243 items indexed · AI tools, prompts, hooks & techniques
This guide provides developers with links to complete, real-world project templates for setting up automated AI assistants. These examples demonstrate how to make an AI automatically review code, run quality checks, and manage development tasks using pre-built configurations and CI/CD integrations.
OpenCLAW, a tool for building custom AI assistants, has a major update that makes it more reliable for business use. It can now automatically switch between different AI models (like from OpenAI or Google) if one fails, and offers a new 'ContextEngine' for developers to create more advanced memory and data retrieval systems for their AI agents.
OpenCLAW provides a framework for deploying multiple AI agents that work together to automate complex business processes like customer onboarding or billing. This guide outlines a structured, 7-phase roadmap for taking these agent systems from planning to a secure, production-ready deployment.
This is a massive library containing over 170 pre-built AI agent templates for the OpenClaw framework, covering categories from productivity and development to marketing and finance. It allows you to quickly deploy a specialized AI assistant by copying a configuration file, saving significant development time.
This entry outlines a structured workflow for using the Claude Code AI assistant to build software projects more efficiently. By defining project context upfront in a CLAUDE.md file and using custom commands, development teams can reduce errors, manage API costs, and accelerate the entire coding process from planning to commit.
This document outlines a structured workflow that uses the Claude Code AI assistant to manage large software development tasks from start to finish. By breaking down work into planned steps with human approval checkpoints, developers can automate up to 90% of the coding, resulting in significant productivity gains while maintaining quality control.
This guide provides developers with concrete examples of how to build AI agents that are reliable enough for production use. It highlights specific open-source projects that focus on testing and handling unexpected scenarios, or 'edge cases', to ensure the AI doesn't break when faced with unusual user requests or data.