Fullstack AI: The Building Blocks for Production Ready AI-Powered Software
summary
Claims a production-ready AI agent requires explicit context management, tool calls and memory rather than a bigger LLM; builds a demo research agent using Next.js, TypeScript and the Vercel AI SDK that spawns subagents, stores articles on disk (not a DB), creates summaries, and supports human approval. Uses RAG with vector search and embeddings to inject local knowledge, shows instruction reliability dropping after ~50 steps (10–20 near-perfect, 20–50 mostly reliable, degradation after that). Integrates external actions via Model Context Protocol (MCP); demo works but shows verbosity and context-bloat tradeoffs.
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chapters (14)
- 0:00AI everywhere suddenly
- 2:34Core problem in AI software
- 6:45Speaker and context windows
- 10:37AI solutions with LEGO pieces
- 13:26Demo of AI research agent
- 18:23Structured output in AI
- 21:30Workflow vs agentic patterns
- 24:41What is RAG and why essential
- 27:11Managing context in RAG
- 31:23Tools empower LLMs
- 35:39Agent loops and human approval
- 38:49Observability and testing for AI
- 40:35Key takeaways for production AI
- 43:28Live demo results and closing
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