Practical tips for AI coding assistants, prompts, and productivity workflows you can use today.
Explains how to use the .github/copilot-instructions.md file to enforce coding standards and framework-specific patterns across an entire development team. Compares this repository-level configuration to individual user settings.
Guides developers through configuring Claude Desktop with MCP servers to query local SQLite or PostgreSQL databases directly from the chat interface. Shows how this replaces the manual process of copy-pasting schema definitions.
A working mental model for prompting AI coding tools across the dev lifecycle, with direct routes into templates, libraries, context management and review frameworks.
A practical hub for matching autocomplete, chat, agent, and self-hosted AI coding tools to the way your team actually works.
Evaluates JetBrains AI Assistant specifically for JVM developers working with Java, Kotlin, and Spring Boot. Compares its deep IDE integration and type-aware refactoring against GitHub Copilot's general-purpose suggestions.
Strategies for preventing AI coding assistants from getting confused by massive monorepos. Covers workspace configuration, targeted indexing, and scoping queries.
Compares the terminal-native AI tools from GitHub and Anthropic. Focuses on command execution, shell integration, and complex task handling.
A working model for how AI tools actually fit into engineering practice, from rollout and compliance through debugging, testing, and cost control.
A practical map of Cursor's AI-native workflow: context control, MCP servers, rules, indexing, and Composer, with links to the deep-dive guides for each.
Local models still trail Claude on multi-step reasoning, but for regulated environments where code genuinely can't leave the machine, that tradeoff is well worth making.