐩𝐚𝐫𝐞𝐝 "Let the flow guide me" seemed like a fun way to build a side… | Jason Agostoni
𝐃𝐮𝐦𝐛𝐐𝐮𝐞𝐬𝐭𝐢𝐨𝐧.𝐚𝐢 - "𝐉𝐮𝐬𝐭 𝐁𝐮𝐢𝐥𝐝 𝐈𝐭" 𝐁𝐞𝐜𝐨𝐦𝐞𝐬 𝐎𝐯𝐞𝐫𝐥𝐲 𝐎𝐫𝐠𝐚𝐧𝐢𝐳𝐞𝐝 𝐚𝐧𝐝 𝐏𝐫𝐞𝐩𝐚𝐫𝐞𝐝 "Let the flow guide me" seemed like a fun way to build a side project. That lasted about 10 minutes. Turns out, even side projects benefit from structure. Especially when you're using AI coding agents that will happily generate code for whatever half-baked idea you throw at them. Without precise direction, AI coding agents will build you something half-baked every time. Some people vibe code, this guy needs absolute control. 𝐄𝐧𝐭𝐞𝐫 𝐁𝐌𝐀𝐃: Breakthrough Method of Agile AI Driven Development. It's a workflow for using AI agents throughout the entire SDLC, not just for code generation. Sure, using a formal methodology for a lone-wolf side project sounds like overkill. But being prepared in advance is the way to succeed with AI coding agents. I used the 𝐀𝐧𝐚𝐥𝐲𝐬𝐭 𝐚𝐠𝐞𝐧𝐭 to brainstorm product direction and develop a proper backlog. What started as "build a sarcastic Q&A bot" turned into a structured set of epics, features, and technical constraints. (Don't judge, organizing is very relaxing) 𝐓𝐡𝐞 𝐩𝐫𝐨𝐝𝐮𝐜𝐭 𝐞𝐯𝐨𝐥𝐯𝐞𝐝: - Not just Q&A, but shareable "receipts" of roasts - Not just sarcastic, but multiple personas with different personalities - Not just answers, but a hidden narrative layer (more on that later) - Not just ads but merch (really, Jason?) 𝐓𝐡𝐞 𝐟𝐢𝐫𝐬𝐭 𝐫𝐞𝐚𝐥 𝐭𝐞𝐜𝐡𝐧𝐢𝐜𝐚𝐥 𝐜𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞𝐬 𝐞𝐦𝐞𝐫𝐠𝐞𝐝: 𝐃𝐞𝐯𝐞𝐥𝐨𝐩𝐢𝐧𝐠 𝐚𝐧𝐝 𝐩𝐚𝐜𝐤𝐚𝐠𝐢𝐧𝐠 𝐭𝐡𝐞 𝐩𝐞𝐫𝐬𝐨𝐧𝐚𝐬: How do you get an LLM to consistently stay in character as "Overqualified and Annoyed" or "Weary Tech Support" without it either going too soft or crossing into genuinely mean? This wasn't just prompt engineering. It was product design masked as technical constraints. 𝐋𝐋𝐌 𝐦𝐨𝐝𝐞𝐥 𝐞𝐯𝐚𝐥𝐮𝐚𝐭𝐢𝐨𝐧: I needed models that could follow persona instructions reliably while staying brutally efficient on cost. That meant testing dozens of models across multiple providers. Some were too expensive. Some ignored instructions. Some were painfully slow. The goal: $0.02 to $0.20 per million output tokens. The result: a multi-model fallback system through OpenRouter that could hit the $30 per million questions target. These first challenges were just the warmup. The real fun was still ahead. AI agents are incredible at implementation, but they need constraints. They need a backlog. They need someone saying "build THIS, not that." The Analyst agent helped me think through the product. The coding agents helped me build it. But the architecture? Can't take that away from me. Code is getting cheaper. Knowing what to build and why? Still a nice challenge. dumbquestion.ai