AI editors – how they work, and how to master them
summary
Explains how semantic search and embeddings put only relevant code chunks into context, with vectors stored in a vector store for cosine-similarity retrieval, and describes a reimplementation of cursor’s pipeline and the real-world problem that cursor reindexes only every 10 minutes so branch switches return stale context. Describes agent rules using agents.mmd, using Context7 and MCP servers to serve up versioned docs, compares models (GPToss 120B, GPT5 codex, clousonet 4.5) and cites Cerebras running ~3,000 tokens per second, and notes a migration platform called app tape that rewrites PHP to TypeScript.
AI-written summary · about
chapters (8)
- 0:00AI editors introduction
- 3:53AI editor usage patterns
- 5:40Mastering context in AI editors
- 8:27Semantic search in AI editors
- 13:28Agent rules for AI editors
- 15:18Choosing model for tasks
- 16:45Documentation with AI
- 18:54Technical debt in AI coding
related talks
48mAI Coding Agents and How to Code Them
01h 01mAI Agents In-Depth – Function Calling, MCP and Tool Use Under the Hood
31mAI Coding Agents Don't Need Bigger Models. They Need Better Tools.
46mFullstack AI: The Building Blocks for Production Ready AI-Powered Software
53mAgents, Tools, and Mcp, Oh My! Next Level AI Concepts for Developers
40mAI Coding Agents and how to code them
45mAgents, Tools, and MCP, oh my! Next-level AI concepts for developers
37mRAG, Tooling, MCP, Agents ... and What is Missing?