A product's data model—the core concepts and objects it prioritizes—determines whether new features create compounding advantages or just add to a feature list. Companies like Slack (persistent channels), Notion (blocks), Figma (shared canvas), and Rippling (employee records) succeeded by choosing non-obvious data models that became impossible for competitors to replicate without rebuilding from scratch. As AI commoditizes code execution, the data model becomes the primary moat. Horizontal tools innovate on how products are built, while vertical tools succeed by elevating the right domain objects. The key is identifying the atomic unit of work in your domain and ensuring every new feature strengthens that central concept.

Visual Studio Code version 1.105 introduces AI-powered merge conflict resolution, OS notifications for task completion and chat responses, and native macOS authentication. The release expands MCP marketplace support for installing servers directly from the Extensions view, adds fully-qualified tool names to avoid conflicts, and improves chat features with recent session history, chain of thought display, and better edit tools for custom models. Additional enhancements include terminal dictation controls, test coverage reporting in the runTests tool, and support for nested AGENTS.md files for workspace-specific AI instructions.

Microsoft released Amplifier, an experimental development environment that enhances AI coding assistants with 20+ specialized agents, a knowledge extraction system, parallel worktree workflows, and automatic conversation transcript preservation. The tool provides pre-loaded patterns, context management, and automation to transform AI assistants into more capable development partners. It requires Python 3.11+, UV, Node.js, and works primarily in WSL2, though it's explicitly marked as early-stage research software with no stability guarantees or official support.