OpenCode + gpt-oss: Will Local, Open Source Power the Future?

In this episode of The Build, Cameron Rohn and Tom Spencer examine how local, open-source AI and developer tooling reshape startup architecture. They begin by surveying the landscape with a Pre-ChatGPT Tool Survey and discussion of Chinese OSS models, weighing lightweight desktop AI use cases against cloud-first approaches like Anthropic Cloud Code and the Ollama Inference Tool. The conversation then shifts to AI development and tools, where they compare agent patterns, memory systems, and an Alternative Inference Workflow for productionizing models. They explore building in public strategies next, describing practical tactics for community feedback, monetization experiments, and transparent telemetry while citing MCP tools for iteration speed. The technical architecture decisions segment delves into service composition and deployment, referencing Langsmith for orchestration, Vercel for frontend hosting, and Supabase for backend and realtime state. Entrepreneurship insights follow, focusing on startup go-to-market, developer workflows, and how to package open-source components into viable products. Throughout, Cameron and Tom balance concrete implementation notes—toolchains, data flows, and memory caching strategies—with business implications of open-source adoption. They close with a forward-looking takeaway: local, open-source stacks combined with disciplined architecture and public building practices will accelerate practical AI products for developers and entrepreneurs.