When AI Writes More Than Humans Can Review скачать в хорошем качестве

When AI Writes More Than Humans Can Review

In this episode of The Context Window, Tracy Lee, Ben Lesh, and Brandon Mathis dig into what people miss with Cursor and Claude Code, like feeding the tools the right documentation, using multiple agents to review output, and treating the model less like a genius and more like a fast junior developer that needs constant direction and code review. They talk about why tab development still wins in some real world refactor work, why the future problem is not generating code but reviewing it, and what happens when AI can produce more than humans can realistically validate. The conversation also explores building agents inside companies, the emerging SDK race between platforms like Vercel and TanStack, why MCP isn’t dead but has growing pains around context bloat, and what enterprise teams can do when they are stuck with slower setups like AWS Bedrock. What You’ll Learn: How to stop AI coding tools from producing “confident garbage” by giving them the right context and constraints Why using Cursor or Claude Code well means supervising an assistant like a fast junior dev and doing real code review again How to use multiple agents to review the same change so you can spot issues without reading every line manually When “tab development” beats full agent mode and how to recognize those refactor style use cases What’s next in agent building inside companies including tool calling workflows, MCP growing pains, and surviving enterprise realities like Bedrock Tracy Lee on Linkedin:   / tracyslee   Ben Lesh on Linkedin:   / blesh   Brandon Mathis on Linkedin:   / mathisbrandon   This Dot Labs Twitter: https://x.com/ThisDotLabs This Dot Media Twitter: https://x.com/ThisDotMedia This Dot Labs Instagram:   / thisdotlabs   This Dot Labs Facebook:   / thisdot   Chapters 0:00 Welcome to 2026 + episode roadmap 0:42 What engineers still get wrong about AI coding tools 2:34 Managing AI as a fast but unreliable developer 5:32 Reviewing AI output at scale 8:30 Tab coding vs agent-driven workflows 11:53 Building agents: Cursor, Claude, Vercel, TanStack, LangGraph 17:03 MCP debate: context, tools, and scale 22:29 AWS Bedrock realities in the enterprise 26:56 How companies actually use AI in production 31:14 Chat as the ultimate UI 35:22 Multi-model systems, robots, and what’s next 37:50 Bias, access, and who gets left behind 43:43 Wrap-up and closing thoughts Sponsored by This Dot Labs: https://ai.thisdot.co/