End-to-End Project: Building an Automated Video Clipping AI Pipeline

In this episode of The Build, Cameron Rohn and Tom Spencer walk through an end-to-end project to build an automated video clipping AI pipeline and unpack both the technical and business trade-offs behind it. They begin by mapping the toolchain and agent systems, naming MCP tools and MCP servers alongside OpenAI Agent Systems, Cloudflare Agent OS, Daytona and E2B, with Langsmith used for orchestration and Vercel plus Supabase for deployment and storage. The conversation then shifts to architecture and memory: designing Memory Systems, secure resource connections and API integration patterns that support the End-to-End AI Pipeline and the From Ideas to Production framework. They explore development workflows and developer tooling—debugging agents, local-first MVPs, CI, and instrumentation—to make AI Agents reliable in production. Next they examine building in public strategies and entrepreneurship insights, debating Interactive AI Agent Demos, an AI-Driven Video Clipping Service, monetization paths, and the value of Share AI Snippets Openly to attract community contributions. Through temporal examples and concrete stacks the hosts balance technical architecture decisions with go-to-market thinking. The episode closes with a forward-looking takeaway encouraging builders to iterate publicly, prioritize secure, observable pipelines, and ship small experiments that validate product-market fit.