AI Development Tool Guides
These AI development tool guides focus on the software around the model: command-line clients, editor integrations, extensions, and utilities that make an AI workflow practical.
4 guides
What you can learn
- Install AI command-line and editor tools
- Connect tools to local models or cloud APIs
- Troubleshoot common configuration and environment issues
A model is only one part of a dependable developer workflow. The client needs the right permissions, project context, provider settings, and safety boundaries. Follow the installation guide for your tool, verify the command or extension on a sample repository, and keep configuration files free of credentials. This approach makes troubleshooting much easier when a provider, model, or editor changes.
Setup Continue.dev AI Autocomplete in VS Code
Add AI autocomplete, inline editing, and chat to VS Code using free local or API models.
9 Git Commands Every Developer Should Know
Learn nine practical Git commands for saving work, undoing commits, inspecting changes, tracing bugs, and recovering lost history.
7 Two-Color UI Combinations for Developer Projects
Seven ready-to-use two-color palettes with hex codes, contrast guidance, and practical UI roles for dashboards, apps, and landing pages.
The $20 per Month Startup Stack
Build and launch a lean software product with an AI coding plan and free tiers for backend, deployment, email, analytics, monitoring, and data.
Development Tools questions
Should I use a local model or a cloud model with a development tool?
Use a local model when privacy and offline access are priorities and your hardware can support it. Use a cloud model when you need faster setup or a model that is too large for your computer.
What is the safest way to test an AI development tool?
Use a non-critical repository, review proposed file changes, and grant only the permissions the tool needs. Keep provider keys outside the project files.
Need a recommendation first? Use the free AI setup wizard or compare every AI model.