Ep 8 (Audio Only)

In this episode of The Build, Cameron Rohn and Tom Spencer unpack practical strategies and technical trade-offs for shipping developer-facing AI products and startups at velocity. They begin by mapping AI development and tools, comparing models like Moonshot Kimi and Unsloth Local Version, and demoing interoperability with Claude CLI and the Klein VS Code Agent while highlighting memory systems and agent orchestration. The conversation then shifts to technical architecture decisions, arguing for an agent-based architecture pivot, Sparse MoE for Efficiency, and Synthetic Data RL to improve agent robustness and lower inference cost using MCP tools and Langsmith for observability. They explore building in public strategies and entrepreneurship insights, discussing monetization for a low-cost coding assistant, the trade-offs between open-source vs closed coding LLMs, and how community signals guide product-market fit. Next, they examine developer tooling and deployment: integrating with Vercel and Supabase for rapid frontend/back-end iteration, and leveraging Cloudflare's Cost-Effective Services to minimize hosting spend for prototypes. Throughout, the hosts alternate technical depth with startup playbooks—CI workflows, metrics, and feedback loops—illustrating how developer-focused AI agents can scale. The episode closes with a forward-looking takeaway: prioritize modular agent architectures and transparent public builds to accelerate learning and product-market fit for founders and engineers.